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2302.12178 | Prabhakar Kudva | Prabhakar Kudva, Rajesh Bordawekar, Apoorva Nitsure | A Scalable Space-efficient In-database Interpretability Framework for
Embedding-based Semantic SQL Queries | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AI-Powered database (AI-DB) is a novel relational database system that uses a
self-supervised neural network, database embedding, to enable semantic SQL
queries on relational tables. In this paper, we describe an architecture and
implementation of in-database interpretability infrastructure designed to
provide simple, transparent, and relatable insights into ranked results of
semantic SQL queries supported by AI-DB. We introduce a new co-occurrence based
interpretability approach to capture relationships between relational entities
and describe a space-efficient probabilistic Sketch implementation to store and
process co-occurrence counts. Our approach provides both query-agnostic
(global) and query-specific (local) interpretabilities. Experimental evaluation
demonstrate that our in-database probabilistic approach provides the same
interpretability quality as the precise space-inefficient approach, while
providing scalable and space efficient runtime behavior (up to 8X space
savings), without any user intervention.
| [
{
"version": "v1",
"created": "Thu, 23 Feb 2023 17:18:40 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Feb 2023 17:22:52 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 17:41:11 GMT"
}
] | 1,677,715,200,000 | [
[
"Kudva",
"Prabhakar",
""
],
[
"Bordawekar",
"Rajesh",
""
],
[
"Nitsure",
"Apoorva",
""
]
] |
2302.12195 | Paulo Shakarian | Paulo Shakarian, Gerardo I. Simari | Extensions to Generalized Annotated Logic and an Equivalent Neural
Architecture | Accepted to IEEE TransAI, 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | While deep neural networks have led to major advances in image recognition,
language translation, data mining, and game playing, there are well-known
limits to the paradigm such as lack of explainability, difficulty of
incorporating prior knowledge, and modularity. Neuro symbolic hybrid systems
have recently emerged as a straightforward way to extend deep neural networks
by incorporating ideas from symbolic reasoning such as computational logic. In
this paper, we propose a list desirable criteria for neuro symbolic systems and
examine how some of the existing approaches address these criteria. We then
propose an extension to generalized annotated logic that allows for the
creation of an equivalent neural architecture comprising an alternate neuro
symbolic hybrid. However, unlike previous approaches that rely on continuous
optimization for the training process, our framework is designed as a binarized
neural network that uses discrete optimization. We provide proofs of
correctness and discuss several of the challenges that must be overcome to
realize this framework in an implemented system.
| [
{
"version": "v1",
"created": "Thu, 23 Feb 2023 17:39:46 GMT"
}
] | 1,677,196,800,000 | [
[
"Shakarian",
"Paulo",
""
],
[
"Simari",
"Gerardo I.",
""
]
] |
2302.12314 | Nicholas Soultanian | Theresa Chadwick, James Chao, Christianne Izumigawa, George Galdorisi,
Hector Ortiz-Pena, Elias Loup, Nicholas Soultanian, Mitch Manzanares, Adrian
Mai, Richmond Yen, and Douglas S. Lange | Characterizing Novelty in the Military Domain | Submitted to ICCRTS: International Command and Control Research and
Technology Symposium. 8 pages. 5 Figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A critical factor in utilizing agents with Artificial Intelligence (AI) is
their robustness to novelty. AI agents include models that are either
engineered or trained. Engineered models include knowledge of those aspects of
the environment that are known and considered important by the engineers.
Learned models form embeddings of aspects of the environment based on
connections made through the training data. In operation, however, a rich
environment is likely to present challenges not seen in training sets or
accounted for in engineered models. Worse still, adversarial environments are
subject to change by opponents. A program at the Defense Advanced Research
Project Agency (DARPA) seeks to develop the science necessary to develop and
evaluate agents that are robust to novelty. This capability will be required,
before AI has the role envisioned within mission critical environments. As part
of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are
mapping possible military domain novelty types to a domain-independent ontology
developed as part of a theory of novelty. Characterizing the possible space of
novelty mathematically and ontologically will allow us to experiment with agent
designs that are coming from the DARPA SAIL-ON program in relevant military
environments. Utilizing the same techniques as being used in laboratory
experiments, we will be able to measure agent ability to detect, characterize,
and accommodate novelty.
| [
{
"version": "v1",
"created": "Thu, 23 Feb 2023 20:21:24 GMT"
}
] | 1,677,456,000,000 | [
[
"Chadwick",
"Theresa",
""
],
[
"Chao",
"James",
""
],
[
"Izumigawa",
"Christianne",
""
],
[
"Galdorisi",
"George",
""
],
[
"Ortiz-Pena",
"Hector",
""
],
[
"Loup",
"Elias",
""
],
[
"Soultanian",
"Nicholas",
""
],
[
"Manzanares",
"Mitch",
""
],
[
"Mai",
"Adrian",
""
],
[
"Yen",
"Richmond",
""
],
[
"Lange",
"Douglas S.",
""
]
] |
2302.12592 | Liang Wang | Liang Wang and Zhuangkun Wei and Weisi Guo | Securing IoT Communication using Physical Sensor Data -- Graph Layer
Security with Federated Multi-Agent Deep Reinforcement Learning | 6 pages | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Internet-of-Things (IoT) devices are often used to transmit physical sensor
data over digital wireless channels. Traditional Physical Layer Security
(PLS)-based cryptography approaches rely on accurate channel estimation and
information exchange for key generation, which irrevocably ties key quality
with digital channel estimation quality. Recently, we proposed a new concept
called Graph Layer Security (GLS), where digital keys are derived from physical
sensor readings. The sensor readings between legitimate users are correlated
through a common background infrastructure environment (e.g., a common water
distribution network or electric grid). The challenge for GLS has been how to
achieve distributed key generation. This paper presents a Federated multi-agent
Deep reinforcement learning-assisted Distributed Key generation scheme (FD2K),
which fully exploits the common features of physical dynamics to establish
secret key between legitimate users. We present for the first time initial
experimental results of GLS with federated learning, achieving considerable
security performance in terms of key agreement rate (KAR), and key randomness.
| [
{
"version": "v1",
"created": "Fri, 24 Feb 2023 12:10:23 GMT"
}
] | 1,677,456,000,000 | [
[
"Wang",
"Liang",
""
],
[
"Wei",
"Zhuangkun",
""
],
[
"Guo",
"Weisi",
""
]
] |
2302.12676 | Stelios Triantafyllou | Stelios Triantafyllou, Goran Radanovic | Towards Computationally Efficient Responsibility Attribution in
Decentralized Partially Observable MDPs | AAMAS 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Responsibility attribution is a key concept of accountable multi-agent
decision making. Given a sequence of actions, responsibility attribution
mechanisms quantify the impact of each participating agent to the final
outcome. One such popular mechanism is based on actual causality, and it
assigns (causal) responsibility based on the actions that were found to be
pivotal for the considered outcome. However, the inherent problem of
pinpointing actual causes and consequently determining the exact responsibility
assignment has shown to be computationally intractable. In this paper, we aim
to provide a practical algorithmic solution to the problem of responsibility
attribution under a computational budget. We first formalize the problem in the
framework of Decentralized Partially Observable Markov Decision Processes
(Dec-POMDPs) augmented by a specific class of Structural Causal Models (SCMs).
Under this framework, we introduce a Monte Carlo Tree Search (MCTS) type of
method which efficiently approximates the agents' degrees of responsibility.
This method utilizes the structure of a novel search tree and a pruning
technique, both tailored to the problem of responsibility attribution. Other
novel components of our method are (a) a child selection policy based on linear
scalarization and (b) a backpropagation procedure that accounts for a
minimality condition that is typically used to define actual causality. We
experimentally evaluate the efficacy of our algorithm through a
simulation-based test-bed, which includes three team-based card games.
| [
{
"version": "v1",
"created": "Fri, 24 Feb 2023 14:56:25 GMT"
}
] | 1,677,456,000,000 | [
[
"Triantafyllou",
"Stelios",
""
],
[
"Radanovic",
"Goran",
""
]
] |
2302.12691 | Riccardo Albertoni | Riccardo Albertoni and Sara Colantonio and Piotr Skrzypczy\'nski and
Jerzy Stefanowski | Reproducibility of Machine Learning: Terminology, Recommendations and
Open Issues | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reproducibility is one of the core dimensions that concur to deliver
Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be
defined as the possibility to reproduce the same or a similar experiment or
method, thereby obtaining the same or similar results as the original
scientists. It is an essential ingredient of the scientific method and crucial
for gaining trust in relevant claims. A reproducibility crisis has been
recently acknowledged by scientists and this seems to affect even more
Artificial Intelligence and Machine Learning, due to the complexity of the
models at the core of their recent successes. Notwithstanding the recent debate
on Artificial Intelligence reproducibility, its practical implementation is
still insufficient, also because many technical issues are overlooked. In this
survey, we critically review the current literature on the topic and highlight
the open issues. Our contribution is three-fold. We propose a concise
terminological review of the terms coming into play. We collect and systematize
existing recommendations for achieving reproducibility, putting forth the means
to comply with them. We identify key elements often overlooked in modern
Machine Learning and provide novel recommendations for them. We further
specialize these for two critical application domains, namely the biomedical
and physical artificial intelligence fields.
| [
{
"version": "v1",
"created": "Fri, 24 Feb 2023 15:33:20 GMT"
}
] | 1,677,456,000,000 | [
[
"Albertoni",
"Riccardo",
""
],
[
"Colantonio",
"Sara",
""
],
[
"Skrzypczyński",
"Piotr",
""
],
[
"Stefanowski",
"Jerzy",
""
]
] |
2302.13115 | Majid Khonji | Rashid Alyassi and Majid Khonji | Dual Formulation for Chance Constrained Stochastic Shortest Path with
Application to Autonomous Vehicle Behavior Planning | null | null | 10.1109/CDC45484.2021.9683656 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Autonomous vehicles face the problem of optimizing the expected performance
of subsequent maneuvers while bounding the risk of collision with surrounding
dynamic obstacles. These obstacles, such as agent vehicles, often exhibit
stochastic transitions that should be accounted for in a timely and safe
manner. The Constrained Stochastic Shortest Path problem (C-SSP) is a formalism
for planning in stochastic environments under certain types of operating
constraints. While C-SSP allows specifying constraints in the planning problem,
it does not allow for bounding the probability of constraint violation, which
is desired in safety-critical applications. This work's first contribution is
an exact integer linear programming formulation for Chance-constrained SSP
(CC-SSP) that attains deterministic policies. Second, a randomized rounding
procedure is presented for stochastic policies. Third, we show that the CC-SSP
formalism can be generalized to account for constraints that span through
multiple time steps. Evaluation results show the usefulness of our approach in
benchmark problems compared to existing approaches.
| [
{
"version": "v1",
"created": "Sat, 25 Feb 2023 16:40:00 GMT"
}
] | 1,677,542,400,000 | [
[
"Alyassi",
"Rashid",
""
],
[
"Khonji",
"Majid",
""
]
] |
2302.13187 | Luc\'ia G\'omez \'Alvarez | Luc\'ia G\'omez \'Alvarez, Sebastian Rudolph and Hannes Strass | Tractable Diversity: Scalable Multiperspective Ontology Management via
Standpoint EL | null | 32nd International Joint Conference on Artificial Intelligence,
IJCAI 2023 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The tractability of the lightweight description logic EL has allowed for the
construction of large and widely used ontologies that support semantic
interoperability. However, comprehensive domains with a broad user base are
often at odds with strong axiomatisations otherwise useful for inferencing,
since these are usually context-dependent and subject to diverging
perspectives. In this paper we introduce Standpoint EL, a multi-modal extension
of EL that allows for the integrated representation of domain knowledge
relative to diverse, possibly conflicting standpoints (or contexts), which can
be hierarchically organised and put in relation to each other. We establish
that Standpoint EL still exhibits EL's favourable PTime standard reasoning,
whereas introducing additional features like empty standpoints, rigid roles,
and nominals makes standard reasoning tasks intractable.
| [
{
"version": "v1",
"created": "Sat, 25 Feb 2023 22:59:04 GMT"
}
] | 1,683,763,200,000 | [
[
"Álvarez",
"Lucía Gómez",
""
],
[
"Rudolph",
"Sebastian",
""
],
[
"Strass",
"Hannes",
""
]
] |
2302.13189 | Luc\'ia G\'omez \'Alvarez | Brandon Bennett and Luc\'ia G\'omez \'Alvarez | Vagueness in Predicates and Objects | null | 13th International Conference on Formal Ontology in Information
Systems, FOIS 2023 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Classical semantics assumes that one can model reference, predication and
quantification with respect to a fixed domain of precise referent objects.
Non-logical terms and quantification are then interpreted directly in terms of
elements and subsets of this domain. We explore ways to generalise this
classical picture of precise predicates and objects to account for variability
of meaning due to factors such as vagueness, context and diversity of
definitions or opinions. Both names and predicative expressions can be given
either multiple semantic referents or be associated with semantic referents
that incorporate some model of variability. We present a semantic framework,
Variable Reference Semantics, that can accommodate several modes of variability
in relation to both predicates and objects.
| [
{
"version": "v1",
"created": "Sat, 25 Feb 2023 23:05:33 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 07:59:01 GMT"
}
] | 1,683,763,200,000 | [
[
"Bennett",
"Brandon",
""
],
[
"Álvarez",
"Lucía Gómez",
""
]
] |
2302.13225 | Hui Wang | Hui Wang, Abdallah Saffidine, Tristan Cazenave | Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local
Search | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work proposed the UCTMAXSAT algorithm to address Maximum
Satisfiability Problems (MaxSAT) and shown improved performance over pure
Stochastic Local Search algorithms (SLS). UCTMAXSAT is based on Monte Carlo
Tree Search but it uses SLS instead of purely random playouts. In this work, we
introduce two algorithmic variations over UCTMAXSAT. We carry an empirical
analysis on MaxSAT benchmarks from recent competitions and establish that both
ideas lead to performance improvements. First, a nesting of the tree search
inspired by the Nested Monte Carlo Search algorithm is effective on most
instance types in the benchmark. Second, we observe that using a static flip
limit in SLS, the ideal budget depends heavily on the instance size and we
propose to set it dynamically. We show that it is a robust way to achieve
comparable performance on a variety of instances without requiring additional
tuning.
| [
{
"version": "v1",
"created": "Sun, 26 Feb 2023 03:37:26 GMT"
}
] | 1,677,542,400,000 | [
[
"Wang",
"Hui",
""
],
[
"Saffidine",
"Abdallah",
""
],
[
"Cazenave",
"Tristan",
""
]
] |
2302.13591 | Mattia Fumagalli | Mattia Fumagalli, Daqian Shi, Fausto Giunchiglia | Towards Ranking Schemas by Focus | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The main goal of this paper is to evaluate knowledge base schemas, modeled as
a set of entity types, each such type being associated with a set of
properties, according to their focus. We intuitively model the notion of focus
as ''the state or quality of being relevant in storing and retrieving
information''. This definition of focus is adapted from the notion of
''categorization purpose'', as first defined in cognitive psychology, thus
giving us a high level of understandability on the side of users. In turn, this
notion is formalized based on a set of knowledge metrics that, for any given
focus, rank knowledge base schemas according to their quality. We apply the
proposed methodology to more than 200 state-of-the-art knowledge base schemas.
The experimental results show the utility of our approach
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2023 08:53:39 GMT"
}
] | 1,677,542,400,000 | [
[
"Fumagalli",
"Mattia",
""
],
[
"Shi",
"Daqian",
""
],
[
"Giunchiglia",
"Fausto",
""
]
] |
2302.14146 | Fabio Cozman | Fabio Gagliardi Cozman | Markov Conditions and Factorization in Logical Credal Networks | 10 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We examine the recently proposed language of Logical Credal Networks, in
particular investigating the consequences of various Markov conditions. We
introduce the notion of structure for a Logical Credal Network and show that a
structure without directed cycles leads to a well-known factorization result.
For networks with directed cycles, we analyze the differences between Markov
conditions, factorization results, and specification requirements.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2023 21:06:20 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Mar 2023 12:26:09 GMT"
},
{
"version": "v3",
"created": "Fri, 17 Mar 2023 18:53:47 GMT"
}
] | 1,679,356,800,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
2302.14208 | Tung Thai | Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh
Soni, Mudit Verma, Sriram Gopalakrishnan, Neeraj Varshney, Chitta Baral,
Subbarao Kambhampati, Jivko Sinapov, and Matthias Scheutz | Methods and Mechanisms for Interactive Novelty Handling in Adversarial
Environments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning to detect, characterize and accommodate novelties is a challenge
that agents operating in open-world domains need to address to be able to
guarantee satisfactory task performance. Certain novelties (e.g., changes in
environment dynamics) can interfere with the performance or prevent agents from
accomplishing task goals altogether. In this paper, we introduce general
methods and architectural mechanisms for detecting and characterizing different
types of novelties, and for building an appropriate adaptive model to
accommodate them utilizing logical representations and reasoning methods. We
demonstrate the effectiveness of the proposed methods in evaluations performed
by a third party in the adversarial multi-agent board game Monopoly. The
results show high novelty detection and accommodation rates across a variety of
novelty types, including changes to the rules of the game, as well as changes
to the agent's action capabilities.
| [
{
"version": "v1",
"created": "Tue, 28 Feb 2023 00:05:48 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Mar 2023 02:04:39 GMT"
}
] | 1,678,147,200,000 | [
[
"Thai",
"Tung",
""
],
[
"Shen",
"Ming",
""
],
[
"Garg",
"Mayank",
""
],
[
"Kalani",
"Ayush",
""
],
[
"Vaidya",
"Nakul",
""
],
[
"Soni",
"Utkarsh",
""
],
[
"Verma",
"Mudit",
""
],
[
"Gopalakrishnan",
"Sriram",
""
],
[
"Varshney",
"Neeraj",
""
],
[
"Baral",
"Chitta",
""
],
[
"Kambhampati",
"Subbarao",
""
],
[
"Sinapov",
"Jivko",
""
],
[
"Scheutz",
"Matthias",
""
]
] |
2302.14442 | Girish Varma | Shreevignesh Suriyanarayanan, Praveen Paruchuri, Girish Varma | City-scale Pollution Aware Traffic Routing by Sampling Max Flows using
MCMC | Accepted in AAAI 2023 (AI for Social Impact Track) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A significant cause of air pollution in urban areas worldwide is the high
volume of road traffic. Long-term exposure to severe pollution can cause
serious health issues. One approach towards tackling this problem is to design
a pollution-aware traffic routing policy that balances multiple objectives of
i) avoiding extreme pollution in any area ii) enabling short transit times, and
iii) making effective use of the road capacities. We propose a novel
sampling-based approach for this problem. We provide the first construction of
a Markov Chain that can sample integer max flow solutions of a planar graph,
with theoretical guarantees that the probabilities depend on the aggregate
transit length. We designed a traffic policy using diverse samples and
simulated traffic on real-world road maps using the SUMO traffic simulator. We
observe a considerable decrease in areas with severe pollution when
experimented with maps of large cities across the world compared to other
approaches.
| [
{
"version": "v1",
"created": "Tue, 28 Feb 2023 09:40:37 GMT"
}
] | 1,677,628,800,000 | [
[
"Suriyanarayanan",
"Shreevignesh",
""
],
[
"Paruchuri",
"Praveen",
""
],
[
"Varma",
"Girish",
""
]
] |
2302.14688 | Simon Gottschalk | Simon Gottschalk, Endri Kacupaj, Sara Abdollahi, Diego Alves, Gabriel
Amaral, Elisavet Koutsiana, Tin Kuculo, Daniela Major, Caio Mello, Gullal S.
Cheema, Abdul Sittar, Swati, Golsa Tahmasebzadeh, Gaurish Thakkar | OEKG: The Open Event Knowledge Graph | The definitive version of this work was published in the Proceedings
of the 2nd International Workshop on Cross-lingual Event-centric Open
Analytics co-located with the 30th The Web Conference (WWW 2021) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accessing and understanding contemporary and historical events of global
impact such as the US elections and the Olympic Games is a major prerequisite
for cross-lingual event analytics that investigate event causes, perception and
consequences across country borders. In this paper, we present the Open Event
Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph
composed of seven different data sets from multiple application domains,
including question answering, entity recommendation and named entity
recognition. These data sets are all integrated through an easy-to-use and
robust pipeline and by linking to the event-centric knowledge graph EventKG. We
describe their common schema and demonstrate the use of the OEKG at the example
of three use cases: type-specific image retrieval, hybrid question answering
over knowledge graphs and news articles, as well as language-specific event
recommendation. The OEKG and its query endpoint are publicly available.
| [
{
"version": "v1",
"created": "Tue, 28 Feb 2023 15:58:28 GMT"
}
] | 1,677,628,800,000 | [
[
"Gottschalk",
"Simon",
""
],
[
"Kacupaj",
"Endri",
""
],
[
"Abdollahi",
"Sara",
""
],
[
"Alves",
"Diego",
""
],
[
"Amaral",
"Gabriel",
""
],
[
"Koutsiana",
"Elisavet",
""
],
[
"Kuculo",
"Tin",
""
],
[
"Major",
"Daniela",
""
],
[
"Mello",
"Caio",
""
],
[
"Cheema",
"Gullal S.",
""
],
[
"Sittar",
"Abdul",
""
],
[
"Swati",
"",
""
],
[
"Tahmasebzadeh",
"Golsa",
""
],
[
"Thakkar",
"Gaurish",
""
]
] |
2303.00446 | Yang Yuan | Yang Yuan | Succinct Representations for Concepts | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Foundation models like chatGPT have demonstrated remarkable performance on
various tasks. However, for many questions, they may produce false answers that
look accurate. How do we train the model to precisely understand the concepts?
In this paper, we introduce succinct representations of concepts based on
category theory. Such representation yields concept-wise invariance properties
under various tasks, resulting a new learning algorithm that can provably and
accurately learn complex concepts or fix misconceptions. Moreover, by
recursively expanding the succinct representations, one can generate a
hierarchical decomposition, and manually verify the concept by individually
examining each part inside the decomposition.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2023 12:11:23 GMT"
}
] | 1,677,715,200,000 | [
[
"Yuan",
"Yang",
""
]
] |
2303.00672 | Willy Reis A. S. | Willy Arthur Silva Reis, Denis Benevolo Pais, Valdinei Freire, Karina
Valdivia Delgado | Forward-PECVaR Algorithm: Exact Evaluation for CVaR SSPs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The Stochastic Shortest Path (SSP) problem models probabilistic
sequential-decision problems where an agent must pursue a goal while minimizing
a cost function. Because of the probabilistic dynamics, it is desired to have a
cost function that considers risk. Conditional Value at Risk (CVaR) is a
criterion that allows modeling an arbitrary level of risk by considering the
expectation of a fraction $\alpha$ of worse trajectories. Although an optimal
policy is non-Markovian, solutions of CVaR-SSP can be found approximately with
Value Iteration based algorithms such as CVaR Value Iteration with Linear
Interpolation (CVaRVIQ) and CVaR Value Iteration via Quantile Representation
(CVaRVILI). These type of solutions depends on the algorithm's parameters such
as the number of atoms and $\alpha_0$ (the minimum $\alpha$). To compare the
policies returned by these algorithms, we need a way to exactly evaluate
stationary policies of CVaR-SSPs. Although there is an algorithm that evaluates
these policies, this only works on problems with uniform costs. In this paper,
we propose a new algorithm, Forward-PECVaR (ForPECVaR), that evaluates exactly
stationary policies of CVaR-SSPs with non-uniform costs. We evaluate
empirically CVaR Value Iteration algorithms that found solutions approximately
regarding their quality compared with the exact solution, and the influence of
the algorithm parameters in the quality and scalability of the solutions.
Experiments in two domains show that it is important to use an $\alpha_0$
smaller than the $\alpha$ target and an adequate number of atoms to obtain a
good approximation.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2023 17:10:22 GMT"
}
] | 1,677,715,200,000 | [
[
"Reis",
"Willy Arthur Silva",
""
],
[
"Pais",
"Denis Benevolo",
""
],
[
"Freire",
"Valdinei",
""
],
[
"Delgado",
"Karina Valdivia",
""
]
] |
2303.00752 | Zsolt Zombori | Andr\'as Kornai and Michael Bukatin and Zsolt Zombori | Safety without alignment | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Currently, the dominant paradigm in AI safety is alignment with human values.
Here we describe progress on developing an alternative approach to safety,
based on ethical rationalism (Gewirth:1978), and propose an inherently safe
implementation path via hybrid theorem provers in a sandbox. As AGIs evolve,
their alignment may fade, but their rationality can only increase (otherwise
more rational ones will have a significant evolutionary advantage) so an
approach that ties their ethics to their rationality has clear long-term
advantages.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2023 13:07:50 GMT"
},
{
"version": "v2",
"created": "Sat, 18 Mar 2023 04:59:26 GMT"
}
] | 1,679,356,800,000 | [
[
"Kornai",
"András",
""
],
[
"Bukatin",
"Michael",
""
],
[
"Zombori",
"Zsolt",
""
]
] |
2303.00822 | Brittany Cates | Brittany Cates, Anagha Kulkarni, Sarath Sreedharan | Planning for Attacker Entrapment in Adversarial Settings | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a planning framework to generate a defense strategy
against an attacker who is working in an environment where a defender can
operate without the attacker's knowledge. The objective of the defender is to
covertly guide the attacker to a trap state from which the attacker cannot
achieve their goal. Further, the defender is constrained to achieve its goal
within K number of steps, where K is calculated as a pessimistic lower bound
within which the attacker is unlikely to suspect a threat in the environment.
Such a defense strategy is highly useful in real world systems like honeypots
or honeynets, where an unsuspecting attacker interacts with a simulated
production system while assuming it is the actual production system. Typically,
the interaction between an attacker and a defender is captured using game
theoretic frameworks. Our problem formulation allows us to capture it as a much
simpler infinite horizon discounted MDP, in which the optimal policy for the
MDP gives the defender's strategy against the actions of the attacker. Through
empirical evaluation, we show the merits of our problem formulation.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2023 21:08:27 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Apr 2023 21:15:02 GMT"
}
] | 1,680,825,600,000 | [
[
"Cates",
"Brittany",
""
],
[
"Kulkarni",
"Anagha",
""
],
[
"Sreedharan",
"Sarath",
""
]
] |
2303.00968 | Robin Burke | Amanda Aird, Paresha Farastu, Joshua Sun, Elena \v{S}tefancov\'a,
Cassidy All, Amy Voida, Nicholas Mattei, Robin Burke | Dynamic fairness-aware recommendation through multi-agent social choice | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Algorithmic fairness in the context of personalized recommendation presents
significantly different challenges to those commonly encountered in
classification tasks. Researchers studying classification have generally
considered fairness to be a matter of achieving equality of outcomes between a
protected and unprotected group, and built algorithmic interventions on this
basis. We argue that fairness in real-world application settings in general,
and especially in the context of personalized recommendation, is much more
complex and multi-faceted, requiring a more general approach. We propose a
model to formalize multistakeholder fairness in recommender systems as a two
stage social choice problem. In particular, we express recommendation fairness
as a novel combination of an allocation and an aggregation problem, which
integrate both fairness concerns and personalized recommendation provisions,
and derive new recommendation techniques based on this formulation. Simulations
demonstrate the ability of the framework to integrate multiple fairness
concerns in a dynamic way.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2023 05:06:17 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Mar 2023 20:39:24 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Feb 2024 18:44:19 GMT"
}
] | 1,709,078,400,000 | [
[
"Aird",
"Amanda",
""
],
[
"Farastu",
"Paresha",
""
],
[
"Sun",
"Joshua",
""
],
[
"Štefancová",
"Elena",
""
],
[
"All",
"Cassidy",
""
],
[
"Voida",
"Amy",
""
],
[
"Mattei",
"Nicholas",
""
],
[
"Burke",
"Robin",
""
]
] |
2303.01049 | Shuai Xiao | Shuai Xiao, Le Guo, Zaifan Jiang, Lei Lv, Yuanbo Chen, Jun Zhu, Shuang
Yang | Model-based Constrained MDP for Budget Allocation in Sequential
Incentive Marketing | Published at CIKM '19: Proceedings of the 28th ACM International
Conference on Information and Knowledge Management | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Sequential incentive marketing is an important approach for online businesses
to acquire customers, increase loyalty and boost sales. How to effectively
allocate the incentives so as to maximize the return (e.g., business
objectives) under the budget constraint, however, is less studied in the
literature. This problem is technically challenging due to the facts that 1)
the allocation strategy has to be learned using historically logged data, which
is counterfactual in nature, and 2) both the optimality and feasibility (i.e.,
that cost cannot exceed budget) needs to be assessed before being deployed to
online systems. In this paper, we formulate the problem as a constrained Markov
decision process (CMDP). To solve the CMDP problem with logged counterfactual
data, we propose an efficient learning algorithm which combines bisection
search and model-based planning. First, the CMDP is converted into its dual
using Lagrangian relaxation, which is proved to be monotonic with respect to
the dual variable. Furthermore, we show that the dual problem can be solved by
policy learning, with the optimal dual variable being found efficiently via
bisection search (i.e., by taking advantage of the monotonicity). Lastly, we
show that model-based planing can be used to effectively accelerate the joint
optimization process without retraining the policy for every dual variable.
Empirical results on synthetic and real marketing datasets confirm the
effectiveness of our methods.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2023 08:10:45 GMT"
}
] | 1,677,801,600,000 | [
[
"Xiao",
"Shuai",
""
],
[
"Guo",
"Le",
""
],
[
"Jiang",
"Zaifan",
""
],
[
"Lv",
"Lei",
""
],
[
"Chen",
"Yuanbo",
""
],
[
"Zhu",
"Jun",
""
],
[
"Yang",
"Shuang",
""
]
] |
2303.01300 | Andrew Fuchs | Andrew Fuchs, Andrea Passarella, Marco Conti | Compensating for Sensing Failures via Delegation in Human-AI Hybrid
Systems | null | Sensors 2023, 23, 3409 | 10.3390/s23073409 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Given an increasing prevalence of intelligent systems capable of autonomous
actions or augmenting human activities, it is important to consider scenarios
in which the human, autonomous system, or both can exhibit failures as a result
of one of several contributing factors (e.g. perception). Failures for either
humans or autonomous agents can lead to simply a reduced performance level, or
a failure can lead to something as severe as injury or death. For our topic, we
consider the hybrid human-AI teaming case where a managing agent is tasked with
identifying when to perform a delegation assignment and whether the human or
autonomous system should gain control. In this context, the manager will
estimate its best action based on the likelihood of either (human, autonomous)
agent failure as a result of their sensing capabilities and possible
deficiencies. We model how the environmental context can contribute to, or
exacerbate, the sensing deficiencies. These contexts provide cases where the
manager must learn to attribute capabilities to suitability for
decision-making. As such, we demonstrate how a Reinforcement Learning (RL)
manager can correct the context-delegation association and assist the hybrid
team of agents in outperforming the behavior of any agent working in isolation.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2023 14:27:01 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Mar 2023 14:28:47 GMT"
}
] | 1,679,875,200,000 | [
[
"Fuchs",
"Andrew",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Conti",
"Marco",
""
]
] |
2303.01325 | Chen Chen | Chen Chen, Jie Fu, Lingjuan Lyu | A Pathway Towards Responsible AI Generated Content | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI Generated Content (AIGC) has received tremendous attention within the past
few years, with content generated in the format of image, text, audio, video,
etc. Meanwhile, AIGC has become a double-edged sword and recently received much
criticism regarding its responsible usage. In this article, we focus on 8 main
concerns that may hinder the healthy development and deployment of AIGC in
practice, including risks from (1) privacy; (2) bias, toxicity, misinformation;
(3) intellectual property (IP); (4) robustness; (5) open source and
explanation; (6) technology abuse; (7) consent, credit, and compensation; (8)
environment. Additionally, we provide insights into the promising directions
for tackling these risks while constructing generative models, enabling AIGC to
be used more responsibly to truly benefit society.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2023 14:58:40 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Mar 2023 23:32:57 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Dec 2023 08:21:12 GMT"
}
] | 1,703,808,000,000 | [
[
"Chen",
"Chen",
""
],
[
"Fu",
"Jie",
""
],
[
"Lyu",
"Lingjuan",
""
]
] |
2303.01618 | Th\'eophile Champion | Th\'eophile Champion and Marek Grze\'s and Lisa Bonheme and Howard
Bowman | Deconstructing deep active inference | 59 pages, 46 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Active inference is a theory of perception, learning and decision making,
which can be applied to neuroscience, robotics, and machine learning. Recently,
reasearch has been taking place to scale up this framework using Monte-Carlo
tree search and deep learning. The goal of this activity is to solve more
complicated tasks using deep active inference. First, we review the existing
literature, then, we progresively build a deep active inference agent. For two
agents, we have experimented with five definitions of the expected free energy
and three different action selection strategies. According to our experiments,
the models able to solve the dSprites environment are the ones that maximise
rewards. Finally, we compare the similarity of the representation learned by
the layers of various agents using centered kernel alignment. Importantly, the
agent maximising reward and the agent minimising expected free energy learn
very similar representations except for the last layer of the critic network
(reflecting the difference in learning objective), and the variance layers of
the transition and encoder networks. We found that the reward maximising agent
is a lot more certain than the agent minimising expected free energy. This is
because the agent minimising expected free energy always picks the action down,
and does not gather enough data for the other actions. In contrast, the agent
maximising reward, keeps on selecting the actions left and right, enabling it
to successfully solve the task. The only difference between those two agents is
the epistemic value, which aims to make the outputs of the transition and
encoder networks as close as possible. Thus, the agent minimising expected free
energy picks a single action (down), and becomes an expert at predicting the
future when selecting this action. This makes the KL divergence between the
output of the transition and encoder networks small.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2023 22:39:56 GMT"
},
{
"version": "v2",
"created": "Mon, 8 May 2023 08:20:23 GMT"
}
] | 1,683,590,400,000 | [
[
"Champion",
"Théophile",
""
],
[
"Grześ",
"Marek",
""
],
[
"Bonheme",
"Lisa",
""
],
[
"Bowman",
"Howard",
""
]
] |
2303.01860 | Giacomo De Bernardi Dr. | Giacomo De Bernardi, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli | Rule-based Out-Of-Distribution Detection | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Out-of-distribution detection is one of the most critical issue in the
deployment of machine learning. The data analyst must assure that data in
operation should be compliant with the training phase as well as understand if
the environment has changed in a way that autonomous decisions would not be
safe anymore. The method of the paper is based on eXplainable Artificial
Intelligence (XAI); it takes into account different metrics to identify any
resemblance between in-distribution and out of, as seen by the XAI model. The
approach is non-parametric and distributional assumption free. The validation
over complex scenarios (predictive maintenance, vehicle platooning, covert
channels in cybersecurity) corroborates both precision in detection and
evaluation of training-operation conditions proximity. Results are available
via open source and open data at the following link:
https://github.com/giacomo97cnr/Rule-based-ODD.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2023 11:26:28 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Mar 2023 08:19:23 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Apr 2023 08:02:34 GMT"
},
{
"version": "v4",
"created": "Mon, 21 Aug 2023 10:13:58 GMT"
}
] | 1,692,662,400,000 | [
[
"De Bernardi",
"Giacomo",
""
],
[
"Narteni",
"Sara",
""
],
[
"Cambiaso",
"Enrico",
""
],
[
"Mongelli",
"Maurizio",
""
]
] |
2303.02536 | Atticus Geiger | Atticus Geiger and Zhengxuan Wu and Christopher Potts and Thomas Icard
and Noah D. Goodman | Finding Alignments Between Interpretable Causal Variables and
Distributed Neural Representations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Causal abstraction is a promising theoretical framework for explainable
artificial intelligence that defines when an interpretable high-level causal
model is a faithful simplification of a low-level deep learning system.
However, existing causal abstraction methods have two major limitations: they
require a brute-force search over alignments between the high-level model and
the low-level one, and they presuppose that variables in the high-level model
will align with disjoint sets of neurons in the low-level one. In this paper,
we present distributed alignment search (DAS), which overcomes these
limitations. In DAS, we find the alignment between high-level and low-level
models using gradient descent rather than conducting a brute-force search, and
we allow individual neurons to play multiple distinct roles by analyzing
representations in non-standard bases-distributed representations. Our
experiments show that DAS can discover internal structure that prior approaches
miss. Overall, DAS removes previous obstacles to conducting causal abstraction
analyses and allows us to find conceptual structure in trained neural nets.
| [
{
"version": "v1",
"created": "Sun, 5 Mar 2023 00:57:49 GMT"
},
{
"version": "v2",
"created": "Wed, 31 May 2023 01:44:59 GMT"
},
{
"version": "v3",
"created": "Tue, 21 Nov 2023 01:02:31 GMT"
},
{
"version": "v4",
"created": "Wed, 21 Feb 2024 23:23:18 GMT"
}
] | 1,708,646,400,000 | [
[
"Geiger",
"Atticus",
""
],
[
"Wu",
"Zhengxuan",
""
],
[
"Potts",
"Christopher",
""
],
[
"Icard",
"Thomas",
""
],
[
"Goodman",
"Noah D.",
""
]
] |
2303.02664 | Shahaf S. Shperberg | Amihay Elboher, Ava Bensoussan, Erez Karpas, Wheeler Ruml, Shahaf S.
Shperberg, Solomon E. Shimony | A Formal Metareasoning Model of Concurrent Planning and Execution | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Agents that plan and act in the real world must deal with the fact that time
passes as they are planning. When timing is tight, there may be insufficient
time to complete the search for a plan before it is time to act. By commencing
execution before search concludes, one gains time to search by making planning
and execution concurrent. However, this incurs the risk of making incorrect
action choices, especially if actions are irreversible. This tradeoff between
opportunity and risk is the problem addressed in this paper. Our main
contribution is to formally define this setting as an abstract metareasoning
problem. We find that the abstract problem is intractable. However, we identify
special cases that are solvable in polynomial time, develop greedy solution
algorithms, and, through tests on instances derived from search problems, find
several methods that achieve promising practical performance. This work lays
the foundation for a principled time-aware executive that concurrently plans
and executes.
| [
{
"version": "v1",
"created": "Sun, 5 Mar 2023 13:05:26 GMT"
}
] | 1,678,147,200,000 | [
[
"Elboher",
"Amihay",
""
],
[
"Bensoussan",
"Ava",
""
],
[
"Karpas",
"Erez",
""
],
[
"Ruml",
"Wheeler",
""
],
[
"Shperberg",
"Shahaf S.",
""
],
[
"Shimony",
"Solomon E.",
""
]
] |
2303.03091 | Jean Dezert | Jean Dezert, Albena Tchamova | On Kenn's Rule of Combination Applied to Breast Cancer Precision Therapy | 5 pages, technical note | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This short technical note points out an erroneous claim about a new rule of
combination of basic belief assignments presented recently by Kenn et al. in
2023, referred as Kenn's rule of combination (or just as KRC for short). We
prove thanks a very simple counter-example that Kenn's rule is not associative.
Consequently, the results of the method proposed by Kenn et al. highly depends
on the ad-hoc sequential order chosen for the fusion process as proposed by the
authors. This serious problem casts in doubt the interest of this method and
its real ability to provide trustful results and to make good decisions to help
for precise breast cancer therapy.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2023 12:16:06 GMT"
}
] | 1,678,147,200,000 | [
[
"Dezert",
"Jean",
""
],
[
"Tchamova",
"Albena",
""
]
] |
2303.03581 | Kewei Cheng | Kewei Cheng, Nesreen K. Ahmed, Yizhou Sun | Neural Compositional Rule Learning for Knowledge Graph Reasoning | ICLR 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Learning logical rules is critical to improving reasoning in KGs. This is due
to their ability to provide logical and interpretable explanations when used
for predictions, as well as their ability to generalize to other tasks,
domains, and data. While recent methods have been proposed to learn logical
rules, the majority of these methods are either restricted by their
computational complexity and can not handle the large search space of
large-scale KGs, or show poor generalization when exposed to data outside the
training set. In this paper, we propose an end-to-end neural model for learning
compositional logical rules called NCRL. NCRL detects the best compositional
structure of a rule body, and breaks it into small compositions in order to
infer the rule head. By recurrently merging compositions in the rule body with
a recurrent attention unit, NCRL finally predicts a single rule head.
Experimental results show that NCRL learns high-quality rules, as well as being
generalizable. Specifically, we show that NCRL is scalable, efficient, and
yields state-of-the-art results for knowledge graph completion on large-scale
KGs. Moreover, we test NCRL for systematic generalization by learning to reason
on small-scale observed graphs and evaluating on larger unseen ones.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2023 01:29:52 GMT"
}
] | 1,678,233,600,000 | [
[
"Cheng",
"Kewei",
""
],
[
"Ahmed",
"Nesreen K.",
""
],
[
"Sun",
"Yizhou",
""
]
] |
2303.03961 | Stefanie Rinderle-Ma | Beate Scheibel and Stefanie Rinderle-Ma | An End-to-End Approach for Online Decision Mining and Decision Drift
Analysis in Process-Aware Information Systems: Extended Version | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision mining enables the discovery of decision rules from event logs or
streams, and constitutes an important part of in-depth analysis and
optimisation of business processes. So far, decision mining has been merely
applied in an ex-post way resulting in a snapshot of decision rules for the
given chunk of log data. Online decision mining, by contrast, enables
continuous monitoring of decision rule evolution and decision drift. Hence this
paper presents an end-to-end approach for the discovery as well as monitoring
of decision points and the corresponding decision rules during runtime,
bridging the gap between online control flow discovery and decision mining. The
approach provides automatic decision support for process-aware information
systems with efficient decision drift discovery and monitoring. For monitoring,
not only the performance, in terms of accuracy, of decision rules is taken into
account, but also the occurrence of data elements and changes in branching
frequency. The paper provides two algorithms, which are evaluated on four
synthetic and one real-life data set, showing feasibility and applicability of
the approach. Overall, the approach fosters the understanding of decisions in
business processes and hence contributes to an improved human-process
interaction.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2023 15:04:49 GMT"
}
] | 1,678,233,600,000 | [
[
"Scheibel",
"Beate",
""
],
[
"Rinderle-Ma",
"Stefanie",
""
]
] |
2303.04141 | Katarina Doctor Z | Katarina Doctor, Christine Task, Eric Kildebeck, Mayank Kejriwal,
Lawrence Holder, and Russell Leong | Toward Defining a Domain Complexity Measure Across Domains | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Artificial Intelligence (AI) systems planned for deployment in real-world
applications frequently are researched and developed in closed simulation
environments where all variables are controlled and known to the simulator or
labeled benchmark datasets are used. Transition from these simulators,
testbeds, and benchmark datasets to more open-world domains poses significant
challenges to AI systems, including significant increases in the complexity of
the domain and the inclusion of real-world novelties; the open-world
environment contains numerous out-of-distribution elements that are not part in
the AI systems' training set. Here, we propose a path to a general,
domain-independent measure of domain complexity level. We distinguish two
aspects of domain complexity: intrinsic and extrinsic. The intrinsic domain
complexity is the complexity that exists by itself without any action or
interaction from an AI agent performing a task on that domain. This is an
agent-independent aspect of the domain complexity. The extrinsic domain
complexity is agent- and task-dependent. Intrinsic and extrinsic elements
combined capture the overall complexity of the domain. We frame the components
that define and impact domain complexity levels in a domain-independent light.
Domain-independent measures of complexity could enable quantitative predictions
of the difficulty posed to AI systems when transitioning from one testbed or
environment to another, when facing out-of-distribution data in open-world
tasks, and when navigating the rapidly expanding solution and search spaces
encountered in open-world domains.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2023 18:56:50 GMT"
}
] | 1,678,233,600,000 | [
[
"Doctor",
"Katarina",
""
],
[
"Task",
"Christine",
""
],
[
"Kildebeck",
"Eric",
""
],
[
"Kejriwal",
"Mayank",
""
],
[
"Holder",
"Lawrence",
""
],
[
"Leong",
"Russell",
""
]
] |
2303.04283 | Francesco Fabiano | Francesco Fabiano, Vishal Pallagani, Marianna Bergamaschi Ganapini,
Lior Horesh, Andrea Loreggia, Keerthiram Murugesan, Francesca Rossi, Biplav
Srivastava | Fast and Slow Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The concept of Artificial Intelligence has gained a lot of attention over the
last decade. In particular, AI-based tools have been employed in several
scenarios and are, by now, pervading our everyday life. Nonetheless, most of
these systems lack many capabilities that we would naturally consider to be
included in a notion of "intelligence". In this work, we present an
architecture that, inspired by the cognitive theory known as Thinking Fast and
Slow by D. Kahneman, is tasked with solving planning problems in different
settings, specifically: classical and multi-agent epistemic. The system
proposed is an instance of a more general AI paradigm, referred to as SOFAI
(for Slow and Fast AI). SOFAI exploits multiple solving approaches, with
different capabilities that characterize them as either fast or slow, and a
metacognitive module to regulate them. This combination of components, which
roughly reflects the human reasoning process according to D. Kahneman, allowed
us to enhance the reasoning process that, in this case, is concerned with
planning in two different settings. The behavior of this system is then
compared to state-of-the-art solvers, showing that the newly introduced system
presents better results in terms of generality, solving a wider set of problems
with an acceptable trade-off between solving times and solution accuracy.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2023 23:05:38 GMT"
}
] | 1,678,320,000,000 | [
[
"Fabiano",
"Francesco",
""
],
[
"Pallagani",
"Vishal",
""
],
[
"Ganapini",
"Marianna Bergamaschi",
""
],
[
"Horesh",
"Lior",
""
],
[
"Loreggia",
"Andrea",
""
],
[
"Murugesan",
"Keerthiram",
""
],
[
"Rossi",
"Francesca",
""
],
[
"Srivastava",
"Biplav",
""
]
] |
2303.04352 | Robert Wray | Robert E. Wray, Steven J. Jones, John E. Laird | Computational-level Analysis of Constraint Compliance for General
Intelligence | 10 pages, 2 figures. Accepted for presentation at AGI 2023. Corrected
author list (segmented list) and abstract text artifacts | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Human behavior is conditioned by codes and norms that constrain action.
Rules, ``manners,'' laws, and moral imperatives are examples of classes of
constraints that govern human behavior. These systems of constraints are
"messy:" individual constraints are often poorly defined, what constraints are
relevant in a particular situation may be unknown or ambiguous, constraints
interact and conflict with one another, and determining how to act within the
bounds of the relevant constraints may be a significant challenge, especially
when rapid decisions are needed. Despite such messiness, humans incorporate
constraints in their decisions robustly and rapidly. General,
artificially-intelligent agents must also be able to navigate the messiness of
systems of real-world constraints in order to behave predictability and
reliably. In this paper, we characterize sources of complexity in constraint
processing for general agents and describe a computational-level analysis for
such constraint compliance. We identify key algorithmic requirements based on
the computational-level analysis and outline an initial, exploratory
implementation of a general approach to constraint compliance.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2023 03:25:24 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Apr 2023 17:58:41 GMT"
},
{
"version": "v3",
"created": "Thu, 15 Jun 2023 15:03:11 GMT"
}
] | 1,686,873,600,000 | [
[
"Wray",
"Robert E.",
""
],
[
"Jones",
"Steven J.",
""
],
[
"Laird",
"John E.",
""
]
] |
2303.04534 | Laura Giordano | Mario Alviano, Laura Giordano, Daniele Theseider Dupr\'e | Complexity and scalability of defeasible reasoning in many-valued
weighted knowledge bases with typicality | 14 pages 4, figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Weighted knowledge bases for description logics with typicality under a
"concept-wise" multi-preferential semantics provide a logical interpretation of
MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been
shown to be suitable for addressing defeasible reasoning in the finitely
many-valued case, providing a $\Pi^p_2$ upper bound on the complexity of the
problem, nonetheless leaving unknown the exact complexity and only providing a
proof-of-concept implementation. This paper fulfils the lack by providing a
$P^{NP[log]}$-completeness result and new ASP encodings that deal with weighted
knowledge bases with large search spaces.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2023 12:08:53 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Mar 2023 17:12:47 GMT"
}
] | 1,679,961,600,000 | [
[
"Alviano",
"Mario",
""
],
[
"Giordano",
"Laura",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
] |
2303.04571 | Yang Yuan | Yang Yuan | A Categorical Framework of General Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Can machines think? Since Alan Turing asked this question in 1950, nobody is
able to give a direct answer, due to the lack of solid mathematical foundations
for general intelligence. In this paper, we introduce a categorical framework
towards this goal, with two main results. First, we investigate object
representation through presheaves, introducing the notion of self-state
awareness as a categorical analogue to self-consciousness, along with
corresponding algorithms for its enforcement and evaluation. Secondly, we
extend object representation to scenario representation using diagrams and
limits, which then become building blocks for mathematical modeling,
interpretability and AI safety. As an ancillary result, our framework
introduces various categorical invariance properties that can serve as the
alignment signals for model training.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2023 13:37:01 GMT"
},
{
"version": "v2",
"created": "Wed, 3 May 2023 12:50:14 GMT"
}
] | 1,683,158,400,000 | [
[
"Yuan",
"Yang",
""
]
] |
2303.06152 | Seng-Beng Ho | Seng-Beng Ho | Why is That a Good or Not a Good Frying Pan? -- Knowledge Representation
for Functions of Objects and Tools for Design Understanding, Improvement, and
Generation | 11 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The understanding of the functional aspects of objects and tools is of
paramount importance in supporting an intelligent system in navigating around
in the environment and interacting with various objects, structures, and
systems, to help fulfil its goals. A detailed understanding of functionalities
can also lead to design improvements and novel designs that would enhance the
operations of AI and robotic systems on the one hand, and human lives on the
other. This paper demonstrates how a particular object - in this case, a frying
pan - and its participation in the processes it is designed to support - in
this case, the frying process - can be represented in a general function
representational language and framework, that can be used to flesh out the
processes and functionalities involved, leading to a deep conceptual
understanding with explainability of functionalities that allows the system to
answer "why" questions - why is something a good frying pan, say, or why a
certain part on the frying pan is designed in a certain way? Or, why is
something not a good frying pan? This supports the re-design and improvement on
design of objects, artifacts, and tools, as well as the potential for
generating novel designs that are functionally accurate, usable, and
satisfactory.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2023 03:47:59 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Mar 2023 09:06:48 GMT"
},
{
"version": "v3",
"created": "Tue, 21 Mar 2023 03:20:57 GMT"
}
] | 1,679,443,200,000 | [
[
"Ho",
"Seng-Beng",
""
]
] |
2303.06252 | Subhash Nerella | Subhash Nerella, Ziyuan Guan, Scott Siegel, Jiaqing Zhang, Kia
Khezeli, Azra Bihorac, Parisa Rashidi | AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with
Pervasive Sensing | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The intensive care unit (ICU) is a specialized hospital space where
critically ill patients receive intensive care and monitoring. Comprehensive
monitoring is imperative in assessing patients conditions, in particular
acuity, and ultimately the quality of care. However, the extent of patient
monitoring in the ICU is limited due to time constraints and the workload on
healthcare providers. Currently, visual assessments for acuity, including fine
details such as facial expressions, posture, and mobility, are sporadically
captured, or not captured at all. These manual observations are subjective to
the individual, prone to documentation errors, and overburden care providers
with the additional workload. Artificial Intelligence (AI) enabled systems has
the potential to augment the patient visual monitoring and assessment due to
their exceptional learning capabilities. Such systems require robust annotated
data to train. To this end, we have developed pervasive sensing and data
processing system which collects data from multiple modalities depth images,
color RGB images, accelerometry, electromyography, sound pressure, and light
levels in ICU for developing intelligent monitoring systems for continuous and
granular acuity, delirium risk, pain, and mobility assessment. This paper
presents the Intelligent Intensive Care Unit (I2CU) system architecture we
developed for real-time patient monitoring and visual assessment.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2023 00:25:55 GMT"
}
] | 1,678,752,000,000 | [
[
"Nerella",
"Subhash",
""
],
[
"Guan",
"Ziyuan",
""
],
[
"Siegel",
"Scott",
""
],
[
"Zhang",
"Jiaqing",
""
],
[
"Khezeli",
"Kia",
""
],
[
"Bihorac",
"Azra",
""
],
[
"Rashidi",
"Parisa",
""
]
] |
2303.06430 | Zijian Ding | Zijian Ding, Joel Chan | Mapping the Design Space of Interactions in Human-AI Text Co-creation
Tasks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have demonstrated impressive text generation
capabilities, prompting us to reconsider the future of human-AI co-creation and
how humans interact with LLMs. In this paper, we present a spectrum of content
generation tasks and their corresponding human-AI interaction patterns. These
tasks include: 1) fixed-scope content curation tasks with minimal human-AI
interactions, 2) independent creative tasks with precise human-AI interactions,
and 3) complex and interdependent creative tasks with iterative human-AI
interactions. We encourage the generative AI and HCI research communities to
focus on the more complex and interdependent tasks, which require greater
levels of human involvement.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2023 15:45:47 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Mar 2023 13:44:40 GMT"
}
] | 1,678,838,400,000 | [
[
"Ding",
"Zijian",
""
],
[
"Chan",
"Joel",
""
]
] |
2303.06605 | Tengtao Song | Tengtao Song, Nuo Chen, Ji Jiang, Zhihong Zhu, Yuexian Zou | Improve Retrieval-based Dialogue System via Syntax-Informed Attention | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-turn response selection is a challenging task due to its high demands
on efficient extraction of the matching features from abundant information
provided by context utterances. Since incorporating syntactic information like
dependency structures into neural models can promote a better understanding of
the sentences, such a method has been widely used in NLP tasks. Though
syntactic information helps models achieved pleasing results, its application
in retrieval-based dialogue systems has not been fully explored. Meanwhile,
previous works focus on intra-sentence syntax alone, which is far from
satisfactory for the task of multi-turn response where dialogues usually
contain multiple sentences. To this end, we propose SIA, Syntax-Informed
Attention, considering both intra- and inter-sentence syntax information. While
the former restricts attention scope to only between tokens and corresponding
dependents in the syntax tree, the latter allows attention in cross-utterance
pairs for those syntactically important tokens. We evaluate our method on three
widely used benchmarks and experimental results demonstrate the general
superiority of our method on dialogue response selection.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2023 08:14:16 GMT"
}
] | 1,678,752,000,000 | [
[
"Song",
"Tengtao",
""
],
[
"Chen",
"Nuo",
""
],
[
"Jiang",
"Ji",
""
],
[
"Zhu",
"Zhihong",
""
],
[
"Zou",
"Yuexian",
""
]
] |
2303.06691 | Kwabena Nuamah | Kwabena Nuamah and Alan Bundy | ALIST: Associative Logic for Inference, Storage and Transfer. A Lingua
Franca for Inference on the Web | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent developments in support for constructing knowledge graphs have led to
a rapid rise in their creation both on the Web and within organisations. Added
to existing sources of data, including relational databases, APIs, etc., there
is a strong demand for techniques to query these diverse sources of knowledge.
While formal query languages, such as SPARQL, exist for querying some knowledge
graphs, users are required to know which knowledge graphs they need to query
and the unique resource identifiers of the resources they need. Although
alternative techniques in neural information retrieval embed the content of
knowledge graphs in vector spaces, they fail to provide the representation and
query expressivity needed (e.g. inability to handle non-trivial aggregation
functions such as regression). We believe that a lingua franca, i.e. a
formalism, that enables such representational flexibility will increase the
ability of intelligent automated agents to combine diverse data sources by
inference.
Our work proposes a flexible representation (alists) to support intelligent
federated querying of diverse knowledge sources. Our contribution includes (1)
a formalism that abstracts the representation of queries from the specific
query language of a knowledge graph; (2) a representation to dynamically curate
data and functions (operations) to perform non-trivial inference over diverse
knowledge sources; (3) a demonstration of the expressiveness of alists to
represent the diversity of representational formalisms, including SPARQL
queries, and more generally first-order logic expressions.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2023 15:55:56 GMT"
}
] | 1,678,752,000,000 | [
[
"Nuamah",
"Kwabena",
""
],
[
"Bundy",
"Alan",
""
]
] |
2303.06775 | Hyeonchang Jeon | Hyeonchang Jeon and Kyung-Joong Kim | Behavioral Differences is the Key of Ad-hoc Team Cooperation in
Multiplayer Games Hanabi | 8 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ad-hoc team cooperation is the problem of cooperating with other players that
have not been seen in the learning process. Recently, this problem has been
considered in the context of Hanabi, which requires cooperation without
explicit communication with the other players. While in self-play strategies
cooperating on reinforcement learning (RL) process has shown success, there is
the problem of failing to cooperate with other unseen agents after the initial
learning is completed. In this paper, we categorize the results of ad-hoc team
cooperation into Failure, Success, and Synergy and analyze the associated
failures. First, we confirm that agents learning via RL converge to one
strategy each, but not necessarily the same strategy and that these agents can
deploy different strategies even though they utilize the same hyperparameters.
Second, we confirm that the larger the behavioral difference, the more
pronounced the failure of ad-hoc team cooperation, as demonstrated using
hierarchical clustering and Pearson correlation. We confirm that such agents
are grouped into distinctly different groups through hierarchical clustering,
such that the correlation between behavioral differences and ad-hoc team
performance is -0.978. Our results improve understanding of key factors to form
successful ad-hoc team cooperation in multi-player games.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2023 23:25:55 GMT"
}
] | 1,678,752,000,000 | [
[
"Jeon",
"Hyeonchang",
""
],
[
"Kim",
"Kyung-Joong",
""
]
] |
2303.07128 | Zhou Wen | Wen Zhou | VMCDL: Vulnerability Mining Based on Cascaded Deep Learning Under Source
Control Flow | The relevant mathematical derivation has some problems such as lack
of coherence, and the location of sensitive words and the formation of slices
need to be further elaborated | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development of the computer industry and computer software,
the risk of software vulnerabilities being exploited has greatly increased.
However, there are still many shortcomings in the existing mining techniques
for leakage source research, such as high false alarm rate, coarse-grained
detection, and dependence on expert experience. In this paper, we mainly use
the c/c++ source code data of the SARD dataset, process the source code of
CWE476, CWE469, CWE516 and CWE570 vulnerability types, test the Joern
vulnerability scanning function of the cutting-edge tool, and propose a new
cascading deep learning model VMCDL based on source code control flow to
effectively detect vulnerabilities. First, this paper uses joern to locate and
extract sensitive functions and statements to form a sensitive statement
library of vulnerable code. Then, the CFG flow vulnerability code snippets are
generated by bidirectional breadth-first traversal, and then vectorized by
Doc2vec. Finally, the cascade deep learning model based on source code control
flow is used for classification to obtain the classification results. In the
experimental evaluation, we give the test results of Joern on specific
vulnerabilities, and give the confusion matrix and label data of the binary
classification results of the model algorithm on single vulnerability type
source code, and compare and verify the five indicators of FPR, FNR, ACC, P and
F1, respectively reaching 10.30%, 5.20%, 92.50%,85.10% and 85.40%,which shows
that it can effectively reduce the false alarm rate of static analysis.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2023 13:58:39 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Mar 2023 14:33:30 GMT"
}
] | 1,679,875,200,000 | [
[
"Zhou",
"Wen",
""
]
] |
2303.07181 | Tim Puphal Dr. | Tim Puphal, Malte Probst and Julian Eggert | Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic
Driving | null | Transactions on Intelligent Vehicles 2019 | 10.1109/TIV.2019.2919465 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Risk assessment is a central element for the development and validation of
Autonomous Vehicles (AV). It comprises a combination of occurrence probability
and severity of future critical events. Time Headway (TH) as well as
Time-To-Contact (TTC) are commonly used risk metrics and have qualitative
relations to occurrence probability. However, they lack theoretical derivations
and additionally they are designed to only cover special types of traffic
scenarios (e.g. following between single car pairs). In this paper, we present
a probabilistic situation risk model based on survival analysis considerations
and extend it to naturally incorporate sensory, temporal and behavioral
uncertainties as they arise in real-world scenarios. The resulting Risk Spot
Detector (RSD) is applied and tested on naturalistic driving data of a
multi-lane boulevard with several intersections, enabling the visualization of
road criticality maps. Compared to TH and TTC, our approach is more selective
and specific in predicting risk. RSD concentrates on driving sections of high
vehicle density where large accelerations and decelerations or approaches with
high velocity occur.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2023 15:22:51 GMT"
}
] | 1,678,752,000,000 | [
[
"Puphal",
"Tim",
""
],
[
"Probst",
"Malte",
""
],
[
"Eggert",
"Julian",
""
]
] |
2303.07192 | Umberto Straccia | Franco Alberto Cardillo and Franca Debole and Umberto Straccia | PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL
Ontologies | arXiv admin note: substantial text overlap with arXiv:2008.05297 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | OWL ontologies are a quite popular way to describe structured knowledge in
terms of classes, relations among classes and class instances. In this paper,
given a target class T of an OWL ontology, with a focus on ontologies with
real- and boolean-valued data properties, we address the problem of learning
graded fuzzy concept inclusion axioms with the aim of describing enough
conditions for being an individual classified as instance of the class T. To do
so, we present PN-OWL that is a two-stage learning algorithm made of a P-stage
and an N-stage. Roughly, in the P-stage the algorithm tries to cover as many
positive examples as possible (increase recall), without compromising too much
precision, while in the N-stage, the algorithm tries to rule out as many false
positives, covered by the P-stage, as possible. PN-OWL then aggregates the
fuzzy inclusion axioms learnt at the P-stage and the N-stage by combining them
via aggregation functions to allow for a final decision whether an individual
is instance of T or not. We also illustrate its effectiveness by means of an
experimentation. An interesting feature is that fuzzy datatypes are built
automatically, the learnt fuzzy concept inclusions can be represented directly
into Fuzzy OWL 2 and, thus, any Fuzzy OWL 2 reasoner can then be used to
automatically determine/classify (and to which degree) whether an individual
belongs to the target class T or not.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2023 09:08:55 GMT"
}
] | 1,678,752,000,000 | [
[
"Cardillo",
"Franco Alberto",
""
],
[
"Debole",
"Franca",
""
],
[
"Straccia",
"Umberto",
""
]
] |
2303.07435 | Atrisha Sarkar | Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki | Revealed Multi-Objective Utility Aggregation in Human Driving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central design problem in game theoretic analysis is the estimation of the
players' utilities. In many real-world interactive situations of human decision
making, including human driving, the utilities are multi-objective in nature;
therefore, estimating the parameters of aggregation, i.e., mapping of
multi-objective utilities to a scalar value, becomes an essential part of game
construction. However, estimating this parameter from observational data
introduces several challenges due to a host of unobservable factors, including
the underlying modality of aggregation and the possibly boundedly rational
behaviour model that generated the observation. Based on the concept of
rationalisability, we develop algorithms for estimating multi-objective
aggregation parameters for two common aggregation methods, weighted and
satisficing aggregation, and for both strategic and non-strategic reasoning
models. Based on three different datasets, we provide insights into how human
drivers aggregate the utilities of safety and progress, as well as the
situational dependence of the aggregation process. Additionally, we show that
irrespective of the specific solution concept used for solving the games, a
data-driven estimation of utility aggregation significantly improves the
predictive accuracy of behaviour models with respect to observed human
behaviour.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2023 19:29:17 GMT"
}
] | 1,678,838,400,000 | [
[
"Sarkar",
"Atrisha",
""
],
[
"Larson",
"Kate",
""
],
[
"Czarnecki",
"Krzysztof",
""
]
] |
2303.07439 | Shimon Edelman | Shimon Edelman | On the ethics of constructing conscious AI | Revised version to appear in "Computational Approaches to Conscious
AI", edited by A. Chella (World Scientific, in preparation). arXiv admin
note: substantial text overlap with arXiv:2002.05652 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In its pragmatic turn, the new discipline of AI ethics came to be dominated
by humanity's collective fear of its creatures, as reflected in an extensive
and perennially popular literary tradition. Dr. Frankenstein's monster in the
novel by Mary Shelley rising against its creator; the unorthodox golem in H.
Leivick's 1920 play going on a rampage; the rebellious robots of Karel
\v{C}apek -- these and hundreds of other examples of the genre are the
background against which the preoccupation of AI ethics with preventing robots
from behaving badly towards people is best understood. In each of these three
fictional cases (as well as in many others), the miserable artificial creature
-- mercilessly exploited, or cornered by a murderous mob, and driven to
violence in self-defense -- has its author's sympathy. In real life, with very
few exceptions, things are different: theorists working on the ethics of AI
completely ignore the possibility of robots needing protection from their
creators. The present book chapter takes up this, less commonly considered,
ethical angle of AI.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2023 19:36:16 GMT"
}
] | 1,678,838,400,000 | [
[
"Edelman",
"Shimon",
""
]
] |
2303.07957 | Kazem Taghandiki | Kazem Taghandiki, Mohammad Hassan Ahmadi, Elnaz Rezaei Ehsan | Automatic summarisation of Instagram social network posts Combining
semantic and statistical approaches | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The proliferation of data and text documents such as articles, web pages,
books, social network posts, etc. on the Internet has created a fundamental
challenge in various fields of text processing under the title of "automatic
text summarisation". Manual processing and summarisation of large volumes of
textual data is a very difficult, expensive, time-consuming and impossible
process for human users. Text summarisation systems are divided into extractive
and abstract categories. In the extractive summarisation method, the final
summary of a text document is extracted from the important sentences of the
same document without any modification. In this method, it is possible to
repeat a series of sentences and to interfere with pronouns. However, in the
abstract summarisation method, the final summary of a textual document is
extracted from the meaning and significance of the sentences and words of the
same document or other documents. Many of the works carried out have used
extraction methods or abstracts to summarise the collection of web documents,
each of which has advantages and disadvantages in the results obtained in terms
of similarity or size. In this work, a crawler has been developed to extract
popular text posts from the Instagram social network with appropriate
preprocessing, and a set of extraction and abstraction algorithms have been
combined to show how each of the abstraction algorithms can be used.
Observations made on 820 popular text posts on the social network Instagram
show the accuracy (80%) of the proposed system.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2023 14:59:20 GMT"
}
] | 1,678,838,400,000 | [
[
"Taghandiki",
"Kazem",
""
],
[
"Ahmadi",
"Mohammad Hassan",
""
],
[
"Ehsan",
"Elnaz Rezaei",
""
]
] |
2303.08119 | Jiuhai Chen | Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou | How Many Demonstrations Do You Need for In-context Learning? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) are capable to perform complex reasoning by
in-context learning (ICL) when provided with a few input-output demonstrations
(demos) and more powerful when intermediate reasoning steps ("chain of thoughts
(CoT)") of the demos are given. Is it necessary to use multi-demo in ICL? In
this paper, we study ICL using fewer demos for each test query on the tasks
in~\cite{wei2022chain}. Surprisingly, we do not observe significant degradation
when using only one randomly chosen demo. To study this phenomenon, for each
test query, we categorize demos into "correct demos" leading to the correct
answer, and "wrong demos" resulting in wrong answers. Our analysis reveals an
inherent bias in those widely studied datasets: most demos are correct for a
majority of test queries, which explains the good performance of using one
random demo. Moreover, ICL (with and w/o CoT) using only one correct demo
significantly outperforms all-demo ICL adopted by most previous works,
indicating the weakness of LLMs in finding correct demo(s) for input queries,
which is difficult to evaluate on the biased datasets. Furthermore, we observe
a counterintuitive behavior of ICL using multi-demo, i.e., its accuracy
degrades(improves) when given more correct(wrong) demos. This implies that ICL
can be easily misguided by interference among demos and their spurious
correlations. Our analyses highlight several fundamental challenges that need
to be addressed in LLMs training, ICL, and benchmark design.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2023 17:50:45 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Apr 2023 14:38:04 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Apr 2023 22:28:54 GMT"
}
] | 1,682,467,200,000 | [
[
"Chen",
"Jiuhai",
""
],
[
"Chen",
"Lichang",
""
],
[
"Zhu",
"Chen",
""
],
[
"Zhou",
"Tianyi",
""
]
] |
2303.08177 | Andrew Smart | Jamila Smith-Loud, Andrew Smart, Darlene Neal, Amber Ebinama, Eric
Corbett, Paul Nicholas, Qazi Rashid, Anne Peckham, Sarah Murphy-Gray, Nicole
Morris, Elisha Smith Arrillaga, Nicole-Marie Cotton, Emnet Almedom, Olivia
Araiza, Eliza McCullough, Abbie Langston, Christopher Nellum | The Equitable AI Research Roundtable (EARR): Towards Community-Based
Decision Making in Responsible AI Development | 14 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper reports on our initial evaluation of The Equitable AI Research
Roundtable -- a coalition of experts in law, education, community engagement,
social justice, and technology. EARR was created in collaboration among a large
tech firm, nonprofits, NGO research institutions, and universities to provide
critical research based perspectives and feedback on technology's emergent
ethical and social harms. Through semi-structured workshops and discussions
within the large tech firm, EARR has provided critical perspectives and
feedback on how to conceptualize equity and vulnerability as they relate to AI
technology. We outline three principles in practice of how EARR has operated
thus far that are especially relevant to the concerns of the FAccT community:
how EARR expands the scope of expertise in AI development, how it fosters
opportunities for epistemic curiosity and responsibility, and that it creates a
space for mutual learning. This paper serves as both an analysis and
translation of lessons learned through this engagement approach, and the
possibilities for future research.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2023 18:57:20 GMT"
}
] | 1,678,924,800,000 | [
[
"Smith-Loud",
"Jamila",
""
],
[
"Smart",
"Andrew",
""
],
[
"Neal",
"Darlene",
""
],
[
"Ebinama",
"Amber",
""
],
[
"Corbett",
"Eric",
""
],
[
"Nicholas",
"Paul",
""
],
[
"Rashid",
"Qazi",
""
],
[
"Peckham",
"Anne",
""
],
[
"Murphy-Gray",
"Sarah",
""
],
[
"Morris",
"Nicole",
""
],
[
"Arrillaga",
"Elisha Smith",
""
],
[
"Cotton",
"Nicole-Marie",
""
],
[
"Almedom",
"Emnet",
""
],
[
"Araiza",
"Olivia",
""
],
[
"McCullough",
"Eliza",
""
],
[
"Langston",
"Abbie",
""
],
[
"Nellum",
"Christopher",
""
]
] |
2303.08264 | David Chanin | David Chanin, Anthony Hunter | Neuro-symbolic Commonsense Social Reasoning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Social norms underlie all human social interactions, yet formalizing and
reasoning with them remains a major challenge for AI systems. We present a
novel system for taking social rules of thumb (ROTs) in natural language from
the Social Chemistry 101 dataset and converting them to first-order logic where
reasoning is performed using a neuro-symbolic theorem prover. We accomplish
this in several steps. First, ROTs are converted into Abstract Meaning
Representation (AMR), which is a graphical representation of the concepts in a
sentence, and align the AMR with RoBERTa embeddings. We then generate alternate
simplified versions of the AMR via a novel algorithm, recombining and merging
embeddings for added robustness against different wordings of text, and
incorrect AMR parses. The AMR is then converted into first-order logic, and is
queried with a neuro-symbolic theorem prover. The goal of this paper is to
develop and evaluate a neuro-symbolic method which performs explicit reasoning
about social situations in a logical form.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2023 22:37:33 GMT"
}
] | 1,678,924,800,000 | [
[
"Chanin",
"David",
""
],
[
"Hunter",
"Anthony",
""
]
] |
2303.08546 | Fuhui Zhou | Fuhui Zhou and Yihao Li and Ming Xu and Lu Yuan and Qihui Wu and Rose
Qingyang Hu and Naofal Al-Dhahir | Cognitive Semantic Communication Systems Driven by Knowledge Graph:
Principle, Implementation, and Performance Evaluation | arXiv admin note: text overlap with arXiv:2202.11958 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic communication is envisioned as a promising technique to break
through the Shannon limit. However, semantic inference and semantic error
correction have not been well studied. Moreover, error correction methods of
existing semantic communication frameworks are inexplicable and inflexible,
which limits the achievable performance. In this paper, to tackle this issue, a
knowledge graph is exploited to develop semantic communication systems. Two
cognitive semantic communication frameworks are proposed for the single-user
and multiple-user communication scenarios. Moreover, a simple, general, and
interpretable semantic alignment algorithm for semantic information detection
is proposed. Furthermore, an effective semantic correction algorithm is
proposed by mining the inference rule from the knowledge graph. Additionally,
the pre-trained model is fine-tuned to recover semantic information. For the
multi-user cognitive semantic communication system, a message recovery
algorithm is proposed to distinguish messages of different users by matching
the knowledge level between the source and the destination. Extensive
simulation results conducted on a public dataset demonstrate that our proposed
single-user and multi-user cognitive semantic communication systems are
superior to benchmark communication systems in terms of the data compression
rate and communication reliability. Finally, we present realistic single-user
and multi-user cognitive semantic communication systems results by building a
software-defined radio prototype system.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2023 12:01:43 GMT"
}
] | 1,678,924,800,000 | [
[
"Zhou",
"Fuhui",
""
],
[
"Li",
"Yihao",
""
],
[
"Xu",
"Ming",
""
],
[
"Yuan",
"Lu",
""
],
[
"Wu",
"Qihui",
""
],
[
"Hu",
"Rose Qingyang",
""
],
[
"Al-Dhahir",
"Naofal",
""
]
] |
2303.08792 | Kazem Taghandiki | Kazem Taghandiki | Building an Effective Email Spam Classification Model with spaCy | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Today, people use email services such as Gmail, Outlook, AOL Mail, etc. to
communicate with each other as quickly as possible to send information and
official letters. Spam or junk mail is a major challenge to this type of
communication, usually sent by botnets with the aim of advertising, harming and
stealing information in bulk to different people. Receiving unwanted spam
emails on a daily basis fills up the inbox folder. Therefore, spam detection is
a fundamental challenge, so far many works have been done to detect spam using
clustering and text categorisation methods. In this article, the author has
used the spaCy natural language processing library and 3 machine learning (ML)
algorithms Naive Bayes (NB), Decision Tree C45 and Multilayer Perceptron (MLP)
in the Python programming language to detect spam emails collected from the
Gmail service. Observations show the accuracy rate (96%) of the Multilayer
Perceptron (MLP) algorithm in spam detection.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2023 17:41:11 GMT"
}
] | 1,678,924,800,000 | [
[
"Taghandiki",
"Kazem",
""
]
] |
2303.09056 | Gary An | Gary An and Chase Cockrell | Generating synthetic multi-dimensional molecular-mediator time series
data for artificial intelligence-based disease trajectory forecasting and
drug development digital twins: Considerations | 16 pages, 2 Figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The use of synthetic data is recognized as a crucial step in the development
of neural network-based Artificial Intelligence (AI) systems. While the methods
for generating synthetic data for AI applications in other domains have a role
in certain biomedical AI systems, primarily related to image processing, there
is a critical gap in the generation of time series data for AI tasks where it
is necessary to know how the system works. This is most pronounced in the
ability to generate synthetic multi-dimensional molecular time series data
(SMMTSD); this is the type of data that underpins research into biomarkers and
mediator signatures for forecasting various diseases and is an essential
component of the drug development pipeline. We argue the insufficiency of
statistical and data-centric machine learning (ML) means of generating this
type of synthetic data is due to a combination of factors: perpetual data
sparsity due to the Curse of Dimensionality, the inapplicability of the Central
Limit Theorem, and the limits imposed by the Causal Hierarchy Theorem.
Alternatively, we present a rationale for using complex multi-scale
mechanism-based simulation models, constructed and operated on to account for
epistemic incompleteness and the need to provide maximal expansiveness in
concordance with the Principle of Maximal Entropy. These procedures provide for
the generation of SMMTD that minimizes the known shortcomings associated with
neural network AI systems, namely overfitting and lack of generalizability. The
generation of synthetic data that accounts for the identified factors of
multi-dimensional time series data is an essential capability for the
development of mediator-biomarker based AI forecasting systems, and therapeutic
control development and optimization through systems like Drug Development
Digital Twins.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 03:13:53 GMT"
}
] | 1,679,011,200,000 | [
[
"An",
"Gary",
""
],
[
"Cockrell",
"Chase",
""
]
] |
2303.09058 | Shuhao Zhang | Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang, Jing Xiao, Xuan
Wang | SVDE: Scalable Value-Decomposition Exploration for Cooperative
Multi-Agent Reinforcement Learning | 13 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Value-decomposition methods, which reduce the difficulty of a multi-agent
system by decomposing the joint state-action space into local
observation-action spaces, have become popular in cooperative multi-agent
reinforcement learning (MARL). However, value-decomposition methods still have
the problems of tremendous sample consumption for training and lack of active
exploration. In this paper, we propose a scalable value-decomposition
exploration (SVDE) method, which includes a scalable training mechanism,
intrinsic reward design, and explorative experience replay. The scalable
training mechanism asynchronously decouples strategy learning with
environmental interaction, so as to accelerate sample generation in a MapReduce
manner. For the problem of lack of exploration, an intrinsic reward design and
explorative experience replay are proposed, so as to enhance exploration to
produce diverse samples and filter non-novel samples, respectively.
Empirically, our method achieves the best performance on almost all maps
compared to other popular algorithms in a set of StarCraft II micromanagement
games. A data-efficiency experiment also shows the acceleration of SVDE for
sample collection and policy convergence, and we demonstrate the effectiveness
of factors in SVDE through a set of ablation experiments.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 03:17:20 GMT"
}
] | 1,679,011,200,000 | [
[
"Qi",
"Shuhan",
""
],
[
"Zhang",
"Shuhao",
""
],
[
"Wang",
"Qiang",
""
],
[
"Zhang",
"Jiajia",
""
],
[
"Xiao",
"Jing",
""
],
[
"Wang",
"Xuan",
""
]
] |
2303.09197 | Yann Munro | Y. Munro (1), C. Sarmiento (1), I. Bloch (1), G. Bourgne (1), M.-J.
Lesot (1) ((1) Sorbonne Universit\'e, CNRS, LIP6, Paris, France) | Integrating Temporality and Causality into Acyclic Argumentation
Frameworks using a Transition System | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In the context of abstract argumentation, we present the benefits of
considering temporality, i.e. the order in which arguments are enunciated, as
well as causality. We propose a formal method to rewrite the concepts of
acyclic abstract argumentation frameworks into an action language, that allows
us to model the evolution of the world, and to establish causal relationships
between the enunciation of arguments and their consequences, whether direct or
indirect. An Answer Set Programming implementation is also proposed, as well as
perspectives towards explanations.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 10:13:47 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Feb 2024 08:42:45 GMT"
}
] | 1,707,264,000,000 | [
[
"Munro",
"Y.",
"",
"Sorbonne Université, CNRS, LIP6, Paris, France"
],
[
"Sarmiento",
"C.",
"",
"Sorbonne Université, CNRS, LIP6, Paris, France"
],
[
"Bloch",
"I.",
"",
"Sorbonne Université, CNRS, LIP6, Paris, France"
],
[
"Bourgne",
"G.",
"",
"Sorbonne Université, CNRS, LIP6, Paris, France"
],
[
"Lesot",
"M. -J.",
"",
"Sorbonne Université, CNRS, LIP6, Paris, France"
]
] |
2303.09209 | Massimiliano Ronzani | Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino, Chiara
Ghidini, Francesca Meneghello, Massimiliano Ronzani | Recommending the optimal policy by learning to act from temporal data | 10 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Prescriptive Process Monitoring is a prominent problem in Process Mining,
which consists in identifying a set of actions to be recommended with the goal
of optimising a target measure of interest or Key Performance Indicator (KPI).
One challenge that makes this problem difficult is the need to provide
Prescriptive Process Monitoring techniques only based on temporally annotated
(process) execution data, stored in, so-called execution logs, due to the lack
of well crafted and human validated explicit models. In this paper we aim at
proposing an AI based approach that learns, by means of Reinforcement Learning
(RL), an optimal policy (almost) only from the observation of past executions
and recommends the best activities to carry on for optimizing a KPI of
interest. This is achieved first by learning a Markov Decision Process for the
specific KPIs from data, and then by using RL training to learn the optimal
policy. The approach is validated on real and synthetic datasets and compared
with off-policy Deep RL approaches. The ability of our approach to compare
with, and often overcome, Deep RL approaches provides a contribution towards
the exploitation of white box RL techniques in scenarios where only temporal
execution data are available.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 10:30:36 GMT"
}
] | 1,679,011,200,000 | [
[
"Branchi",
"Stefano",
""
],
[
"Buliga",
"Andrei",
""
],
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Meneghello",
"Francesca",
""
],
[
"Ronzani",
"Massimiliano",
""
]
] |
2303.09311 | Frederic lardeux | Tomasz Jastrzab, Fr\'ed\'eric Lardeux (LERIA), Eric Monfroy (LERIA) | Taking advantage of a very simple property to efficiently infer NFAs | null | 2022 IEEE 34rd International Conference on Tools with Artificial
Intelligence (ICTAI), Oct 2022, Virtual, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Grammatical inference consists in learning a formal grammar as a finite state
machine or as a set of rewrite rules. In this paper, we are concerned with
inferring Nondeterministic Finite Automata (NFA) that must accept some words,
and reject some other words from a given sample. This problem can naturally be
modeled in SAT. The standard model being enormous, some models based on
prefixes, suffixes, and hybrids were designed to generate smaller SAT
instances. There is a very simple and obvious property that says: if there is
an NFA of size k for a given sample, there is also an NFA of size k+1. We first
strengthen this property by adding some characteristics to the NFA of size k+1.
Hence, we can use this property to tighten the bounds of the size of the
minimal NFA for a given sample. We then propose simplified and refined models
for NFA of size k+1 that are smaller than the initial models for NFA of size k.
We also propose a reduction algorithm to build an NFA of size k from a specific
NFA of size k+1. Finally, we validate our proposition with some experimentation
that shows the efficiency of our approach.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 13:36:55 GMT"
}
] | 1,679,011,200,000 | [
[
"Jastrzab",
"Tomasz",
"",
"LERIA"
],
[
"Lardeux",
"Frédéric",
"",
"LERIA"
],
[
"Monfroy",
"Eric",
"",
"LERIA"
]
] |
2303.09449 | Jakub Kowalski | Jakub Kowalski, Elliot Doe, Mark H. M. Winands, Daniel G\'orski,
Dennis J. N. J. Soemers | Proof Number Based Monte-Carlo Tree Search | Extended version of IEEE Transactions on Games 2024 article (which is
a journal version of IEEE CoG 2022 paper available at arXiv:2206.03965) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper proposes a new game-search algorithm, PN-MCTS, which combines
Monte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These two
algorithms have been successfully applied for decision making in a range of
domains. We define three areas where the additional knowledge provided by the
proof and disproof numbers gathered in MCTS trees might be used: final move
selection, solving subtrees, and the UCB1 selection mechanism. We test all
possible combinations on different time settings, playing against vanilla UCT
on several games: Lines of Action ($7$$\times$$7$ and $8$$\times$$8$ board
sizes), MiniShogi, Knightthrough, and Awari. Furthermore, we extend this new
algorithm to properly address games with draws, like Awari, by adding an
additional layer of PNS on top of the MCTS tree. The experiments show that
PN-MCTS is able to outperform MCTS in all tested game domains, achieving win
rates up to 96.2% for Lines of Action.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 16:27:07 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Dec 2023 18:30:55 GMT"
},
{
"version": "v3",
"created": "Sat, 25 May 2024 16:39:16 GMT"
},
{
"version": "v4",
"created": "Wed, 29 May 2024 06:49:31 GMT"
}
] | 1,717,027,200,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Doe",
"Elliot",
""
],
[
"Winands",
"Mark H. M.",
""
],
[
"Górski",
"Daniel",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
]
] |
2303.10118 | Susana Hahn Martin Lunas | Susana Hahn, Orkunt Sabuncu, Torsten Schaub, Tobias Stolzmann | Clingraph: A System for ASP-based Visualization | Short version presented at the International Conference on Logic
Programming and Non-monotonic Reasoning (LPNMR'22). Extended version under
consideration in Theory and Practice of Logic Programming (TPLP'22), 24
pages, 10 figures | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | We present the ASP-based visualization tool, clingraph, which aims at
visualizing various concepts of ASP by means of ASP itself. This idea traces
back to the aspviz tool and clingraph redevelops and extends it in the context
of modern ASP systems. More precisely, clingraph takes graph specifications in
terms of ASP facts and hands them over to the graph visualization system
graphviz. The use of ASP provides a great interface between logic programs
and/or answer sets and their visualization. Also, clingraph offers a python API
that extends this ease of interfacing to clingo's API, and in turn to connect
and monitor various aspects of the solving process.
| [
{
"version": "v1",
"created": "Fri, 17 Mar 2023 16:59:14 GMT"
}
] | 1,681,430,400,000 | [
[
"Hahn",
"Susana",
""
],
[
"Sabuncu",
"Orkunt",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Stolzmann",
"Tobias",
""
]
] |
2303.10142 | Samuel Magaz-Romero | Vicente Moret-Bonillo, Eduardo Mosqueira-Rey, Samuel Magaz-Romero,
Diego Alvarez-Estevez | Hybrid Classic-Quantum Computing for Staging of Invasive Ductal
Carcinoma of Breast | Submitted to Information (ISSN 2078-2489) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite the great current relevance of Artificial Intelligence, and the
extraordinary innovations that this discipline has brought to many fields
-among which, without a doubt, medicine is found-, experts in medical
applications of Artificial Intelligence are looking for new alternatives to
solve problems for which current Artificial Intelligence programs do not
provide with optimal solutions. For this, one promising option could be the use
of the concepts and ideas of Quantum Mechanics, for the construction of
quantum-based Artificial Intelligence systems. From a hybrid classical-quantum
perspective, this article deals with the application of quantum computing
techniques for the staging of Invasive Ductal Carcinoma of the breast. It
includes: (1) a general explanation of a classical, and well-established,
approach for medical reasoning, (2) a description of the clinical problem, (3)
a conceptual model for staging invasive ductal carcinoma, (4) some basic
notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the
proposed approach for quantum staging of the invasive ductal carcinoma, and (6)
the results obtained after running the quantum system on a significant number
of use cases. A detailed discussion is also provided at the end of this paper.
| [
{
"version": "v1",
"created": "Fri, 17 Mar 2023 17:28:30 GMT"
}
] | 1,679,270,400,000 | [
[
"Moret-Bonillo",
"Vicente",
""
],
[
"Mosqueira-Rey",
"Eduardo",
""
],
[
"Magaz-Romero",
"Samuel",
""
],
[
"Alvarez-Estevez",
"Diego",
""
]
] |
2303.10268 | Giuseppe Sanfilippo | Angelo Gilio, David E. Over, Niki Pfeifer, Giuseppe Sanfilippo | On Trivalent Logics, Compound Conditionals, and Probabilistic Deduction
Theorems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we recall some results for conditional events, compound
conditionals, conditional random quantities, p-consistency, and p-entailment.
Then, we show the equivalence between bets on conditionals and conditional
bets, by reviewing de Finetti's trivalent analysis of conditionals. But our
approach goes beyond de Finetti's early trivalent logical analysis and is based
on his later ideas, aiming to take his proposals to a higher level. We examine
two recent articles that explore trivalent logics for conditionals and their
definitions of logical validity and compare them with our approach to compound
conditionals. We prove a Probabilistic Deduction Theorem for conditional
events. After that, we study some probabilistic deduction theorems, by
presenting several examples. We focus on iterated conditionals and the
invalidity of the Import-Export principle in the light of our Probabilistic
Deduction Theorem. We use the inference from a disjunction, "$A$ or $B$", to
the conditional,"if not-$A$ then $B$", as an example to show the invalidity of
the Import-Export principle. We also introduce a General Import-Export
principle and we illustrate it by examining some p-valid inference rules of
System P. Finally, we briefly discuss some related work relevant to AI.
| [
{
"version": "v1",
"created": "Fri, 17 Mar 2023 22:35:06 GMT"
}
] | 1,679,356,800,000 | [
[
"Gilio",
"Angelo",
""
],
[
"Over",
"David E.",
""
],
[
"Pfeifer",
"Niki",
""
],
[
"Sanfilippo",
"Giuseppe",
""
]
] |
2303.10714 | Aldo Ricioppo | Giovanni Amendola, Marco Manna, Aldo Ricioppo | Characterizing Nexus of Similarity within Knowledge Bases: A Logic-based
Framework and its Computational Complexity Aspects | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Similarities between entities occur frequently in many real-world scenarios.
For over a century, researchers in different fields have proposed a range of
approaches to measure the similarity between entities. More recently, inspired
by "Google Sets", significant academic and commercial efforts have been devoted
to expanding a given set of entities with similar ones. As a result, existing
approaches nowadays are able to take into account properties shared by
entities, hereinafter called nexus of similarity. Accordingly, machines are
largely able to deal with both similarity measures and set expansions. To the
best of our knowledge, however, there is no way to characterize nexus of
similarity between entities, namely identifying such nexus in a formal and
comprehensive way so that they are both machine- and human-readable; moreover,
there is a lack of consensus on evaluating existing approaches for weakly
similar entities. As a first step towards filling these gaps, we aim to
complement existing literature by developing a novel logic-based framework to
formally and automatically characterize nexus of similarity between tuples of
entities within a knowledge base. Furthermore, we analyze computational
complexity aspects of this framework.
| [
{
"version": "v1",
"created": "Sun, 19 Mar 2023 16:50:59 GMT"
}
] | 1,679,356,800,000 | [
[
"Amendola",
"Giovanni",
""
],
[
"Manna",
"Marco",
""
],
[
"Ricioppo",
"Aldo",
""
]
] |
2303.11899 | Hankang Gu | Hankang Gu, Shangbo Wang, Xiaoguang Ma, Dongyao Jia, Guoqiang Mao, Eng
Gee Lim, Cheuk Pong Ryan Wong | Large-Scale Traffic Signal Control Using Constrained Network Partition
and Adaptive Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-agent Deep Reinforcement Learning (MADRL) based traffic signal control
becomes a popular research topic in recent years. To alleviate the scalability
issue of completely centralized RL techniques and the non-stationarity issue of
completely decentralized RL techniques on large-scale traffic networks, some
literature utilizes a regional control approach where the whole network is
firstly partitioned into multiple disjoint regions, followed by applying the
centralized RL approach to each region. However, the existing partitioning
rules either have no constraints on the topology of regions or require the same
topology for all regions. Meanwhile, no existing regional control approach
explores the performance of optimal joint action in an exponentially growing
regional action space when intersections are controlled by 4-phase traffic
signals (EW, EWL, NS, NSL). In this paper, we propose a novel RL training
framework named RegionLight to tackle the above limitations. Specifically, the
topology of regions is firstly constrained to a star network which comprises
one center and an arbitrary number of leaves. Next, the network partitioning
problem is modeled as an optimization problem to minimize the number of
regions. Then, an Adaptive Branching Dueling Q-Network (ABDQ) model is proposed
to decompose the regional control task into several joint signal control
sub-tasks corresponding to particular intersections. Subsequently, these
sub-tasks maximize the regional benefits cooperatively. Finally, the global
control strategy for the whole network is obtained by concatenating the optimal
joint actions of all regions. Experimental results demonstrate the superiority
of our proposed framework over all baselines under both real and synthetic
datasets in all evaluation metrics.
| [
{
"version": "v1",
"created": "Tue, 21 Mar 2023 14:42:58 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Mar 2023 07:34:22 GMT"
},
{
"version": "v3",
"created": "Fri, 7 Apr 2023 06:38:44 GMT"
},
{
"version": "v4",
"created": "Mon, 26 Jun 2023 04:08:48 GMT"
},
{
"version": "v5",
"created": "Thu, 7 Sep 2023 04:42:45 GMT"
}
] | 1,694,131,200,000 | [
[
"Gu",
"Hankang",
""
],
[
"Wang",
"Shangbo",
""
],
[
"Ma",
"Xiaoguang",
""
],
[
"Jia",
"Dongyao",
""
],
[
"Mao",
"Guoqiang",
""
],
[
"Lim",
"Eng Gee",
""
],
[
"Wong",
"Cheuk Pong Ryan",
""
]
] |
2303.12040 | Mark Stefik | Mark Stefik | Roots and Requirements for Collaborative AIs | 24 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The vision of AI collaborators is a staple of mythology and science fiction,
where artificial agents with special talents assist human partners and teams.
In this dream, sophisticated AIs understand nuances of collaboration and human
communication. The AI as collaborator dream is different from computer tools
that augment human intelligence (IA) or intermediate human collaboration. Those
tools have their roots in the 1960s and helped to drive an information
technology revolution. They can be useful but they are not intelligent and do
not collaborate as effectively as skilled people. With the increase of hybrid
and remote work since the COVID pandemic, the benefits and requirements for
better coordination, collaboration, and communication are becoming hot topics
in the workplace. Employers and workers face choices and trade-offs as they
negotiate the options for working from home versus working at the office. Many
factors such as the high costs of homes near employers are impeding a mass
return to the office. Government advisory groups and leaders in AI have
advocated for years that AIs should be transparent and effective collaborators.
Nonetheless, robust AIs that collaborate like talented people remain out of
reach. Are AI teammates part of a solution? How artificially intelligent (AI)
could and should they be? This position paper reviews the arc of technology and
public calls for human-machine teaming. It draws on earlier research in
psychology and the social sciences about what human-like collaboration
requires. This paper sets a context for a second science-driven paper that
advocates a radical shift in technology and methodology for creating resilient,
intelligent, and human-compatible AIs (Stefik & Price, 2023). The aspirational
goal is that such AIs would learn, share what they learn, and collaborate to
achieve high capabilities.
| [
{
"version": "v1",
"created": "Tue, 21 Mar 2023 17:27:38 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Jun 2023 21:06:00 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Nov 2023 16:53:26 GMT"
},
{
"version": "v4",
"created": "Thu, 2 Nov 2023 23:00:28 GMT"
},
{
"version": "v5",
"created": "Tue, 7 Nov 2023 17:57:12 GMT"
},
{
"version": "v6",
"created": "Thu, 28 Dec 2023 17:52:14 GMT"
},
{
"version": "v7",
"created": "Mon, 22 Apr 2024 20:56:24 GMT"
}
] | 1,713,916,800,000 | [
[
"Stefik",
"Mark",
""
]
] |
2303.12336 | Siyuan Feng | Siyuan Feng, Taijie Chen, Yuhao Zhang, Jintao Ke, Zhengfei Zheng and
Hai Yang | A multi-functional simulation platform for on-demand ride service
operations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On-demand ride services or ride-sourcing services have been experiencing fast
development in the past decade. Various mathematical models and optimization
algorithms have been developed to help ride-sourcing platforms design
operational strategies with higher efficiency. However, due to cost and
reliability issues (implementing an immature algorithm for real operations may
result in system turbulence), it is commonly infeasible to validate these
models and train/test these optimization algorithms within real-world ride
sourcing platforms. Acting as a useful test bed, a simulation platform for
ride-sourcing systems will be very important to conduct algorithm
training/testing or model validation through trails and errors. While previous
studies have established a variety of simulators for their own tasks, it lacks
a fair and public platform for comparing the models or algorithms proposed by
different researchers. In addition, the existing simulators still face many
challenges, ranging from their closeness to real environments of ride-sourcing
systems, to the completeness of different tasks they can implement. To address
the challenges, we propose a novel multi-functional and open-sourced simulation
platform for ride-sourcing systems, which can simulate the behaviors and
movements of various agents on a real transportation network. It provides a few
accessible portals for users to train and test various optimization algorithms,
especially reinforcement learning algorithms, for a variety of tasks, including
on-demand matching, idle vehicle repositioning, and dynamic pricing. In
addition, it can be used to test how well the theoretical models approximate
the simulated outcomes. Evaluated on real-world data based experiments, the
simulator is demonstrated to be an efficient and effective test bed for various
tasks related to on-demand ride service operations.
| [
{
"version": "v1",
"created": "Wed, 22 Mar 2023 06:25:19 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Aug 2023 09:47:18 GMT"
}
] | 1,691,366,400,000 | [
[
"Feng",
"Siyuan",
""
],
[
"Chen",
"Taijie",
""
],
[
"Zhang",
"Yuhao",
""
],
[
"Ke",
"Jintao",
""
],
[
"Zheng",
"Zhengfei",
""
],
[
"Yang",
"Hai",
""
]
] |
2303.13191 | Giorgio Terracina | Francesco Cauteruccio and Giorgio Terracina | Extended High Utility Pattern Mining: An Answer Set Programming Based
Framework and Applications | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Detecting sets of relevant patterns from a given dataset is an important
challenge in data mining. The relevance of a pattern, also called utility in
the literature, is a subjective measure and can be actually assessed from very
different points of view. Rule-based languages like Answer Set Programming
(ASP) seem well suited for specifying user-provided criteria to assess pattern
utility in a form of constraints; moreover, declarativity of ASP allows for a
very easy switch between several criteria in order to analyze the dataset from
different points of view. In this paper, we make steps toward extending the
notion of High Utility Pattern Mining (HUPM); in particular we introduce a new
framework that allows for new classes of utility criteria not considered in the
previous literature. We also show how recent extensions of ASP with external
functions can support a fast and effective encoding and testing of the new
framework. To demonstrate the potential of the proposed framework, we exploit
it as a building block for the definition of an innovative method for
predicting ICU admission for COVID-19 patients. Finally, an extensive
experimental activity demonstrates both from a quantitative and a qualitative
point of view the effectiveness of the proposed approach. Under consideration
in Theory and Practice of Logic Programming (TPLP)
| [
{
"version": "v1",
"created": "Thu, 23 Mar 2023 11:42:57 GMT"
}
] | 1,679,616,000,000 | [
[
"Cauteruccio",
"Francesco",
""
],
[
"Terracina",
"Giorgio",
""
]
] |
2303.13494 | Thomas Bolander | Gaia Belardinelli and Thomas Bolander | Attention! Dynamic Epistemic Logic Models of (In)attentive Agents | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Attention is the crucial cognitive ability that limits and selects what
information we observe. Previous work by Bolander et al. (2016) proposes a
model of attention based on dynamic epistemic logic (DEL) where agents are
either fully attentive or not attentive at all. While introducing the realistic
feature that inattentive agents believe nothing happens, the model does not
represent the most essential aspect of attention: its selectivity. Here, we
propose a generalization that allows for paying attention to subsets of atomic
formulas. We introduce the corresponding logic for propositional attention, and
show its axiomatization to be sound and complete. We then extend the framework
to account for inattentive agents that, instead of assuming nothing happens,
may default to a specific truth-value of what they failed to attend to (a sort
of prior concerning the unattended atoms). This feature allows for a more
cognitively plausible representation of the inattentional blindness phenomenon,
where agents end up with false beliefs due to their failure to attend to
conspicuous but unexpected events. Both versions of the model define
attention-based learning through appropriate DEL event models based on a few
and clear edge principles. While the size of such event models grow
exponentially both with the number of agents and the number of atoms, we
introduce a new logical language for describing event models syntactically and
show that using this language our event models can be represented linearly in
the number of agents and atoms. Furthermore, representing our event models
using this language is achieved by a straightforward formalisation of the
aforementioned edge principles.
| [
{
"version": "v1",
"created": "Thu, 23 Mar 2023 17:55:32 GMT"
},
{
"version": "v2",
"created": "Thu, 18 May 2023 13:41:27 GMT"
}
] | 1,684,454,400,000 | [
[
"Belardinelli",
"Gaia",
""
],
[
"Bolander",
"Thomas",
""
]
] |
2303.13512 | Sander Schulhoff | Stephanie Milani, Anssi Kanervisto, Karolis Ramanauskas, Sander
Schulhoff, Brandon Houghton, Sharada Mohanty, Byron Galbraith, Ke Chen, Yan
Song, Tianze Zhou, Bingquan Yu, He Liu, Kai Guan, Yujing Hu, Tangjie Lv,
Federico Malato, Florian Leopold, Amogh Raut, Ville Hautam\"aki, Andrew
Melnik, Shu Ishida, Jo\~ao F. Henriques, Robert Klassert, Walter Laurito,
Ellen Novoseller, Vinicius G. Goecks, Nicholas Waytowich, David Watkins, Josh
Miller, Rohin Shah | Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the
MineRL BASALT 2022 Competition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To facilitate research in the direction of fine-tuning foundation models from
human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human
Feedback at NeurIPS 2022. The BASALT challenge asks teams to compete to develop
algorithms to solve tasks with hard-to-specify reward functions in Minecraft.
Through this competition, we aimed to promote the development of algorithms
that use human feedback as channels to learn the desired behavior. We describe
the competition and provide an overview of the top solutions. We conclude by
discussing the impact of the competition and future directions for improvement.
| [
{
"version": "v1",
"created": "Thu, 23 Mar 2023 17:59:17 GMT"
}
] | 1,679,616,000,000 | [
[
"Milani",
"Stephanie",
""
],
[
"Kanervisto",
"Anssi",
""
],
[
"Ramanauskas",
"Karolis",
""
],
[
"Schulhoff",
"Sander",
""
],
[
"Houghton",
"Brandon",
""
],
[
"Mohanty",
"Sharada",
""
],
[
"Galbraith",
"Byron",
""
],
[
"Chen",
"Ke",
""
],
[
"Song",
"Yan",
""
],
[
"Zhou",
"Tianze",
""
],
[
"Yu",
"Bingquan",
""
],
[
"Liu",
"He",
""
],
[
"Guan",
"Kai",
""
],
[
"Hu",
"Yujing",
""
],
[
"Lv",
"Tangjie",
""
],
[
"Malato",
"Federico",
""
],
[
"Leopold",
"Florian",
""
],
[
"Raut",
"Amogh",
""
],
[
"Hautamäki",
"Ville",
""
],
[
"Melnik",
"Andrew",
""
],
[
"Ishida",
"Shu",
""
],
[
"Henriques",
"João F.",
""
],
[
"Klassert",
"Robert",
""
],
[
"Laurito",
"Walter",
""
],
[
"Novoseller",
"Ellen",
""
],
[
"Goecks",
"Vinicius G.",
""
],
[
"Waytowich",
"Nicholas",
""
],
[
"Watkins",
"David",
""
],
[
"Miller",
"Josh",
""
],
[
"Shah",
"Rohin",
""
]
] |
2303.13531 | Irina Lomazova | Antonina K. Begicheva, Irina A. Lomazova, Roman A. Nesterov | Discovering Hierarchical Process Models: an Approach Based on Events
Clustering | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Process mining is a field of computer science that deals with discovery and
analysis of process models based on automatically generated event logs.
Currently, many companies use this technology for optimization and improving
their processes. However, a discovered process model may be too detailed,
sophisticated and difficult for experts to understand. In this paper, we
consider the problem of discovering a hierarchical business process model from
a low-level event log, i.e., the problem of automatic synthesis of more
readable and understandable process models based on information stored in event
logs of information systems.
Discovery of better structured and more readable process models is
intensively studied in the frame of process mining research from different
perspectives. In this paper, we present an algorithm for discovering
hierarchical process models represented as two-level workflow nets. The
algorithm is based on predefined event ilustering so that the cluster defines a
sub-process corresponding to a high-level transition at the top level of the
net. Unlike existing solutions, our algorithm does not impose restrictions on
the process control flow and allows for concurrency and iteration.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2023 11:05:40 GMT"
}
] | 1,679,875,200,000 | [
[
"Begicheva",
"Antonina K.",
""
],
[
"Lomazova",
"Irina A.",
""
],
[
"Nesterov",
"Roman A.",
""
]
] |
2303.13532 | Ala-Eddine Yahiaoui | Ala-Eddine Yahiaoui, Sohaib Afifi and Hamid Afifi | Enhanced Iterated local search for the technician routing and scheduling
problem | Submitted manuscript to Computers and Operations Research journal. 34
pages, 7 figures, 6 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Most public facilities in the European countries, including France, Germany,
and the UK, were built during the reconstruction projects between 1950 and
1980. Owing to the deteriorating state of such vital infrastructure has become
relatively expensive in the recent decades. A significant part of the
maintenance operation costs is spent on the technical staff. Therefore, the
optimal use of the available workforce is essential to optimize the operation
costs. This includes planning technical interventions, workload balancing,
productivity improvement, etc. In this paper, we focus on the routing of
technicians and scheduling of their tasks. We address for this purpose a
variant of the workforce scheduling problem called the technician routing and
scheduling problem (TRSP). This problem has applications in different fields,
such as transportation infrastructure (rail and road networks),
telecommunications, and sewage facilities. To solve the TRSP, we propose an
enhanced iterated local search (eILS) approach. The enhancement of the ILS
firstly includes an intensification procedure that incorporates a set of local
search operators and removal-repair heuristics crafted for the TRSP. Next, four
different mechanisms are used in the perturbation phase. Finally, an elite set
of solutions is used to extensively explore the neighborhood of local optima as
well as to enhance diversification during search space exploration. To measure
the performance of the proposed method, experiments were conducted based on
benchmark instances from the literature, and the results obtained were compared
with those of an existing method. Our method achieved very good results, since
it reached the best overall gap, which is three times lower than that of the
literature. Furthermore, eILS improved the best-known solution for $34$
instances among a total of $56$ while maintaining reasonable computational
times.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2023 23:44:49 GMT"
}
] | 1,679,875,200,000 | [
[
"Yahiaoui",
"Ala-Eddine",
""
],
[
"Afifi",
"Sohaib",
""
],
[
"Afifi",
"Hamid",
""
]
] |
2303.13948 | Ciyuan Peng | Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, Francesco Osborne | Knowledge Graphs: Opportunities and Challenges | 43pages, 5 figures, 3 tables | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | With the explosive growth of artificial intelligence (AI) and big data, it
has become vitally important to organize and represent the enormous volume of
knowledge appropriately. As graph data, knowledge graphs accumulate and convey
knowledge of the real world. It has been well-recognized that knowledge graphs
effectively represent complex information; hence, they rapidly gain the
attention of academia and industry in recent years. Thus to develop a deeper
understanding of knowledge graphs, this paper presents a systematic overview of
this field. Specifically, we focus on the opportunities and challenges of
knowledge graphs. We first review the opportunities of knowledge graphs in
terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential
application fields of knowledge graphs. Then, we thoroughly discuss severe
technical challenges in this field, such as knowledge graph embeddings,
knowledge acquisition, knowledge graph completion, knowledge fusion, and
knowledge reasoning. We expect that this survey will shed new light on future
research and the development of knowledge graphs.
| [
{
"version": "v1",
"created": "Fri, 24 Mar 2023 12:10:42 GMT"
}
] | 1,679,875,200,000 | [
[
"Peng",
"Ciyuan",
""
],
[
"Xia",
"Feng",
""
],
[
"Naseriparsa",
"Mehdi",
""
],
[
"Osborne",
"Francesco",
""
]
] |
2303.14332 | Ashwin Kumar | Ashwin Kumar, Yevgeniy Vorobeychik, William Yeoh | Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing
Systems | Accepted for publication at the International Conference on Automated
Planning and Scheduling (ICAPS) 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | State-of-the-art order dispatching algorithms for ridesharing batch passenger
requests and allocate them to a fleet of vehicles in a centralized manner,
optimizing over the estimated values of each passenger-vehicle matching using
integer linear programming (ILP). Using good estimates of future values, such
ILP-based approaches are able to significantly increase the service rates
(percentage of requests served) for a fixed fleet of vehicles. However, such
approaches that focus solely on maximizing efficiency can lead to disparities
for both drivers (e.g., income inequality) and passengers (e.g., inequality of
service for different groups). Existing approaches that consider fairness only
do it for naive assignment policies, require extensive training, or look at
only single-sided fairness. We propose a simple incentive-based fairness scheme
that can be implemented online as a part of this ILP formulation that allows us
to improve fairness over a variety of fairness metrics. Deriving from a lens of
variance minimization, we describe how these fairness incentives can be
formulated for two distinct use cases for passenger groups and driver fairness.
We show that under mild conditions, our approach can guarantee an improvement
in the chosen metric for the worst-off individual. We also show empirically
that our Simple Incentives approach significantly outperforms prior art,
despite requiring no retraining; indeed, it often leads to a large improvement
over the state-of-the-art fairness-aware approach in both overall service rate
and fairness.
| [
{
"version": "v1",
"created": "Sat, 25 Mar 2023 02:24:27 GMT"
}
] | 1,679,961,600,000 | [
[
"Kumar",
"Ashwin",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
],
[
"Yeoh",
"William",
""
]
] |
2303.14363 | Dillon Chen | Dillon Chen, Felipe Trevizan, Sylvie Thi\'ebaux | Heuristic Search for Multi-Objective Probabilistic Planning | 11 pages, 4 figures, 3 tables, accepted to AAAI23 | null | 10.1609/aaai.v37i10.26409 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Heuristic search is a powerful approach that has successfully been applied to
a broad class of planning problems, including classical planning,
multi-objective planning, and probabilistic planning modelled as a stochastic
shortest path (SSP) problem. Here, we extend the reach of heuristic search to a
more expressive class of problems, namely multi-objective stochastic shortest
paths (MOSSPs), which require computing a coverage set of non-dominated
policies. We design new heuristic search algorithms MOLAO* and MOLRTDP, which
extend well-known SSP algorithms to the multi-objective case. We further
construct a spectrum of domain-independent heuristic functions differing in
their ability to take into account the stochastic and multi-objective features
of the problem to guide the search. Our experiments demonstrate the benefits of
these algorithms and the relative merits of the heuristics.
| [
{
"version": "v1",
"created": "Sat, 25 Mar 2023 05:18:22 GMT"
}
] | 1,711,411,200,000 | [
[
"Chen",
"Dillon",
""
],
[
"Trevizan",
"Felipe",
""
],
[
"Thiébaux",
"Sylvie",
""
]
] |
2303.14894 | Md Solimul Chowdhury | Md Solimul Chowdhury and Cayden R. Codel and Marijn J.H. Heule | A Linear Weight Transfer Rule for Local Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The Divide and Distribute Fixed Weights algorithm (ddfw) is a dynamic local
search SAT-solving algorithm that transfers weight from satisfied to falsified
clauses in local minima. ddfw is remarkably effective on several hard
combinatorial instances. Yet, despite its success, it has received little study
since its debut in 2005. In this paper, we propose three modifications to the
base algorithm: a linear weight transfer method that moves a dynamic amount of
weight between clauses in local minima, an adjustment to how satisfied clauses
are chosen in local minima to give weight, and a weighted-random method of
selecting variables to flip. We implemented our modifications to ddfw on top of
the solver yalsat. Our experiments show that our modifications boost the
performance compared to the original ddfw algorithm on multiple benchmarks,
including those from the past three years of SAT competitions. Moreover, our
improved solver exclusively solves hard combinatorial instances that refute a
conjecture on the lower bound of two Van der Waerden numbers set forth by Ahmed
et al. (2014), and it performs well on a hard graph-coloring instance that has
been open for over three decades.
| [
{
"version": "v1",
"created": "Mon, 27 Mar 2023 03:06:34 GMT"
}
] | 1,679,961,600,000 | [
[
"Chowdhury",
"Md Solimul",
""
],
[
"Codel",
"Cayden R.",
""
],
[
"Heule",
"Marijn J. H.",
""
]
] |
2303.15027 | Uzma Hasan | Uzma Hasan, Emam Hossain, Md Osman Gani | A Survey on Causal Discovery Methods for I.I.D. and Time Series Data | Published (05 Sept 2023) in Transactions on Machine Learning Research
(TMLR) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The ability to understand causality from data is one of the major milestones
of human-level intelligence. Causal Discovery (CD) algorithms can identify the
cause-effect relationships among the variables of a system from related
observational data with certain assumptions. Over the years, several methods
have been developed primarily based on the statistical properties of data to
uncover the underlying causal mechanism. In this study, we present an extensive
discussion on the methods designed to perform causal discovery from both
independent and identically distributed (I.I.D.) data and time series data. For
this purpose, we first introduce the common terminologies used in causal
discovery literature and then provide a comprehensive discussion of the
algorithms designed to identify causal relations in different settings. We
further discuss some of the benchmark datasets available for evaluating the
algorithmic performance, off-the-shelf tools or software packages to perform
causal discovery readily, and the common metrics used to evaluate these
methods. We also evaluate some widely used causal discovery algorithms on
multiple benchmark datasets and compare their performances. Finally, we
conclude by discussing the research challenges and the applications of causal
discovery algorithms in multiple areas of interest.
| [
{
"version": "v1",
"created": "Mon, 27 Mar 2023 09:21:41 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Apr 2023 06:34:08 GMT"
},
{
"version": "v3",
"created": "Wed, 25 Oct 2023 23:52:29 GMT"
},
{
"version": "v4",
"created": "Tue, 12 Mar 2024 20:14:45 GMT"
}
] | 1,710,374,400,000 | [
[
"Hasan",
"Uzma",
""
],
[
"Hossain",
"Emam",
""
],
[
"Gani",
"Md Osman",
""
]
] |
2303.15113 | Fajar Ekaputra | Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart,
Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine
Farfar, S\"oren Auer | Describing and Organizing Semantic Web and Machine Learning Systems in
the SWeMLS-KG | Preprint of a paper in the resource track of the 20th Extended
Semantic Web Conference (ESWC'23) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In line with the general trend in artificial intelligence research to create
intelligent systems that combine learning and symbolic components, a new
sub-area has emerged that focuses on combining machine learning (ML) components
with techniques developed by the Semantic Web (SW) community - Semantic Web
Machine Learning (SWeML for short). Due to its rapid growth and impact on
several communities in the last two decades, there is a need to better
understand the space of these SWeML Systems, their characteristics, and trends.
Yet, surveys that adopt principled and unbiased approaches are missing. To fill
this gap, we performed a systematic study and analyzed nearly 500 papers
published in the last decade in this area, where we focused on evaluating
architectural, and application-specific features. Our analysis identified a
rapidly growing interest in SWeML Systems, with a high impact on several
application domains and tasks. Catalysts for this rapid growth are the
increased application of deep learning and knowledge graph technologies. By
leveraging the in-depth understanding of this area acquired through this study,
a further key contribution of this paper is a classification system for SWeML
Systems which we publish as ontology.
| [
{
"version": "v1",
"created": "Mon, 27 Mar 2023 11:31:42 GMT"
}
] | 1,679,961,600,000 | [
[
"Ekaputra",
"Fajar J.",
""
],
[
"Llugiqi",
"Majlinda",
""
],
[
"Sabou",
"Marta",
""
],
[
"Ekelhart",
"Andreas",
""
],
[
"Paulheim",
"Heiko",
""
],
[
"Breit",
"Anna",
""
],
[
"Revenko",
"Artem",
""
],
[
"Waltersdorfer",
"Laura",
""
],
[
"Farfar",
"Kheir Eddine",
""
],
[
"Auer",
"Sören",
""
]
] |
2303.15935 | Xiang Li | Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu,
Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu,
Dinggang Shen, Tianming Liu | When Brain-inspired AI Meets AGI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial General Intelligence (AGI) has been a long-standing goal of
humanity, with the aim of creating machines capable of performing any
intellectual task that humans can do. To achieve this, AGI researchers draw
inspiration from the human brain and seek to replicate its principles in
intelligent machines. Brain-inspired artificial intelligence is a field that
has emerged from this endeavor, combining insights from neuroscience,
psychology, and computer science to develop more efficient and powerful AI
systems. In this article, we provide a comprehensive overview of brain-inspired
AI from the perspective of AGI. We begin with the current progress in
brain-inspired AI and its extensive connection with AGI. We then cover the
important characteristics for both human intelligence and AGI (e.g., scaling,
multimodality, and reasoning). We discuss important technologies toward
achieving AGI in current AI systems, such as in-context learning and prompt
tuning. We also investigate the evolution of AGI systems from both algorithmic
and infrastructural perspectives. Finally, we explore the limitations and
future of AGI.
| [
{
"version": "v1",
"created": "Tue, 28 Mar 2023 12:46:38 GMT"
}
] | 1,680,048,000,000 | [
[
"Zhao",
"Lin",
""
],
[
"Zhang",
"Lu",
""
],
[
"Wu",
"Zihao",
""
],
[
"Chen",
"Yuzhong",
""
],
[
"Dai",
"Haixing",
""
],
[
"Yu",
"Xiaowei",
""
],
[
"Liu",
"Zhengliang",
""
],
[
"Zhang",
"Tuo",
""
],
[
"Hu",
"Xintao",
""
],
[
"Jiang",
"Xi",
""
],
[
"Li",
"Xiang",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Shen",
"Dinggang",
""
],
[
"Liu",
"Tianming",
""
]
] |
2303.16680 | Stefanie Rinderle-Ma | Janik-Vasily Benzin, Gyunam Park, Stefanie Rinderle-Ma | Preventing Object-centric Discovery of Unsound Process Models for Object
Interactions with Loops in Collaborative Systems: Extended Version | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Object-centric process discovery (OCPD) constitutes a paradigm shift in
process mining. Instead of assuming a single case notion present in the event
log, OCPD can handle events without a single case notion, but that are instead
related to a collection of objects each having a certain type. The object types
constitute multiple, interacting case notions. The output of OCPD is an
object-centric Petri net, i.e. a Petri net with object-typed places, that
represents the parallel execution of multiple execution flows corresponding to
object types. Similar to classical process discovery, where we aim for
behaviorally sound process models as a result, in OCPD, we aim for soundness of
the resulting object-centric Petri nets. However, the existing OCPD approach
can result in violations of soundness. As we will show, one violation arises
for multiple interacting object types with loops that arise in collaborative
systems. This paper proposes an extended OCPD approach and proves that it does
not suffer from this violation of soundness of the resulting object-centric
Petri nets. We also show how we prevent the OCPD approach from introducing
spurious interactions in the discovered object-centric Petri net. The proposed
framework is prototypically implemented.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2023 13:31:46 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2023 16:26:26 GMT"
}
] | 1,680,480,000,000 | [
[
"Benzin",
"Janik-Vasily",
""
],
[
"Park",
"Gyunam",
""
],
[
"Rinderle-Ma",
"Stefanie",
""
]
] |
2303.16949 | Irfansha Shaik | Irfansha Shaik and Jaco van de Pol | Concise QBF Encodings for Games on a Grid (extended version) | 15 pages (main paper), 20 listings, 3 figures, 3 tables and 2
appendix sections | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Encoding 2-player games in QBF correctly and efficiently is challenging and
error-prone. To enable concise specifications and uniform encodings of games
played on grid boards, like Tic-Tac-Toe, Connect-4, Domineering, Pursuer-Evader
and Breakthrough, we introduce Board-game Domain Definition Language (BDDL),
inspired by the success of PDDL in the planning domain.
We provide an efficient translation from BDDL into QBF, encoding the
existence of a winning strategy of bounded depth. Our lifted encoding treats
board positions symbolically and allows concise definitions of conditions,
effects and winning configurations, relative to symbolic board positions. The
size of the encoding grows linearly in the input model and the considered
depth.
To show the feasibility of such a generic approach, we use QBF solvers to
compute the critical depths of winning strategies for instances of several
known games. For several games, our work provides the first QBF encoding.
Unlike plan validation in SAT-based planning, validating QBF-based winning
strategies is difficult. We show how to validate winning strategies using QBF
certificates and interactive game play.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2023 18:11:41 GMT"
}
] | 1,680,220,800,000 | [
[
"Shaik",
"Irfansha",
""
],
[
"van de Pol",
"Jaco",
""
]
] |
2303.16967 | Sachin Grover | Wiktor Piotrowski, Yoni Sher, Sachin Grover, Roni Stern, Shiwali Mohan | Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+ | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | This paper studies how a domain-independent planner and combinatorial search
can be employed to play Angry Birds, a well established AI challenge problem.
To model the game, we use PDDL+, a planning language for mixed
discrete/continuous domains that supports durative processes and exogenous
events. The paper describes the model and identifies key design decisions that
reduce the problem complexity. In addition, we propose several domain-specific
enhancements including heuristics and a search technique similar to preferred
operators. Together, they alleviate the complexity of combinatorial search. We
evaluate our approach by comparing its performance with dedicated
domain-specific solvers on a range of Angry Birds levels. The results show that
our performance is on par with these domain-specific approaches in most levels,
even without using our domain-specific search enhancements.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2023 19:01:51 GMT"
}
] | 1,680,220,800,000 | [
[
"Piotrowski",
"Wiktor",
""
],
[
"Sher",
"Yoni",
""
],
[
"Grover",
"Sachin",
""
],
[
"Stern",
"Roni",
""
],
[
"Mohan",
"Shiwali",
""
]
] |
2303.17018 | Yuliya Lierler | Daniel Bresnahan, Nicholas Hippen, Yuliya Lierler | System Predictor: Grounding Size Estimator for Logic Programs under
Answer Set Semantics | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Answer set programming is a declarative logic programming paradigm geared
towards solving difficult combinatorial search problems. While different logic
programs can encode the same problem, their performance may vary significantly.
It is not always easy to identify which version of the program performs the
best. We present the system Predictor (and its algorithmic backend) for
estimating the grounding size of programs, a metric that can influence a
performance of a system processing a program. We evaluate the impact of
Predictor when used as a guide for rewritings produced by the answer set
programming rewriting tools Projector and Lpopt. The results demonstrate
potential to this approach.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2023 20:49:40 GMT"
}
] | 1,680,220,800,000 | [
[
"Bresnahan",
"Daniel",
""
],
[
"Hippen",
"Nicholas",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
2303.17075 | Lenore Blum | Lenore Blum, Manuel Blum | Viewpoint: A Theoretical Computer Science Perspective on Consciousness
and Artificial General Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We have defined the Conscious Turing Machine (CTM) for the purpose of
investigating a Theoretical Computer Science (TCS) approach to consciousness.
For this, we have hewn to the TCS demand for simplicity and understandability.
The CTM is consequently and intentionally a simple machine. It is not a model
of the brain, though its design has greatly benefited - and continues to
benefit - from neuroscience and psychology. The CTM is a model of and for
consciousness.
Although it is developed to understand consciousness, the CTM offers a
thoughtful and novel guide to the creation of an Artificial General
Intelligence (AGI). For example, the CTM has an enormous number of powerful
processors, some with specialized expertise, others unspecialized but poised to
develop an expertise. For whatever problem must be dealt with, the CTM has an
excellent way to utilize those processors that have the required knowledge,
ability, and time to work on the problem, even if it is not aware of which ones
these may be.
| [
{
"version": "v1",
"created": "Thu, 30 Mar 2023 00:39:10 GMT"
}
] | 1,680,220,800,000 | [
[
"Blum",
"Lenore",
""
],
[
"Blum",
"Manuel",
""
]
] |
2303.17262 | Salvatore Flavio Pileggi Ph.D. | Salvatore F. Pileggi | Ontology in Hybrid Intelligence: a concise literature review | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In a context of constant evolution and proliferation of AI technology,Hybrid
Intelligence is gaining popularity to refer a balanced coexistence between
human and artificial intelligence. The term has been extensively used in the
past two decades to define models of intelligence involving more than one
technology. This paper aims to provide (i) a concise and focused overview of
the adoption of Ontology in the broad context of Hybrid Intelligence regardless
of its definition and (ii) a critical discussion on the possible role of
Ontology to reduce the gap between human and artificial intelligence within
hybrid intelligent systems. Beside the typical benefits provided by an
effective use of ontologies, at a conceptual level, the conducted analysis has
pointed out a significant contribution of Ontology to improve quality and
accuracy, as well as a more specific role to enable extended interoperability,
system engineering and explainable/transparent systems. Additionally, an
application-oriented analysis has shown a significant role in present systems
(70+% of the cases) and, potentially, in future systems. However, despite the
relatively consistent number of papers on the topic, a proper holistic
discussion on the establishment of the next generation of hybrid-intelligent
environments with a balanced co-existence of human and artificial intelligence
is fundamentally missed in literature. Last but not the least, there is
currently a relatively low explicit focus on automatic reasoning and inference
in hybrid intelligent systems.
| [
{
"version": "v1",
"created": "Thu, 30 Mar 2023 09:55:29 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Oct 2023 01:02:15 GMT"
}
] | 1,697,587,200,000 | [
[
"Pileggi",
"Salvatore F.",
""
]
] |
2303.17892 | Samy Badreddine | Samy Badreddine and Gianluca Apriceno and Andrea Passerini and Luciano
Serafini | Interval Logic Tensor Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic
that interprets knowledge such as sequential properties (traces) and event
properties using sequences of real-featured data. We interpret connectives
using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy
temporal relations using relationships between the intervals' areas. We propose
Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by
propagating gradients through IRL. In order to support effective learning, ILTN
defines smoothened versions of the fuzzy intervals and temporal relations of
IRL using softplus activations. We show that ILTN can successfully leverage
knowledge expressed in IRL in synthetic tasks that require reasoning about
events to predict their fuzzy durations. Our results show that the system is
capable of making events compliant with background temporal knowledge.
| [
{
"version": "v1",
"created": "Fri, 31 Mar 2023 08:51:44 GMT"
}
] | 1,680,480,000,000 | [
[
"Badreddine",
"Samy",
""
],
[
"Apriceno",
"Gianluca",
""
],
[
"Passerini",
"Andrea",
""
],
[
"Serafini",
"Luciano",
""
]
] |
2304.00002 | Nick DiSanto | Nick DiSanto | Beyond Interpretable Benchmarks: Contextual Learning through Cognitive
and Multimodal Perception | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With state-of-the-art models achieving high performance on standard
benchmarks, contemporary research paradigms continue to emphasize general
intelligence as an enduring objective. However, this pursuit overlooks the
fundamental disparities between the high-level data perception abilities of
artificial and natural intelligence systems. This study questions the Turing
Test as a criterion of generally intelligent thought and contends that it is
misinterpreted as an attempt to anthropomorphize computer systems. Instead, it
emphasizes tacit learning as a cornerstone of general-purpose intelligence,
despite its lack of overt interpretability. This abstract form of intelligence
necessitates contextual cognitive attributes that are crucial for human-level
perception: generalizable experience, moral responsibility, and implicit
prioritization. The absence of these features yields undeniable perceptual
disparities and constrains the cognitive capacity of artificial systems to
effectively contextualize their environments. Additionally, this study
establishes that, despite extensive exploration of potential architecture for
future systems, little consideration has been given to how such models will
continuously absorb and adapt to contextual data. While conventional models may
continue to improve in benchmark performance, disregarding these contextual
considerations will lead to stagnation in human-like comprehension. Until
general intelligence can be abstracted from task-specific domains and systems
can learn implicitly from their environments, research standards should instead
prioritize the disciplines in which AI thrives.
| [
{
"version": "v1",
"created": "Sun, 4 Dec 2022 08:30:04 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Sep 2023 03:19:16 GMT"
}
] | 1,696,291,200,000 | [
[
"DiSanto",
"Nick",
""
]
] |
2304.00004 | Mayukh Bagchi | Mayukh Bagchi and Subhashis Das | Disentangling Domain Ontologies | In: Proceedings of the 19th Italian Research Conference on Digital
Libraries (IRCDL), February 23-24, 2023, Bari, Italy | null | null | IRCDL2023 | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we introduce and illustrate the novel phenomenon of Conceptual
Entanglement which emerges due to the representational manifoldness immanent
while incrementally modelling domain ontologies step-by-step across the
following five levels: perception, labelling, semantic alignment, hierarchical
modelling and intensional definition. In turn, we propose Conceptual
Disentanglement, a multi-level conceptual modelling strategy which enforces and
explicates, via guiding principles, semantic bijections with respect to each
level of conceptual entanglement (across all the above five levels) paving the
way for engineering conceptually disentangled domain ontologies. We also
briefly argue why state-of-the-art ontology development methodologies and
approaches are insufficient with respect to our characterization.
| [
{
"version": "v1",
"created": "Tue, 21 Mar 2023 08:36:14 GMT"
}
] | 1,680,566,400,000 | [
[
"Bagchi",
"Mayukh",
""
],
[
"Das",
"Subhashis",
""
]
] |
2304.00009 | Siddarth Shandeep Singh | Siddarth Singh and Benjamin Rosman | The challenge of redundancy on multi-agent value factorisation | Published at the 22nd International Conference on Autonomous Agents
and Multiagent Systems (AAMAS 2023). 2 Pages, 1 Figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the field of cooperative multi-agent reinforcement learning (MARL), the
standard paradigm is the use of centralised training and decentralised
execution where a central critic conditions the policies of the cooperative
agents based on a central state. It has been shown, that in cases with large
numbers of redundant agents these methods become less effective. In a more
general case, there is likely to be a larger number of agents in an environment
than is required to solve the task. These redundant agents reduce performance
by enlarging the dimensionality of both the state space and and increasing the
size of the joint policy used to solve the environment. We propose leveraging
layerwise relevance propagation (LRP) to instead separate the learning of the
joint value function and generation of local reward signals and create a new
MARL algorithm: relevance decomposition network (RDN). We find that although
the performance of both baselines VDN and Qmix degrades with the number of
redundant agents, RDN is unaffected.
| [
{
"version": "v1",
"created": "Tue, 28 Mar 2023 20:41:12 GMT"
}
] | 1,680,566,400,000 | [
[
"Singh",
"Siddarth",
""
],
[
"Rosman",
"Benjamin",
""
]
] |
2304.00755 | Xianghua Zeng | Xianghua Zeng, Hao Peng, Angsheng Li | Effective and Stable Role-Based Multi-Agent Collaboration by Structural
Information Principles | 9 pages, 8 figures,2 references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Role-based learning is a promising approach to improving the performance of
Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual
assistance, current role-based methods cannot guarantee stably discovering a
set of roles to effectively decompose a complex task, as they assume either a
predefined role structure or practical experience for selecting
hyperparameters. In this article, we propose a mathematical Structural
Information principles-based Role Discovery method, namely SIRD, and then
present a SIRD optimizing MARL framework, namely SR-MARL, for multi-agent
collaboration. The SIRD transforms role discovery into a hierarchical action
space clustering. Specifically, the SIRD consists of structuralization,
sparsification, and optimization modules, where an optimal encoding tree is
generated to perform abstracting to discover roles. The SIRD is agnostic to
specific MARL algorithms and flexibly integrated with various value function
factorization approaches. Empirical evaluations on the StarCraft II
micromanagement benchmark demonstrate that, compared with state-of-the-art MARL
algorithms, the SR-MARL framework improves the average test win rate by 0.17%,
6.08%, and 3.24%, and reduces the deviation by 16.67%, 30.80%, and 66.30%,
under easy, hard, and super hard scenarios.
| [
{
"version": "v1",
"created": "Mon, 3 Apr 2023 07:13:44 GMT"
}
] | 1,680,566,400,000 | [
[
"Zeng",
"Xianghua",
""
],
[
"Peng",
"Hao",
""
],
[
"Li",
"Angsheng",
""
]
] |
2304.00879 | Pietro Totis | Pietro Totis, Angelika Kimmig, Luc De Raedt | smProbLog: Stable Model Semantics in ProbLog for Probabilistic
Argumentation | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation problems are concerned with determining the acceptability of a
set of arguments from their relational structure. When the available
information is uncertain, probabilistic argumentation frameworks provide
modelling tools to account for it. The first contribution of this paper is a
novel interpretation of probabilistic argumentation frameworks as probabilistic
logic programs. Probabilistic logic programs are logic programs in which some
of the facts are annotated with probabilities. We show that the programs
representing probabilistic argumentation frameworks do not satisfy a common
assumption in probabilistic logic programming (PLP) semantics, which is, that
probabilistic facts fully capture the uncertainty in the domain under
investigation. The second contribution of this paper is then a novel PLP
semantics for programs where a choice of probabilistic facts does not uniquely
determine the truth assignment of the logical atoms. The third contribution of
this paper is the implementation of a PLP system supporting this semantics:
smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic
programming language ProbLog. smProbLog supports many inference and learning
tasks typical of PLP, which, together with our first contribution, provide
novel reasoning tools for probabilistic argumentation. We evaluate our approach
with experiments analyzing the computational cost of the proposed algorithms
and their application to a dataset of argumentation problems.
| [
{
"version": "v1",
"created": "Mon, 3 Apr 2023 10:59:25 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Apr 2023 09:21:03 GMT"
}
] | 1,681,776,000,000 | [
[
"Totis",
"Pietro",
""
],
[
"Kimmig",
"Angelika",
""
],
[
"De Raedt",
"Luc",
""
]
] |
2304.01204 | Jakub Dylag | Jakub J. Dylag, Victor Suarez, James Wald, Aneesha Amodini Uvara | Automatic Geo-alignment of Artwork in Children's Story Books | Master's project | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A study was conducted to prove AI software could be used to translate and
generate illustrations without any human intervention. This was done with the
purpose of showing and distributing it to the external customer, Pratham Books.
The project aligns with the company's vision by leveraging the generalisation
and scalability of Machine Learning algorithms, offering significant cost
efficiency increases to a wide range of literary audiences in varied
geographical locations. A comparative study methodology was utilised to
determine the best performant method out of the 3 devised, Prompt Augmentation
using Keywords, CLIP Embedding Mask, and Cross Attention Control with Editorial
Prompts. A thorough evaluation process was completed using both quantitative
and qualitative measures. Each method had its own strengths and weaknesses, but
through the evaluation, method 1 was found to have the best yielding results.
Promising future advancements may be made to further increase image quality by
incorporating Large Language Models and personalised stylistic models. The
presented approach can also be adapted to Video and 3D sculpture generation for
novel illustrations in digital webbooks.
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2023 06:23:06 GMT"
}
] | 1,680,652,800,000 | [
[
"Dylag",
"Jakub J.",
""
],
[
"Suarez",
"Victor",
""
],
[
"Wald",
"James",
""
],
[
"Uvara",
"Aneesha Amodini",
""
]
] |
2304.01366 | Thomas Kunz | Li Li, Jean-Pierre S. El Rami, Adrian Taylor, James Hailing Rao,
Thomas Kunz | Enabling A Network AI Gym for Autonomous Cyber Agents | To appear in Proceedings of the 2022 International Conference on
Computational Science and Computational Intelligence | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work aims to enable autonomous agents for network cyber operations
(CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The
required RL training environment is particularly challenging, as it must
balance the need for high-fidelity, best achieved through real network
emulation, with the need for running large numbers of training episodes, best
achieved using simulation. A unified training environment, namely the Cyber Gym
for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E
automatically generates a simulated CyGIL-S. From preliminary experimental
results, CyGIL-S is capable to train agents in minutes compared with the days
required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly
to CyGIL-E showing full decision proficiency in the emulated "real" network.
Enabling offline RL, the CyGIL solution presents a promising direction towards
sim-to-real for leveraging RL agents in real-world cyber networks.
| [
{
"version": "v1",
"created": "Mon, 3 Apr 2023 20:47:03 GMT"
}
] | 1,680,652,800,000 | [
[
"Li",
"Li",
""
],
[
"Rami",
"Jean-Pierre S. El",
""
],
[
"Taylor",
"Adrian",
""
],
[
"Rao",
"James Hailing",
""
],
[
"Kunz",
"Thomas",
""
]
] |
2304.01503 | Jonathan Freedman | Jonathan D. Freedman and Ian A. Nappier | GPT-4 to GPT-3.5: 'Hold My Scalpel' -- A Look at the Competency of
OpenAI's GPT on the Plastic Surgery In-Service Training Exam | 30 pages, 1 table, 8 figures, Appendix | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Plastic Surgery In-Service Training Exam (PSITE) is an important
indicator of resident proficiency and serves as a useful benchmark for
evaluating OpenAI's GPT. Unlike many of the simulated tests or practice
questions shown in the GPT-4 Technical Paper, the multiple-choice questions
evaluated here are authentic PSITE questions. These questions offer realistic
clinical vignettes that a plastic surgeon commonly encounters in practice and
scores highly correlate with passing the written boards required to become a
Board Certified Plastic Surgeon. Our evaluation shows dramatic improvement of
GPT-4 (without vision) over GPT-3.5 with both the 2022 and 2021 exams
respectively increasing the score from 8th to 88th percentile and 3rd to 99th
percentile. The final results of the 2023 PSITE are set to be released on April
11, 2023, and this is an exciting moment to continue our research with a fresh
exam. Our evaluation pipeline is ready for the moment that the exam is released
so long as we have access via OpenAI to the GPT-4 API. With multimodal input,
we may achieve superhuman performance on the 2023.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 03:30:12 GMT"
}
] | 1,680,652,800,000 | [
[
"Freedman",
"Jonathan D.",
""
],
[
"Nappier",
"Ian A.",
""
]
] |
2304.01539 | Keehang Kwon | Keehang Kwon | Implementing Dynamic Programming in Computability Logic Web | 9 pages. It contains an interesting definition of an algorithm | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a novel definition of an algorithm and its corresponding algorithm
language called CoLweb. The merit of CoLweb [1] is that it makes algorithm
design so versatile. That is, it forces us to a high-level, proof-carrying,
distributed-style approach to algorithm design for both non-distributed
computing and distributed one. We argue that this approach simplifies algorithm
design. In addition, it unifies other approaches including recursive
logical/functional algorithms, imperative algorithms, object-oriented
imperative algorithms, neural-nets, interaction nets, proof-carrying code, etc.
As an application, we refine Horn clause definitions into two kinds:
blind-univerally-quantified (BUQ) ones and parallel-universally-quantified
(PUQ) ones. BUQ definitions corresponds to the traditional ones such as those
in Prolog where knowledgebase is $not$ expanding and its proof procedure is
based on the backward chaining. On the other hand, in PUQ definitions,
knowledgebase is $expanding$ and its proof procedure leads to forward chaining
and {\it automatic memoization}.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 05:33:43 GMT"
}
] | 1,680,652,800,000 | [
[
"Kwon",
"Keehang",
""
]
] |
2304.01543 | Roohallah Alizadehsani Dr | Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Cifci, Samina
Kausar, Rizwan Rehman, Priyakshi Mahanta, Pranjal Kumar Bora, Ammar Almasri,
Rami S. Alkhawaldeh, Sadiq Hussain, Bilal Alatas, Afshin Shoeibi, Hossein
Moosaei, Milan Hladik, Saeid Nahavandi, Panos M. Pardalos | A Brief Review of Explainable Artificial Intelligence in Healthcare | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | XAI refers to the techniques and methods for building AI applications which
assist end users to interpret output and predictions of AI models. Black box AI
applications in high-stakes decision-making situations, such as medical domain
have increased the demand for transparency and explainability since wrong
predictions may have severe consequences. Model explainability and
interpretability are vital successful deployment of AI models in healthcare
practices. AI applications' underlying reasoning needs to be transparent to
clinicians in order to gain their trust. This paper presents a systematic
review of XAI aspects and challenges in the healthcare domain. The primary
goals of this study are to review various XAI methods, their challenges, and
related machine learning models in healthcare. The methods are discussed under
six categories: Features-oriented methods, global methods, concept models,
surrogate models, local pixel-based methods, and human-centric methods. Most
importantly, the paper explores XAI role in healthcare problems to clarify its
necessity in safety-critical applications. The paper intends to establish a
comprehensive understanding of XAI-related applications in the healthcare field
by reviewing the related experimental results. To facilitate future research
for filling research gaps, the importance of XAI models from different
viewpoints and their limitations are investigated.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 05:41:57 GMT"
}
] | 1,680,652,800,000 | [
[
"Sadeghi",
"Zahra",
""
],
[
"Alizadehsani",
"Roohallah",
""
],
[
"Cifci",
"Mehmet Akif",
""
],
[
"Kausar",
"Samina",
""
],
[
"Rehman",
"Rizwan",
""
],
[
"Mahanta",
"Priyakshi",
""
],
[
"Bora",
"Pranjal Kumar",
""
],
[
"Almasri",
"Ammar",
""
],
[
"Alkhawaldeh",
"Rami S.",
""
],
[
"Hussain",
"Sadiq",
""
],
[
"Alatas",
"Bilal",
""
],
[
"Shoeibi",
"Afshin",
""
],
[
"Moosaei",
"Hossein",
""
],
[
"Hladik",
"Milan",
""
],
[
"Nahavandi",
"Saeid",
""
],
[
"Pardalos",
"Panos M.",
""
]
] |
2304.01547 | Ruben Solozabal | Talal Algumaei, Ruben Solozabal, Reda Alami, Hakim Hacid, Merouane
Debbah, Martin Takac | Regularization of the policy updates for stabilizing Mean Field Games | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL)
where multiple agents interact in the same environment and whose goal is to
maximize the individual returns. Challenges arise when scaling up the number of
agents due to the resultant non-stationarity that the many agents introduce. In
order to address this issue, Mean Field Games (MFG) rely on the symmetry and
homogeneity assumptions to approximate games with very large populations.
Recently, deep Reinforcement Learning has been used to scale MFG to games with
larger number of states. Current methods rely on smoothing techniques such as
averaging the q-values or the updates on the mean-field distribution. This work
presents a different approach to stabilize the learning based on proximal
updates on the mean-field policy. We name our algorithm Mean Field Proximal
Policy Optimization (MF-PPO), and we empirically show the effectiveness of our
method in the OpenSpiel framework.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 05:45:42 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Apr 2023 13:53:24 GMT"
}
] | 1,681,430,400,000 | [
[
"Algumaei",
"Talal",
""
],
[
"Solozabal",
"Ruben",
""
],
[
"Alami",
"Reda",
""
],
[
"Hacid",
"Hakim",
""
],
[
"Debbah",
"Merouane",
""
],
[
"Takac",
"Martin",
""
]
] |
2304.01559 | Jianlin Liu | Lixia Wu, Jianlin Liu, Junhong Lou, Haoyuan Hu, Jianbin Zheng, Haomin
Wen, Chao Song, Shu He | G2PTL: A Pre-trained Model for Delivery Address and its Applications in
Logistics System | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Text-based delivery addresses, as the data foundation for logistics systems,
contain abundant and crucial location information. How to effectively encode
the delivery address is a core task to boost the performance of downstream
tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural
Language Process (NLP) have emerged as the dominant tools for encoding semantic
information in text. Though promising, those NLP-based PTMs fall short of
encoding geographic knowledge in the delivery address, which considerably trims
down the performance of delivery-related tasks in logistic systems such as
Cainiao. To tackle the above problem, we propose a domain-specific pre-trained
model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in
Logistics field. G2PTL combines the semantic learning capabilities of text
pre-training with the geographical-relationship encoding abilities of graph
modeling. Specifically, we first utilize real-world logistics delivery data to
construct a large-scale heterogeneous graph of delivery addresses, which
contains abundant geographic knowledge and delivery information. Then, G2PTL is
pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive
experiments are conducted to demonstrate the effectiveness of G2PTL through
four downstream tasks in logistics systems on real-world datasets. G2PTL has
been deployed in production in Cainiao's logistics system, which significantly
improves the performance of delivery-related tasks. The code of G2PTL is
available at https://huggingface.co/Cainiao-AI/G2PTL.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 06:33:03 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Jun 2023 07:41:23 GMT"
},
{
"version": "v3",
"created": "Thu, 31 Aug 2023 11:14:51 GMT"
}
] | 1,693,526,400,000 | [
[
"Wu",
"Lixia",
""
],
[
"Liu",
"Jianlin",
""
],
[
"Lou",
"Junhong",
""
],
[
"Hu",
"Haoyuan",
""
],
[
"Zheng",
"Jianbin",
""
],
[
"Wen",
"Haomin",
""
],
[
"Song",
"Chao",
""
],
[
"He",
"Shu",
""
]
] |
2304.01592 | Mohit Prashant | Mohit Prashant and Arvind Easwaran | PAC-Based Formal Verification for Out-of-Distribution Data Detection | 10 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Cyber-physical systems (CPS) like autonomous vehicles, that utilize learning
components, are often sensitive to noise and out-of-distribution (OOD)
instances encountered during runtime. As such, safety critical tasks depend
upon OOD detection subsystems in order to restore the CPS to a known state or
interrupt execution to prevent safety from being compromised. However, it is
difficult to guarantee the performance of OOD detectors as it is difficult to
characterize the OOD aspect of an instance, especially in high-dimensional
unstructured data.
To distinguish between OOD data and data known to the learning component
through the training process, an emerging technique is to incorporate
variational autoencoders (VAE) within systems and apply classification or
anomaly detection techniques on their latent spaces. The rationale for doing so
is the reduction of the data domain size through the encoding process, which
benefits real-time systems through decreased processing requirements,
facilitates feature analysis for unstructured data and allows more explainable
techniques to be implemented.
This study places probably approximately correct (PAC) based guarantees on
OOD detection using the encoding process within VAEs to quantify image features
and apply conformal constraints over them. This is used to bound the detection
error on unfamiliar instances with user-defined confidence. The approach used
in this study is to empirically establish these bounds by sampling the latent
probability distribution and evaluating the error with respect to the
constraint violations that are encountered. The guarantee is then verified
using data generated from CARLA, an open-source driving simulator.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 07:33:02 GMT"
}
] | 1,680,652,800,000 | [
[
"Prashant",
"Mohit",
""
],
[
"Easwaran",
"Arvind",
""
]
] |
2304.01664 | Keyu Wang | Keyu Wang, Site Li, Jiaye Li, Guilin Qi and Qiu Ji | An Embedding-based Approach to Inconsistency-tolerant Reasoning with
Inconsistent Ontologies | 9 pages,1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inconsistency handling is an important issue in knowledge management.
Especially in ontology engineering, logical inconsistencies may occur during
ontology construction. A natural way to reason with an inconsistent ontology is
to utilize the maximal consistent subsets of the ontology. However, previous
studies on selecting maximum consistent subsets have rarely considered the
semantics of the axioms, which may result in irrational inference. In this
paper, we propose a novel approach to reasoning with inconsistent ontologies in
description logics based on the embeddings of axioms. We first give a method
for turning axioms into distributed semantic vectors to compute the semantic
connections between the axioms. We then define an embedding-based method for
selecting the maximum consistent subsets and use it to define an
inconsistency-tolerant inference relation. We show the rationality of our
inference relation by considering some logical properties. Finally, we conduct
experiments on several ontologies to evaluate the reasoning power of our
inference relation. The experimental results show that our embedding-based
method can outperform existing inconsistency-tolerant reasoning methods based
on maximal consistent subsets.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 09:38:02 GMT"
},
{
"version": "v2",
"created": "Sun, 26 Nov 2023 08:11:57 GMT"
}
] | 1,701,129,600,000 | [
[
"Wang",
"Keyu",
""
],
[
"Li",
"Site",
""
],
[
"Li",
"Jiaye",
""
],
[
"Qi",
"Guilin",
""
],
[
"Ji",
"Qiu",
""
]
] |
2304.01771 | Fangzhen Lin | Fangzhen Lin and Ziyi Shou and Chengcai Chen | Using Language Models For Knowledge Acquisition in Natural Language
Reasoning Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a natural language problem that requires some non-trivial reasoning to
solve, there are at least two ways to do it using a large language model (LLM).
One is to ask it to solve it directly. The other is to use it to extract the
facts from the problem text and then use a theorem prover to solve it. In this
note, we compare the two methods using ChatGPT and GPT4 on a series of logic
word puzzles, and conclude that the latter is the right approach.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 13:01:48 GMT"
}
] | 1,680,652,800,000 | [
[
"Lin",
"Fangzhen",
""
],
[
"Shou",
"Ziyi",
""
],
[
"Chen",
"Chengcai",
""
]
] |
2304.01844 | Jingyi Feng | Jingyi Feng and Chenming Zhang | Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning | 21 pages, 8 figures, 8 formulas | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Comprehending how the brain interacts with the external world through
generated neural data is crucial for determining its working mechanism,
treating brain diseases, and understanding intelligence. Although many
theoretical models have been proposed, they have thus far been difficult to
integrate and develop. In this study, we were inspired in part by grid cells in
creating a more general and robust grid module and constructing an interactive
and self-reinforcing cognitive system together with Bayesian reasoning, an
approach called space-division and exploration-exploitation with grid-feedback
(Grid-SD2E). Here, a grid module can be used as an interaction medium between
the outside world and a system, as well as a self-reinforcement medium within
the system. The space-division and exploration-exploitation (SD2E) receives the
0/1 signals of a grid through its space-division (SD) module. The system
described in this paper is also a theoretical model derived from experiments
conducted by other researchers and our experience on neural decoding. Herein,
we analyse the rationality of the system based on the existing theories in both
neuroscience and cognitive science, and attempt to propose special and general
rules to explain the different interactions between people and between people
and the external world. What's more, based on this framework, the smallest
computing unit is extracted, which is analogous to a single neuron in the
brain.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 14:54:12 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Jun 2023 09:28:20 GMT"
},
{
"version": "v3",
"created": "Sun, 10 Dec 2023 08:12:22 GMT"
}
] | 1,702,339,200,000 | [
[
"Feng",
"Jingyi",
""
],
[
"Zhang",
"Chenming",
""
]
] |
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