id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
sequencelengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2207.12166 | Maxime Amblard | Maxime Amblard (SEMAGRAMME, LORIA), Bruno Guillaume (SEMAGRAMME,
LORIA), Siyana Pavlova (SEMAGRAMME, LORIA), Guy Perrier (SEMAGRAMME, LORIA) | Graph Querying for Semantic Annotations | null | f ISA-18 Workshop at LREC2022, Jun 2022, Marseille, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents how the online tool GREW-MATCH can be used to make
queries and visualise data from existing semantically annotated corpora. A
dedicated syntax is available to construct simple to complex queries and
execute them against a corpus. Such queries give transverse views of the
annotated data, these views can help for checking the consistency of
annotations in one corpus or across several corpora. GREW-MATCH can then be
seen as an error mining tool: when inconsistencies are detected, it helps
finding the sentences which should be fixed. Finally, GREW-MATCH can also be
used as a side tool to assist annotation tasks helping to find annotation
examples in existing corpora to be compared to the data to be annotated.
| [
{
"version": "v1",
"created": "Mon, 25 Jul 2022 13:08:15 GMT"
}
] | 1,658,793,600,000 | [
[
"Amblard",
"Maxime",
"",
"SEMAGRAMME, LORIA"
],
[
"Guillaume",
"Bruno",
"",
"SEMAGRAMME,\n LORIA"
],
[
"Pavlova",
"Siyana",
"",
"SEMAGRAMME, LORIA"
],
[
"Perrier",
"Guy",
"",
"SEMAGRAMME, LORIA"
]
] |
2207.12174 | Maxime Amblard | Siyana Pavlova (SEMAGRAMME, LORIA), Maxime Amblard (SEMAGRAMME,
LORIA), Bruno Guillaume (SEMAGRAMME, LORIA) | How much of UCCA can be predicted from AMR? | null | f ISA-18 Workshop at LREC2022, Jun 2022, Marseille, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider two of the currently popular semantic frameworks:
Abstract Meaning Representation (AMR)a more abstract framework, and Universal
Conceptual Cognitive Annotation (UCCA)-an anchored framework. We use a
corpus-based approach to build two graph rewriting systems, a deterministic and
a non-deterministic one, from the former to the latter framework. We present
their evaluation and a number of ambiguities that we discovered while building
our rules. Finally, we provide a discussion and some future work directions in
relation to comparing semantic frameworks of different flavors.
| [
{
"version": "v1",
"created": "Mon, 25 Jul 2022 13:13:34 GMT"
}
] | 1,658,793,600,000 | [
[
"Pavlova",
"Siyana",
"",
"SEMAGRAMME, LORIA"
],
[
"Amblard",
"Maxime",
"",
"SEMAGRAMME,\n LORIA"
],
[
"Guillaume",
"Bruno",
"",
"SEMAGRAMME, LORIA"
]
] |
2207.12252 | Tom Westermann | Tom Westermann, Nemanja Hranisavljevic, Alexander Fay | Accessing and Interpreting OPC UA Event Traces based on Semantic Process
Descriptions | Copyright 2022 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other works | 2022 IEEE 27th International Conference on Emerging Technologies
and Factory Automation (ETFA), pp. 1-7 | 10.1109/ETFA52439.2022.9921565 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analysis of event data from production systems is the basis for many
applications associated with Industry 4.0. However, heterogeneous and disjoint
data is common in this domain. As a consequence, contextual information of an
event might be incomplete or improperly interpreted which results in suboptimal
analysis results. This paper proposes an approach to access a production
systems' event data based on the event data's context (such as the product
type, process type or process parameters). The approach extracts filtered event
logs from a database system by combining: 1) a semantic model of a production
system's hierarchical structure, 2) a formalized process description and 3) an
OPC UA information model. As a proof of concept we demonstrate our approach
using a sample server based on OPC UA for Machinery Companion Specifications.
| [
{
"version": "v1",
"created": "Mon, 25 Jul 2022 15:13:44 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Oct 2022 08:58:02 GMT"
}
] | 1,666,915,200,000 | [
[
"Westermann",
"Tom",
""
],
[
"Hranisavljevic",
"Nemanja",
""
],
[
"Fay",
"Alexander",
""
]
] |
2207.12763 | Till Hofmann | Till Hofmann, Vaishak Belle | Using Abstraction for Interpretable Robot Programs in Stochastic Domains | Presented at the KR'22 Workshop on Explainable Logic-Based Knowledge
Representation (XLoKR). arXiv admin note: substantial text overlap with
arXiv:2204.03536 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | A robot's actions are inherently stochastic, as its sensors are noisy and its
actions do not always have the intended effects. For this reason, the agent
language Golog has been extended to models with degrees of belief and
stochastic actions. While this allows more precise robot models, the resulting
programs are much harder to comprehend, because they need to deal with the
noise, e.g., by looping until some desired state has been reached with
certainty, and because the resulting action traces consist of a large number of
actions cluttered with sensor noise. To alleviate these issues, we propose to
use abstraction. We define a high-level and nonstochastic model of the robot
and then map the high-level model into the lower-level stochastic model. The
resulting programs are much easier to understand, often do not require belief
operators or loops, and produce much shorter action traces.
| [
{
"version": "v1",
"created": "Tue, 26 Jul 2022 09:15:37 GMT"
}
] | 1,677,715,200,000 | [
[
"Hofmann",
"Till",
""
],
[
"Belle",
"Vaishak",
""
]
] |
2207.12764 | Anahita Farhang Ghahfarokhi | Anahita Farhang Ghahfarokhi, Fatemeh Akoochekian, Fareed Zandkarimi,
Wil M.P. van der Aalst | Clustering Object-Centric Event Logs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Process mining provides various algorithms to analyze process executions
based on event data. Process discovery, the most prominent category of process
mining techniques, aims to discover process models from event logs, however, it
leads to spaghetti models when working with real-life data. Therefore, several
clustering techniques have been proposed on top of traditional event logs
(i.e., event logs with a single case notion) to reduce the complexity of
process models and discover homogeneous subsets of cases. Nevertheless, in
real-life processes, particularly in the context of Business-to-Business (B2B)
processes, multiple objects are involved in a process. Recently, Object-Centric
Event Logs (OCELs) have been introduced to capture the information of such
processes, and several process discovery techniques have been developed on top
of OCELs. Yet, the output of the proposed discovery techniques on real OCELs
leads to more informative but also more complex models. In this paper, we
propose a clustering-based approach to cluster similar objects in OCELs to
simplify the obtained process models. Using a case study of a real B2B process,
we demonstrate that our approach reduces the complexity of the process models
and generates coherent subsets of objects which help the end-users gain
insights into the process.
| [
{
"version": "v1",
"created": "Tue, 26 Jul 2022 09:16:39 GMT"
}
] | 1,658,880,000,000 | [
[
"Ghahfarokhi",
"Anahita Farhang",
""
],
[
"Akoochekian",
"Fatemeh",
""
],
[
"Zandkarimi",
"Fareed",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] |
2207.13181 | Kevin Osanlou Mr | Kevin Osanlou, Christophe Guettier, Tristan Cazenave, Eric Jacopin | Planning and Learning: Path-Planning for Autonomous Vehicles, a Review
of the Literature | AAAI-format & updated | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This short review aims to make the reader familiar with state-of-the-art
works relating to planning, scheduling and learning. First, we study
state-of-the-art planning algorithms. We give a brief introduction of neural
networks. Then we explore in more detail graph neural networks, a recent
variant of neural networks suited for processing graph-structured inputs. We
describe briefly the concept of reinforcement learning algorithms and some
approaches designed to date. Next, we study some successful approaches
combining neural networks for path-planning. Lastly, we focus on temporal
planning problems with uncertainty.
| [
{
"version": "v1",
"created": "Tue, 26 Jul 2022 20:56:18 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Oct 2023 21:02:54 GMT"
}
] | 1,697,673,600,000 | [
[
"Osanlou",
"Kevin",
""
],
[
"Guettier",
"Christophe",
""
],
[
"Cazenave",
"Tristan",
""
],
[
"Jacopin",
"Eric",
""
]
] |
2207.13857 | Ekaterina Nikonova | Ekaterina Nikonova, Cheng Xue, Vimukthini Pinto, Chathura Gamage, Peng
Zhang, Jochen Renz | Measuring Difficulty of Novelty Reaction | null | AAAI 2022, Designing Artificial Intelligence for Open Worlds | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Current AI systems are designed to solve close-world problems with the
assumption that the underlying world is remaining more or less the same.
However, when dealing with real-world problems such assumptions can be invalid
as sudden and unexpected changes can occur. To effectively deploy AI-powered
systems in the real world, AI systems should be able to deal with open-world
novelty quickly. Inevitably, dealing with open-world novelty raises an
important question of novelty difficulty. Knowing whether one novelty is harder
to deal with than another, can help researchers to train their systems
systematically. In addition, it can also serve as a measurement of the
performance of novelty robust AI systems. In this paper, we propose to define
the novelty reaction difficulty as a relative difficulty of performing the
known task after the introduction of the novelty. We propose a universal method
that can be applied to approximate the difficulty. We present the
approximations of the difficulty using our method and show how it aligns with
the results of the evaluation of AI agents designed to deal with novelty.
| [
{
"version": "v1",
"created": "Thu, 28 Jul 2022 02:16:07 GMT"
}
] | 1,681,171,200,000 | [
[
"Nikonova",
"Ekaterina",
""
],
[
"Xue",
"Cheng",
""
],
[
"Pinto",
"Vimukthini",
""
],
[
"Gamage",
"Chathura",
""
],
[
"Zhang",
"Peng",
""
],
[
"Renz",
"Jochen",
""
]
] |
2207.14119 | Christian Kindermann | Christian Kindermann and Martin G. Skj{\ae}veland | A Survey of Syntactic Modelling Structures in Biomedical Ontologies | Accepted at The 21st International Semantic Web Conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the large-scale uptake of semantic technologies in the biomedical
domain, little is known about common modelling practices in published
ontologies. OWL ontologies are often published only in the crude form of sets
of axioms leaving the underlying design opaque. However, a principled and
systematic ontology development life cycle is likely to be reflected in
regularities of the ontology's emergent syntactic structure. To develop an
understanding of this emergent structure, we propose to reverse-engineer
ontologies taking a syntax-directed approach for identifying and analysing
regularities for axioms and sets of axioms. We survey BioPortal in terms of
syntactic modelling trends and common practices for OWL axioms and class
frames. Our findings suggest that biomedical ontologies only share simple
syntactic structures in which OWL constructors are not deeply nested or
combined in a complex manner. While such simple structures often account for
large proportions of axioms in a given ontology, many ontologies also contain
non-trivial amounts of more complex syntactic structures that are not common
across ontologies.
| [
{
"version": "v1",
"created": "Thu, 28 Jul 2022 14:33:00 GMT"
}
] | 1,659,052,800,000 | [
[
"Kindermann",
"Christian",
""
],
[
"Skjæveland",
"Martin G.",
""
]
] |
2207.14760 | Hang Chu | Hang Chu, Amir Hosein Khasahmadi, Karl D.D. Willis, Fraser Anderson,
Yaoli Mao, Linh Tran, Justin Matejka, Jo Vermeulen | SimCURL: Simple Contrastive User Representation Learning from Command
Sequences | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | User modeling is crucial to understanding user behavior and essential for
improving user experience and personalized recommendations. When users interact
with software, vast amounts of command sequences are generated through logging
and analytics systems. These command sequences contain clues to the users'
goals and intents. However, these data modalities are highly unstructured and
unlabeled, making it difficult for standard predictive systems to learn from.
We propose SimCURL, a simple yet effective contrastive self-supervised deep
learning framework that learns user representation from unlabeled command
sequences. Our method introduces a user-session network architecture, as well
as session dropout as a novel way of data augmentation. We train and evaluate
our method on a real-world command sequence dataset of more than half a billion
commands. Our method shows significant improvement over existing methods when
the learned representation is transferred to downstream tasks such as
experience and expertise classification.
| [
{
"version": "v1",
"created": "Fri, 29 Jul 2022 16:06:03 GMT"
}
] | 1,659,312,000,000 | [
[
"Chu",
"Hang",
""
],
[
"Khasahmadi",
"Amir Hosein",
""
],
[
"Willis",
"Karl D. D.",
""
],
[
"Anderson",
"Fraser",
""
],
[
"Mao",
"Yaoli",
""
],
[
"Tran",
"Linh",
""
],
[
"Matejka",
"Justin",
""
],
[
"Vermeulen",
"Jo",
""
]
] |
2207.14772 | Steven James | Nicholas Muir, Steven James | Combining Evolutionary Search with Behaviour Cloning for Procedurally
Generated Content | null | Proceedings of 43rd Conference of the South African Institute of
Computer Scientists and Information Technologists, July 2022 | 10.29007/qpkt | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we consider the problem of procedural content generation for
video game levels. Prior approaches have relied on evolutionary search (ES)
methods capable of generating diverse levels, but this generation procedure is
slow, which is problematic in real-time settings. Reinforcement learning (RL)
has also been proposed to tackle the same problem, and while level generation
is fast, training time can be prohibitively expensive. We propose a framework
to tackle the procedural content generation problem that combines the best of
ES and RL. In particular, our approach first uses ES to generate a sequence of
levels evolved over time, and then uses behaviour cloning to distil these
levels into a policy, which can then be queried to produce new levels quickly.
We apply our approach to a maze game and Super Mario Bros, with our results
indicating that our approach does in fact decrease the time required for level
generation, especially when an increasing number of valid levels are required.
| [
{
"version": "v1",
"created": "Fri, 29 Jul 2022 16:25:52 GMT"
}
] | 1,659,312,000,000 | [
[
"Muir",
"Nicholas",
""
],
[
"James",
"Steven",
""
]
] |
2208.00316 | Guilherme Paulino-Passos | Guilherme Paulino-Passos and Francesca Toni | On Interactive Explanations as Non-Monotonic Reasoning | Corrected version for the XAI-IJCAI 2022 workshop, expands on the
XLoKR-KR 2022 workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work shows issues of consistency with explanations, with methods
generating local explanations that seem reasonable instance-wise, but that are
inconsistent across instances. This suggests not only that instance-wise
explanations can be unreliable, but mainly that, when interacting with a system
via multiple inputs, a user may actually lose confidence in the system. To
better analyse this issue, in this work we treat explanations as objects that
can be subject to reasoning and present a formal model of the interactive
scenario between user and system, via sequences of inputs, outputs, and
explanations. We argue that explanations can be thought of as committing to
some model behaviour (even if only prima facie), suggesting a form of
entailment, which, we argue, should be thought of as non-monotonic. This
allows: 1) to solve some considered inconsistencies in explanation, such as via
a specificity relation; 2) to consider properties from the non-monotonic
reasoning literature and discuss their desirability, gaining more insight on
the interactive explanation scenario.
| [
{
"version": "v1",
"created": "Sat, 30 Jul 2022 22:08:35 GMT"
}
] | 1,659,398,400,000 | [
[
"Paulino-Passos",
"Guilherme",
""
],
[
"Toni",
"Francesca",
""
]
] |
2208.00894 | Fabio Massimo Zennaro | Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas | Towards Computing an Optimal Abstraction for Structural Causal Models | 6 pages, 5 pages appendix, 2 figures Submitted to Causal
Representation Learning workshop at the 38th Conference on Uncertainty in
Artificial Intelligence (UAI CRL 2022) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Working with causal models at different levels of abstraction is an important
feature of science. Existing work has already considered the problem of
expressing formally the relation of abstraction between causal models. In this
paper, we focus on the problem of learning abstractions. We start by defining
the learning problem formally in terms of the optimization of a standard
measure of consistency. We then point out the limitation of this approach, and
we suggest extending the objective function with a term accounting for
information loss. We suggest a concrete measure of information loss, and we
illustrate its contribution to learning new abstractions.
| [
{
"version": "v1",
"created": "Mon, 1 Aug 2022 14:35:57 GMT"
}
] | 1,659,398,400,000 | [
[
"Zennaro",
"Fabio Massimo",
""
],
[
"Turrini",
"Paolo",
""
],
[
"Damoulas",
"Theodoros",
""
]
] |
2208.01093 | Luc\'ia Prieto Santamar\'ia | Andrea \'Alvarez P\'erez, Ana Iglesias-Molina, Luc\'ia Prieto
Santamar\'ia, Mar\'ia Poveda-Villal\'on, Carlos Badenes-Olmedo, Alejandro
Rodr\'iguez-Gonz\'alez | EBOCA: Evidences for BiOmedical Concepts Association Ontology | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | There is a large number of online documents data sources available nowadays.
The lack of structure and the differences between formats are the main
difficulties to automatically extract information from them, which also has a
negative impact on its use and reuse. In the biomedical domain, the DISNET
platform emerged to provide researchers with a resource to obtain information
in the scope of human disease networks by means of large-scale heterogeneous
sources. Specifically in this domain, it is critical to offer not only the
information extracted from different sources, but also the evidence that
supports it. This paper proposes EBOCA, an ontology that describes (i)
biomedical domain concepts and associations between them, and (ii) evidences
supporting these associations; with the objective of providing an schema to
improve the publication and description of evidences and biomedical
associations in this domain. The ontology has been successfully evaluated to
ensure there are no errors, modelling pitfalls and that it meets the previously
defined functional requirements. Test data coming from a subset of DISNET and
automatic association extractions from texts has been transformed according to
the proposed ontology to create a Knowledge Graph that can be used in real
scenarios, and which has also been used for the evaluation of the presented
ontology.
| [
{
"version": "v1",
"created": "Mon, 1 Aug 2022 18:47:03 GMT"
}
] | 1,659,484,800,000 | [
[
"Pérez",
"Andrea Álvarez",
""
],
[
"Iglesias-Molina",
"Ana",
""
],
[
"Santamaría",
"Lucía Prieto",
""
],
[
"Poveda-Villalón",
"María",
""
],
[
"Badenes-Olmedo",
"Carlos",
""
],
[
"Rodríguez-González",
"Alejandro",
""
]
] |
2208.02029 | Timo Bertram | Timo Bertram, Johannes F\"urnkranz, Martin M\"uller | Supervised and Reinforcement Learning from Observations in
Reconnaissance Blind Chess | 4 Pages, IEEE Conference on Games 2022 short paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work, we adapt a training approach inspired by the original AlphaGo
system to play the imperfect information game of Reconnaissance Blind Chess.
Using only the observations instead of a full description of the game state, we
first train a supervised agent on publicly available game records. Next, we
increase the performance of the agent through self-play with the on-policy
reinforcement learning algorithm Proximal Policy Optimization. We do not use
any search to avoid problems caused by the partial observability of game states
and only use the policy network to generate moves when playing. With this
approach, we achieve an ELO of 1330 on the RBC leaderboard, which places our
agent at position 27 at the time of this writing. We see that self-play
significantly improves performance and that the agent plays acceptably well
without search and without making assumptions about the true game state.
| [
{
"version": "v1",
"created": "Wed, 3 Aug 2022 12:50:19 GMT"
}
] | 1,659,571,200,000 | [
[
"Bertram",
"Timo",
""
],
[
"Fürnkranz",
"Johannes",
""
],
[
"Müller",
"Martin",
""
]
] |
2208.02187 | Eduardo C. Garrido-Merch\'an | Eduardo C. Garrido Merch\'an, Sara Lumbreras | On the independence between phenomenal consciousness and computational
intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consciousness and intelligence are properties commonly understood as
dependent by folk psychology and society in general. The term artificial
intelligence and the kind of problems that it managed to solve in the recent
years has been shown as an argument to establish that machines experience some
sort of consciousness. Following the analogy of Russell, if a machine is able
to do what a conscious human being does, the likelihood that the machine is
conscious increases. However, the social implications of this analogy are
catastrophic. Concretely, if rights are given to entities that can solve the
kind of problems that a neurotypical person can, does the machine have
potentially more rights that a person that has a disability? For example, the
autistic syndrome disorder spectrum can make a person unable to solve the kind
of problems that a machine solves. We believe that the obvious answer is no, as
problem solving does not imply consciousness. Consequently, we will argue in
this paper how phenomenal consciousness and, at least, computational
intelligence are independent and why machines do not possess phenomenal
consciousness, although they can potentially develop a higher computational
intelligence that human beings. In order to do so, we try to formulate an
objective measure of computational intelligence and study how it presents in
human beings, animals and machines. Analogously, we study phenomenal
consciousness as a dichotomous variable and how it is distributed in humans,
animals and machines. As phenomenal consciousness and computational
intelligence are independent, this fact has critical implications for society
that we also analyze in this work.
| [
{
"version": "v1",
"created": "Wed, 3 Aug 2022 16:17:11 GMT"
}
] | 1,659,571,200,000 | [
[
"Merchán",
"Eduardo C. Garrido",
""
],
[
"Lumbreras",
"Sara",
""
]
] |
2208.02443 | Branko Ristic | Branko Ristic, Alessio Benavoli, Sanjeev Arulampalam | Credal Valuation Networks for Machine Reasoning Under Uncertainty | 16 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Contemporary undertakings provide limitless opportunities for widespread
application of machine reasoning and artificial intelligence in situations
characterised by uncertainty, hostility and sheer volume of data. The paper
develops a valuation network as a graphical system for higher-level fusion and
reasoning under uncertainty in support of the human operators. Valuations,
which are mathematical representation of (uncertain) knowledge and collected
data, are expressed as credal sets, defined as coherent interval probabilities
in the framework of imprecise probability theory. The basic operations with
such credal sets, combination and marginalisation, are defined to satisfy the
axioms of a valuation algebra. A practical implementation of the credal
valuation network is discussed and its utility demonstrated on a small scale
example.
| [
{
"version": "v1",
"created": "Thu, 4 Aug 2022 04:33:16 GMT"
}
] | 1,659,657,600,000 | [
[
"Ristic",
"Branko",
""
],
[
"Benavoli",
"Alessio",
""
],
[
"Arulampalam",
"Sanjeev",
""
]
] |
2208.02914 | Tan Zhi-Xuan | Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B.
Tenenbaum, Vikash K. Mansinghka | Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian
Theory of Mind | 6 pages, 2 figures. Presented at the Robotics: Science and Systems
2022 Workshop on Social Intelligence in Humans and Robots | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To facilitate the development of new models to bridge the gap between machine
and human social intelligence, the recently proposed Baby Intuitions Benchmark
(arXiv:2102.11938) provides a suite of tasks designed to evaluate commonsense
reasoning about agents' goals and actions that even young infants exhibit. Here
we present a principled Bayesian solution to this benchmark, based on a
hierarchically Bayesian Theory of Mind (HBToM). By including hierarchical
priors on agent goals and dispositions, inference over our HBToM model enables
few-shot learning of the efficiency and preferences of an agent, which can then
be used in commonsense plausibility judgements about subsequent agent behavior.
This approach achieves near-perfect accuracy on most benchmark tasks,
outperforming deep learning and imitation learning baselines while producing
interpretable human-like inferences, demonstrating the advantages of structured
Bayesian models of human social cognition.
| [
{
"version": "v1",
"created": "Thu, 4 Aug 2022 22:27:11 GMT"
}
] | 1,659,916,800,000 | [
[
"Zhi-Xuan",
"Tan",
""
],
[
"Gothoskar",
"Nishad",
""
],
[
"Pollok",
"Falk",
""
],
[
"Gutfreund",
"Dan",
""
],
[
"Tenenbaum",
"Joshua B.",
""
],
[
"Mansinghka",
"Vikash K.",
""
]
] |
2208.03097 | EPTCS | Matteo Cardellini (Politecnico di Torino) | An ASP Framework for Efficient Urban Traffic Optimization | In Proceedings ICLP 2022, arXiv:2208.02685 | EPTCS 364, 2022, pp. 217-227 | 10.4204/EPTCS.364.37 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Avoiding congestion and controlling traffic in urban scenarios is becoming
nowadays of paramount importance due to the rapid growth of our cities'
population and vehicles. The effective control of urban traffic as a means to
mitigate congestion can be beneficial in an economic, environmental and health
way. In this paper, a framework which allows to efficiently simulate and
optimize traffic flow in a large roads' network with hundreds of vehicles is
presented. The framework leverages on an Answer Set Programming (ASP) encoding
to formally describe the movements of vehicles inside a network. Taking
advantage of the ability to specify optimization constraints in ASP and the
off-the-shelf solver Clingo, it is then possible to optimize the routes of
vehicles inside the network to reduce a range of relevant metrics (e.g., travel
times or emissions). Finally, an analysis on real-world traffic data is
performed, utilizing the state-of-the-art Urban Mobility Simulator (SUMO) to
keep track of the state of the network, test the correctness of the solution
and to prove the efficiency and capabilities of the presented solution.
| [
{
"version": "v1",
"created": "Fri, 5 Aug 2022 10:50:38 GMT"
}
] | 1,659,916,800,000 | [
[
"Cardellini",
"Matteo",
"",
"Politecnico di Torino"
]
] |
2208.03121 | Johan Kwisthout | Johan Kwisthout | Motivating explanations in Bayesian networks using MAP-independence | Manuscript currently under review for International Journal of
Approximate Reasoning, special issue "Papers from ECSQARU 2021" | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In decision support systems the motivation and justification of the system's
diagnosis or classification is crucial for the acceptance of the system by the
human user. In Bayesian networks a diagnosis or classification is typically
formalized as the computation of the most probable joint value assignment to
the hypothesis variables, given the observed values of the evidence variables
(generally known as the MAP problem). While solving the MAP problem gives the
most probable explanation of the evidence, the computation is a black box as
far as the human user is concerned and it does not give additional insights
that allow the user to appreciate and accept the decision. For example, a user
might want to know to whether an unobserved variable could potentially (upon
observation) impact the explanation, or whether it is irrelevant in this
aspect. In this paper we introduce a new concept, MAP- independence, which
tries to capture this notion of relevance, and explore its role towards a
potential justification of an inference to the best explanation. We formalize
several computational problems based on this concept and assess their
computational complexity.
| [
{
"version": "v1",
"created": "Fri, 5 Aug 2022 12:26:54 GMT"
}
] | 1,659,916,800,000 | [
[
"Kwisthout",
"Johan",
""
]
] |
2208.03423 | Manuel Paredes | Manuel Paredes (ICA), Marc Sartor (ICA), C\'edric Masclet (LGMT) | Advantages in Using a Stock Spring Selection Tool that Manages the
Uncertainty of the Designer Requirements | null | Evolutionary Design and Manufacture, Springer London, pp.69-80,
2000 | 10.1007/978-1-4471-0519-0_6 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyses the advantages of using a stock spring selection tool
that manages the uncertainty of designer requirements. Firstly, the manual
search and its main drawbacks are described. Then a computer assisted stock
spring selection tool is presented which performs all necessary calculations to
extract the most suitable spring from within a database. The algorithm analyses
data set with interval values using both multi-criteria analysis and fuzzy
logic. Two examples, comparing manual and assisted search, are presented. They
show not only that the results are significantly better using the assisted
search but it helps designers to detail easily and precisely their
specifications and thus increase design process flexibility.
| [
{
"version": "v1",
"created": "Thu, 4 Aug 2022 09:47:04 GMT"
}
] | 1,660,003,200,000 | [
[
"Paredes",
"Manuel",
"",
"ICA"
],
[
"Sartor",
"Marc",
"",
"ICA"
],
[
"Masclet",
"Cédric",
"",
"LGMT"
]
] |
2208.03685 | Kaifeng Yang | Kaifeng Yang, Guozhi Dong, Michael Affenzeller | A Parallel Technique for Multi-objective Bayesian Global Optimization:
Using a Batch Selection of Probability of Improvement | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian global optimization (BGO) is an efficient surrogate-assisted
technique for problems involving expensive evaluations. A parallel technique
can be used to parallelly evaluate the true-expensive objective functions in
one iteration to boost the execution time. An effective and straightforward
approach is to design an acquisition function that can evaluate the performance
of a bath of multiple solutions, instead of a single point/solution, in one
iteration. This paper proposes five alternatives of \emph{Probability of
Improvement} (PoI) with multiple points in a batch (q-PoI) for multi-objective
Bayesian global optimization (MOBGO), taking the covariance among multiple
points into account. Both exact computational formulas and the Monte Carlo
approximation algorithms for all proposed q-PoIs are provided. Based on the
distribution of the multiple points relevant to the Pareto-front, the
position-dependent behavior of the five q-PoIs is investigated. Moreover, the
five q-PoIs are compared with the other nine state-of-the-art and recently
proposed batch MOBGO algorithms on twenty bio-objective benchmarks. The
empirical experiments on different variety of benchmarks are conducted to
demonstrate the effectiveness of two greedy q-PoIs ($\kpoi_{\mbox{best}}$ and
$\kpoi_{\mbox{all}}$) on low-dimensional problems and the effectiveness of two
explorative q-PoIs ($\kpoi_{\mbox{one}}$ and $\kpoi_{\mbox{worst}}$) on
high-dimensional problems with difficult-to-approximate Pareto front
boundaries.
| [
{
"version": "v1",
"created": "Sun, 7 Aug 2022 09:28:44 GMT"
}
] | 1,660,003,200,000 | [
[
"Yang",
"Kaifeng",
""
],
[
"Dong",
"Guozhi",
""
],
[
"Affenzeller",
"Michael",
""
]
] |
2208.04148 | Youheng Zhang | Youheng Zhang | A Historical Interaction between Artificial Intelligence and Philosophy | null | Teorie V\v{e}dy / Theory of Science, 1(1), Article 1 (2023) | 10.46938/tv.2023.579 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper reviews the historical development of AI and representative
philosophical thinking from the perspective of the research paradigm.
Additionally, it considers the methodology and applications of AI from a
philosophical perspective and anticipates its continued advancement. In the
history of AI, Symbolism and connectionism are the two main paradigms in AI
research. Symbolism holds that the world can be explained by symbols and dealt
with through precise, logical processes, but connectionism believes this
process should be implemented through artificial neural networks. Regardless of
how intelligent machines or programs should achieve their smart goals, the
historical development of AI demonstrates the best answer at this time. Still,
it is not the final answer of AI research.
| [
{
"version": "v1",
"created": "Sat, 23 Jul 2022 22:37:22 GMT"
}
] | 1,699,315,200,000 | [
[
"Zhang",
"Youheng",
""
]
] |
2208.04153 | Shreya Bhatt | Shreya Bhatt, Aayush Jain, Parv Maheshwari, Animesh Jha, Debashish
Chakravarty | [Reproducibility Report] Path Planning using Neural A* Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The following paper is a reproducibility report for "Path Planning using
Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility
Challenge 2021. The original paper proposes the Neural A* planner, and claims
it achieves an optimal balance between the reduction of node expansions and
path accuracy. We verify this claim by reimplementing the model in a different
framework and reproduce the data published in the original paper. We have also
provided a code-flow diagram to aid comprehension of the code structure. As
extensions to the original paper, we explore the effects of (1) generalizing
the model by training it on a shuffled dataset, (2) introducing dropout, (3)
implementing empirically chosen hyperparameters as trainable parameters in the
model, (4) altering the network model to Generative Adversarial Networks (GANs)
to introduce stochasticity, (5) modifying the encoder from Unet to Unet++, (6)
incorporating cost maps obtained from the Neural A* module in other variations
of A* search.
| [
{
"version": "v1",
"created": "Sat, 16 Jul 2022 17:25:04 GMT"
}
] | 1,660,003,200,000 | [
[
"Bhatt",
"Shreya",
""
],
[
"Jain",
"Aayush",
""
],
[
"Maheshwari",
"Parv",
""
],
[
"Jha",
"Animesh",
""
],
[
"Chakravarty",
"Debashish",
""
]
] |
2208.04273 | Benjamin J. Smith | Benjamin J Smith and Robert Klassert and Roland Pihlakas | Improving performance in multi-objective decision-making in Bottles
environments with soft maximin approaches | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Balancing multiple competing and conflicting objectives is an essential task
for any artificial intelligence tasked with satisfying human values or
preferences. Conflict arises both from misalignment between individuals with
competing values, but also between conflicting value systems held by a single
human. Starting with principle of loss-aversion, we designed a set of soft
maximin function approaches to multi-objective decision-making. Bench-marking
these functions in a set of previously-developed environments, we found that
one new approach in particular, 'split-function exp-log loss aversion'
(SFELLA), learns faster than the state of the art thresholded alignment
objective method (Vamplew et al, 2021) on three of four tasks it was tested on,
and achieved the same optimal performance after learning. SFELLA also showed
relative robustness improvements against changes in objective scale, which may
highlight an advantage dealing with distribution shifts in the environment
dynamics. Due to publishing rules, further work could not be presented in the
preprint, but in the final published version, we will further compare SFELLA to
the multi-objective reward exponentials (MORE) approach (Rolf, 2020),
demonstrating that SFELLA performs similarly to MORE in a simple
previously-described foraging task, but in a modified foraging environment with
a new resource that was not depleted as the agent worked, SFELLA collected more
of the new resource with very little cost incurred in terms of the old
resource. Overall, we found SFELLA useful for avoiding problems that sometimes
occur with a thresholded approach, and more reward-responsive than MORE while
retaining its conservative, loss-averse incentive structure.
| [
{
"version": "v1",
"created": "Mon, 8 Aug 2022 17:09:11 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Aug 2022 21:43:09 GMT"
}
] | 1,660,521,600,000 | [
[
"Smith",
"Benjamin J",
""
],
[
"Klassert",
"Robert",
""
],
[
"Pihlakas",
"Roland",
""
]
] |
2208.05438 | Hongyang Du | Hongyang Du, Jiazhen Liu, Dusit Niyato, Jiawen Kang, Zehui Xiong,
Junshan Zhang, and Dong In Kim | Attention-aware Resource Allocation and QoE Analysis for Metaverse
xURLLC Services | Accepted by IEEE Journal on Selected Areas in Communications (IEEE
JSAC) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Metaverse encapsulates our expectations of the next-generation Internet,
while bringing new key performance indicators (KPIs). Although conventional
ultra-reliable and low-latency communications (URLLC) can satisfy objective
KPIs, it is difficult to provide a personalized immersive experience that is a
distinctive feature of the Metaverse. Since the quality of experience (QoE) can
be regarded as a comprehensive KPI, the URLLC is evolved towards the next
generation URLLC (xURLLC) with a personalized resource allocation scheme to
achieve higher QoE. To deploy Metaverse xURLLC services, we study the
interaction between the Metaverse service provider (MSP) and the network
infrastructure provider (InP), and provide an optimal contract design
framework. Specifically, the utility of the MSP, defined as a function of
Metaverse users' QoE, is to be maximized, while ensuring the incentives of the
InP. To model the QoE mathematically, we propose a novel metric named
Meta-Immersion that incorporates both the objective KPIs and subjective
feelings of Metaverse users. Furthermore, we develop an attention-aware
rendering capacity allocation scheme to improve QoE in xURLLC. Using a
user-object-attention level dataset, we validate that the xURLLC can achieve an
average of 20.1% QoE improvement compared to the conventional URLLC with a
uniform resource allocation scheme. The code for this paper is available at
https://github.com/HongyangDu/AttentionQoE
| [
{
"version": "v1",
"created": "Wed, 10 Aug 2022 16:51:27 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Aug 2022 11:55:07 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Sep 2022 02:55:33 GMT"
},
{
"version": "v4",
"created": "Thu, 2 Feb 2023 06:43:35 GMT"
},
{
"version": "v5",
"created": "Mon, 27 Mar 2023 02:20:48 GMT"
},
{
"version": "v6",
"created": "Wed, 28 Jun 2023 11:38:11 GMT"
}
] | 1,687,996,800,000 | [
[
"Du",
"Hongyang",
""
],
[
"Liu",
"Jiazhen",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Kang",
"Jiawen",
""
],
[
"Xiong",
"Zehui",
""
],
[
"Zhang",
"Junshan",
""
],
[
"Kim",
"Dong In",
""
]
] |
2208.06224 | Dmitry Maximov | Dmitry Maximov | Lattice Generalizations of the Concept of Fuzzy Numbers and Zadeh's
Extension Principle | arXiv admin note: text overlap with arXiv:2108.04760 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The concept of a fuzzy number is generalized to the case of a finite carrier
set of partially ordered elements, more precisely, a lattice, when a membership
function also takes values in a partially ordered set (a lattice). Zadeh's
extension principle for determining the degree of membership of a function of
fuzzy numbers is corrected for this generalization. An analogue of the concept
of mean value is also suggested. The use of partially ordered values in
cognitive maps with comparison of expert assessments is considered.
| [
{
"version": "v1",
"created": "Fri, 12 Aug 2022 11:32:33 GMT"
}
] | 1,660,521,600,000 | [
[
"Maximov",
"Dmitry",
""
]
] |
2208.06377 | Alessandro Gianola | Silvio Ghilardi and Alessandro Gianola and Marco Montali and Andrey
Rivkin | Relational Action Bases: Formalization, Effective Safety Verification,
and Invariants (Extended Version) | Extended version of the conference paper 'Safety Verification and
Universal Invariants for Relational Action Bases' by the same authors,
accepted at the 32nd International Joint Conference on Artificial
Intelligence (IJCAI 2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modeling and verification of dynamic systems operating over a relational
representation of states are increasingly investigated problems in AI, Business
Process Management, and Database Theory. To make these systems amenable to
verification, the amount of information stored in each relational state needs
to be bounded, or restrictions are imposed on the preconditions and effects of
actions. We introduce the general framework of relational action bases (RABs),
which generalizes existing models by lifting both these restrictions: unbounded
relational states can be evolved through actions that can quantify both
existentially and universally over the data, and that can exploit numerical
datatypes with arithmetic predicates. We then study parameterized safety of
RABs via (approximated) SMT-based backward search, singling out essential
meta-properties of the resulting procedure, and showing how it can be realized
by an off-the-shelf combination of existing verification modules of the
state-of-the-art MCMT model checker. We demonstrate the effectiveness of this
approach on a benchmark of data-aware business processes. Finally, we show how
universal invariants can be exploited to make this procedure fully correct.
| [
{
"version": "v1",
"created": "Fri, 12 Aug 2022 17:03:50 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Aug 2023 15:54:43 GMT"
}
] | 1,691,971,200,000 | [
[
"Ghilardi",
"Silvio",
""
],
[
"Gianola",
"Alessandro",
""
],
[
"Montali",
"Marco",
""
],
[
"Rivkin",
"Andrey",
""
]
] |
2208.06555 | Foivos Tsimpourlas | Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim
Hazelwood, Ajitha Rajan and Hugh Leather | BenchPress: A Deep Active Benchmark Generator | To appear in PACT 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We develop BenchPress, the first ML benchmark generator for compilers that is
steerable within feature space representations of source code. BenchPress
synthesizes compiling functions by adding new code in any part of an empty or
existing sequence by jointly observing its left and right context, achieving
excellent compilation rate. BenchPress steers benchmark generation towards
desired target features that has been impossible for state of the art
synthesizers (or indeed humans) to reach. It performs better in targeting the
features of Rodinia benchmarks in 3 different feature spaces compared with (a)
CLgen - a state of the art ML synthesizer, (b) CLSmith fuzzer, (c) SRCIROR
mutator or even (d) human-written code from GitHub. BenchPress is the first
generator to search the feature space with active learning in order to generate
benchmarks that will improve a downstream task. We show how using BenchPress,
Grewe's et al. CPU vs GPU heuristic model can obtain a higher speedup when
trained on BenchPress's benchmarks compared to other techniques. BenchPress is
a powerful code generator: Its generated samples compile at a rate of 86%,
compared to CLgen's 2.33%. Starting from an empty fixed input, BenchPress
produces 10x more unique, compiling OpenCL benchmarks than CLgen, which are
significantly larger and more feature diverse.
| [
{
"version": "v1",
"created": "Sat, 13 Aug 2022 03:00:50 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Aug 2022 00:40:44 GMT"
}
] | 1,660,694,400,000 | [
[
"Tsimpourlas",
"Foivos",
""
],
[
"Petoumenos",
"Pavlos",
""
],
[
"Xu",
"Min",
""
],
[
"Cummins",
"Chris",
""
],
[
"Hazelwood",
"Kim",
""
],
[
"Rajan",
"Ajitha",
""
],
[
"Leather",
"Hugh",
""
]
] |
2208.06590 | Hiroshi Yamakawa | Hiroshi Yamakawa and Yutaka Matsuo | Recognition of All Categories of Entities by AI | 7 pages (without references), 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Human-level AI will have significant impacts on human society. However,
estimates for the realization time are debatable. To arrive at human-level AI,
artificial general intelligence (AGI), as opposed to AI systems that are
specialized for a specific task, was set as a technically meaningful long-term
goal. But now, propelled by advances in deep learning, that achievement is
getting much closer. Considering the recent technological developments, it
would be meaningful to discuss the completion date of human-level AI through
the "comprehensive technology map approach," wherein we map human-level
capabilities at a reasonable granularity, identify the current range of
technology, and discuss the technical challenges in traversing unexplored areas
and predict when all of them will be overcome. This paper presents a new
argumentative option to view the ontological sextet, which encompasses entities
in a way that is consistent with our everyday intuition and scientific
practice, as a comprehensive technological map. Because most of the modeling of
the world, in terms of how to interpret it, by an intelligent subject is the
recognition of distal entities and the prediction of their temporal evolution,
being able to handle all distal entities is a reasonable goal. Based on the
findings of philosophy and engineering cognitive technology, we predict that in
the relatively near future, AI will be able to recognize various entities to
the same degree as humans.
| [
{
"version": "v1",
"created": "Sat, 13 Aug 2022 08:00:42 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Aug 2022 01:17:06 GMT"
}
] | 1,660,780,800,000 | [
[
"Yamakawa",
"Hiroshi",
""
],
[
"Matsuo",
"Yutaka",
""
]
] |
2208.06802 | Mrinal Rawat | Mrinal Rawat, Victor Barres | Real-time Caller Intent Detection In Human-Human Customer Support Spoken
Conversations | null | null | null | Accepted in Communication in Human-AI Interaction, IJCAI'22 | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Agent assistance during human-human customer support spoken interactions
requires triggering workflows based on the caller's intent (reason for call).
Timeliness of prediction is essential for a good user experience. The goal is
for a system to detect the caller's intent at the time the agent would have
been able to detect it (Intent Boundary). Some approaches focus on predicting
the output offline, i.e. once the full spoken input (e.g. the whole
conversational turn) has been processed by the ASR system. This introduces an
undesirable latency in the prediction each time the intent could have been
detected earlier in the turn. Recent work on voice assistants has used
incremental real-time predictions at a word-by-word level to detect intent
before the end of a command. Human-directed and machine-directed speech however
have very different characteristics. In this work, we propose to apply a method
developed in the context of voice-assistant to the problem of online real time
caller's intent detection in human-human spoken interactions. We use a dual
architecture in which two LSTMs are jointly trained: one predicting the Intent
Boundary (IB) and then other predicting the intent class at the IB. We conduct
our experiments on our private dataset comprising transcripts of human-human
telephone conversations from the telecom customer support domain. We report
results analyzing both the accuracy of our system as well as the impact of
different architectures on the trade off between overall accuracy and
prediction latency.
| [
{
"version": "v1",
"created": "Sun, 14 Aug 2022 07:50:23 GMT"
}
] | 1,660,608,000,000 | [
[
"Rawat",
"Mrinal",
""
],
[
"Barres",
"Victor",
""
]
] |
2208.06906 | Ernest Davis | Ernest Davis | Limits of an AI program for solving college math problems | 4 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Drori et al. (2022) report that "A neural network solves, explains, and
generates university math problems by program synthesis and few-shot learning
at human level ... [It] automatically answers 81\% of university-level
mathematics problems." The system they describe is indeed impressive; however,
the above description is very much overstated. The work of solving the problems
is done, not by a neural network, but by the symbolic algebra package Sympy.
Problems of various formats are excluded from consideration. The so-called
"explanations" are just rewordings of lines of code. Answers are marked as
correct that are not in the form specified in the problem. Most seriously, it
seems that in many cases the system uses the correct answer given in the test
corpus to guide its path to solving the problem.
| [
{
"version": "v1",
"created": "Sun, 14 Aug 2022 20:10:14 GMT"
}
] | 1,660,608,000,000 | [
[
"Davis",
"Ernest",
""
]
] |
2208.07031 | Rishi Veerapaneni | Rishi Veerapaneni, Maxim Likhachev | Non-Blocking Batch A* (Technical Report) | 4 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Heuristic search has traditionally relied on hand-crafted or programmatically
derived heuristics. Neural networks (NNs) are newer powerful tools which can be
used to learn complex mappings from states to cost-to-go heuristics. However,
their slow single inference time is a large overhead that can substantially
slow down planning time in optimized heuristic search implementations. Several
recent works have described ways to take advantage of NN's batch computations
to decrease overhead in planning, while retaining bounds on (sub)optimality.
However, all these methods have used the NN heuristic in a "blocking" manner
while building up their batches, and have ignored possible fast-to-compute
admissible heuristics (e.g. existing classically derived heuristics) that are
usually available to use. We introduce Non-Blocking Batch A* (NBBA*), a bounded
suboptimal method which lazily computes the NN heuristic in batches while
allowing expansions informed by a non-NN heuristic. We show how this subtle but
important change can lead to substantial reductions in expansions compared to
the current blocking alternative, and see that the performance is related to
the information difference between the batch computed NN and fast non-NN
heuristic.
| [
{
"version": "v1",
"created": "Mon, 15 Aug 2022 07:07:29 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Aug 2022 02:16:48 GMT"
}
] | 1,660,694,400,000 | [
[
"Veerapaneni",
"Rishi",
""
],
[
"Likhachev",
"Maxim",
""
]
] |
2208.07074 | Yusuke Kawamoto | Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga | Sound and Relatively Complete Belief Hoare Logic for Statistical
Hypothesis Testing Programs | Accepted to the journal Artificial Intelligence (AI); an extended
version of the KR'21 conference paper https://proceedings.kr.org/2021/39/ | Artificial Intelligence, Vol.326, 104045, Elsevier, 2024 | 10.1016/j.artint.2023.104045 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new approach to formally describing the requirement for
statistical inference and checking whether a program uses the statistical
method appropriately. Specifically, we define belief Hoare logic (BHL) for
formalizing and reasoning about the statistical beliefs acquired via hypothesis
testing. This program logic is sound and relatively complete with respect to a
Kripke model for hypothesis tests. We demonstrate by examples that BHL is
useful for reasoning about practical issues in hypothesis testing. In our
framework, we clarify the importance of prior beliefs in acquiring statistical
beliefs through hypothesis testing, and discuss the whole picture of the
justification of statistical inference inside and outside the program logic.
| [
{
"version": "v1",
"created": "Mon, 15 Aug 2022 08:42:24 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Apr 2023 13:09:52 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Nov 2023 16:44:52 GMT"
}
] | 1,701,734,400,000 | [
[
"Kawamoto",
"Yusuke",
""
],
[
"Sato",
"Tetsuya",
""
],
[
"Suenaga",
"Kohei",
""
]
] |
2208.07622 | Zhaoxuan Tan | Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua
Zheng, Jundong Li, Minnan Luo | KRACL: Contrastive Learning with Graph Context Modeling for Sparse
Knowledge Graph Completion | Accepted to The Web Conference 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graph Embeddings (KGE) aim to map entities and relations to low
dimensional spaces and have become the \textit{de-facto} standard for knowledge
graph completion. Most existing KGE methods suffer from the sparsity challenge,
where it is harder to predict entities that appear less frequently in knowledge
graphs. In this work, we propose a novel framework KRACL to alleviate the
widespread sparsity in KGs with graph context and contrastive learning.
Firstly, we propose the Knowledge Relational Attention Network (KRAT) to
leverage the graph context by simultaneously projecting neighboring triples to
different latent spaces and jointly aggregating messages with the attention
mechanism. KRAT is capable of capturing the subtle semantic information and
importance of different context triples as well as leveraging multi-hop
information in knowledge graphs. Secondly, we propose the knowledge contrastive
loss by combining the contrastive loss with cross entropy loss, which
introduces more negative samples and thus enriches the feedback to sparse
entities. Our experiments demonstrate that KRACL achieves superior results
across various standard knowledge graph benchmarks, especially on WN18RR and
NELL-995 which have large numbers of low in-degree entities. Extensive
experiments also bear out KRACL's effectiveness in handling sparse knowledge
graphs and robustness against noisy triples.
| [
{
"version": "v1",
"created": "Tue, 16 Aug 2022 09:17:40 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Feb 2023 08:23:41 GMT"
}
] | 1,676,332,800,000 | [
[
"Tan",
"Zhaoxuan",
""
],
[
"Chen",
"Zilong",
""
],
[
"Feng",
"Shangbin",
""
],
[
"Zhang",
"Qingyue",
""
],
[
"Zheng",
"Qinghua",
""
],
[
"Li",
"Jundong",
""
],
[
"Luo",
"Minnan",
""
]
] |
2208.07753 | Qingxu Fu | Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Xiaolin Ai, Wanmai
Yuan | A Policy Resonance Approach to Solve the Problem of Responsibility
Diffusion in Multiagent Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SOTA multiagent reinforcement algorithms distinguish themselves in many ways
from their single-agent equivalences. However, most of them still totally
inherit the single-agent exploration-exploitation strategy. Naively inheriting
this strategy from single-agent algorithms causes potential collaboration
failures, in which the agents blindly follow mainstream behaviors and reject
taking minority responsibility. We name this problem the Responsibility
Diffusion (RD) as it shares similarities with a same-name social psychology
effect. In this work, we start by theoretically analyzing the cause of this RD
problem, which can be traced back to the exploration-exploitation dilemma of
multiagent systems (especially large-scale multiagent systems). We address this
RD problem by proposing a Policy Resonance (PR) approach which modifies the
collaborative exploration strategy of agents by refactoring the joint agent
policy while keeping individual policies approximately invariant. Next, we show
that SOTA algorithms can equip this approach to promote the collaborative
performance of agents in complex cooperative tasks. Experiments are performed
in multiple test benchmark tasks to illustrate the effectiveness of this
approach.
| [
{
"version": "v1",
"created": "Tue, 16 Aug 2022 13:56:00 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Aug 2022 09:14:57 GMT"
},
{
"version": "v3",
"created": "Tue, 5 Dec 2023 03:37:57 GMT"
}
] | 1,701,820,800,000 | [
[
"Fu",
"Qingxu",
""
],
[
"Qiu",
"Tenghai",
""
],
[
"Yi",
"Jianqiang",
""
],
[
"Pu",
"Zhiqiang",
""
],
[
"Ai",
"Xiaolin",
""
],
[
"Yuan",
"Wanmai",
""
]
] |
2208.07777 | Yu Zhang | Enqiang Zhu, Yu Zhang and Chanjuan Liu | An Adaptive Repeated-Intersection-Reduction Local Search for the Maximum
Independent Set Problem | 11 pages, 0 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The maximum independent set (MIS) problem, a classical NP-hard problem with
extensive applications in various areas, aims to find the largest set of
vertices with no edge among them. Due to its computational intractability, it
is difficult to solve the MIS problem effectively, especially on large graphs.
Employing heuristic approaches to obtain a good solution within an acceptable
amount of time has attracted much attention in literature. In this paper, we
propose an efficient local search framework for MIS called ARIR, which
encompasses two main parts: a lightweight adaptive mechanism and a novel
inexact efficient reduction rule to simplify instances. Based on ARIR, three
algorithms -- ARIR-I, ARIR-II, and ARIR-III -- are developed by adopting three
distinct reduction strategies. We conduct experiments on five benchmarks,
encompassing 92 instances. Compared with six state-of-the-art algorithms, our
ARIR-based algorithms offer the best accuracy on the majority of instances,
while obtaining competitive results on the remaining instances.
| [
{
"version": "v1",
"created": "Tue, 16 Aug 2022 14:39:38 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Nov 2022 13:20:25 GMT"
}
] | 1,669,075,200,000 | [
[
"Zhu",
"Enqiang",
""
],
[
"Zhang",
"Yu",
""
],
[
"Liu",
"Chanjuan",
""
]
] |
2208.07805 | John Harwell | John Harwell, Maria Gini | SIERRA: A Modular Framework for Research Automation and Reproducibility | Submitted to IEEE RAM | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Modern intelligent systems researchers form hypotheses about system behavior
and then run experiments using one or more independent variables to test their
hypotheses. We present SIERRA, a novel framework structured around that idea
for accelerating research development and improving reproducibility of results.
SIERRA accelerates research by automating the process of generating executable
experiments from queries over independent variables(s), executing experiments,
and processing the results to generate deliverables such as graphs and videos.
It shifts the paradigm for testing hypotheses from procedural ("Do these steps
to answer the query") to declarative ("Here is the query to test--GO!"),
reducing the burden on researchers. It employs a modular architecture enabling
easy customization and extension for the needs of individual researchers,
thereby eliminating manual configuration and processing via throw-away scripts.
SIERRA improves reproducibility of research by providing automation independent
of the execution environment (HPC hardware, real robots, etc.) and targeted
platform (arbitrary simulator or real robots). This enables exact experiment
replication, up to the limit of the execution environment and platform, as well
as making it easy for researchers to test hypotheses in different computational
environments.
| [
{
"version": "v1",
"created": "Tue, 16 Aug 2022 15:36:34 GMT"
}
] | 1,660,694,400,000 | [
[
"Harwell",
"John",
""
],
[
"Gini",
"Maria",
""
]
] |
2208.08017 | Bingbing Wen | Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah | Towards Generating Robust, Fair, and Emotion-Aware Explanations for
Recommender Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As recommender systems become increasingly sophisticated and complex, they
often suffer from lack of fairness and transparency. Providing robust and
unbiased explanations for recommendations has been drawing more and more
attention as it can help address these issues and improve trustworthiness and
informativeness of recommender systems. However, despite the fact that such
explanations are generated for humans who respond more strongly to messages
with appropriate emotions, there is a lack of consideration for emotions when
generating explanations for recommendations. Current explanation generation
models are found to exaggerate certain emotions without accurately capturing
the underlying tone or the meaning. In this paper, we propose a novel method
based on a multi-head transformer, called Emotion-aware Transformer for
Explainable Recommendation (EmoTER), to generate more robust, fair, and
emotion-enhanced explanations. To measure the linguistic quality and emotion
fairness of the generated explanations, we adopt both automatic text metrics
and human perceptions for evaluation. Experiments on three widely-used
benchmark datasets with multiple evaluation metrics demonstrate that EmoTER
consistently outperforms the existing state-of-the-art explanation generation
models in terms of text quality, explainability, and consideration for fairness
to emotion distribution. Implementation of EmoTER will be released as an
open-source toolkit to support further research.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 01:49:14 GMT"
}
] | 1,660,780,800,000 | [
[
"Wen",
"Bingbing",
""
],
[
"Feng",
"Yunhe",
""
],
[
"Zhang",
"Yongfeng",
""
],
[
"Shah",
"Chirag",
""
]
] |
2208.08058 | Ji Xu | Ji Xu, Gang Ren, Yao Xiao, Shaobo Li, Guoyin Wang | Semi-supervised Learning with Deterministic Labeling and Large Margin
Projection | 12 pages, ready to submit to a journal | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The centrality and diversity of the labeled data are very influential to the
performance of semi-supervised learning (SSL), but most SSL models select the
labeled data randomly. This study first construct a leading forest that forms a
partially ordered topological space in an unsupervised way, and select a group
of most representative samples to label with one shot (differs from active
learning essentially) using property of homeomorphism. Then a kernelized large
margin metric is efficiently learned for the selected data to classify the
remaining unlabeled sample. Optimal leading forest (OLF) has been observed to
have the advantage of revealing the difference evolution along a path within a
subtree. Therefore, we formulate an optimization problem based on OLF to select
the samples. Also with OLF, the multiple local metrics learning is facilitated
to address multi-modal and mix-modal problem in SSL, especially when the number
of class is large. Attribute to this novel design, stableness and accuracy of
the performance is significantly improved when compared with the
state-of-the-art graph SSL methods. The extensive experimental studies have
shown that the proposed method achieved encouraging accuracy and efficiency.
Code has been made available at https://github.com/alanxuji/DeLaLA.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 04:09:35 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Oct 2022 10:11:23 GMT"
}
] | 1,665,446,400,000 | [
[
"Xu",
"Ji",
""
],
[
"Ren",
"Gang",
""
],
[
"Xiao",
"Yao",
""
],
[
"Li",
"Shaobo",
""
],
[
"Wang",
"Guoyin",
""
]
] |
2208.08149 | Haixiao Chi | Haixiao Chi, Dawei Wang, Gaojie Cui, Feng Mao, Beishui Liao | A Concept and Argumentation based Interpretable Model in High Risk
Domains | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Interpretability has become an essential topic for artificial intelligence in
some high-risk domains such as healthcare, bank and security. For commonly-used
tabular data, traditional methods trained end-to-end machine learning models
with numerical and categorical data only, and did not leverage human
understandable knowledge such as data descriptions. Yet mining human-level
knowledge from tabular data and using it for prediction remain a challenge.
Therefore, we propose a concept and argumentation based model (CAM) that
includes the following two components: a novel concept mining method to obtain
human understandable concepts and their relations from both descriptions of
features and the underlying data, and a quantitative argumentation-based method
to do knowledge representation and reasoning. As a result of it, CAM provides
decisions that are based on human-level knowledge and the reasoning process is
intrinsically interpretable. Finally, to visualize the purposed interpretable
model, we provide a dialogical explanation that contain dominated reasoning
path within CAM. Experimental results on both open source benchmark dataset and
real-word business dataset show that (1) CAM is transparent and interpretable,
and the knowledge inside the CAM is coherent with human understanding; (2) Our
interpretable approach can reach competitive results comparing with other
state-of-art models.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 08:29:02 GMT"
}
] | 1,660,780,800,000 | [
[
"Chi",
"Haixiao",
""
],
[
"Wang",
"Dawei",
""
],
[
"Cui",
"Gaojie",
""
],
[
"Mao",
"Feng",
""
],
[
"Liao",
"Beishui",
""
]
] |
2208.08157 | Andre Thevapalan | Andre Thevapalan and Gabriele Kern-Isberner | On Establishing Robust Consistency in Answer Set Programs | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer set programs used in real-world applications often require that the
program is usable with different input data. This, however, can often lead to
contradictory statements and consequently to an inconsistent program. Causes
for potential contradictions in a program are conflicting rules. In this paper,
we show how to ensure that a program $\mathcal{P}$ remains non-contradictory
given any allowed set of such input data. For that, we introduce the notion of
conflict-resolving $\lambda$- extensions. A conflict-resolving
$\lambda$-extension for a conflicting rule $r$ is a set $\lambda$ of (default)
literals such that extending the body of $r$ by $\lambda$ resolves all
conflicts of $r$ at once. We investigate the properties that suitable
$\lambda$-extensions should possess and building on that, we develop a strategy
to compute all such conflict-resolving $\lambda$-extensions for each
conflicting rule in $\mathcal{P}$. We show that by implementing a conflict
resolution process that successively resolves conflicts using
$\lambda$-extensions eventually yields a program that remains non-contradictory
given any allowed set of input data.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 08:56:29 GMT"
}
] | 1,660,780,800,000 | [
[
"Thevapalan",
"Andre",
""
],
[
"Kern-Isberner",
"Gabriele",
""
]
] |
2208.08160 | Luke Thorburn | Luke Thorburn, Maria Polukarov, Carmine Ventre | Error in the Euclidean Preference Model | 11 pages, 5 figures. Accepted as an extended abstract to AAMAS 2023,
full paper IJCAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial models of preference, in the form of vector embeddings, are learned
by many deep learning and multiagent systems, including recommender systems.
Often these models are assumed to approximate a Euclidean structure, where an
individual prefers alternatives positioned closer to their "ideal point", as
measured by the Euclidean metric. However, Bogomolnaia and Laslier (2007)
showed that there exist ordinal preference profiles that cannot be represented
with this structure if the Euclidean space has two fewer dimensions than there
are individuals or alternatives. We extend this result, showing that there are
situations in which almost all preference profiles cannot be represented with
the Euclidean model, and derive a theoretical lower bound on the expected error
when using the Euclidean model to approximate non-Euclidean preference
profiles. Our results have implications for the interpretation and use of
vector embeddings, because in some cases close approximation of arbitrary, true
ordinal relationships can be expected only if the dimensionality of the
embeddings is a substantial fraction of the number of entities represented.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 09:01:17 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Feb 2023 19:29:07 GMT"
},
{
"version": "v3",
"created": "Sat, 13 May 2023 13:28:02 GMT"
}
] | 1,684,195,200,000 | [
[
"Thorburn",
"Luke",
""
],
[
"Polukarov",
"Maria",
""
],
[
"Ventre",
"Carmine",
""
]
] |
2208.08176 | Rita Sevastjanova | Rita Sevastjanova, Eren Cakmak, Shauli Ravfogel, Ryan Cotterell, and
Mennatallah El-Assady | Visual Comparison of Language Model Adaptation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Neural language models are widely used; however, their model parameters often
need to be adapted to the specific domains and tasks of an application, which
is time- and resource-consuming. Thus, adapters have recently been introduced
as a lightweight alternative for model adaptation. They consist of a small set
of task-specific parameters with a reduced training time and simple parameter
composition. The simplicity of adapter training and composition comes along
with new challenges, such as maintaining an overview of adapter properties and
effectively comparing their produced embedding spaces. To help developers
overcome these challenges, we provide a twofold contribution. First, in close
collaboration with NLP researchers, we conducted a requirement analysis for an
approach supporting adapter evaluation and detected, among others, the need for
both intrinsic (i.e., embedding similarity-based) and extrinsic (i.e.,
prediction-based) explanation methods. Second, motivated by the gathered
requirements, we designed a flexible visual analytics workspace that enables
the comparison of adapter properties. In this paper, we discuss several design
iterations and alternatives for interactive, comparative visual explanation
methods. Our comparative visualizations show the differences in the adapted
embedding vectors and prediction outcomes for diverse human-interpretable
concepts (e.g., person names, human qualities). We evaluate our workspace
through case studies and show that, for instance, an adapter trained on the
language debiasing task according to context-0 (decontextualized) embeddings
introduces a new type of bias where words (even gender-independent words such
as countries) become more similar to female than male pronouns. We demonstrate
that these are artifacts of context-0 embeddings.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 09:25:28 GMT"
}
] | 1,660,780,800,000 | [
[
"Sevastjanova",
"Rita",
""
],
[
"Cakmak",
"Eren",
""
],
[
"Ravfogel",
"Shauli",
""
],
[
"Cotterell",
"Ryan",
""
],
[
"El-Assady",
"Mennatallah",
""
]
] |
2208.08218 | Bosong Huang | Jin Huang, Bosong Huang, Weihao Yu, Jing Xiao, Ruzhong Xie, Ke Ruan | ODformer: Spatial-Temporal Transformers for Long Sequence
Origin-Destination Matrix Forecasting Against Cross Application Scenario | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Origin-Destination (OD) matrices record directional flow data between pairs
of OD regions. The intricate spatiotemporal dependency in the matrices makes
the OD matrix forecasting (ODMF) problem not only intractable but also
non-trivial. However, most of the related methods are designed for very short
sequence time series forecasting in specific application scenarios, which
cannot meet the requirements of the variation in scenarios and forecasting
length of practical applications. To address these issues, we propose a
Transformer-like model named ODformer, with two salient characteristics: (i)
the novel OD Attention mechanism, which captures special spatial dependencies
between OD pairs of the same origin (destination), greatly improves the ability
of the model to predict cross-application scenarios after combining with 2D-GCN
that captures spatial dependencies between OD regions. (ii) a PeriodSparse
Self-attention that effectively forecasts long sequence OD matrix series while
adapting to the periodic differences in different scenarios. Generous
experiments in three application backgrounds (i.e., transportation traffic, IP
backbone network traffic, crowd flow) show our method outperforms the
state-of-the-art methods.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 10:58:46 GMT"
}
] | 1,660,780,800,000 | [
[
"Huang",
"Jin",
""
],
[
"Huang",
"Bosong",
""
],
[
"Yu",
"Weihao",
""
],
[
"Xiao",
"Jing",
""
],
[
"Xie",
"Ruzhong",
""
],
[
"Ruan",
"Ke",
""
]
] |
2208.08320 | Zhenyu Lei | Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen,
Jundong Li, Qinghua Zheng, Minnan Luo | BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic
Consistency | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Twitter bots are automatic programs operated by malicious actors to
manipulate public opinion and spread misinformation. Research efforts have been
made to automatically identify bots based on texts and networks on social
media. Existing methods only leverage texts or networks alone, and while few
works explored the shallow combination of the two modalities, we hypothesize
that the interaction and information exchange between texts and graphs could be
crucial for holistically evaluating bot activities on social media. In
addition, according to a recent survey (Cresci, 2020), Twitter bots are
constantly evolving while advanced bots steal genuine users' tweets and dilute
their malicious content to evade detection. This results in greater
inconsistency across the timeline of novel Twitter bots, which warrants more
attention. In light of these challenges, we propose BIC, a Twitter Bot
detection framework with text-graph Interaction and semantic Consistency.
Specifically, in addition to separately modeling the two modalities on social
media, BIC employs a text-graph interaction module to enable information
exchange across modalities in the learning process. In addition, given the
stealing behavior of novel Twitter bots, BIC proposes to model semantic
consistency in tweets based on attention weights while using it to augment the
decision process. Extensive experiments demonstrate that BIC consistently
outperforms state-of-the-art baselines on two widely adopted datasets. Further
analyses reveal that text-graph interactions and modeling semantic consistency
are essential improvements and help combat bot evolution.
| [
{
"version": "v1",
"created": "Wed, 17 Aug 2022 14:34:40 GMT"
},
{
"version": "v2",
"created": "Sat, 18 Feb 2023 03:43:04 GMT"
}
] | 1,676,937,600,000 | [
[
"Lei",
"Zhenyu",
""
],
[
"Wan",
"Herun",
""
],
[
"Zhang",
"Wenqian",
""
],
[
"Feng",
"Shangbin",
""
],
[
"Chen",
"Zilong",
""
],
[
"Li",
"Jundong",
""
],
[
"Zheng",
"Qinghua",
""
],
[
"Luo",
"Minnan",
""
]
] |
2208.08611 | Yinxiao Wang | Jinxin Ding, Yuxin Huang, Keyang Ni, Xueyao Wang, Yinxiao Wang and
Yucheng Wang | Intellectual Property Evaluation Utilizing Machine Learning | 5 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intellectual properties is increasingly important in the economic
development. To solve the pain points by traditional methods in IP evaluation,
we are developing a new technology with machine learning as the core. We have
built an online platform and will expand our business in the Greater Bay Area
with plans.
| [
{
"version": "v1",
"created": "Thu, 18 Aug 2022 03:15:01 GMT"
}
] | 1,660,867,200,000 | [
[
"Ding",
"Jinxin",
""
],
[
"Huang",
"Yuxin",
""
],
[
"Ni",
"Keyang",
""
],
[
"Wang",
"Xueyao",
""
],
[
"Wang",
"Yinxiao",
""
],
[
"Wang",
"Yucheng",
""
]
] |
2208.08620 | Yan-Li Liu | Yanli Liu, Jiming Zhao, Chu-Min Li, Hua Jiang, Kun He | Hybrid Learning with New Value Function for the Maximum Common Subgraph
Problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Maximum Common induced Subgraph (MCS) is an important NP-hard problem with
wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of
efficient algorithms for MCS, consisting in successively selecting vertices to
match and pruning when it is discovered that a solution better than the best
solution found so far does not exist. The method of selecting the vertices to
match is essential for the performance of BnB. In this paper, we propose a new
value function and a hybrid selection strategy used in reinforcement learning
to define a new vertex selection method, and propose a new BnB algorithm,
called McSplitDAL, for MCS. Extensive experiments show that McSplitDAL
significantly improves the current best BnB algorithms, McSplit+LL and
McSplit+RL. An empirical analysis is also performed to illustrate why the new
value function and the hybrid selection strategy are effective.
| [
{
"version": "v1",
"created": "Thu, 18 Aug 2022 03:43:50 GMT"
}
] | 1,660,867,200,000 | [
[
"Liu",
"Yanli",
""
],
[
"Zhao",
"Jiming",
""
],
[
"Li",
"Chu-Min",
""
],
[
"Jiang",
"Hua",
""
],
[
"He",
"Kun",
""
]
] |
2208.08790 | Ravi Vadlamani | Satyam Kumar, Mendhikar Vishal and Vadlamani Ravi | Explainable Reinforcement Learning on Financial Stock Trading using SHAP | 28 pages; 3 Tables; 21 Figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Explainable Artificial Intelligence (XAI) research gained prominence in
recent years in response to the demand for greater transparency and trust in AI
from the user communities. This is especially critical because AI is adopted in
sensitive fields such as finance, medicine etc., where implications for
society, ethics, and safety are immense. Following thorough systematic
evaluations, work in XAI has primarily focused on Machine Learning (ML) for
categorization, decision, or action. To the best of our knowledge, no work is
reported that offers an Explainable Reinforcement Learning (XRL) method for
trading financial stocks. In this paper, we proposed to employ SHapley Additive
exPlanation (SHAP) on a popular deep reinforcement learning architecture viz.,
deep Q network (DQN) to explain an action of an agent at a given instance in
financial stock trading. To demonstrate the effectiveness of our method, we
tested it on two popular datasets namely, SENSEX and DJIA, and reported the
results.
| [
{
"version": "v1",
"created": "Thu, 18 Aug 2022 12:03:28 GMT"
}
] | 1,660,867,200,000 | [
[
"Kumar",
"Satyam",
""
],
[
"Vishal",
"Mendhikar",
""
],
[
"Ravi",
"Vadlamani",
""
]
] |
2208.08968 | Maarten Grachten | Emmanuel Deruty, Maarten Grachten | "Melatonin": A Case Study on AI-induced Musical Style | Accepted paper at the 3rd Conference on AI Music Creativity
(September 2022) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Although the use of AI tools in music composition and production is steadily
increasing, as witnessed by the newly founded AI song contest, analysis of
music produced using these tools is still relatively uncommon as a mean to gain
insight in the ways AI tools impact music production. In this paper we present
a case study of "Melatonin", a song produced by extensive use of BassNet, an AI
tool originally designed to generate bass lines. Through analysis of the
artists' work flow and song project, we identify style characteristics of the
song in relation to the affordances of the tool, highlighting manifestations of
style in terms of both idiom and sound.
| [
{
"version": "v1",
"created": "Thu, 18 Aug 2022 17:17:53 GMT"
}
] | 1,660,867,200,000 | [
[
"Deruty",
"Emmanuel",
""
],
[
"Grachten",
"Maarten",
""
]
] |
2208.09137 | Yun-Cheng Wang | Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo | GreenKGC: A Lightweight Knowledge Graph Completion Method | Accepted to ACL2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Knowledge graph completion (KGC) aims to discover missing relationships
between entities in knowledge graphs (KGs). Most prior KGC work focuses on
learning embeddings for entities and relations through a simple scoring
function. Yet, a higher-dimensional embedding space is usually required for a
better reasoning capability, which leads to a larger model size and hinders
applicability to real-world problems (e.g., large-scale KGs or mobile/edge
computing). A lightweight modularized KGC solution, called GreenKGC, is
proposed in this work to address this issue. GreenKGC consists of three
modules: representation learning, feature pruning, and decision learning, to
extract discriminant KG features and make accurate predictions on missing
relationships using classifiers and negative sampling. Experimental results
demonstrate that, in low dimensions, GreenKGC can outperform SOTA methods in
most datasets. In addition, low-dimensional GreenKGC can achieve competitive or
even better performance against high-dimensional models with a much smaller
model size.
| [
{
"version": "v1",
"created": "Fri, 19 Aug 2022 03:33:45 GMT"
},
{
"version": "v2",
"created": "Sun, 9 Jul 2023 09:34:39 GMT"
}
] | 1,689,206,400,000 | [
[
"Wang",
"Yun-Cheng",
""
],
[
"Ge",
"Xiou",
""
],
[
"Wang",
"Bin",
""
],
[
"Kuo",
"C. -C. Jay",
""
]
] |
2208.09554 | Robert Wray | James R. Kirk, Robert E. Wray, Peter Lindes, John E. Laird | Integrating Diverse Knowledge Sources for Online One-shot Learning of
Novel Tasks | 20 pages, 3 figures. (Added technical appendix based on reviewer
feedback.) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Autonomous agents are able to draw on a wide variety of potential sources of
task knowledge; however current approaches invariably focus on only one or two.
Here we investigate the challenges and impact of exploiting diverse knowledge
sources to learn online, in one-shot, new tasks for a simulated office mobile
robot. The resulting agent, developed in the Soar cognitive architecture, uses
the following sources of domain and task knowledge: interaction with the
environment, task execution and search knowledge, human natural language
instruction, and responses retrieved from a large language model (GPT-3). We
explore the distinct contributions of these knowledge sources and evaluate the
performance of different combinations in terms of learning correct task
knowledge and human workload. Results show that an agent's online integration
of diverse knowledge sources improves one-shot task learning overall, reducing
human feedback needed for rapid and reliable task learning.
| [
{
"version": "v1",
"created": "Fri, 19 Aug 2022 21:53:15 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Feb 2023 02:55:43 GMT"
},
{
"version": "v3",
"created": "Mon, 15 May 2023 16:34:58 GMT"
}
] | 1,684,195,200,000 | [
[
"Kirk",
"James R.",
""
],
[
"Wray",
"Robert E.",
""
],
[
"Lindes",
"Peter",
""
],
[
"Laird",
"John E.",
""
]
] |
2208.09568 | Ang Li | Ang Li and Judea Pearl | Probabilities of Causation with Nonbinary Treatment and Effect | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper deals with the problem of estimating the probabilities of
causation when treatment and effect are not binary. Tian and Pearl derived
sharp bounds for the probability of necessity and sufficiency (PNS), the
probability of sufficiency (PS), and the probability of necessity (PN) using
experimental and observational data. In this paper, we provide theoretical
bounds for all types of probabilities of causation to multivalued treatments
and effects. We further discuss examples where our bounds guide practical
decisions and use simulation studies to evaluate how informative the bounds are
for various combinations of data.
| [
{
"version": "v1",
"created": "Fri, 19 Aug 2022 23:54:47 GMT"
}
] | 1,661,212,800,000 | [
[
"Li",
"Ang",
""
],
[
"Pearl",
"Judea",
""
]
] |
2208.09569 | Ang Li | Ang Li and Judea Pearl | Unit Selection with Nonbinary Treatment and Effect | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The unit selection problem aims to identify a set of individuals who are most
likely to exhibit a desired mode of behavior, for example, selecting
individuals who would respond one way if encouraged and a different way if not
encouraged. Using a combination of experimental and observational data, Li and
Pearl derived tight bounds on the "benefit function", which is the payoff/cost
associated with selecting an individual with given characteristics. This paper
extends the benefit function to the general form such that the treatment and
effect are not restricted to binary. We propose an algorithm to test the
identifiability of the nonbinary benefit function and an algorithm to compute
the bounds of the nonbinary benefit function using experimental and
observational data.
| [
{
"version": "v1",
"created": "Sat, 20 Aug 2022 00:01:46 GMT"
}
] | 1,661,212,800,000 | [
[
"Li",
"Ang",
""
],
[
"Pearl",
"Judea",
""
]
] |
2208.09973 | Ardeshir Mirbakhsh | Ardeshir Mirbakhsh, Joyoung Lee, Dejan Besenski | Development of a CAV-based Intersection Control System and Corridor
Level Impact Assessment | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a signal-free intersection control system for CAVs by
combination of a pixel reservation algorithm and a Deep Reinforcement Learning
(DRL) decision-making logic, followed by a corridor-level impact assessment of
the proposed model. The pixel reservation algorithm detects potential colliding
maneuvers and the DRL logic optimizes vehicles' movements to avoid collision
and minimize the overall delay at the intersection. The proposed control system
is called Decentralized Sparse Coordination System (DSCLS) since each vehicle
has its own control logic and interacts with other vehicles in coordinated
states only. Due to the chain impact of taking random actions in the DRL's
training course, the trained model can deal with unprecedented volume
conditions, which poses the main challenge in intersection management. The
performance of the developed model is compared with conventional and CAV-based
control systems, including fixed traffic lights, actuated traffic lights, and
the Longest Queue First (LQF) control system under three volume regimes in a
corridor of four intersections in VISSIM software. The simulation result
revealed that the proposed model reduces delay by 50%, 29%, and 23% in
moderate, high, and extreme volume regimes compared to the other CAV-based
control system. Improvements in travel time, fuel consumption, emission, and
Surrogate Safety Measures (SSM) are also noticeable.
| [
{
"version": "v1",
"created": "Sun, 21 Aug 2022 21:56:20 GMT"
}
] | 1,661,212,800,000 | [
[
"Mirbakhsh",
"Ardeshir",
""
],
[
"Lee",
"Joyoung",
""
],
[
"Besenski",
"Dejan",
""
]
] |
2208.10327 | Pablo Barros | Pablo Barros, Ozge Nilay Yalc{\i}n, Ana Tanevska, Alessandra Sciutti | Incorporating Rivalry in Reinforcement Learning for a Competitive Game | Accepted at the Neural Computing and Applications Journal | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent advances in reinforcement learning with social agents have allowed
such models to achieve human-level performance on specific interaction tasks.
However, most interactive scenarios do not have a version alone as an end goal;
instead, the social impact of these agents when interacting with humans is as
important and largely unexplored. In this regard, this work proposes a novel
reinforcement learning mechanism based on the social impact of rivalry
behavior. Our proposed model aggregates objective and social perception
mechanisms to derive a rivalry score that is used to modulate the learning of
artificial agents. To investigate our proposed model, we design an interactive
game scenario, using the Chef's Hat Card Game, and examine how the rivalry
modulation changes the agent's playing style, and how this impacts the
experience of human players in the game. Our results show that humans can
detect specific social characteristics when playing against rival agents when
compared to common agents, which directly affects the performance of the human
players in subsequent games. We conclude our work by discussing how the
different social and objective features that compose the artificial rivalry
score contribute to our results.
| [
{
"version": "v1",
"created": "Mon, 22 Aug 2022 14:06:06 GMT"
}
] | 1,661,212,800,000 | [
[
"Barros",
"Pablo",
""
],
[
"Yalcın",
"Ozge Nilay",
""
],
[
"Tanevska",
"Ana",
""
],
[
"Sciutti",
"Alessandra",
""
]
] |
2208.10932 | Federico Cerutti | Pietro Baroni, Federico Cerutti, Massimiliano Giacomin, Lance M.
Kaplan, Murat Sensoy | Research Note on Uncertain Probabilities and Abstract Argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sixth assessment of the international panel on climate change (IPCC)
states that "cumulative net CO2 emissions over the last decade (2010-2019) are
about the same size as the 11 remaining carbon budget likely to limit warming
to 1.5C (medium confidence)." Such reports directly feed the public discourse,
but nuances such as the degree of belief and of confidence are often lost. In
this paper, we propose a formal account for allowing such degrees of belief and
the associated confidence to be used to label arguments in abstract
argumentation settings. Differently from other proposals in probabilistic
argumentation, we focus on the task of probabilistic inference over a chosen
query building upon Sato's distribution semantics which has been already shown
to encompass a variety of cases including the semantics of Bayesian networks.
Borrowing from the vast literature on such semantics, we examine how such tasks
can be dealt with in practice when considering uncertain probabilities, and
discuss the connections with existing proposals for probabilistic
argumentation.
| [
{
"version": "v1",
"created": "Tue, 23 Aug 2022 13:03:02 GMT"
}
] | 1,661,299,200,000 | [
[
"Baroni",
"Pietro",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Giacomin",
"Massimiliano",
""
],
[
"Kaplan",
"Lance M.",
""
],
[
"Sensoy",
"Murat",
""
]
] |
2208.11024 | Kiril Gashteovski | Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, Pengfei Liu,
Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig | KGxBoard: Explainable and Interactive Leaderboard for Evaluation of
Knowledge Graph Completion Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graphs (KGs) store information in the form of (head, predicate,
tail)-triples. To augment KGs with new knowledge, researchers proposed models
for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p;
?) or (?; p; t) queries. Such models are usually evaluated with averaged
metrics on a held-out test set. While useful for tracking progress, averaged
single-score metrics cannot reveal what exactly a model has learned -- or
failed to learn. To address this issue, we propose KGxBoard: an interactive
framework for performing fine-grained evaluation on meaningful subsets of the
data, each of which tests individual and interpretable capabilities of a KGC
model. In our experiments, we highlight the findings that we discovered with
the use of KGxBoard, which would have been impossible to detect with standard
averaged single-score metrics.
| [
{
"version": "v1",
"created": "Tue, 23 Aug 2022 15:11:45 GMT"
}
] | 1,661,299,200,000 | [
[
"Widjaja",
"Haris",
""
],
[
"Gashteovski",
"Kiril",
""
],
[
"Rim",
"Wiem Ben",
""
],
[
"Liu",
"Pengfei",
""
],
[
"Malon",
"Christopher",
""
],
[
"Ruffinelli",
"Daniel",
""
],
[
"Lawrence",
"Carolin",
""
],
[
"Neubig",
"Graham",
""
]
] |
2208.11349 | Zijian Gao | Zijian Gao, YiYing Li, Kele Xu, Yuanzhao Zhai, Dawei Feng, Bo Ding,
XinJun Mao, Huaimin Wang | Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sparsity of extrinsic rewards poses a serious challenge for reinforcement
learning (RL). Currently, many efforts have been made on curiosity which can
provide a representative intrinsic reward for effective exploration. However,
the challenge is still far from being solved. In this paper, we present a novel
curiosity for RL, named DyMeCu, which stands for Dynamic Memory-based
Curiosity. Inspired by human curiosity and information theory, DyMeCu consists
of a dynamic memory and dual online learners. The curiosity arouses if
memorized information can not deal with the current state, and the information
gap between dual learners can be formulated as the intrinsic reward for agents,
and then such state information can be consolidated into the dynamic memory.
Compared with previous curiosity methods, DyMeCu can better mimic human
curiosity with dynamic memory, and the memory module can be dynamically grown
based on a bootstrap paradigm with dual learners. On multiple benchmarks
including DeepMind Control Suite and Atari Suite, large-scale empirical
experiments are conducted and the results demonstrate that DyMeCu outperforms
competitive curiosity-based methods with or without extrinsic rewards. We will
release the code to enhance reproducibility.
| [
{
"version": "v1",
"created": "Wed, 24 Aug 2022 07:56:12 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Nov 2023 02:27:06 GMT"
}
] | 1,700,524,800,000 | [
[
"Gao",
"Zijian",
""
],
[
"Li",
"YiYing",
""
],
[
"Xu",
"Kele",
""
],
[
"Zhai",
"Yuanzhao",
""
],
[
"Feng",
"Dawei",
""
],
[
"Ding",
"Bo",
""
],
[
"Mao",
"XinJun",
""
],
[
"Wang",
"Huaimin",
""
]
] |
2208.11652 | Mohamad Zamini | Mohamad Zamini, Hassan Reza, Minou Rabiei | A Review of Knowledge Graph Completion | null | Information 2022, 13(8), 396 | 10.3390/info13080396 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information extraction methods proved to be effective at triple extraction
from structured or unstructured data. The organization of such triples in the
form of (head entity, relation, tail entity) is called the construction of
Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In
order to use KGs in downstream tasks, it is desirable to predict missing links
in KGs. Different approaches have been recently proposed for representation
learning of KGs by embedding both entities and relations into a low-dimensional
vector space aiming to predict unknown triples based on previously visited
triples. According to how the triples will be treated independently or
dependently, we divided the task of knowledge graph completion into
conventional and graph neural network representation learning and we discuss
them in more detail. In conventional approaches, each triple will be processed
independently and in GNN-based approaches, triples also consider their local
neighborhood. View Full-Text
| [
{
"version": "v1",
"created": "Wed, 24 Aug 2022 16:42:59 GMT"
}
] | 1,661,385,600,000 | [
[
"Zamini",
"Mohamad",
""
],
[
"Reza",
"Hassan",
""
],
[
"Rabiei",
"Minou",
""
]
] |
2208.12047 | K Anitha | R.Nithya, K.Anitha | Even vertex $\zeta$-graceful labeling on Rough Graph | 10 pages, 13 figures | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Rough graph is the graphical structure of information system with imprecise
knowledge. Tong He designed the properties of rough graph in 2006[6] and
following that He and Shi introduced the notion of edge rough graph[7]. He et
al developed the concept of weighted rough graph with weighted attributes[6].
In this paper, we introduce a new type of labeling called Even vertex {\zeta}-
graceful labeling as weight value for edges. We investigate this labeling for
some special graphs like rough path graph, rough cycle graph, rough comb graph,
rough ladder graph and rough star graph.
| [
{
"version": "v1",
"created": "Tue, 23 Aug 2022 16:53:10 GMT"
}
] | 1,661,472,000,000 | [
[
"Nithya",
"R.",
""
],
[
"Anitha",
"K.",
""
]
] |
2208.12210 | Ragib Ahsan | Ragib Ahsan, David Arbour, Elena Zheleva | Learning Relational Causal Models with Cycles through Relational
Acyclification | Published in the 37th AAAI Conference on Artificial Intelligence
(AAAI 2023) | AAAI 2023 | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In real-world phenomena which involve mutual influence or causal effects
between interconnected units, equilibrium states are typically represented with
cycles in graphical models. An expressive class of graphical models, relational
causal models, can represent and reason about complex dynamic systems
exhibiting such cycles or feedback loops. Existing cyclic causal discovery
algorithms for learning causal models from observational data assume that the
data instances are independent and identically distributed which makes them
unsuitable for relational causal models. At the same time, causal discovery
algorithms for relational causal models assume acyclicity. In this work, we
examine the necessary and sufficient conditions under which a constraint-based
relational causal discovery algorithm is sound and complete for cyclic
relational causal models. We introduce relational acyclification, an operation
specifically designed for relational models that enables reasoning about the
identifiability of cyclic relational causal models. We show that under the
assumptions of relational acyclification and $\sigma$-faithfulness, the
relational causal discovery algorithm RCD (Maier et al. 2013) is sound and
complete for cyclic models. We present experimental results to support our
claim.
| [
{
"version": "v1",
"created": "Thu, 25 Aug 2022 17:00:42 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2022 15:54:07 GMT"
},
{
"version": "v3",
"created": "Tue, 13 Sep 2022 16:20:08 GMT"
},
{
"version": "v4",
"created": "Thu, 20 Oct 2022 16:54:30 GMT"
},
{
"version": "v5",
"created": "Tue, 6 Dec 2022 07:02:08 GMT"
},
{
"version": "v6",
"created": "Fri, 24 Feb 2023 18:40:13 GMT"
},
{
"version": "v7",
"created": "Fri, 17 Mar 2023 06:26:35 GMT"
}
] | 1,679,270,400,000 | [
[
"Ahsan",
"Ragib",
""
],
[
"Arbour",
"David",
""
],
[
"Zheleva",
"Elena",
""
]
] |
2208.12386 | Adam Hepworth | Adam Hepworth, Aya Hussein, Darryn Reid and Hussein Abbass | Swarm Analytics: Designing Information Markers to Characterise Swarm
Systems in Shepherding Contexts | 28 pages, 15 tables, 13 figures | null | 10.1177/10597123221137090 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Contemporary swarm indicators are often used in isolation, focused on
extracting information at the individual or collective levels. Consequently,
these are seldom integrated to infer a top-level operating picture of the
swarm, its members, and its overall collective dynamics. The primary
contribution of this paper is to organise a suite of indicators about swarms
into an ontologically-arranged collection of information markers to
characterise the swarm from the perspective of an external observer\textemdash,
a recognition agent. Our contribution shows the foundations for a new area of
research that we tile swarm analytics, whose primary concern is with the design
and organisation of collections of swarm markers to understand, detect,
recognise, track, and learn a particular insight about a swarm system. We
present our designed framework of information markers that offer a new avenue
for swarm research, especially for heterogeneous and cognitive swarms that may
require more advanced capabilities to detect agencies and categorise agent
influences and responses.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 00:43:24 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 22:14:51 GMT"
}
] | 1,669,075,200,000 | [
[
"Hepworth",
"Adam",
""
],
[
"Hussein",
"Aya",
""
],
[
"Reid",
"Darryn",
""
],
[
"Abbass",
"Hussein",
""
]
] |
2208.12480 | Trupti Padiya | Trupti Padiya, Frank L\"offler, and Friederike Klan | Need for Design Patterns: Interoperability Issues and Modelling
Challenges for Observational Data | 5 pages with 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Interoperability issues concerning observational data have gained attention
in recent times. Automated data integration is important when it comes to the
scientific analysis of observational data from different sources. However, it
is hampered by various data interoperability issues. We focus exclusively on
semantic interoperability issues for observational characteristics. We propose
a use-case-driven approach to identify general classes of interoperability
issues. In this paper, this is exemplarily done for the use-case of citizen
science fireball observations. We derive key concepts for the identified
interoperability issues that are generalizable to observational data in other
fields of science. These key concepts contain several modeling challenges, and
we broadly describe each modeling challenges associated with its
interoperability issue. We believe, that addressing these challenges with a set
of ontology design patterns will be an effective means for unified semantic
modeling, paving the way for a unified approach for resolving interoperability
issues in observational data. We demonstrate this with one design pattern,
highlighting the importance and need for ontology design patterns for
observational data, and leave the remaining patterns to future work. Our paper
thus describes interoperability issues along with modeling challenges as a
starting point for developing a set of extensible and reusable design patterns.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 07:40:19 GMT"
}
] | 1,661,731,200,000 | [
[
"Padiya",
"Trupti",
""
],
[
"Löffler",
"Frank",
""
],
[
"Klan",
"Friederike",
""
]
] |
2208.12523 | Martin Glauer | Martin Glauer, Robert West, Susan Michie, Janna Hastings | ESC-Rules: Explainable, Semantically Constrained Rule Sets | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We describe a novel approach to explainable prediction of a continuous
variable based on learning fuzzy weighted rules. Our model trains a set of
weighted rules to maximise prediction accuracy and minimise an ontology-based
'semantic loss' function including user-specified constraints on the rules that
should be learned in order to maximise the explainability of the resulting rule
set from a user perspective. This system fuses quantitative sub-symbolic
learning with symbolic learning and constraints based on domain knowledge. We
illustrate our system on a case study in predicting the outcomes of behavioural
interventions for smoking cessation, and show that it outperforms other
interpretable approaches, achieving performance close to that of a deep
learning model, while offering transparent explainability that is an essential
requirement for decision-makers in the health domain.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 09:29:30 GMT"
}
] | 1,661,731,200,000 | [
[
"Glauer",
"Martin",
""
],
[
"West",
"Robert",
""
],
[
"Michie",
"Susan",
""
],
[
"Hastings",
"Janna",
""
]
] |
2208.12551 | Wensheng Gan | Jiahui Chen, Yixin Xu, Shicheng Wan, Wensheng Gan, and Jerry Chun-Wei
Lin | Itemset Utility Maximization with Correlation Measure | Preprint. 5 figures, 7 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As an important data mining technology, high utility itemset mining (HUIM) is
used to find out interesting but hidden information (e.g., profit and risk).
HUIM has been widely applied in many application scenarios, such as market
analysis, medical detection, and web click stream analysis. However, most
previous HUIM approaches often ignore the relationship between items in an
itemset. Therefore, many irrelevant combinations (e.g., \{gold, apple\} and
\{notebook, book\}) are discovered in HUIM. To address this limitation, many
algorithms have been proposed to mine correlated high utility itemsets
(CoHUIs). In this paper, we propose a novel algorithm called the Itemset
Utility Maximization with Correlation Measure (CoIUM), which considers both a
strong correlation and the profitable values of the items. Besides, the novel
algorithm adopts a database projection mechanism to reduce the cost of database
scanning. Moreover, two upper bounds and four pruning strategies are utilized
to effectively prune the search space. And a concise array-based structure
named utility-bin is used to calculate and store the adopted upper bounds in
linear time and space. Finally, extensive experimental results on dense and
sparse datasets demonstrate that CoIUM significantly outperforms the
state-of-the-art algorithms in terms of runtime and memory consumption.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 10:06:24 GMT"
}
] | 1,661,731,200,000 | [
[
"Chen",
"Jiahui",
""
],
[
"Xu",
"Yixin",
""
],
[
"Wan",
"Shicheng",
""
],
[
"Gan",
"Wensheng",
""
],
[
"Lin",
"Jerry Chun-Wei",
""
]
] |
2208.12726 | Maria Concetta Morelli | Nicola Leone, Marco Manna, Maria Concetta Morelli, and Simona Perri | A Formal Comparison between Datalog-based Languages for Stream Reasoning
(extended version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper investigates the relative expressiveness of two logic-based
languages for reasoning over streams, namely LARS Programs -- the language of
the Logic-based framework for Analytic Reasoning over Streams called LARS --
and LDSR -- the language of the recent extension of the I-DLV system for stream
reasoning called I-DLV-sr. Although these two languages build over Datalog,
they do differ both in syntax and semantics. To reconcile their expressive
capabilities for stream reasoning, we define a comparison framework that allows
us to show that, without any restrictions, the two languages are incomparable
and to identify fragments of each language that can be expressed via the other
one.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 15:27:21 GMT"
}
] | 1,661,731,200,000 | [
[
"Leone",
"Nicola",
""
],
[
"Manna",
"Marco",
""
],
[
"Morelli",
"Maria Concetta",
""
],
[
"Perri",
"Simona",
""
]
] |
2208.12789 | Ximing Qiao | Ximing Qiao, Hai Li | Learning and Compositionality: a Unification Attempt via Connectionist
Probabilistic Programming | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We consider learning and compositionality as the key mechanisms towards
simulating human-like intelligence. While each mechanism is successfully
achieved by neural networks and symbolic AIs, respectively, it is the
combination of the two mechanisms that makes human-like intelligence possible.
Despite the numerous attempts on building hybrid neuralsymbolic systems, we
argue that our true goal should be unifying learning and compositionality, the
core mechanisms, instead of neural and symbolic methods, the surface approaches
to achieve them. In this work, we review and analyze the strengths and
weaknesses of neural and symbolic methods by separating their forms and
meanings (structures and semantics), and propose Connectionist Probabilistic
Program (CPPs), a framework that connects connectionist structures (for
learning) and probabilistic program semantics (for compositionality). Under the
framework, we design a CPP extension for small scale sequence modeling and
provide a learning algorithm based on Bayesian inference. Although challenges
exist in learning complex patterns without supervision, our early results
demonstrate CPP's successful extraction of concepts and relations from raw
sequential data, an initial step towards compositional learning.
| [
{
"version": "v1",
"created": "Fri, 26 Aug 2022 17:20:58 GMT"
}
] | 1,661,731,200,000 | [
[
"Qiao",
"Ximing",
""
],
[
"Li",
"Hai",
""
]
] |
2208.13390 | Majid Mohammadi | Majid Mohammadi | Unified Bayesian Frameworks for Multi-criteria Decision-making Problems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces Bayesian frameworks for tackling various aspects of
multi-criteria decision-making (MCDM) problems, leveraging a probabilistic
interpretation of MCDM methods and challenges. By harnessing the flexibility of
Bayesian models, the proposed frameworks offer statistically elegant solutions
to key challenges in MCDM, such as group decision-making problems and criteria
correlation. Additionally, these models can accommodate diverse forms of
uncertainty in decision makers' (DMs) preferences, including normal and
triangular distributions, as well as interval preferences. To address
large-scale group MCDM scenarios, a probabilistic mixture model is developed,
enabling the identification of homogeneous subgroups of DMs. Furthermore, a
probabilistic ranking scheme is devised to assess the relative importance of
criteria and alternatives based on DM(s) preferences. Through experimentation
on various numerical examples, the proposed frameworks are validated,
demonstrating their effectiveness and highlighting their distinguishing
features in comparison to alternative methods.
| [
{
"version": "v1",
"created": "Mon, 29 Aug 2022 06:47:05 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Sep 2022 07:31:08 GMT"
},
{
"version": "v3",
"created": "Sun, 22 Jan 2023 17:01:47 GMT"
},
{
"version": "v4",
"created": "Wed, 6 Sep 2023 13:44:40 GMT"
}
] | 1,694,044,800,000 | [
[
"Mohammadi",
"Majid",
""
]
] |
2208.13515 | Mahnaz Sadat Qafari | Christian Kohlschmidt and Mahnaz Sadat Qafari and Wil M. P. van der
Aalst | Detecting Surprising Situations in Event Data | 12 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Process mining is a set of techniques that are used by organizations to
understand and improve their operational processes. The first essential step in
designing any process reengineering procedure is to find process improvement
opportunities. In existing work, it is usually assumed that the set of
problematic process instances in which an undesirable outcome occurs is known
prior or is easily detectable. So the process enhancement procedure involves
finding the root causes and the treatments for the problem in those process
instances. For example, the set of problematic instances is considered as those
with outlier values or with values smaller/bigger than a given threshold in one
of the process features. However, on various occasions, using this approach,
many process enhancement opportunities, not captured by these problematic
process instances, are missed. To overcome this issue, we formulate finding the
process enhancement areas as a context-sensitive anomaly/outlier detection
problem. We define a process enhancement area as a set of situations (process
instances or prefixes of process instances) where the process performance is
surprising. We aim to characterize those situations where process
performance/outcome is significantly different from what was expected
considering its performance/outcome in similar situations. To evaluate the
validity and relevance of the proposed approach, we have implemented and
evaluated it on several real-life event logs.
| [
{
"version": "v1",
"created": "Mon, 29 Aug 2022 11:33:58 GMT"
}
] | 1,661,817,600,000 | [
[
"Kohlschmidt",
"Christian",
""
],
[
"Qafari",
"Mahnaz Sadat",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] |
2208.13841 | Yuan Yang | Yuan Yang, Keith McGreggor, Maithilee Kunda | Visual-Imagery-Based Analogical Construction in Geometric Matrix
Reasoning Task | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Raven's Progressive Matrices is a family of classical intelligence tests that
have been widely used in both research and clinical settings. There have been
many exciting efforts in AI communities to computationally model various
aspects of problem solving such figural analogical reasoning problems. In this
paper, we present a series of computational models for solving Raven's
Progressive Matrices using analogies and image transformations. We run our
models following three different strategies usually adopted by human testees.
These models are tested on the standard version of Raven's Progressive
Matrices, in which we can solve 57 out 60 problems in it. Therefore, analogy
and image transformation are proved to be effective in solving RPM problems.
| [
{
"version": "v1",
"created": "Mon, 29 Aug 2022 19:09:53 GMT"
}
] | 1,661,904,000,000 | [
[
"Yang",
"Yuan",
""
],
[
"McGreggor",
"Keith",
""
],
[
"Kunda",
"Maithilee",
""
]
] |
2208.14037 | Ajay Vishwanath | Ajay Vishwanath, Einar Duenger B{\o}hn, Ole-Christoffer Granmo, Charl
Maree and Christian Omlin | Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning | Premature submission of revised revision | null | 10.1007/s43681-022-00251-8 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Machine ethics has received increasing attention over the past few years
because of the need to ensure safe and reliable artificial intelligence (AI).
The two dominantly used theories in machine ethics are deontological and
utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned
as an alternative ethical theory. While this interesting approach has certain
advantages over popular ethical theories, little effort has been put into
engineering artificial virtuous agents due to challenges in their
formalization, codifiability, and the resolution of ethical dilemmas to train
virtuous agents. We propose to bridge this gap by using role-playing games
riddled with moral dilemmas. There are several such games in existence, such as
Papers, Please and Life is Strange, where the main character encounters
situations where they must choose the right course of action by giving up
something else dear to them. We draw inspiration from such games to show how a
systemic role-playing game can be designed to develop virtues within an
artificial agent. Using modern day AI techniques, such as affinity-based
reinforcement learning and explainable AI, we motivate the implementation of
virtuous agents that play such role-playing games, and the examination of their
decisions through a virtue ethical lens. The development of such agents and
environments is a first step towards practically formalizing and demonstrating
the value of virtue ethics in the development of ethical agents.
| [
{
"version": "v1",
"created": "Tue, 30 Aug 2022 07:37:03 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 13:44:51 GMT"
},
{
"version": "v3",
"created": "Sat, 10 Dec 2022 08:28:58 GMT"
}
] | 1,673,308,800,000 | [
[
"Vishwanath",
"Ajay",
""
],
[
"Bøhn",
"Einar Duenger",
""
],
[
"Granmo",
"Ole-Christoffer",
""
],
[
"Maree",
"Charl",
""
],
[
"Omlin",
"Christian",
""
]
] |
2208.14820 | Nikos Katzouris | Nikos Katzouris and Georgios Paliouras | Learning Automata-Based Complex Event Patterns in Answer Set Programming | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Complex Event Recognition and Forecasting (CER/F) techniques attempt to
detect, or even forecast ahead of time, event occurrences in streaming input
using predefined event patterns. Such patterns are not always known in advance,
or they frequently change over time, making machine learning techniques,
capable of extracting such patterns from data, highly desirable in CER/F. Since
many CER/F systems use symbolic automata to represent such patterns, we propose
a family of such automata where the transition-enabling conditions are defined
by Answer Set Programming (ASP) rules, and which, thanks to the strong
connections of ASP to symbolic learning, are directly learnable from data. We
present such a learning approach in ASP and an incremental version thereof that
trades optimality for efficiency and is capable to scale to large datasets. We
evaluate our approach on two CER datasets and compare it to state-of-the-art
automata learning techniques, demonstrating empirically a superior performance,
both in terms of predictive accuracy and scalability.
| [
{
"version": "v1",
"created": "Wed, 31 Aug 2022 12:40:44 GMT"
}
] | 1,661,990,400,000 | [
[
"Katzouris",
"Nikos",
""
],
[
"Paliouras",
"Georgios",
""
]
] |
2209.00210 | Xiuyi Fan | Xiuyi Fan | Probabilistic Deduction: an Approach to Probabilistic Structured
Argumentation | 70 pages, 13 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces Probabilistic Deduction (PD) as an approach to
probabilistic structured argumentation. A PD framework is composed of
probabilistic rules (p-rules). As rules in classical structured argumentation
frameworks, p-rules form deduction systems. In addition, p-rules also represent
conditional probabilities that define joint probability distributions. With PD
frameworks, one performs probabilistic reasoning by solving Rule-Probabilistic
Satisfiability. At the same time, one can obtain an argumentative reading to
the probabilistic reasoning with arguments and attacks. In this work, we
introduce a probabilistic version of the Closed-World Assumption (P-CWA) and
prove that our probabilistic approach coincides with the complete extension in
classical argumentation under P-CWA and with maximum entropy reasoning. We
present several approaches to compute the joint probability distribution from
p-rules for achieving a practical proof theory for PD. PD provides a framework
to unify probabilistic reasoning with argumentative reasoning. This is the
first work in probabilistic structured argumentation where the joint
distribution is not assumed form external sources.
| [
{
"version": "v1",
"created": "Thu, 1 Sep 2022 03:58:38 GMT"
}
] | 1,662,076,800,000 | [
[
"Fan",
"Xiuyi",
""
]
] |
2209.00686 | Marco Zaffalon | Enrique Miranda and Marco Zaffalon | Nonlinear desirability theory | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Desirability can be understood as an extension of Anscombe and Aumann's
Bayesian decision theory to sets of expected utilities. At the core of
desirability lies an assumption of linearity of the scale in which rewards are
measured. It is a traditional assumption used to derive the expected utility
model, which clashes with a general representation of rational decision making,
though. Allais has, in particular, pointed this out in 1953 with his famous
paradox. We note that the utility scale plays the role of a closure operator
when we regard desirability as a logical theory. This observation enables us to
extend desirability to the nonlinear case by letting the utility scale be
represented via a general closure operator. The new theory directly expresses
rewards in actual nonlinear currency (money), much in Savage's spirit, while
arguably weakening the founding assumptions to a minimum. We characterise the
main properties of the new theory both from the perspective of sets of gambles
and of their lower and upper prices (previsions). We show how Allais paradox
finds a solution in the new theory, and discuss the role of sets of
probabilities in the theory.
| [
{
"version": "v1",
"created": "Thu, 1 Sep 2022 18:44:29 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Nov 2022 11:57:06 GMT"
}
] | 1,668,988,800,000 | [
[
"Miranda",
"Enrique",
""
],
[
"Zaffalon",
"Marco",
""
]
] |
2209.00917 | Christophe Lecoutre | Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca | Proceedings of the 2022 XCSP3 Competition | arXiv admin note: text overlap with arXiv:1901.01830 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This document represents the proceedings of the 2022 XCSP3 Competition. The
results of this competition of constraint solvers were presented at FLOC
(Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from
31th July 2022 to 7th August, 2022.
| [
{
"version": "v1",
"created": "Fri, 2 Sep 2022 09:52:29 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Dec 2023 12:55:48 GMT"
}
] | 1,702,339,200,000 | [
[
"Audemard",
"Gilles",
""
],
[
"Lecoutre",
"Christophe",
""
],
[
"Lonca",
"Emmanuel",
""
]
] |
2209.01410 | Quan Zhou | Quan Zhou and Ramen Ghosh and Robert Shorten and Jakub Marecek | Closed-Loop View of the Regulation of AI: Equal Impact across Repeated
Interactions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been much recent interest in the regulation of AI. We argue for a
view based on civil-rights legislation, built on the notions of equal treatment
and equal impact. In a closed-loop view of the AI system and its users, the
equal treatment concerns one pass through the loop. Equal impact, in our view,
concerns the long-run average behaviour across repeated interactions. In order
to establish the existence of the average and its properties, one needs to
study the ergodic properties of the closed-loop and its unique stationary
measure.
| [
{
"version": "v1",
"created": "Sat, 3 Sep 2022 12:25:42 GMT"
},
{
"version": "v2",
"created": "Sun, 25 Feb 2024 11:16:15 GMT"
}
] | 1,708,992,000,000 | [
[
"Zhou",
"Quan",
""
],
[
"Ghosh",
"Ramen",
""
],
[
"Shorten",
"Robert",
""
],
[
"Marecek",
"Jakub",
""
]
] |
2209.01619 | Martin Biehl | Martin Biehl and Nathaniel Virgo | Interpreting systems as solving POMDPs: a step towards a formal
understanding of agency | 17 pages, no figures, to be presented at 3rd International Workshop
on Active Inference 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Under what circumstances can a system be said to have beliefs and goals, and
how do such agency-related features relate to its physical state? Recent work
has proposed a notion of interpretation map, a function that maps the state of
a system to a probability distribution representing its beliefs about an
external world. Such a map is not completely arbitrary, as the beliefs it
attributes to the system must evolve over time in a manner that is consistent
with Bayes' theorem, and consequently the dynamics of a system constrain its
possible interpretations. Here we build on this approach, proposing a notion of
interpretation not just in terms of beliefs but in terms of goals and actions.
To do this we make use of the existing theory of partially observable Markov
processes (POMDPs): we say that a system can be interpreted as a solution to a
POMDP if it not only admits an interpretation map describing its beliefs about
the hidden state of a POMDP but also takes actions that are optimal according
to its belief state. An agent is then a system together with an interpretation
of this system as a POMDP solution. Although POMDPs are not the only possible
formulation of what it means to have a goal, this nevertheless represents a
step towards a more general formal definition of what it means for a system to
be an agent.
| [
{
"version": "v1",
"created": "Sun, 4 Sep 2022 13:40:33 GMT"
}
] | 1,662,508,800,000 | [
[
"Biehl",
"Martin",
""
],
[
"Virgo",
"Nathaniel",
""
]
] |
2209.01728 | Peiwang Tang | Peiwang Tang and Xianchao Zhang | Features Fusion Framework for Multimodal Irregular Time-series Events | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Some data from multiple sources can be modeled as multimodal time-series
events which have different sampling frequencies, data compositions, temporal
relations and characteristics. Different types of events have complex nonlinear
relationships, and the time of each event is irregular. Neither the classical
Recurrent Neural Network (RNN) model nor the current state-of-the-art
Transformer model can deal with these features well. In this paper, a features
fusion framework for multimodal irregular time-series events is proposed based
on the Long Short-Term Memory networks (LSTM). Firstly, the complex features
are extracted according to the irregular patterns of different events.
Secondly, the nonlinear correlation and complex temporal dependencies
relationship between complex features are captured and fused into a tensor.
Finally, a feature gate are used to control the access frequency of different
tensors. Extensive experiments on MIMIC-III dataset demonstrate that the
proposed framework significantly outperforms to the existing methods in terms
of AUC (the area under Receiver Operating Characteristic curve) and AP (Average
Precision).
| [
{
"version": "v1",
"created": "Mon, 5 Sep 2022 02:27:12 GMT"
}
] | 1,662,508,800,000 | [
[
"Tang",
"Peiwang",
""
],
[
"Zhang",
"Xianchao",
""
]
] |
2209.02157 | Yang Ruiyang | Ruiyang Yang, Siheng Li, Beihong Jin | A New Approach to Training Multiple Cooperative Agents for Autonomous
Driving | 8pages, IJCNN2022, Accepted | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training multiple agents to perform safe and cooperative control in the
complex scenarios of autonomous driving has been a challenge. For a small fleet
of cars moving together, this paper proposes Lepus, a new approach to training
multiple agents. Lepus adopts a pure cooperative manner for training multiple
agents, featured with the shared parameters of policy networks and the shared
reward function of multiple agents. In particular, Lepus pre-trains the policy
networks via an adversarial process, improving its collaborative
decision-making capability and further the stability of car driving. Moreover,
for alleviating the problem of sparse rewards, Lepus learns an approximate
reward function from expert trajectories by combining a random network and a
distillation network. We conduct extensive experiments on the MADRaS simulation
platform. The experimental results show that multiple agents trained by Lepus
can avoid collisions as many as possible while driving simultaneously and
outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and
MAGAIL(DDPG) in terms of stability.
| [
{
"version": "v1",
"created": "Mon, 5 Sep 2022 22:35:33 GMT"
}
] | 1,662,508,800,000 | [
[
"Yang",
"Ruiyang",
""
],
[
"Li",
"Siheng",
""
],
[
"Jin",
"Beihong",
""
]
] |
2209.02390 | Mojtaba Moattari | Mojtaba Moattari, Sahar Vahdati, Farhana Zulkernine | ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph
Completion | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graph Embedding (KGE) methods have gained enormous attention from a
wide range of AI communities including Natural Language Processing (NLP) for
text generation, classification and context induction. Embedding a huge number
of inter-relationships in terms of a small number of dimensions, require proper
modeling in both cognitive and computational aspects. Recently, numerous
objective functions regarding cognitive and computational aspects of natural
languages are developed. Among which are the state-of-the-art methods of
linearity, bilinearity, manifold-preserving kernels, projection-subspace, and
analogical inference. However, the major challenge of such models lies in their
loss functions that associate the dimension of relation embeddings to
corresponding entity dimension. This leads to inaccurate prediction of
corresponding relations among entities when counterparts are estimated wrongly.
ProjE KGE, published by Bordes et al., due to low computational complexity and
high potential for model improvement, is improved in this work regarding all
translative and bilinear interactions while capturing entity nonlinearity.
Experimental results on benchmark Knowledge Graphs (KGs) such as FB15K and WN18
show that the proposed approach outperforms the state-of-the-art models in
entity prediction task using linear and bilinear methods and other recent
powerful ones. In addition, a parallel processing structure is proposed for the
model in order to improve the scalability on large KGs. The effects of
different adaptive clustering and newly proposed sampling approaches are also
explained which prove to be effective in improving the accuracy of knowledge
graph completion.
| [
{
"version": "v1",
"created": "Mon, 15 Aug 2022 18:18:05 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Sep 2022 20:55:06 GMT"
}
] | 1,663,545,600,000 | [
[
"Moattari",
"Mojtaba",
""
],
[
"Vahdati",
"Sahar",
""
],
[
"Zulkernine",
"Farhana",
""
]
] |
2209.02414 | Riccardo Emanuele Landi | Gerardo Iovane, Riccardo Emanuele Landi | From Smart Sensing to Consciousness: An info-structural model of
computational consciousness for non-interacting agents | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This study proposes a model of computational consciousness for
non-interacting agents. The phenomenon of interest was assumed as sequentially
dependent on the cognitive tasks of sensation, perception, emotion, affection,
attention, awareness, and consciousness. Starting from the Smart Sensing
prodromal study, the cognitive layers associated with the processes of
attention, awareness, and consciousness were formally defined and tested
together with the other processes concerning sensation, perception, emotion,
and affection. The output of the model consists of an index that synthesizes
the energetic and entropic contributions of consciousness from a
computationally moral perspective. Attention was modeled through a bottom-up
approach, while awareness and consciousness by distinguishing environment from
subjective cognitive processes. By testing the solution on visual stimuli
eliciting the emotions of happiness, anger, fear, surprise, contempt, sadness,
disgust, and the neutral state, it was found that the proposed model is
concordant with the scientific evidence concerning covert attention. Comparable
results were also obtained regarding studies investigating awareness as a
consequence of visual stimuli repetition, as well as those investigating moral
judgments to visual stimuli eliciting disgust and sadness. The solution
represents a novel approach for defining computational consciousness through
artificial emotional activity and morality.
| [
{
"version": "v1",
"created": "Mon, 29 Aug 2022 16:49:51 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Feb 2023 17:08:27 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 09:18:14 GMT"
}
] | 1,677,715,200,000 | [
[
"Iovane",
"Gerardo",
""
],
[
"Landi",
"Riccardo Emanuele",
""
]
] |
2209.02427 | Qian Cao | Qian Cao, Xu Chen, Ruihua Song, Hao Jiang, Guang Yang, Zhao Cao | Multi-Modal Experience Inspired AI Creation | Accepted by ACM Multimedia 2022 | null | 10.1145/3503161.3548189 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI creation, such as poem or lyrics generation, has attracted increasing
attention from both industry and academic communities, with many promising
models proposed in the past few years. Existing methods usually estimate the
outputs based on single and independent visual or textual information. However,
in reality, humans usually make creations according to their experiences, which
may involve different modalities and be sequentially correlated. To model such
human capabilities, in this paper, we define and solve a novel AI creation
problem based on human experiences. More specifically, we study how to generate
texts based on sequential multi-modal information. Compared with the previous
works, this task is much more difficult because the designed model has to well
understand and adapt the semantics among different modalities and effectively
convert them into the output in a sequential manner. To alleviate these
difficulties, we firstly design a multi-channel sequence-to-sequence
architecture equipped with a multi-modal attention network. For more effective
optimization, we then propose a curriculum negative sampling strategy tailored
for the sequential inputs. To benchmark this problem and demonstrate the
effectiveness of our model, we manually labeled a new multi-modal experience
dataset. With this dataset, we conduct extensive experiments by comparing our
model with a series of representative baselines, where we can demonstrate
significant improvements in our model based on both automatic and
human-centered metrics. The code and data are available at:
\url{https://github.com/Aman-4-Real/MMTG}.
| [
{
"version": "v1",
"created": "Fri, 2 Sep 2022 11:50:41 GMT"
}
] | 1,662,508,800,000 | [
[
"Cao",
"Qian",
""
],
[
"Chen",
"Xu",
""
],
[
"Song",
"Ruihua",
""
],
[
"Jiang",
"Hao",
""
],
[
"Yang",
"Guang",
""
],
[
"Cao",
"Zhao",
""
]
] |
2209.02562 | Boris Shminke | Boris Shminke | Project proposal: A modular reinforcement learning based automated
theorem prover | 6 pages, submitted to AITP (http://aitp-conference.org/2022/) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose to build a reinforcement learning prover of independent
components: a deductive system (an environment), the proof state representation
(how an agent sees the environment), and an agent training algorithm. To that
purpose, we contribute an additional Vampire-based environment to
$\texttt{gym-saturation}$ package of OpenAI Gym environments for saturation
provers. We demonstrate a prototype of using $\texttt{gym-saturation}$ together
with a popular reinforcement learning framework (Ray $\texttt{RLlib}$).
Finally, we discuss our plans for completing this work in progress to a
competitive automated theorem prover.
| [
{
"version": "v1",
"created": "Tue, 6 Sep 2022 15:12:53 GMT"
}
] | 1,662,508,800,000 | [
[
"Shminke",
"Boris",
""
]
] |
2209.02646 | Hanqun Cao | Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen,
Pheng-Ann Heng, and Stan Z. Li | A Survey on Generative Diffusion Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep generative models have unlocked another profound realm of human
creativity. By capturing and generalizing patterns within data, we have entered
the epoch of all-encompassing Artificial Intelligence for General Creativity
(AIGC). Notably, diffusion models, recognized as one of the paramount
generative models, materialize human ideation into tangible instances across
diverse domains, encompassing imagery, text, speech, biology, and healthcare.
To provide advanced and comprehensive insights into diffusion, this survey
comprehensively elucidates its developmental trajectory and future directions
from three distinct angles: the fundamental formulation of diffusion,
algorithmic enhancements, and the manifold applications of diffusion. Each
layer is meticulously explored to offer a profound comprehension of its
evolution. Structured and summarized approaches are presented in
https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.
| [
{
"version": "v1",
"created": "Tue, 6 Sep 2022 16:56:21 GMT"
},
{
"version": "v10",
"created": "Sat, 23 Dec 2023 13:03:21 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Sep 2022 09:07:15 GMT"
},
{
"version": "v3",
"created": "Wed, 14 Sep 2022 02:16:03 GMT"
},
{
"version": "v4",
"created": "Sat, 17 Sep 2022 10:05:43 GMT"
},
{
"version": "v5",
"created": "Wed, 21 Sep 2022 16:16:23 GMT"
},
{
"version": "v6",
"created": "Sat, 8 Oct 2022 05:04:59 GMT"
},
{
"version": "v7",
"created": "Wed, 19 Oct 2022 11:39:42 GMT"
},
{
"version": "v8",
"created": "Tue, 13 Dec 2022 14:24:48 GMT"
},
{
"version": "v9",
"created": "Mon, 3 Jul 2023 15:37:01 GMT"
}
] | 1,703,635,200,000 | [
[
"Cao",
"Hanqun",
""
],
[
"Tan",
"Cheng",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Xu",
"Yilun",
""
],
[
"Chen",
"Guangyong",
""
],
[
"Heng",
"Pheng-Ann",
""
],
[
"Li",
"Stan Z.",
""
]
] |
2209.02902 | Bingchen Jiang | Bingchen Jiang and Zhao Li | Defending Against Backdoor Attack on Graph Nerual Network by
Explainability | 10 pages, 10 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Backdoor attack is a powerful attack algorithm to deep learning model.
Recently, GNN's vulnerability to backdoor attack has been proved especially on
graph classification task. In this paper, we propose the first backdoor
detection and defense method on GNN. Most backdoor attack depends on injecting
small but influential trigger to the clean sample. For graph data, current
backdoor attack focus on manipulating the graph structure to inject the
trigger. We find that there are apparent differences between benign samples and
malicious samples in some explanatory evaluation metrics, such as fidelity and
infidelity. After identifying the malicious sample, the explainability of the
GNN model can help us capture the most significant subgraph which is probably
the trigger in a trojan graph. We use various dataset and different attack
settings to prove the effectiveness of our defense method. The attack success
rate all turns out to decrease considerably.
| [
{
"version": "v1",
"created": "Wed, 7 Sep 2022 03:19:29 GMT"
}
] | 1,662,595,200,000 | [
[
"Jiang",
"Bingchen",
""
],
[
"Li",
"Zhao",
""
]
] |
2209.03070 | Zhe Yu | Zhe Yu and Yiwei Lu | An Argumentation-Based Legal Reasoning Approach for DL-Ontology | 16 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontology is a popular method for knowledge representation in different
domains, including the legal domain, and description logics (DL) is commonly
used as its description language. To handle reasoning based on inconsistent
DL-based legal ontologies, the current paper presents a structured
argumentation framework particularly for reasoning in legal contexts on the
basis of ASPIC+, and translates the legal ontology into formulas and rules of
an argumentation theory. With a particular focus on the design of autonomous
vehicles from the perspective of legal AI, we show that using this combined
theory of formal argumentation and DL-based legal ontology, acceptable
assertions can be obtained based on inconsistent ontologies, and the
traditional reasoning tasks of DL ontologies can also be accomplished. In
addition, a formal definition of explanations for the result of reasoning is
presented.
| [
{
"version": "v1",
"created": "Wed, 7 Sep 2022 11:08:08 GMT"
},
{
"version": "v2",
"created": "Sun, 18 Sep 2022 13:32:11 GMT"
}
] | 1,663,632,000,000 | [
[
"Yu",
"Zhe",
""
],
[
"Lu",
"Yiwei",
""
]
] |
2209.03499 | Behnam Mohammadi | Behnam Mohammadi, Nikhil Malik, Tim Derdenger, Kannan Srinivasan | Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers | Corrected the title | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent AI algorithms are black box models whose decisions are difficult to
interpret. eXplainable AI (XAI) is a class of methods that seek to address lack
of AI interpretability and trust by explaining to customers their AI decisions.
The common wisdom is that regulating AI by mandating fully transparent XAI
leads to greater social welfare. Our paper challenges this notion through a
game theoretic model of a policy-maker who maximizes social welfare, firms in a
duopoly competition that maximize profits, and heterogenous consumers. The
results show that XAI regulation may be redundant. In fact, mandating fully
transparent XAI may make firms and consumers worse off. This reveals a tradeoff
between maximizing welfare and receiving explainable AI outputs. We extend the
existing literature on method and substantive fronts, and we introduce and
study the notion of XAI fairness, which may be impossible to guarantee even
under mandatory XAI. Finally, the regulatory and managerial implications of our
results for policy-makers and businesses are discussed, respectively.
| [
{
"version": "v1",
"created": "Wed, 7 Sep 2022 23:36:11 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Sep 2022 17:51:07 GMT"
},
{
"version": "v3",
"created": "Fri, 29 Mar 2024 20:22:00 GMT"
}
] | 1,712,016,000,000 | [
[
"Mohammadi",
"Behnam",
""
],
[
"Malik",
"Nikhil",
""
],
[
"Derdenger",
"Tim",
""
],
[
"Srinivasan",
"Kannan",
""
]
] |
2209.03580 | Sophia Sun | Sophia Sun | Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A
Survey | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Machine learning methods are increasingly widely used in high-risk settings
such as healthcare, transportation, and finance. In these settings, it is
important that a model produces calibrated uncertainty to reflect its own
confidence and avoid failures. In this paper we survey recent works on
uncertainty quantification (UQ) for deep learning, in particular
distribution-free Conformal Prediction method for its mathematical properties
and wide applicability. We will cover the theoretical guarantees of conformal
methods, introduce techniques that improve calibration and efficiency for UQ in
the context of spatiotemporal data, and discuss the role of UQ in the context
of safe decision making.
| [
{
"version": "v1",
"created": "Thu, 8 Sep 2022 06:08:48 GMT"
}
] | 1,662,681,600,000 | [
[
"Sun",
"Sophia",
""
]
] |
2209.03990 | Zlatan Ajanovic | Zlatan Ajanovi\'c, Emina Ali\v{c}kovi\'c, Aida Brankovi\'c, Sead
Delali\'c, Eldar Kurti\'c, Salem Maliki\'c, Adnan Mehoni\'c, Hamza Merzi\'c,
Kenan \v{S}ehi\'c, Bahrudin Trbali\'c | Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global
Trends, Potential Opportunities, Selected Use-cases and Realistic Goals | 25 pages, 3 figures, Bosnian language. Presented at Naucno-strucna
konferencija o umjetnoj inteligenciji. Federalno ministarstvo obrazovanja i
nauke, Mostar, Bosna i Hercegovina, April 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI) is one of the most promising technologies of the
21. century, with an already noticeable impact on society and the economy. With
this work, we provide a short overview of global trends, applications in
industry and selected use-cases from our international experience and work in
industry and academia. The goal is to present global and regional positive
practices and provide an informed opinion on the realistic goals and
opportunities for positioning B&H on the global AI scene.
| [
{
"version": "v1",
"created": "Thu, 8 Sep 2022 18:20:01 GMT"
}
] | 1,662,940,800,000 | [
[
"Ajanović",
"Zlatan",
""
],
[
"Aličković",
"Emina",
""
],
[
"Branković",
"Aida",
""
],
[
"Delalić",
"Sead",
""
],
[
"Kurtić",
"Eldar",
""
],
[
"Malikić",
"Salem",
""
],
[
"Mehonić",
"Adnan",
""
],
[
"Merzić",
"Hamza",
""
],
[
"Šehić",
"Kenan",
""
],
[
"Trbalić",
"Bahrudin",
""
]
] |
2209.04022 | Chulin Xie | Chulin Xie, Zhong Cao, Yunhui Long, Diange Yang, Ding Zhao, Bo Li | Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future
Directions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in machine learning have enabled its wide application in
different domains, and one of the most exciting applications is autonomous
vehicles (AVs), which have encouraged the development of a number of ML
algorithms from perception to prediction to planning. However, training AVs
usually requires a large amount of training data collected from different
driving environments (e.g., cities) as well as different types of personal
information (e.g., working hours and routes). Such collected large data,
treated as the new oil for ML in the data-centric AI era, usually contains a
large amount of privacy-sensitive information which is hard to remove or even
audit. Although existing privacy protection approaches have achieved certain
theoretical and empirical success, there is still a gap when applying them to
real-world applications such as autonomous vehicles. For instance, when
training AVs, not only can individually identifiable information reveal
privacy-sensitive information, but also population-level information such as
road construction within a city, and proprietary-level commercial secrets of
AVs. Thus, it is critical to revisit the frontier of privacy risks and
corresponding protection approaches in AVs to bridge this gap. Following this
goal, in this work, we provide a new taxonomy for privacy risks and protection
methods in AVs, and we categorize privacy in AVs into three levels: individual,
population, and proprietary. We explicitly list out recent challenges to
protect each of these levels of privacy, summarize existing solutions to these
challenges, discuss the lessons and conclusions, and provide potential future
directions and opportunities for both researchers and practitioners. We believe
this work will help to shape the privacy research in AV and guide the privacy
protection technology design.
| [
{
"version": "v1",
"created": "Thu, 8 Sep 2022 20:16:21 GMT"
}
] | 1,662,940,800,000 | [
[
"Xie",
"Chulin",
""
],
[
"Cao",
"Zhong",
""
],
[
"Long",
"Yunhui",
""
],
[
"Yang",
"Diange",
""
],
[
"Zhao",
"Ding",
""
],
[
"Li",
"Bo",
""
]
] |
2209.04100 | Xianqi Zhang | Xianqi Zhang, Xingtao Wang, Xu Liu, Wenrui Wang, Xiaopeng Fan, and
Debin Zhao | Task-Agnostic Learning to Accomplish New Tasks | 11 pages, 11 figures, Under Review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning (RL) and Imitation Learning (IL) have made great
progress in robotic control in recent years. However, these methods show
obvious deterioration for new tasks that need to be completed through new
combinations of actions. RL methods heavily rely on reward functions that
cannot generalize well for new tasks, while IL methods are limited by expert
demonstrations which do not cover new tasks. In contrast, humans can easily
complete these tasks with the fragmented knowledge learned from task-agnostic
experience. Inspired by this observation, this paper proposes a task-agnostic
learning method (TAL for short) that can learn fragmented knowledge from
task-agnostic data to accomplish new tasks. TAL consists of four stages. First,
the task-agnostic exploration is performed to collect data from interactions
with the environment. The collected data is organized via a knowledge graph.
Compared with the previous sequential structure, the knowledge graph
representation is more compact and fits better for environment exploration.
Second, an action feature extractor is proposed and trained using the collected
knowledge graph data for task-agnostic fragmented knowledge learning. Third, a
candidate action generator is designed, which applies the action feature
extractor on a new task to generate multiple candidate action sets. Finally, an
action proposal is designed to produce the probabilities for actions in a new
task according to the environmental information. The probabilities are then
used to select actions to be executed from multiple candidate action sets to
form the plan. Experiments on a virtual indoor scene show that the proposed
method outperforms the state-of-the-art offline RL method: CQL by 35.28% and
the IL method: BC by 22.22%.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 03:02:49 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Feb 2023 09:47:25 GMT"
}
] | 1,676,592,000,000 | [
[
"Zhang",
"Xianqi",
""
],
[
"Wang",
"Xingtao",
""
],
[
"Liu",
"Xu",
""
],
[
"Wang",
"Wenrui",
""
],
[
"Fan",
"Xiaopeng",
""
],
[
"Zhao",
"Debin",
""
]
] |
2209.04160 | Gadekallu Thippa Reddy | Rajeswari Chengoden, Nancy Victor, Thien Huynh-The, Gokul Yenduri,
Rutvij H.Jhaveri, Mamoun Alazab, Sweta Bhattacharya, Pawan Hegde, Praveen
Kumar Reddy Maddikunta, and Thippa Reddy Gadekallu | Metaverse for Healthcare: A Survey on Potential Applications, Challenges
and Future Directions | In peer review | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The rapid progress in digitalization and automation have led to an
accelerated growth in healthcare, generating novel models that are creating new
channels for rendering treatment with reduced cost. The Metaverse is an
emerging technology in the digital space which has huge potential in
healthcare, enabling realistic experiences to the patients as well as the
medical practitioners. The Metaverse is a confluence of multiple enabling
technologies such as artificial intelligence, virtual reality, augmented
reality, internet of medical devices, robotics, quantum computing, etc. through
which new directions for providing quality healthcare treatment and services
can be explored. The amalgamation of these technologies ensures immersive,
intimate and personalized patient care. It also provides adaptive intelligent
solutions that eliminates the barriers between healthcare providers and
receivers. This article provides a comprehensive review of the Metaverse for
healthcare, emphasizing on the state of the art, the enabling technologies for
adopting the Metaverse for healthcare, the potential applications and the
related projects. The issues in the adaptation of the Metaverse for healthcare
applications are also identified and the plausible solutions are highlighted as
part of future research directions.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 07:40:11 GMT"
}
] | 1,662,940,800,000 | [
[
"Chengoden",
"Rajeswari",
""
],
[
"Victor",
"Nancy",
""
],
[
"Huynh-The",
"Thien",
""
],
[
"Yenduri",
"Gokul",
""
],
[
"Jhaveri",
"Rutvij H.",
""
],
[
"Alazab",
"Mamoun",
""
],
[
"Bhattacharya",
"Sweta",
""
],
[
"Hegde",
"Pawan",
""
],
[
"Maddikunta",
"Praveen Kumar Reddy",
""
],
[
"Gadekallu",
"Thippa Reddy",
""
]
] |
2209.04189 | Abhiram Katuri | Abhiram Katuri, Sindhu Salugu, Gelli Tharuni, Challa Sri Gouri | Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using
Natural Language Processing | 5 Pages, 3 figures | null | 10.35940/ijeat.A3802.1012122 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most crucial aspects of communication in daily life is speech
recognition. Speech recognition that is based on natural language processing is
one of the essential elements in the conversion of one system to another. In
this paper, we created an interface that transforms speech and other auditory
inputs into text using a digital filter. Contrary to the many methods for this
conversion, it is also possible for linguistic faults to appear occasionally,
gender recognition, speech recognition that is unsuccessful (cannot recognize
voice), and gender recognition to fail. Since technical problems are involved,
we developed a program that acts as a mediator to prevent initiating software
issues in order to eliminate even this little deviation. Its planned MFCC and
HMM are in sync with its AI system. As a result, technical errors have been
avoided.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 08:55:34 GMT"
}
] | 1,662,940,800,000 | [
[
"Katuri",
"Abhiram",
""
],
[
"Salugu",
"Sindhu",
""
],
[
"Tharuni",
"Gelli",
""
],
[
"Gouri",
"Challa Sri",
""
]
] |
2209.04265 | Yubin Liu | Yubin Liu, Qiming Ye, Jose Escribano-Macias, Yuxiang Feng, Eduardo
Candela, and Panagiotis Angeloudis | Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A
Hybrid Q-Learning Network Approach | 54 pages, 18 figures. This paper has been submitted to Transportation
Research Part E: Logistics and Transportation Review (Manuscript Number:
TRE-D-23-00202) | null | 10.1016/j.tre.2023.103234 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Mobile parcel lockers have been recently proposed by logistics operators as a
technology that could help reduce traffic congestion and operational costs in
urban freight distribution. Given their ability to relocate throughout their
area of deployment, they hold the potential to improve customer accessibility
and convenience. In this study, we formulate the Mobile Parcel Locker Problem
(MPLP) , a special case of the Location-Routing Problem (LRP) which determines
the optimal stopover location for MPLs throughout the day and plans
corresponding delivery routes. A Hybrid Q Learning Network based Method (HQM)
is developed to resolve the computational complexity of the resulting large
problem instances while escaping local optima. In addition, the HQM is
integrated with global and local search mechanisms to resolve the dilemma of
exploration and exploitation faced by classic reinforcement learning methods.
We examine the performance of HQM under different problem sizes (up to 200
nodes) and benchmarked it against the exact approach and Genetic Algorithm
(GA). Our results indicate that HQM achieves better optimisation performance
with shorter computation time than the exact approach solved by the Gurobi
solver in large problem instances. Additionally, the average reward obtained by
HQM is 1.96 times greater than GA, which demonstrates that HQM has a better
optimisation ability. Further, we identify critical factors that contribute to
fleet size requirements, travel distances, and service delays. Our findings
outline that the efficiency of MPLs is mainly contingent on the length of time
windows and the deployment of MPL stopovers. Finally, we highlight managerial
implications based on parametric analysis to provide guidance for logistics
operators in the context of efficient last-mile distribution operations.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 11:59:42 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Nov 2022 08:05:17 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Feb 2023 02:39:29 GMT"
}
] | 1,691,452,800,000 | [
[
"Liu",
"Yubin",
""
],
[
"Ye",
"Qiming",
""
],
[
"Escribano-Macias",
"Jose",
""
],
[
"Feng",
"Yuxiang",
""
],
[
"Candela",
"Eduardo",
""
],
[
"Angeloudis",
"Panagiotis",
""
]
] |
2209.04309 | Jiawei Zheng | Jiawei Zheng and Petros Papapanagiotou and Jacques D. Fleuriot | Alignment-based conformance checking over probabilistic events | Extended version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conformance checking techniques allow us to evaluate how well some exhibited
behaviour, represented by a trace of monitored events, conforms to a specified
process model. Modern monitoring and activity recognition technologies, such as
those relying on sensors, the IoT, statistics and AI, can produce a wealth of
relevant event data. However, this data is typically characterised by noise and
uncertainty, in contrast to the assumption of a deterministic event log
required by conformance checking algorithms. In this paper, we extend
alignment-based conformance checking to function under a probabilistic event
log. We introduce a weighted trace model and weighted alignment cost function,
and a custom threshold parameter that controls the level of confidence on the
event data vs. the process model. The resulting algorithm considers activities
of lower but sufficiently high probability that better align with the process
model. We explain the algorithm and its motivation both from formal and
intuitive perspectives, and demonstrate its functionality in comparison with
deterministic alignment using real-life datasets.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 14:07:37 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Mar 2023 14:16:27 GMT"
}
] | 1,680,220,800,000 | [
[
"Zheng",
"Jiawei",
""
],
[
"Papapanagiotou",
"Petros",
""
],
[
"Fleuriot",
"Jacques D.",
""
]
] |
2209.04355 | Hanlei Zhang | Hanlei Zhang, Hua Xu, Xin Wang, Qianrui Zhou, Shaojie Zhao, Jiayan
Teng | MIntRec: A New Dataset for Multimodal Intent Recognition | Accepted by ACM MM 2022 (Main Track, Long Paper) | null | 10.1145/3503161.3547906 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal intent recognition is a significant task for understanding human
language in real-world multimodal scenes. Most existing intent recognition
methods have limitations in leveraging the multimodal information due to the
restrictions of the benchmark datasets with only text information. This paper
introduces a novel dataset for multimodal intent recognition (MIntRec) to
address this issue. It formulates coarse-grained and fine-grained intent
taxonomies based on the data collected from the TV series Superstore. The
dataset consists of 2,224 high-quality samples with text, video, and audio
modalities and has multimodal annotations among twenty intent categories.
Furthermore, we provide annotated bounding boxes of speakers in each video
segment and achieve an automatic process for speaker annotation. MIntRec is
helpful for researchers to mine relationships between different modalities to
enhance the capability of intent recognition. We extract features from each
modality and model cross-modal interactions by adapting three powerful
multimodal fusion methods to build baselines. Extensive experiments show that
employing the non-verbal modalities achieves substantial improvements compared
with the text-only modality, demonstrating the effectiveness of using
multimodal information for intent recognition. The gap between the
best-performing methods and humans indicates the challenge and importance of
this task for the community. The full dataset and codes are available for use
at https://github.com/thuiar/MIntRec.
| [
{
"version": "v1",
"created": "Fri, 9 Sep 2022 15:37:39 GMT"
}
] | 1,675,900,800,000 | [
[
"Zhang",
"Hanlei",
""
],
[
"Xu",
"Hua",
""
],
[
"Wang",
"Xin",
""
],
[
"Zhou",
"Qianrui",
""
],
[
"Zhao",
"Shaojie",
""
],
[
"Teng",
"Jiayan",
""
]
] |
2209.04759 | Nico Roos | Nico Roos | A Semantic Tableau Method for Argument Construction | Post proceedings of the BNAIC 2020 | Artificial Intelligence and Machine Learning, Communications in
Computer and Information Science 1398 (2021) 122-140 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A semantic tableau method, called an argumentation tableau, that enables the
derivation of arguments, is proposed. First, the derivation of arguments for
standard propositional and predicate logic is addressed. Next, an extension
that enables reasoning with defeasible rules is presented. Finally, reasoning
by cases using an argumentation tableau is discussed.
| [
{
"version": "v1",
"created": "Sat, 10 Sep 2022 23:40:22 GMT"
}
] | 1,663,027,200,000 | [
[
"Roos",
"Nico",
""
]
] |
2209.04911 | M Charity | M Charity and Julian Togelius | Keke AI Competition: Solving puzzle levels in a dynamically changing
mechanic space | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Keke AI Competition introduces an artificial agent competition for the
game Baba is You - a Sokoban-like puzzle game where players can create rules
that influence the mechanics of the game. Altering a rule can cause temporary
or permanent effects for the rest of the level that could be part of the
solution space. The nature of these dynamic rules and the deterministic aspect
of the game creates a challenge for AI to adapt to a variety of mechanic
combinations in order to solve a level. This paper describes the framework and
evaluation metrics used to rank submitted agents and baseline results from
sample tree search agents.
| [
{
"version": "v1",
"created": "Sun, 11 Sep 2022 17:50:27 GMT"
}
] | 1,663,027,200,000 | [
[
"Charity",
"M",
""
],
[
"Togelius",
"Julian",
""
]
] |
2209.05090 | Alexander Steen | Alexander Steen, David Fuenmayor | Bridging between LegalRuleML and TPTP for Automated Normative Reasoning
(extended version) | 19 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LegalRuleML is a comprehensive XML-based representation framework for
modeling and exchanging normative rules. The TPTP input and output formats, on
the other hand, are general-purpose standards for the interaction with
automated reasoning systems. In this paper we provide a bridge between the two
communities by (i) defining a logic-pluralistic normative reasoning language
based on the TPTP format, (ii) providing a translation scheme between relevant
fragments of LegalRuleML and this language, and (iii) proposing a flexible
architecture for automated normative reasoning based on this translation. We
exemplarily instantiate and demonstrate the approach with three different
normative logics.
| [
{
"version": "v1",
"created": "Mon, 12 Sep 2022 08:42:34 GMT"
}
] | 1,663,027,200,000 | [
[
"Steen",
"Alexander",
""
],
[
"Fuenmayor",
"David",
""
]
] |
2209.05170 | Sarit Kraus | Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S.S. Ravi | Resource Allocation to Agents with Restrictions: Maximizing Likelihood
with Minimum Compromise | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many scenarios where agents with restrictions compete for resources can be
cast as maximum matching problems on bipartite graphs. Our focus is on resource
allocation problems where agents may have restrictions that make them
incompatible with some resources. We assume that a Principle chooses a maximum
matching randomly so that each agent is matched to a resource with some
probability. Agents would like to improve their chances of being matched by
modifying their restrictions within certain limits. The Principle's goal is to
advise an unsatisfied agent to relax its restrictions so that the total cost of
relaxation is within a budget (chosen by the agent) and the increase in the
probability of being assigned a resource is maximized. We establish hardness
results for some variants of this budget-constrained maximization problem and
present algorithmic results for other variants. We experimentally evaluate our
methods on synthetic datasets as well as on two novel real-world datasets: a
vacation activities dataset and a classrooms dataset.
| [
{
"version": "v1",
"created": "Mon, 12 Sep 2022 11:58:19 GMT"
}
] | 1,663,027,200,000 | [
[
"Trabelsi",
"Yohai",
""
],
[
"Adiga",
"Abhijin",
""
],
[
"Kraus",
"Sarit",
""
],
[
"Ravi",
"S. S.",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.