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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2102.08771 | Saeid Barati | Saeid Barati, Gordon Kindlmann, Hank Hoffmann | Comparing and Combining Approximate Computing Frameworks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Approximate computing frameworks configure applications so they can operate
at a range of points in an accuracy-performance trade-off space. Prior work has
introduced many frameworks to create approximate programs. As approximation
frameworks proliferate, it is natural to ask how they can be compared and
combined to create even larger, richer trade-off spaces. We address these
questions by presenting VIPER and BOA. VIPER compares trade-off spaces induced
by different approximation frameworks by visualizing performance improvements
across the full range of possible accuracies. BOA is a family of exploration
techniques that quickly locate Pareto-efficient points in the immense trade-off
space produced by the combination of two or more approximation frameworks. We
use VIPER and BOA to compare and combine three different approximation
frameworks from across the system stack, including: one that changes numerical
precision, one that skips loop iterations, and one that manipulates existing
application parameters. Compared to simply looking at Pareto-optimal curves, we
find VIPER's visualizations provide a quicker and more convenient way to
determine the best approximation technique for any accuracy loss. Compared to a
state-of-the-art evolutionary algorithm, we find that BOA explores 14x fewer
configurations yet locates 35% more Pareto-efficient points.
| [
{
"version": "v1",
"created": "Tue, 16 Feb 2021 04:52:43 GMT"
}
] | 1,613,606,400,000 | [
[
"Barati",
"Saeid",
""
],
[
"Kindlmann",
"Gordon",
""
],
[
"Hoffmann",
"Hank",
""
]
] |
2102.08845 | Gadekallu Thippa Reddy | Shaashwat Agrawal, Sagnik Sarkar, Gautam Srivastava, Praveen Kumar
Reddy Maddikunta, Thippa Reddy Gadekallu | Genetically Optimized Prediction of Remaining Useful Life | Submitted to SUSCOM, Elsevier | null | 10.1016/j.suscom.2021.100565 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The application of remaining useful life (RUL) prediction has taken great
importance in terms of energy optimization, cost-effectiveness, and risk
mitigation. The existing RUL prediction algorithms mostly constitute deep
learning frameworks. In this paper, we implement LSTM and GRU models and
compare the obtained results with a proposed genetically trained neural
network. The current models solely depend on Adam and SGD for optimization and
learning. Although the models have worked well with these optimizers, even
little uncertainties in prognostics prediction can result in huge losses. We
hope to improve the consistency of the predictions by adding another layer of
optimization using Genetic Algorithms. The hyper-parameters - learning rate and
batch size are optimized beyond manual capacity. These models and the proposed
architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized
architecture can predict the given hyper-parameters autonomously and provide
superior results.
| [
{
"version": "v1",
"created": "Wed, 17 Feb 2021 16:09:23 GMT"
}
] | 1,621,900,800,000 | [
[
"Agrawal",
"Shaashwat",
""
],
[
"Sarkar",
"Sagnik",
""
],
[
"Srivastava",
"Gautam",
""
],
[
"Maddikunta",
"Praveen Kumar Reddy",
""
],
[
"Gadekallu",
"Thippa Reddy",
""
]
] |
2102.09005 | Alexander Felfernig | Alexander Felfernig and Monika Schubert and Christoph Zehentner | An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets | Preprint of: A. Felfernig, M. Schubert, and C. Zehentner. An
Efficient Diagnosis Algorithm for Inconsistent Constraint Sets. Artificial
Intelligence for Engineering Design, Analysis, and Manufacturing (AIEDAM),
Cambridge University Press, vol. 26, no.1, pp. 53-62, 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint sets can become inconsistent in different contexts. For example,
during a configuration session the set of customer requirements can become
inconsistent with the configuration knowledge base. Another example is the
engineering phase of a configuration knowledge base where the underlying
constraints can become inconsistent with a set of test cases. In such
situations we are in the need of techniques that support the identification of
minimal sets of faulty constraints that have to be deleted in order to restore
consistency. In this paper we introduce a divide-and-conquer based diagnosis
algorithm (FastDiag) which identifies minimal sets of faulty constraints in an
over-constrained problem. This algorithm is specifically applicable in
scenarios where the efficient identification of leading (preferred) diagnoses
is crucial. We compare the performance of FastDiag with the conflict-directed
calculation of hitting sets and present an in-depth performance analysis that
shows the advantages of our approach.
| [
{
"version": "v1",
"created": "Wed, 17 Feb 2021 19:55:42 GMT"
}
] | 1,613,692,800,000 | [
[
"Felfernig",
"Alexander",
""
],
[
"Schubert",
"Monika",
""
],
[
"Zehentner",
"Christoph",
""
]
] |
2102.09076 | Niels Leadholm | Niels Leadholm (1 and 2), Marcus Lewis (1), Subutai Ahmad (1) ((1)
Numenta, (2) The University of Oxford) | Grid Cell Path Integration For Movement-Based Visual Object Recognition | 15 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Grid cells enable the brain to model the physical space of the world and
navigate effectively via path integration, updating self-position using
information from self-movement. Recent proposals suggest that the brain might
use similar mechanisms to understand the structure of objects in diverse
sensory modalities, including vision. In machine vision, object recognition
given a sequence of sensory samples of an image, such as saccades, is a
challenging problem when the sequence does not follow a consistent, fixed
pattern - yet this is something humans do naturally and effortlessly. We
explore how grid cell-based path integration in a cortical network can support
reliable recognition of objects given an arbitrary sequence of inputs. Our
network (GridCellNet) uses grid cell computations to integrate visual
information and make predictions based on movements. We use local Hebbian
plasticity rules to learn rapidly from a handful of examples (few-shot
learning), and consider the task of recognizing MNIST digits given only a
sequence of image feature patches. We compare GridCellNet to k-Nearest
Neighbour (k-NN) classifiers as well as recurrent neural networks (RNNs), both
of which lack explicit mechanisms for handling arbitrary sequences of input
samples. We show that GridCellNet can reliably perform classification,
generalizing to both unseen examples and completely novel sequence
trajectories. We further show that inference is often successful after sampling
a fraction of the input space, enabling the predictive GridCellNet to
reconstruct the rest of the image given just a few movements. We propose that
dynamically moving agents with active sensors can use grid cell representations
not only for navigation, but also for efficient recognition and feature
prediction of seen objects.
| [
{
"version": "v1",
"created": "Wed, 17 Feb 2021 23:52:57 GMT"
}
] | 1,613,692,800,000 | [
[
"Leadholm",
"Niels",
"",
"1 and 2"
],
[
"Lewis",
"Marcus",
""
],
[
"Ahmad",
"Subutai",
""
]
] |
2102.09312 | Luis Claudio Sugi Afonso | Luis C. S. Afonso, Clayton R. Pereira, Silke A. T. Weber, Christian
Hook, Alexandre X. Falc\~ao, Jo\~ao P. Papa | Hierarchical Learning Using Deep Optimum-Path Forest | null | null | 10.1016/j.jvcir.2020.102823 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used
in several domains, which include computer-assisted medical diagnoses. In this
work, we are interested in developing tools for the automatic identification of
Parkinson's disease using machine learning and the concept of BoVW. The
proposed approach concerns a hierarchical-based learning technique to design
visual dictionaries through the Deep Optimum-Path Forest classifier. The
proposed method was evaluated in six datasets derived from data collected from
individuals when performing handwriting exams. Experimental results showed the
potential of the technique, with robust achievements.
| [
{
"version": "v1",
"created": "Thu, 18 Feb 2021 13:02:40 GMT"
}
] | 1,613,692,800,000 | [
[
"Afonso",
"Luis C. S.",
""
],
[
"Pereira",
"Clayton R.",
""
],
[
"Weber",
"Silke A. T.",
""
],
[
"Hook",
"Christian",
""
],
[
"Falcão",
"Alexandre X.",
""
],
[
"Papa",
"João P.",
""
]
] |
2102.10062 | Stephen Bonner | Stephen Bonner and Ian P Barrett and Cheng Ye and Rowan Swiers and Ola
Engkvist and Andreas Bender and Charles Tapley Hoyt and William L Hamilton | A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge
Graph Perspective | null | Briefings in Bioinformatics, 2022 | 10.1093/bib/bbac404 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drug discovery and development is a complex and costly process. Machine
learning approaches are being investigated to help improve the effectiveness
and speed of multiple stages of the drug discovery pipeline. Of these, those
that use Knowledge Graphs (KG) have promise in many tasks, including drug
repurposing, drug toxicity prediction and target gene-disease prioritisation.
In a drug discovery KG, crucial elements including genes, diseases and drugs
are represented as entities, whilst relationships between them indicate an
interaction. However, to construct high-quality KGs, suitable data is required.
In this review, we detail publicly available sources suitable for use in
constructing drug discovery focused KGs. We aim to help guide machine learning
and KG practitioners who are interested in applying new techniques to the drug
discovery field, but who may be unfamiliar with the relevant data sources. The
datasets are selected via strict criteria, categorised according to the primary
type of information contained within and are considered based upon what
information could be extracted to build a KG. We then present a comparative
analysis of existing public drug discovery KGs and a evaluation of selected
motivating case studies from the literature. Additionally, we raise numerous
and unique challenges and issues associated with the domain and its datasets,
whilst also highlighting key future research directions. We hope this review
will motivate KGs use in solving key and emerging questions in the drug
discovery domain.
| [
{
"version": "v1",
"created": "Fri, 19 Feb 2021 17:49:38 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Feb 2021 15:26:09 GMT"
},
{
"version": "v3",
"created": "Thu, 1 Apr 2021 10:28:50 GMT"
},
{
"version": "v4",
"created": "Fri, 26 Nov 2021 10:56:59 GMT"
}
] | 1,664,236,800,000 | [
[
"Bonner",
"Stephen",
""
],
[
"Barrett",
"Ian P",
""
],
[
"Ye",
"Cheng",
""
],
[
"Swiers",
"Rowan",
""
],
[
"Engkvist",
"Ola",
""
],
[
"Bender",
"Andreas",
""
],
[
"Hoyt",
"Charles Tapley",
""
],
[
"Hamilton",
"William L",
""
]
] |
2102.10247 | Michael Green | Michael Cerny Green, Ahmed Khalifa, Philip Bontrager, Rodrigo Canaan
and Julian Togelius | Game Mechanic Alignment Theory and Discovery | 11 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new concept called Game Mechanic Alignment theory as a way to
organize game mechanics through the lens of systemic rewards and agential
motivations. By disentangling player and systemic influences, mechanics may be
better identified for use in an automated tutorial generation system, which
could tailor tutorials for a particular playstyle or player. Within, we apply
this theory to several well-known games to demonstrate how designers can
benefit from it, we describe a methodology for how to estimate "mechanic
alignment", and we apply this methodology on multiple games in the GVGAI
framework. We discuss how effectively this estimation captures agential
motivations and systemic rewards and how our theory could be used as an
alternative way to find mechanics for tutorial generation.
| [
{
"version": "v1",
"created": "Sat, 20 Feb 2021 03:41:03 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Aug 2021 19:50:56 GMT"
}
] | 1,628,726,400,000 | [
[
"Green",
"Michael Cerny",
""
],
[
"Khalifa",
"Ahmed",
""
],
[
"Bontrager",
"Philip",
""
],
[
"Canaan",
"Rodrigo",
""
],
[
"Togelius",
"Julian",
""
]
] |
2102.10581 | Benjamin Goertzel | Ben Goertzel | Patterns of Cognition: Cognitive Algorithms as Galois Connections
Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is argued that a broad class of AGI-relevant algorithms can be expressed
in a common formal framework, via specifying Galois connections linking search
and optimization processes on directed metagraphs whose edge targets are
labeled with probabilistic dependent types, and then showing these connections
are fulfilled by processes involving metagraph chronomorphisms. Examples are
drawn from the core cognitive algorithms used in the OpenCog AGI framework:
Probabilistic logical inference, evolutionary program learning, pattern mining,
agglomerative clustering, pattern mining and nonlinear-dynamical attention
allocation.
The analysis presented involves representing these cognitive algorithms as
recursive discrete decision processes involving optimizing functions defined
over metagraphs, in which the key decisions involve sampling from probability
distributions over metagraphs and enacting sets of combinatory operations on
selected sub-metagraphs. The mutual associativity of the combinatory operations
involved in a cognitive process is shown to often play a key role in enabling
the decomposition of the process into folding and unfolding operations; a
conclusion that has some practical implications for the particulars of
cognitive processes, e.g. militating toward use of reversible logic and
reversible program execution. It is also observed that where this mutual
associativity holds, there is an alignment between the hierarchy of subgoals
used in recursive decision process execution and a hierarchy of subpatterns
definable in terms of formal pattern theory.
| [
{
"version": "v1",
"created": "Sun, 21 Feb 2021 10:50:40 GMT"
}
] | 1,614,038,400,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2102.10717 | Melanie Mitchell | Melanie Mitchell | Abstraction and Analogy-Making in Artificial Intelligence | Revised version. 30 pages, 9 figures. To appear in Annals of the New
York Academy of Sciences | null | 10.1111/nyas.14619 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conceptual abstraction and analogy-making are key abilities underlying
humans' abilities to learn, reason, and robustly adapt their knowledge to new
domains. Despite of a long history of research on constructing AI systems with
these abilities, no current AI system is anywhere close to a capability of
forming humanlike abstractions or analogies. This paper reviews the advantages
and limitations of several approaches toward this goal, including symbolic
methods, deep learning, and probabilistic program induction. The paper
concludes with several proposals for designing challenge tasks and evaluation
measures in order to make quantifiable and generalizable progress in this area.
| [
{
"version": "v1",
"created": "Mon, 22 Feb 2021 00:12:48 GMT"
},
{
"version": "v2",
"created": "Fri, 14 May 2021 15:27:01 GMT"
}
] | 1,642,550,400,000 | [
[
"Mitchell",
"Melanie",
""
]
] |
2102.10865 | Federico Cerutti | Federico Cerutti, Lance M. Kaplan, Angelika Kimmig, Murat Sensoy | Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits | Under submission to MACH | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When collaborating with an AI system, we need to assess when to trust its
recommendations. If we mistakenly trust it in regions where it is likely to
err, catastrophic failures may occur, hence the need for Bayesian approaches
for probabilistic reasoning in order to determine the confidence (or epistemic
uncertainty) in the probabilities in light of the training data. We propose an
approach to overcome the independence assumption behind most of the approaches
dealing with a large class of probabilistic reasoning that includes Bayesian
networks as well as several instances of probabilistic logic. We provide an
algorithm for Bayesian learning from sparse, albeit complete, observations, and
for deriving inferences and their confidences keeping track of the dependencies
between variables when they are manipulated within the unifying computational
formalism provided by probabilistic circuits. Each leaf of such circuits is
labelled with a beta-distributed random variable that provides us with an
elegant framework for representing uncertain probabilities. We achieve better
estimation of epistemic uncertainty than state-of-the-art approaches, including
highly engineered ones, while being able to handle general circuits and with
just a modest increase in the computational effort compared to using point
probabilities.
| [
{
"version": "v1",
"created": "Mon, 22 Feb 2021 10:03:15 GMT"
}
] | 1,614,038,400,000 | [
[
"Cerutti",
"Federico",
""
],
[
"Kaplan",
"Lance M.",
""
],
[
"Kimmig",
"Angelika",
""
],
[
"Sensoy",
"Murat",
""
]
] |
2102.11137 | Yichen Yang | Yichen David Yang, Jeevana Priya Inala, Osbert Bastani, Yewen Pu,
Armando Solar-Lezama, Martin Rinard | Program Synthesis Guided Reinforcement Learning for Partially Observed
Environments | null | NeurIPS 2021 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key challenge for reinforcement learning is solving long-horizon planning
problems. Recent work has leveraged programs to guide reinforcement learning in
these settings. However, these approaches impose a high manual burden on the
user since they must provide a guiding program for every new task. Partially
observed environments further complicate the programming task because the
program must implement a strategy that correctly, and ideally optimally,
handles every possible configuration of the hidden regions of the environment.
We propose a new approach, model predictive program synthesis (MPPS), that uses
program synthesis to automatically generate the guiding programs. It trains a
generative model to predict the unobserved portions of the world, and then
synthesizes a program based on samples from this model in a way that is robust
to its uncertainty. In our experiments, we show that our approach significantly
outperforms non-program-guided approaches on a set of challenging benchmarks,
including a 2D Minecraft-inspired environment where the agent must complete a
complex sequence of subtasks to achieve its goal, and achieves a similar
performance as using handcrafted programs to guide the agent. Our results
demonstrate that our approach can obtain the benefits of program-guided
reinforcement learning without requiring the user to provide a new guiding
program for every new task.
| [
{
"version": "v1",
"created": "Mon, 22 Feb 2021 16:05:32 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Nov 2021 18:04:02 GMT"
}
] | 1,635,897,600,000 | [
[
"Yang",
"Yichen David",
""
],
[
"Inala",
"Jeevana Priya",
""
],
[
"Bastani",
"Osbert",
""
],
[
"Pu",
"Yewen",
""
],
[
"Solar-Lezama",
"Armando",
""
],
[
"Rinard",
"Martin",
""
]
] |
2102.11232 | Mirza Rami\v{c}i\'c | Mirza Ramicic and Andrea Bonarini | Uncertainty Maximization in Partially Observable Domains: A Cognitive
Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Faced with an ever-increasing complexity of their domains of application,
artificial learning agents are now able to scale up in their ability to process
an overwhelming amount of information coming from their interaction with an
environment. However, this process of scaling does come with a cost of encoding
and processing an increasing amount of redundant information that is not
necessarily beneficial to the learning process itself. This work exploits the
properties of the learning systems defined over partially observable domains by
selectively focusing on the specific type of information that is more likely to
express the causal interaction among the transitioning states of the
environment. Adaptive masking of the observation space based on the temporal
difference displacement criterion enabled a significant improvement in
convergence of temporal difference algorithms defined over a partially
observable Markov process.
| [
{
"version": "v1",
"created": "Mon, 22 Feb 2021 18:05:41 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Feb 2021 15:02:21 GMT"
},
{
"version": "v3",
"created": "Wed, 10 Mar 2021 20:16:03 GMT"
},
{
"version": "v4",
"created": "Sat, 2 Apr 2022 21:53:59 GMT"
}
] | 1,649,116,800,000 | [
[
"Ramicic",
"Mirza",
""
],
[
"Bonarini",
"Andrea",
""
]
] |
2102.11352 | Julie Jiang | Julie Jiang, Kristina Lerman, Emilio Ferrara | Individualized Context-Aware Tensor Factorization for Online Games
Predictions | null | 2020 International Conference on Data Mining Workshops (ICDMW) | 10.1109/ICDMW51313.2020.00048 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Individual behavior and decisions are substantially influenced by their
contexts, such as location, environment, and time. Changes along these
dimensions can be readily observed in Multiplayer Online Battle Arena games
(MOBA), where players face different in-game settings for each match and are
subject to frequent game patches. Existing methods utilizing contextual
information generalize the effect of a context over the entire population, but
contextual information tailored to each individual can be more effective. To
achieve this, we present the Neural Individualized Context-aware Embeddings
(NICE) model for predicting user performance and game outcomes. Our proposed
method identifies individual behavioral differences in different contexts by
learning latent representations of users and contexts through non-negative
tensor factorization. Using a dataset from the MOBA game League of Legends, we
demonstrate that our model substantially improves the prediction of winning
outcome, individual user performance, and user engagement.
| [
{
"version": "v1",
"created": "Mon, 22 Feb 2021 20:46:02 GMT"
}
] | 1,614,124,800,000 | [
[
"Jiang",
"Julie",
""
],
[
"Lerman",
"Kristina",
""
],
[
"Ferrara",
"Emilio",
""
]
] |
2102.11529 | Matthieu Zimmer | Matthieu Zimmer and Xuening Feng and Claire Glanois and Zhaohui Jiang
and Jianyi Zhang and Paul Weng and Dong Li and Jianye Hao and Wulong Liu | Differentiable Logic Machines | Transactions on Machine Learning Research (TMLR) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of reasoning, learning, and decision-making is key to build
more general artificial intelligence systems. As a step in this direction, we
propose a novel neural-logic architecture, called differentiable logic machine
(DLM), that can solve both inductive logic programming (ILP) and reinforcement
learning (RL) problems, where the solution can be interpreted as a first-order
logic program. Our proposition includes several innovations. Firstly, our
architecture defines a restricted but expressive continuous relaxation of the
space of first-order logic programs by assigning weights to predicates instead
of rules, in contrast to most previous neural-logic approaches. Secondly, with
this differentiable architecture, we propose several (supervised and RL)
training procedures, based on gradient descent, which can recover a
fully-interpretable solution (i.e., logic formula). Thirdly, to accelerate RL
training, we also design a novel critic architecture that enables actor-critic
algorithms. Fourthly, to solve hard problems, we propose an incremental
training procedure that can learn a logic program progressively. Compared to
state-of-the-art (SOTA) differentiable ILP methods, DLM successfully solves all
the considered ILP problems with a higher percentage of successful seeds (up to
3.5$\times$). On RL problems, without requiring an interpretable solution, DLM
outperforms other non-interpretable neural-logic RL approaches in terms of
rewards (up to 3.9%). When enforcing interpretability, DLM can solve harder RL
problems (e.g., Sorting, Path) Moreover, we show that deep logic programs can
be learned via incremental supervised training. In addition to this excellent
performance, DLM can scale well in terms of memory and computational time,
especially during the testing phase where it can deal with much more constants
($>$2$\times$) than SOTA.
| [
{
"version": "v1",
"created": "Tue, 23 Feb 2021 07:31:52 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Feb 2021 06:14:03 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Apr 2021 02:40:33 GMT"
},
{
"version": "v4",
"created": "Sun, 12 Dec 2021 11:26:38 GMT"
},
{
"version": "v5",
"created": "Wed, 5 Jul 2023 22:00:05 GMT"
}
] | 1,688,688,000,000 | [
[
"Zimmer",
"Matthieu",
""
],
[
"Feng",
"Xuening",
""
],
[
"Glanois",
"Claire",
""
],
[
"Jiang",
"Zhaohui",
""
],
[
"Zhang",
"Jianyi",
""
],
[
"Weng",
"Paul",
""
],
[
"Li",
"Dong",
""
],
[
"Hao",
"Jianye",
""
],
[
"Liu",
"Wulong",
""
]
] |
2102.11791 | Ramon Fraga Pereira | Kin Max Gusm\~ao, Ramon Fraga Pereira, and Felipe Meneguzzi | Inferring Agents Preferences as Priors for Probabilistic Goal
Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent approaches to goal recognition have leveraged planning landmarks to
achieve high-accuracy with low runtime cost. These approaches, however, lack a
probabilistic interpretation. Furthermore, while most probabilistic models to
goal recognition assume that the recognizer has access to a prior probability
representing, for example, an agent's preferences, virtually no goal
recognition approach actually uses the prior in practice, simply assuming a
uniform prior. In this paper, we provide a model to both extend landmark-based
goal recognition with a probabilistic interpretation and allow the estimation
of such prior probability and its usage to compute posterior probabilities
after repeated interactions of observed agents. We empirically show that our
model can not only recognize goals effectively but also successfully infer the
correct prior probability distribution representing an agent's preferences.
| [
{
"version": "v1",
"created": "Tue, 23 Feb 2021 16:53:23 GMT"
}
] | 1,614,124,800,000 | [
[
"Gusmão",
"Kin Max",
""
],
[
"Pereira",
"Ramon Fraga",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2102.11932 | Thomas Kleine Buening | Thomas Kleine Buening and Meirav Segal and Debabrota Basu and Christos
Dimitrakakis and Anne-Marie George | On Meritocracy in Optimal Set Selection | EAAMO 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Typically, merit is defined with respect to some intrinsic measure of worth.
We instead consider a setting where an individual's worth is \emph{relative}:
when a Decision Maker (DM) selects a set of individuals from a population to
maximise expected utility, it is natural to consider the \emph{Expected
Marginal Contribution} (EMC) of each person to the utility. We show that this
notion satisfies an axiomatic definition of fairness for this setting. We also
show that for certain policy structures, this notion of fairness is aligned
with maximising expected utility, while for linear utility functions it is
identical to the Shapley value. However, for certain natural policies, such as
those that select individuals with a specific set of attributes (e.g. high
enough test scores for college admissions), there is a trade-off between
meritocracy and utility maximisation. We analyse the effect of constraints on
the policy on both utility and fairness in extensive experiments based on
college admissions and outcomes in Norwegian universities.
| [
{
"version": "v1",
"created": "Tue, 23 Feb 2021 20:36:36 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Jul 2021 14:34:21 GMT"
},
{
"version": "v3",
"created": "Fri, 9 Sep 2022 13:37:38 GMT"
}
] | 1,662,940,800,000 | [
[
"Buening",
"Thomas Kleine",
""
],
[
"Segal",
"Meirav",
""
],
[
"Basu",
"Debabrota",
""
],
[
"Dimitrakakis",
"Christos",
""
],
[
"George",
"Anne-Marie",
""
]
] |
2102.12575 | Yuanpeng He | Yuanpeng He | Ordinal relative belief entropy | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Specially customised Entropies are widely applied in measuring the degree of
uncertainties existing in the frame of discernment. However, all of these
entropies regard the frame as a whole that has already been determined which
dose not conform to actual situations. In real life, everything comes in an
order, so how to measure uncertainties of the dynamic process of determining
sequence of propositions contained in a frame of discernment is still an open
issue and no related research has been proceeded. Therefore, a novel ordinal
entropy to measure uncertainties of the frame of discernment considering the
order of confirmation of propositions is proposed in this paper. Compared with
traditional entropies, it manifests effects on degree of uncertainty brought by
orders of propositions existing in a frame of discernment. Besides, some
numerical examples are provided to verify the correctness and validity of the
proposed entropy in this paper.
| [
{
"version": "v1",
"created": "Sun, 21 Feb 2021 04:17:04 GMT"
}
] | 1,614,297,600,000 | [
[
"He",
"Yuanpeng",
""
]
] |
2102.12579 | Alexander Kulikov | Alexander S. Kulikov, Danila Pechenev, Nikita Slezkin | SAT-based Circuit Local Improvement | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Finding exact circuit size is a notorious optimization problem in practice.
Whereas modern computers and algorithmic techniques allow to find a circuit of
size seven in blink of an eye, it may take more than a week to search for a
circuit of size thirteen. One of the reasons of this behavior is that the
search space is enormous: the number of circuits of size $s$ is
$s^{\Theta(s)}$, the number of Boolean functions on $n$ variables is $2^{2^n}$.
In this paper, we explore the following natural heuristic idea for decreasing
the size of a given circuit: go through all its subcircuits of moderate size
and check whether any of them can be improved by reducing to SAT. This may be
viewed as a local search approach: we search for a smaller circuit in a ball
around a given circuit. Through this approach, we prove new upper bounds on the
circuit size of various symmetric functions. We also demonstrate that some
upper bounds that were proved by hand decades ago, nowadays can be found
automatically in a few seconds.
| [
{
"version": "v1",
"created": "Fri, 19 Feb 2021 16:01:50 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Mar 2022 17:12:38 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Apr 2022 09:41:24 GMT"
}
] | 1,651,104,000,000 | [
[
"Kulikov",
"Alexander S.",
""
],
[
"Pechenev",
"Danila",
""
],
[
"Slezkin",
"Nikita",
""
]
] |
2102.13162 | Spencer Killen | Spencer Killen, Jia-Huai You | Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases | 18 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combining the closed-world reasoning of answer set programming (ASP) with the
open-world reasoning of ontologies broadens the space of applications of
reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in
some cases without increasing the complexity of reasoning tasks. However, in
many cases, solver development is lagging behind. As the result, the only known
method of solving disjunctive hybrid MKNF knowledge bases is based on
guess-and-verify, as formulated by Motik and Rosati in their original work. A
main obstacle is understanding how constraint propagation may be performed by a
solver, which, in the context of ASP, centers around the computation of
\textit{unfounded atoms}, the atoms that are false given a partial
interpretation. In this work, we build towards improving solvers for hybrid
MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded
sets for these knowledge bases, identify lower complexity bounds, and
demonstrate how we might integrate these developments into a solver. We discuss
challenges introduced by ontologies that are not present in the development of
solvers for disjunctive logic programs, which warrant some deviations from
traditional definitions of unfounded sets. We compare our work with prior
definitions of unfounded sets.
| [
{
"version": "v1",
"created": "Thu, 25 Feb 2021 20:44:42 GMT"
}
] | 1,616,630,400,000 | [
[
"Killen",
"Spencer",
""
],
[
"You",
"Jia-Huai",
""
]
] |
2102.13307 | Shashi Suman | Shashi Suman, Ali Etemad, Francois Rivest | Potential Impacts of Smart Homes on Human Behavior: A Reinforcement
Learning Approach | in IEEE Transactions on Artificial Intelligence | null | 10.1109/TAI.2021.3127483 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We aim to investigate the potential impacts of smart homes on human behavior.
To this end, we simulate a series of human models capable of performing various
activities inside a reinforcement learning-based smart home. We then
investigate the possibility of human behavior being altered as a result of the
smart home and the human model adapting to one-another. We design a semi-Markov
decision process human task interleaving model based on hierarchical
reinforcement learning that learns to make decisions to either pursue or leave
an activity. We then integrate our human model in the smart home which is based
on Q-learning. We show that a smart home trained on a generic human model is
able to anticipate and learn the thermal preferences of human models with
intrinsic rewards similar to the generic model. The hierarchical human model
learns to complete each activity and set optimal thermal settings for maximum
comfort. With the smart home, the number of time steps required to change the
thermal settings are reduced for the human models. Interestingly, we observe
that small variations in the human model reward structures can lead to the
opposite behavior in the form of unexpected switching between activities which
signals changes in human behavior due to the presence of the smart home.
| [
{
"version": "v1",
"created": "Fri, 26 Feb 2021 05:33:46 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Mar 2021 16:52:17 GMT"
},
{
"version": "v3",
"created": "Mon, 21 Jun 2021 23:05:44 GMT"
}
] | 1,637,625,600,000 | [
[
"Suman",
"Shashi",
""
],
[
"Etemad",
"Ali",
""
],
[
"Rivest",
"Francois",
""
]
] |
2102.13368 | Arianna Casanova | Arianna Casanova, Juerg Kohlas, Marco Zaffalon | Information algebras in the theory of imprecise probabilities | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we show that coherent sets of gambles and coherent lower and
upper previsions can be embedded into the algebraic structure of information
algebra. This leads firstly, to a new perspective of the algebraic and logical
structure of desirability and imprecise probabilities and secondly, it connects
imprecise probabilities to other formalism in computer science sharing the same
underlying structure. Both the domain free and the labeled view of the
resulting information algebras are presented, considering product possibility
spaces. Moreover, it is shown that both are atomistic and therefore they can be
embedded in set algebras.
| [
{
"version": "v1",
"created": "Fri, 26 Feb 2021 09:36:39 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Apr 2021 10:08:28 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Apr 2021 07:35:33 GMT"
}
] | 1,619,568,000,000 | [
[
"Casanova",
"Arianna",
""
],
[
"Kohlas",
"Juerg",
""
],
[
"Zaffalon",
"Marco",
""
]
] |
2102.13564 | Martin Suda | Martin Suda | Improving ENIGMA-Style Clause Selection While Learning From History | 16 page | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We re-examine the topic of machine-learned clause selection guidance in
saturation-based theorem provers. The central idea, recently popularized by the
ENIGMA system, is to learn a classifier for recognizing clauses that appeared
in previously discovered proofs. In subsequent runs, clauses classified
positively are prioritized for selection. We propose several improvements to
this approach and experimentally confirm their viability. For the
demonstration, we use a recursive neural network to classify clauses based on
their derivation history and the presence or absence of automatically supplied
theory axioms therein. The automatic theorem prover Vampire guided by the
network achieves a 41% improvement on a relevant subset of SMT-LIB in a real
time evaluation.
| [
{
"version": "v1",
"created": "Fri, 26 Feb 2021 16:13:45 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Apr 2021 17:46:50 GMT"
}
] | 1,618,444,800,000 | [
[
"Suda",
"Martin",
""
]
] |
2103.00165 | Zifeng Wang | Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, and Yefeng
Zheng | Lifelong Learning based Disease Diagnosis on Clinical Notes | Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD'21) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current deep learning based disease diagnosis systems usually fall short in
catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model
on new tasks usually leads to abrupt decay of performance on previous tasks.
What is worse, the trained diagnosis system would be fixed once deployed but
collecting training data that covers enough diseases is infeasible, which
inspires us to develop a lifelong learning diagnosis system. In this work, we
propose to adopt attention to combine medical entities and context, embedding
episodic memory and consolidation to retain knowledge, such that the learned
model is capable of adapting to sequential disease-diagnosis tasks. Moreover,
we establish a new benchmark, named Jarvis-40, which contains clinical notes
collected from various hospitals. Our experiments show that the proposed method
can achieve state-of-the-art performance on the proposed benchmark.
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 09:23:57 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Mar 2021 03:13:24 GMT"
}
] | 1,615,161,600,000 | [
[
"Wang",
"Zifeng",
""
],
[
"Yang",
"Yifan",
""
],
[
"Wen",
"Rui",
""
],
[
"Chen",
"Xi",
""
],
[
"Huang",
"Shao-Lun",
""
],
[
"Zheng",
"Yefeng",
""
]
] |
2103.00172 | Abubakr Awad | Abubakr Awad, Wei Pang, David Lusseau, George M. Coghill | A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and
Bio-Inspired Applications | arXiv admin note: text overlap with arXiv:1712.02910 by other authors | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In recent years, research on Physarum polycephalum has become more popular
after Nakagaki et al. (2000) performed their famous experiment showing that
Physarum was able to find the shortest route through a maze. Subsequent
researches have confirmed the ability of Physarum-inspired algorithms to solve
a wide range of NP-hard problems. In contrast to previous reviews that either
focus on biological aspects or bio-inspired applications, here we present a
comprehensive review that highlights recent Physarum polycephalum biological
aspects, mathematical models, and Physarum bio-inspired algorithms and their
applications. The novelty of this review stems from our exploration of Physarum
intelligent behaviour in competition settings. Further, we have presented our
new model to simulate Physarum in competition, where multiple Physarum interact
with each other and with their environments. The bio-inspired Physarum in
competition algorithms proved to have great potentials for future research.
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 10:19:41 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Mar 2021 10:49:13 GMT"
},
{
"version": "v3",
"created": "Sat, 8 May 2021 10:22:14 GMT"
}
] | 1,620,691,200,000 | [
[
"Awad",
"Abubakr",
""
],
[
"Pang",
"Wei",
""
],
[
"Lusseau",
"David",
""
],
[
"Coghill",
"George M.",
""
]
] |
2103.00187 | Michael Walton | Michael Walton, Viliam Lisy | Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this report, we present results reproductions for several core algorithms
implemented in the OpenSpiel framework for learning in games. The primary
contribution of this work is a validation of OpenSpiel's re-implemented search
and Reinforcement Learning algorithms against the results reported in their
respective originating works. Additionally, we provide complete documentation
of hyperparameters and source code required to reproduce these experiments
easily and exactly.
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 11:16:09 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Mar 2021 03:41:22 GMT"
}
] | 1,614,729,600,000 | [
[
"Walton",
"Michael",
""
],
[
"Lisy",
"Viliam",
""
]
] |
2103.00200 | Wenrui Gan | Wenrui Gan, Zhulin Liu, C. L. Philip Chen, Tong Zhang | Siamese Labels Auxiliary Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In deep learning, auxiliary training has been widely used to assist the
training of models. During the training phase, using auxiliary modules to
assist training can improve the performance of the model. During the testing
phase, auxiliary modules can be removed, so the test parameters are not
increased. In this paper, we propose a novel auxiliary training method, Siamese
Labels Auxiliary Learning (SiLa). Unlike Deep Mutual Learning (DML), SiLa
emphasizes auxiliary learning and can be easily combined with DML. In general,
the main work of this paper include: (1) propose SiLa Learning, which improves
the performance of common models without increasing test parameters; (2)
compares SiLa with DML and proves that SiLa can improve the generalization of
the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa
can be used for various types of network structures.
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 12:07:30 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Mar 2021 13:26:46 GMT"
},
{
"version": "v3",
"created": "Thu, 26 May 2022 23:51:19 GMT"
}
] | 1,653,868,800,000 | [
[
"Gan",
"Wenrui",
""
],
[
"Liu",
"Zhulin",
""
],
[
"Chen",
"C. L. Philip",
""
],
[
"Zhang",
"Tong",
""
]
] |
2103.00331 | Daniela Kuinchtner | Daniela Kuinchtner, Afonso Sales, Felipe Meneguzzi | CP-MDP: A CANDECOMP-PARAFAC Decomposition Approach to Solve a Markov
Decision Process Multidimensional Problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Markov Decision Process (MDP) is the underlying model for optimal planning
for decision-theoretic agents in stochastic environments. Although much
research focuses on solving MDP problems both in tabular form or using factored
representations, none focused on tensor decomposition methods. Solving MDPs
using tensor algebra offers the prospect of leveraging advances in tensor-based
computations to further increase solver efficiency. In this paper, we develop
an MDP solver for a multidimensional problem using a tensor decomposition
method to compress the transition models and optimize the value iteration and
policy iteration algorithms. We empirically evaluate our approach against
tabular methods and show our approach can compute much larger problems using
substantially less memory, opening up new possibilities for tensor-based
approaches in stochastic planning
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 21:33:19 GMT"
}
] | 1,614,643,200,000 | [
[
"Kuinchtner",
"Daniela",
""
],
[
"Sales",
"Afonso",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2103.00507 | Florentin Hildebrandt | Florentin D Hildebrandt, Barrett Thomas, Marlin W Ulmer | Where the Action is: Let's make Reinforcement Learning for Stochastic
Dynamic Vehicle Routing Problems work! | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | There has been a paradigm-shift in urban logistic services in the last years;
demand for real-time, instant mobility and delivery services grows. This poses
new challenges to logistic service providers as the underlying stochastic
dynamic vehicle routing problems (SDVRPs) require anticipatory real-time
routing actions. Searching the combinatorial action space for efficient routing
actions is by itself a complex task of mixed-integer programming (MIP)
well-known by the operations research community. This complexity is now
multiplied by the challenge of evaluating such actions with respect to their
effectiveness given future dynamism and uncertainty, a potentially ideal case
for reinforcement learning (RL) well-known by the computer science community.
For solving SDVRPs, joint work of both communities is needed, but as we show,
essentially non-existing. Both communities focus on their individual strengths
leaving potential for improvement. Our survey paper highlights this potential
in research originating from both communities. We point out current obstacles
in SDVRPs and guide towards joint approaches to overcome them.
| [
{
"version": "v1",
"created": "Sun, 28 Feb 2021 13:26:35 GMT"
}
] | 1,614,643,200,000 | [
[
"Hildebrandt",
"Florentin D",
""
],
[
"Thomas",
"Barrett",
""
],
[
"Ulmer",
"Marlin W",
""
]
] |
2103.00519 | Andreas Holzinger | Andreas Holzinger, Anna Saranti, Heimo Mueller | KANDINSKYPatterns -- An experimental exploration environment for Pattern
Analysis and Machine Intelligence | 12 pages, submitted to IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI), currently under review | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine intelligence is very successful at standard recognition tasks when
having high-quality training data. There is still a significant gap between
machine-level pattern recognition and human-level concept learning. Humans can
learn under uncertainty from only a few examples and generalize these concepts
to solve new problems. The growing interest in explainable machine
intelligence, requires experimental environments and diagnostic tests to
analyze weaknesses in existing approaches to drive progress in the field. In
this paper, we discuss existing diagnostic tests and test data sets such as
CLEVR, CLEVERER, CLOSURE, CURI, Bongard-LOGO, V-PROM, and present our own
experimental environment: The KANDINSKYPatterns, named after the Russian artist
Wassily Kandinksy, who made theoretical contributions to compositivity, i.e.
that all perceptions consist of geometrically elementary individual components.
This was experimentally proven by Hubel &Wiesel in the 1960s and became the
basis for machine learning approaches such as the Neocognitron and the even
later Deep Learning. While KANDINSKYPatterns have computationally controllable
properties on the one hand, bringing ground truth, they are also easily
distinguishable by human observers, i.e., controlled patterns can be described
by both humans and algorithms, making them another important contribution to
international research in machine intelligence.
| [
{
"version": "v1",
"created": "Sun, 28 Feb 2021 14:09:59 GMT"
}
] | 1,614,643,200,000 | [
[
"Holzinger",
"Andreas",
""
],
[
"Saranti",
"Anna",
""
],
[
"Mueller",
"Heimo",
""
]
] |
2103.00623 | Julien Perolat | Julien Perolat, Sarah Perrin, Romuald Elie, Mathieu Lauri\`ere,
Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin | Scaling up Mean Field Games with Online Mirror Descent | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address scaling up equilibrium computation in Mean Field Games (MFGs)
using Online Mirror Descent (OMD). We show that continuous-time OMD provably
converges to a Nash equilibrium under a natural and well-motivated set of
monotonicity assumptions. This theoretical result nicely extends to
multi-population games and to settings involving common noise. A thorough
experimental investigation on various single and multi-population MFGs shows
that OMD outperforms traditional algorithms such as Fictitious Play (FP). We
empirically show that OMD scales up and converges significantly faster than FP
by solving, for the first time to our knowledge, examples of MFGs with hundreds
of billions states. This study establishes the state-of-the-art for learning in
large-scale multi-agent and multi-population games.
| [
{
"version": "v1",
"created": "Sun, 28 Feb 2021 21:28:36 GMT"
}
] | 1,614,643,200,000 | [
[
"Perolat",
"Julien",
""
],
[
"Perrin",
"Sarah",
""
],
[
"Elie",
"Romuald",
""
],
[
"Laurière",
"Mathieu",
""
],
[
"Piliouras",
"Georgios",
""
],
[
"Geist",
"Matthieu",
""
],
[
"Tuyls",
"Karl",
""
],
[
"Pietquin",
"Olivier",
""
]
] |
2103.00778 | Mahsa Paknezhad | Mahsa Paknezhad, Cuong Phuc Ngo, Amadeus Aristo Winarto, Alistair
Cheong, Chuen Yang Beh, Jiayang Wu, Hwee Kuan Lee | Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis | null | Neurocomputing, 2022 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite many proposed algorithms to provide robustness to deep learning (DL)
models, DL models remain susceptible to adversarial attacks. We hypothesize
that the adversarial vulnerability of DL models stems from two factors. The
first factor is data sparsity which is that in the high dimensional input data
space, there exist large regions outside the support of the data distribution.
The second factor is the existence of many redundant parameters in the DL
models. Owing to these factors, different models are able to come up with
different decision boundaries with comparably high prediction accuracy. The
appearance of the decision boundaries in the space outside the support of the
data distribution does not affect the prediction accuracy of the model.
However, it makes an important difference in the adversarial robustness of the
model. We hypothesize that the ideal decision boundary is as far as possible
from the support of the data distribution. In this paper, we develop a training
framework to observe if DL models are able to learn such a decision boundary
spanning the space around the class distributions further from the data points
themselves. Semi-supervised learning was deployed during training by leveraging
unlabeled data generated in the space outside the support of the data
distribution. We measured adversarial robustness of the models trained using
this training framework against well-known adversarial attacks and by using
robustness metrics. We found that models trained using our framework, as well
as other regularization methods and adversarial training support our hypothesis
of data sparsity and that models trained with these methods learn to have
decision boundaries more similar to the aforementioned ideal decision boundary.
The code for our training framework is available at
https://github.com/MahsaPaknezhad/AdversariallyRobustTraining.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 06:04:31 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Feb 2022 06:50:24 GMT"
},
{
"version": "v3",
"created": "Fri, 18 Feb 2022 04:49:23 GMT"
}
] | 1,645,401,600,000 | [
[
"Paknezhad",
"Mahsa",
""
],
[
"Ngo",
"Cuong Phuc",
""
],
[
"Winarto",
"Amadeus Aristo",
""
],
[
"Cheong",
"Alistair",
""
],
[
"Beh",
"Chuen Yang",
""
],
[
"Wu",
"Jiayang",
""
],
[
"Lee",
"Hwee Kuan",
""
]
] |
2103.00848 | Xiao Huang | Xiao Huang, Hong Qiao, Hui Li and Zhihong Jiang | A Bioinspired Retinal Neural Network for Accurately Extracting
Small-Target Motion Information in Cluttered Backgrounds | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust and accurate detection of small moving targets in cluttered moving
backgrounds is a significant and challenging problem for robotic visual systems
to perform search and tracking tasks. Inspired by the neural circuitry of
elementary motion vision in the mammalian retina, this paper proposes a
bioinspired retinal neural network based on a new neurodynamics-based temporal
filtering and multiform 2-D spatial Gabor filtering. This model can estimate
motion direction accurately via only two perpendicular spatiotemporal filtering
signals, and respond to small targets of different sizes and velocities by
adjusting the dendrite field size of the spatial filter. Meanwhile, an
algorithm of directionally selective inhibition is proposed to suppress the
target-like features in the moving background, which can reduce the influence
of background motion effectively. Extensive synthetic and real-data experiments
show that the proposed model works stably for small targets of a wider size and
velocity range, and has better detection performance than other bioinspired
models. Additionally, it can also extract the information of motion direction
and motion energy accurately and rapidly.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 08:44:27 GMT"
}
] | 1,614,643,200,000 | [
[
"Huang",
"Xiao",
""
],
[
"Qiao",
"Hong",
""
],
[
"Li",
"Hui",
""
],
[
"Jiang",
"Zhihong",
""
]
] |
2103.00891 | Yiwen Liu | Yanzhen Ren, Yiwen Liu, Lina Wang | Using contrastive learning to improve the performance of steganalysis
schemes | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | To improve the detection accuracy and generalization of steganalysis, this
paper proposes the Steganalysis Contrastive Framework (SCF) based on
contrastive learning. The SCF improves the feature representation of
steganalysis by maximizing the distance between features of samples of
different categories and minimizing the distance between features of samples of
the same category. To decrease the computing complexity of the contrastive loss
in supervised learning, we design a novel Steganalysis Contrastive Loss
(StegCL) based on the equivalence and transitivity of similarity. The StegCL
eliminates the redundant computing in the existing contrastive loss. The
experimental results show that the SCF improves the generalization and
detection accuracy of existing steganalysis DNNs, and the maximum promotion is
2% and 3% respectively. Without decreasing the detection accuracy, the training
time of using the StegCL is 10% of that of using the contrastive loss in
supervised learning.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 10:32:02 GMT"
}
] | 1,614,643,200,000 | [
[
"Ren",
"Yanzhen",
""
],
[
"Liu",
"Yiwen",
""
],
[
"Wang",
"Lina",
""
]
] |
2103.01108 | Carl Corea | Carl Corea, Matthias Thimm, Patrick Delfmann | Measuring Inconsistency over Sequences of Business Rule Cases | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this report, we investigate (element-based) inconsistency measures for
multisets of business rule bases. Currently, related works allow to assess
individual rule bases, however, as companies might encounter thousands of such
instances daily, studying not only individual rule bases separately, but rather
also their interrelations becomes necessary, especially in regard to
determining suitable re-modelling strategies. We therefore present an approach
to induce multiset-measures from arbitrary (traditional) inconsistency
measures, propose new rationality postulates for a multiset use-case, and
investigate the complexity of various aspects regarding multi-rule base
inconsistency measurement.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 16:18:26 GMT"
}
] | 1,614,643,200,000 | [
[
"Corea",
"Carl",
""
],
[
"Thimm",
"Matthias",
""
],
[
"Delfmann",
"Patrick",
""
]
] |
2103.01171 | William Macke | William Macke, Reuth Mirsky and Peter Stone | Expected Value of Communication for Planning in Ad Hoc Teamwork | 10 pages, 6 figure, Published at AAAI 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A desirable goal for autonomous agents is to be able to coordinate on the fly
with previously unknown teammates. Known as "ad hoc teamwork", enabling such a
capability has been receiving increasing attention in the research community.
One of the central challenges in ad hoc teamwork is quickly recognizing the
current plans of other agents and planning accordingly. In this paper, we focus
on the scenario in which teammates can communicate with one another, but only
at a cost. Thus, they must carefully balance plan recognition based on
observations vs. that based on communication. This paper proposes a new metric
for evaluating how similar are two policies that a teammate may be following -
the Expected Divergence Point (EDP). We then present a novel planning algorithm
for ad hoc teamwork, determining which query to ask and planning accordingly.
We demonstrate the effectiveness of this algorithm in a range of increasingly
general communication in ad hoc teamwork problems.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 18:09:36 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Mar 2021 18:05:47 GMT"
}
] | 1,616,716,800,000 | [
[
"Macke",
"William",
""
],
[
"Mirsky",
"Reuth",
""
],
[
"Stone",
"Peter",
""
]
] |
2103.01737 | Xinting Hu | Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, Hanwang Zhang | Distilling Causal Effect of Data in Class-Incremental Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a causal framework to explain the catastrophic forgetting in
Class-Incremental Learning (CIL) and then derive a novel distillation method
that is orthogonal to the existing anti-forgetting techniques, such as data
replay and feature/label distillation. We first 1) place CIL into the
framework, 2) answer why the forgetting happens: the causal effect of the old
data is lost in new training, and then 3) explain how the existing techniques
mitigate it: they bring the causal effect back. Based on the framework, we find
that although the feature/label distillation is storage-efficient, its causal
effect is not coherent with the end-to-end feature learning merit, which is
however preserved by data replay. To this end, we propose to distill the
Colliding Effect between the old and the new data, which is fundamentally
equivalent to the causal effect of data replay, but without any cost of replay
storage. Thanks to the causal effect analysis, we can further capture the
Incremental Momentum Effect of the data stream, removing which can help to
retain the old effect overwhelmed by the new data effect, and thus alleviate
the forgetting of the old class in testing. Extensive experiments on three CIL
benchmarks: CIFAR-100, ImageNet-Sub&Full, show that the proposed causal effect
distillation can improve various state-of-the-art CIL methods by a large margin
(0.72%--9.06%).
| [
{
"version": "v1",
"created": "Tue, 2 Mar 2021 14:14:10 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Mar 2021 08:37:50 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Mar 2021 03:16:37 GMT"
}
] | 1,615,248,000,000 | [
[
"Hu",
"Xinting",
""
],
[
"Tang",
"Kaihua",
""
],
[
"Miao",
"Chunyan",
""
],
[
"Hua",
"Xian-Sheng",
""
],
[
"Zhang",
"Hanwang",
""
]
] |
2103.01785 | Felix Mohr | Felix Mohr, Gonzalo Mej\'ia, Francisco Yuraszeck | Single and Parallel Machine Scheduling with Variable Release Dates | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper we study a simple extension of the total weighted flowtime
minimization problem for single and identical parallel machines. While the
standard problem simply defines a set of jobs with their processing times and
weights and assumes that all jobs have release date 0 and have no deadline, we
assume that the release date of each job is a decision variable that is only
constrained by a single global latest arrival deadline. To our knowledge, this
simple yet practically highly relevant extension has never been studied. Our
main contribution is that we show the NP- completeness of the problem even for
the single machine case and provide an exhaustive empirical study of different
typical approaches including genetic algorithms, tree search, and constraint
programming.
| [
{
"version": "v1",
"created": "Tue, 2 Mar 2021 14:52:28 GMT"
}
] | 1,614,729,600,000 | [
[
"Mohr",
"Felix",
""
],
[
"Mejía",
"Gonzalo",
""
],
[
"Yuraszeck",
"Francisco",
""
]
] |
2103.02099 | Alishba Imran | Alishba Imran, William Escobar, Freidoon Barez | Design of an Affordable Prosthetic Arm Equipped with Deep Learning
Vision-Based Manipulation | Pre-print paper, 7 pages, 15 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many amputees throughout the world are left with limited options to
personally own a prosthetic arm due to the expensive cost, mechanical system
complexity, and lack of availability. The three main control methods of
prosthetic hands are: (1) body-powered control, (2) extrinsic mechanical
control, and (3) myoelectric control. These methods can perform well under a
controlled situation but will often break down in clinical and everyday use due
to poor robustness, weak adaptability, long-term training, and heavy mental
burden during use. This paper lays the complete outline of the design process
of an affordable and easily accessible novel prosthetic arm that reduces the
cost of prosthetics from $10,000 to $700 on average. The 3D printed prosthetic
arm is equipped with a depth camera and closed-loop off-policy deep learning
algorithm to help form grasps to the object in view. Current work in
reinforcement learning masters only individual skills and is heavily focused on
parallel jaw grippers for in-hand manipulation. In order to create
generalization, which better performs real-world manipulation, the focus is
specifically on using the general framework of Markov Decision Process (MDP)
through scalable learning with off-policy algorithms such as deep deterministic
policy gradient (DDPG) and to study this question in the context of grasping a
prosthetic arm. We were able to achieve a 78% grasp success rate on previously
unseen objects and generalize across multiple objects for manipulation tasks.
This work will make prosthetics cheaper, easier to use and accessible globally
for amputees. Future work includes applying similar approaches to other medical
assistive devices where a human is interacting with a machine to complete a
task.
| [
{
"version": "v1",
"created": "Wed, 3 Mar 2021 00:35:06 GMT"
}
] | 1,614,816,000,000 | [
[
"Imran",
"Alishba",
""
],
[
"Escobar",
"William",
""
],
[
"Barez",
"Freidoon",
""
]
] |
2103.02362 | Ting Wu | Ting Wu, Junjie Peng, Wenqiang Zhang, Huiran Zhang, Chuanshuai Ma,
Yansong Huang | Video Sentiment Analysis with Bimodal Information-augmented Multi-Head
Attention | 12 pages, 4 figures, content and journal information updated | Knowledge Based Systems 235 (2022) 107676 | 10.1016/j.knosys.2021.107676 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans express feelings or emotions via different channels. Take language as
an example, it entails different sentiments under different visual-acoustic
contexts. To precisely understand human intentions as well as reduce the
misunderstandings caused by ambiguity and sarcasm, we should consider
multimodal signals including textual, visual and acoustic signals. The crucial
challenge is to fuse different modalities of features for sentiment analysis.
To effectively fuse the information carried by different modalities and better
predict the sentiments, we design a novel multi-head attention based fusion
network, which is inspired by the observations that the interactions between
any two pair-wise modalities are different and they do not equally contribute
to the final sentiment prediction. By assigning the acoustic-visual,
acoustic-textual and visual-textual features with reasonable attention and
exploiting a residual structure, we attend to attain the significant features.
We conduct extensive experiments on four public multimodal datasets including
one in Chinese and three in English. The results show that our approach
outperforms the existing methods and can explain the contributions of bimodal
interaction in multiple modalities.
| [
{
"version": "v1",
"created": "Wed, 3 Mar 2021 12:30:11 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Mar 2021 02:54:35 GMT"
},
{
"version": "v3",
"created": "Tue, 16 Nov 2021 07:02:53 GMT"
}
] | 1,637,107,200,000 | [
[
"Wu",
"Ting",
""
],
[
"Peng",
"Junjie",
""
],
[
"Zhang",
"Wenqiang",
""
],
[
"Zhang",
"Huiran",
""
],
[
"Ma",
"Chuanshuai",
""
],
[
"Huang",
"Yansong",
""
]
] |
2103.02363 | Daiki Kimura | Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi, Ryosuke Kohita, Asim
Munawar, Michiaki Tatsubori, Alexander Gray | Reinforcement Learning with External Knowledge by using Logical Neural
Networks | KBRL Workshop at IJCAI-PRICAI 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional deep reinforcement learning methods are sample-inefficient and
usually require a large number of training trials before convergence. Since
such methods operate on an unconstrained action set, they can lead to useless
actions. A recent neuro-symbolic framework called the Logical Neural Networks
(LNNs) can simultaneously provide key-properties of both neural networks and
symbolic logic. The LNNs functions as an end-to-end differentiable network that
minimizes a novel contradiction loss to learn interpretable rules. In this
paper, we utilize LNNs to define an inference graph using basic logical
operations, such as AND and NOT, for faster convergence in reinforcement
learning. Specifically, we propose an integrated method that enables model-free
reinforcement learning from external knowledge sources in an LNNs-based logical
constrained framework such as action shielding and guide. Our results
empirically demonstrate that our method converges faster compared to a
model-free reinforcement learning method that doesn't have such logical
constraints.
| [
{
"version": "v1",
"created": "Wed, 3 Mar 2021 12:34:59 GMT"
}
] | 1,614,816,000,000 | [
[
"Kimura",
"Daiki",
""
],
[
"Chaudhury",
"Subhajit",
""
],
[
"Wachi",
"Akifumi",
""
],
[
"Kohita",
"Ryosuke",
""
],
[
"Munawar",
"Asim",
""
],
[
"Tatsubori",
"Michiaki",
""
],
[
"Gray",
"Alexander",
""
]
] |
2103.02676 | Alvi Ataur Khalil | Alvi Ataur Khalil, Alexander J Byrne, Mohammad Ashiqur Rahman,
Mohammad Hossein Manshaei | Efficient UAV Trajectory-Planning using Economic Reinforcement Learning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Advances in unmanned aerial vehicle (UAV) design have opened up applications
as varied as surveillance, firefighting, cellular networks, and delivery
applications. Additionally, due to decreases in cost, systems employing fleets
of UAVs have become popular. The uniqueness of UAVs in systems creates a novel
set of trajectory or path planning and coordination problems. Environments
include many more points of interest (POIs) than UAVs, with obstacles and
no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement
learning algorithm inspired by economic transactions to distribute tasks
between UAVs. This system revolves around an economic theory, in particular an
auction mechanism where UAVs trade assigned POIs. We formulate the path
planning problem as a multi-agent economic game, where agents can cooperate and
compete for resources. We then translate the problem into a Partially
Observable Markov decision process (POMDP), which is solved using a
reinforcement learning (RL) model deployed on each agent. As the system
computes task distributions via UAV cooperation, it is highly resilient to any
change in the swarm size. Our proposed network and economic game architecture
can effectively coordinate the swarm as an emergent phenomenon while
maintaining the swarm's operation. Evaluation results prove that REPlanner
efficiently outperforms conventional RL-based trajectory search.
| [
{
"version": "v1",
"created": "Wed, 3 Mar 2021 20:54:19 GMT"
}
] | 1,614,902,400,000 | [
[
"Khalil",
"Alvi Ataur",
""
],
[
"Byrne",
"Alexander J",
""
],
[
"Rahman",
"Mohammad Ashiqur",
""
],
[
"Manshaei",
"Mohammad Hossein",
""
]
] |
2103.02943 | Jose Maria Font | Jose M. Font and Tobias Mahlmann | The Dota 2 Bot Competition | 6 pages | IEEE Transactions on Games 2018 | 10.1109/TG.2018.2834566 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multiplayer Online Battle Area (MOBA) games are a recent huge success both in
the video game industry and the international eSports scene. These games
encourage team coordination and cooperation, short and long-term planning,
within a real-time combined action and strategy gameplay.
Artificial Intelligence and Computational Intelligence in Games research
competitions offer a wide variety of challenges regarding the study and
application of AI techniques to different game genres. These events are widely
accepted by the AI/CI community as a sort of AI benchmarking that strongly
influences many other research areas in the field.
This paper presents and describes in detail the Dota 2 Bot competition and
the Dota 2 AI framework that supports it. This challenge aims to join both,
MOBAs and AI/CI game competitions, inviting participants to submit AI
controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2)
to play in 1v1 matches, which aims for fostering research on AI techniques for
real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game
modding capabilities to enable to connect external AI controllers to actual
Dota 2 game matches using the original Free-to-Play game.se of the actual Dota
2 game modding capabilities to enable to connect external AI controllers to
actual Dota 2 game matches using the original Free-to-Play game.
| [
{
"version": "v1",
"created": "Thu, 4 Mar 2021 10:49:47 GMT"
}
] | 1,614,902,400,000 | [
[
"Font",
"Jose M.",
""
],
[
"Mahlmann",
"Tobias",
""
]
] |
2103.03361 | Natesh Ganesh | Natesh Ganesh | From Quantifying Vagueness To Pan-niftyism | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this short paper, we will introduce a simple model for quantifying
philosophical vagueness. There is growing interest in this endeavor to quantify
vague concepts of consciousness, agency, etc. We will then discuss some of the
implications of this model including the conditions under which the
quantification of `nifty' leads to pan-nifty-ism. Understanding this leads to
an interesting insight - the reason a framework to quantify consciousness like
Integrated Information Theory implies (forms of) panpsychism is because there
is favorable structure already implicitly encoded in the construction of the
quantification metric.
| [
{
"version": "v1",
"created": "Mon, 1 Mar 2021 17:00:52 GMT"
}
] | 1,615,161,600,000 | [
[
"Ganesh",
"Natesh",
""
]
] |
2103.03429 | Xiaowei Zhou | Xiaowei Zhou, Jie Yin, Ivor Tsang and Chen Wang | Human-Understandable Decision Making for Visual Recognition | 12 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The widespread use of deep neural networks has achieved substantial success
in many tasks. However, there still exists a huge gap between the operating
mechanism of deep learning models and human-understandable decision making, so
that humans cannot fully trust the predictions made by these models. To date,
little work has been done on how to align the behaviors of deep learning models
with human perception in order to train a human-understandable model. To fill
this gap, we propose a new framework to train a deep neural network by
incorporating the prior of human perception into the model learning process.
Our proposed model mimics the process of perceiving conceptual parts from
images and assessing their relative contributions towards the final
recognition. The effectiveness of our proposed model is evaluated on two
classical visual recognition tasks. The experimental results and analysis
confirm our model is able to provide interpretable explanations for its
predictions, but also maintain competitive recognition accuracy.
| [
{
"version": "v1",
"created": "Fri, 5 Mar 2021 02:07:33 GMT"
}
] | 1,615,161,600,000 | [
[
"Zhou",
"Xiaowei",
""
],
[
"Yin",
"Jie",
""
],
[
"Tsang",
"Ivor",
""
],
[
"Wang",
"Chen",
""
]
] |
2103.03610 | Iain Barclay | Iain Barclay, Harrison Taylor, Alun Preece, Ian Taylor, Dinesh Verma,
Geeth de Mel | A framework for fostering transparency in shared artificial intelligence
models by increasing visibility of contributions | This is the pre-peer reviewed version of the following article:
Barclay I, Taylor H, Preece A, Taylor I, Verma D, de Mel G. A framework for
fostering transparency in shared artificial intelligence models by increasing
visibility of contributions. Concurrency Computat Pract Exper. 2020;e6129.
arXiv admin note: substantial text overlap with arXiv:1907.03483 | null | 10.1002/cpe.6129 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Increased adoption of artificial intelligence (AI) systems into scientific
workflows will result in an increasing technical debt as the distance between
the data scientists and engineers who develop AI system components and
scientists, researchers and other users grows. This could quickly become
problematic, particularly where guidance or regulations change and
once-acceptable best practice becomes outdated, or where data sources are later
discredited as biased or inaccurate. This paper presents a novel method for
deriving a quantifiable metric capable of ranking the overall transparency of
the process pipelines used to generate AI systems, such that users, auditors
and other stakeholders can gain confidence that they will be able to validate
and trust the data sources and contributors in the AI systems that they rely
on. The methodology for calculating the metric, and the type of criteria that
could be used to make judgements on the visibility of contributions to systems
are evaluated through models published at ModelHub and PyTorch Hub, popular
archives for sharing science resources, and is found to be helpful in driving
consideration of the contributions made to generating AI systems and approaches
towards effective documentation and improving transparency in machine learning
assets shared within scientific communities.
| [
{
"version": "v1",
"created": "Fri, 5 Mar 2021 11:28:50 GMT"
}
] | 1,615,161,600,000 | [
[
"Barclay",
"Iain",
""
],
[
"Taylor",
"Harrison",
""
],
[
"Preece",
"Alun",
""
],
[
"Taylor",
"Ian",
""
],
[
"Verma",
"Dinesh",
""
],
[
"de Mel",
"Geeth",
""
]
] |
2103.03666 | Benedikt Kleppmann | Benedikt T. Kleppmann | Tree of Knowledge: an Online Platform for Learning the Behaviour of
Complex Systems | 10 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many social sciences such as psychology and economics try to learn the
behaviour of complex agents such as humans, organisations and countries. The
current statistical methods used for learning this behaviour try to infer
generally valid behaviour, but can only learn from one type of study at a time.
Furthermore, only data from carefully designed studies can be used, as the
phenomenon of interest has to be isolated and confounding factors accounted
for. These restrictions limit the robustness and accuracy of insights that can
be gained from social/economic systems. Here we present the online platform
TreeOfKnowledge which implements a new methodology specifically designed for
learning complex behaviours from complex systems: agent-based behaviour
learning. With agent-based behaviour learning it is possible to gain more
accurate and robust insights as it does not have the restriction of
conventional statistics. It learns agent behaviour from many heterogenous
datasets and can learn from these datasets even if the phenomenon of interest
is not directly observed, but appears deep within complex systems. This new
methodology shows how the internet and advances in computational power allow
for more accurate and powerful mathematical models.
| [
{
"version": "v1",
"created": "Sat, 27 Feb 2021 19:39:14 GMT"
}
] | 1,615,161,600,000 | [
[
"Kleppmann",
"Benedikt T.",
""
]
] |
2103.03798 | Vlad Firoiu | Vlad Firoiu, Eser Aygun, Ankit Anand, Zafarali Ahmed, Xavier Glorot,
Laurent Orseau, Lei Zhang, Doina Precup, Shibl Mourad | Training a First-Order Theorem Prover from Synthetic Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major challenge in applying machine learning to automated theorem proving
is the scarcity of training data, which is a key ingredient in training
successful deep learning models. To tackle this problem, we propose an approach
that relies on training purely with synthetically generated theorems, without
any human data aside from axioms. We use these theorems to train a
neurally-guided saturation-based prover. Our neural prover outperforms the
state-of-the-art E-prover on this synthetic data in both time and search steps,
and shows significant transfer to the unseen human-written theorems from the
TPTP library, where it solves 72\% of first-order problems without equality.
| [
{
"version": "v1",
"created": "Fri, 5 Mar 2021 17:01:34 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Apr 2021 18:41:02 GMT"
}
] | 1,617,840,000,000 | [
[
"Firoiu",
"Vlad",
""
],
[
"Aygun",
"Eser",
""
],
[
"Anand",
"Ankit",
""
],
[
"Ahmed",
"Zafarali",
""
],
[
"Glorot",
"Xavier",
""
],
[
"Orseau",
"Laurent",
""
],
[
"Zhang",
"Lei",
""
],
[
"Precup",
"Doina",
""
],
[
"Mourad",
"Shibl",
""
]
] |
2103.05481 | Damien Pellier | Damien Pellier, Humbert Fiorino | From Classical to Hierarchical: benchmarks for the HTN Track of the
International Planning Competition | null | Proceedings of the International Planning Competition, ICAPS,
Nancy, France, 2020 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this short paper, we outline nine classical benchmarks submitted to the
first hierarchical planning track of the International Planning competition in
2020. All of these benchmarks are based on the HDDL language. The choice of the
benchmarks was based on a questionnaire sent to the HTN community. They are the
following: Barman, Childsnack, Rover, Satellite, Blocksworld, Depots, Gripper,
and Hiking. In the rest of the paper we give a short description of these
benchmarks. All are totally ordered.
| [
{
"version": "v1",
"created": "Tue, 9 Mar 2021 15:11:51 GMT"
}
] | 1,615,334,400,000 | [
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
]
] |
2103.05564 | Marco Pegoraro | Marco Pegoraro, Merih Seran Uysal, Wil M.P. van der Aalst | PROVED: A Tool for Graph Representation and Analysis of Uncertain Event
Data | 11 pages, 6 figures, 1 table, 16 references | Petri Nets (2021) 476-486 | 10.1007/978-3-030-76983-3_24 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The discipline of process mining aims to study processes in a data-driven
manner by analyzing historical process executions, often employing Petri nets.
Event data, extracted from information systems (e.g. SAP), serve as the
starting point for process mining. Recently, novel types of event data have
gathered interest among the process mining community, including uncertain event
data. Uncertain events, process traces and logs contain attributes that are
characterized by quantified imprecisions, e.g., a set of possible attribute
values. The PROVED tool helps to explore, navigate and analyze such uncertain
event data by abstracting the uncertain information using behavior graphs and
nets, which have Petri nets semantics. Based on these constructs, the tool
enables discovery and conformance checking.
| [
{
"version": "v1",
"created": "Tue, 9 Mar 2021 17:11:54 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Apr 2022 13:34:00 GMT"
},
{
"version": "v3",
"created": "Fri, 8 Apr 2022 09:59:26 GMT"
}
] | 1,649,635,200,000 | [
[
"Pegoraro",
"Marco",
""
],
[
"Uysal",
"Merih Seran",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] |
2103.05847 | Yongming He | Yongming He, Guohua Wu, Yingwu Chen and Witold Pedrycz | A Two-stage Framework and Reinforcement Learning-based Optimization
Algorithms for Complex Scheduling Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There hardly exists a general solver that is efficient for scheduling
problems due to their diversity and complexity. In this study, we develop a
two-stage framework, in which reinforcement learning (RL) and traditional
operations research (OR) algorithms are combined together to efficiently deal
with complex scheduling problems. The scheduling problem is solved in two
stages, including a finite Markov decision process (MDP) and a mixed-integer
programming process, respectively. This offers a novel and general paradigm
that combines RL with OR approaches to solving scheduling problems, which
leverages the respective strengths of RL and OR: The MDP narrows down the
search space of the original problem through an RL method, while the
mixed-integer programming process is settled by an OR algorithm. These two
stages are performed iteratively and interactively until the termination
criterion has been met. Under this idea, two implementation versions of the
combination methods of RL and OR are put forward. The agile Earth observation
satellite scheduling problem is selected as an example to demonstrate the
effectiveness of the proposed scheduling framework and methods. The convergence
and generalization capability of the methods are verified by the performance of
training scenarios, while the efficiency and accuracy are tested in 50
untrained scenarios. The results show that the proposed algorithms could stably
and efficiently obtain satisfactory scheduling schemes for agile Earth
observation satellite scheduling problems. In addition, it can be found that
RL-based optimization algorithms have stronger scalability than non-learning
algorithms. This work reveals the advantage of combining reinforcement learning
methods with heuristic methods or mathematical programming methods for solving
complex combinatorial optimization problems.
| [
{
"version": "v1",
"created": "Wed, 10 Mar 2021 03:16:12 GMT"
}
] | 1,615,420,800,000 | [
[
"He",
"Yongming",
""
],
[
"Wu",
"Guohua",
""
],
[
"Chen",
"Yingwu",
""
],
[
"Pedrycz",
"Witold",
""
]
] |
2103.06371 | Himanshu Sahni | Himanshu Sahni and Charles Isbell | Hard Attention Control By Mutual Information Maximization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Biological agents have adopted the principle of attention to limit the rate
of incoming information from the environment. One question that arises is if an
artificial agent has access to only a limited view of its surroundings, how can
it control its attention to effectively solve tasks? We propose an approach for
learning how to control a hard attention window by maximizing the mutual
information between the environment state and the attention location at each
step. The agent employs an internal world model to make predictions about its
state and focuses attention towards where the predictions may be wrong.
Attention is trained jointly with a dynamic memory architecture that stores
partial observations and keeps track of the unobserved state. We demonstrate
that our approach is effective in predicting the full state from a sequence of
partial observations. We also show that the agent's internal representation of
the surroundings, a live mental map, can be used for control in two partially
observable reinforcement learning tasks. Videos of the trained agent can be
found at https://sites.google.com/view/hard-attention-control.
| [
{
"version": "v1",
"created": "Wed, 10 Mar 2021 22:38:28 GMT"
}
] | 1,615,507,200,000 | [
[
"Sahni",
"Himanshu",
""
],
[
"Isbell",
"Charles",
""
]
] |
2103.06602 | Alexandros Nikou PhD | Alexandros Nikou, Anusha Mujumdar, Marin Orlic, Aneta Vulgarakis
Feljan | Symbolic Reinforcement Learning for Safe RAN Control | The paper has been accepted to be presented in 20th International
Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), May 3-7,
London, UK (demo track) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL)
architecture for safe control in Radio Access Network (RAN) applications. In
our automated tool, a user can select a high-level safety specifications
expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a
given cellular network with aim of optimizing network performance, as measured
through certain Key Performance Indicators (KPIs). In the proposed
architecture, network safety shielding is ensured through model-checking
techniques over combined discrete system models (automata) that are abstracted
through reinforcement learning. We demonstrate the user interface (UI) helping
the user set intent specifications to the architecture and inspect the
difference in allowed and blocked actions.
| [
{
"version": "v1",
"created": "Thu, 11 Mar 2021 10:56:49 GMT"
}
] | 1,615,507,200,000 | [
[
"Nikou",
"Alexandros",
""
],
[
"Mujumdar",
"Anusha",
""
],
[
"Orlic",
"Marin",
""
],
[
"Feljan",
"Aneta Vulgarakis",
""
]
] |
2103.06854 | Laura Giordano | Laura Giordano, Valentina Gliozzi, Daniele Theseider Dupr\'e | A conditional, a fuzzy and a probabilistic interpretation of
self-organising maps | 31 pages, 1 figure. arXiv admin note: text overlap with
arXiv:2008.13278 | Journal of Logic and Computation, 2022 | 10.1093/logcom/exab082 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we establish a link between fuzzy and preferential semantics
for description logics and Self-Organising Maps, which have been proposed as
possible candidates to explain the psychological mechanisms underlying category
generalisation. In particular, we show that the input/output behavior of a
Self-Organising Map after training can be described by a fuzzy description
logic interpretation as well as by a preferential interpretation, based on a
concept-wise multipreference semantics, which takes into account preferences
with respect to different concepts and has been recently proposed for ranked
and for weighted defeasible description logics. Properties of the network can
be proven by model checking on the fuzzy or on the preferential interpretation.
Starting from the fuzzy interpretation, we also provide a probabilistic account
for this neural network model.
| [
{
"version": "v1",
"created": "Thu, 11 Mar 2021 18:31:00 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Nov 2021 14:43:54 GMT"
}
] | 1,644,192,000,000 | [
[
"Giordano",
"Laura",
""
],
[
"Gliozzi",
"Valentina",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
] |
2103.06908 | Ivana Dusparic | Ivana Dusparic, Nicolas Cardozo | Adaptation to Unknown Situations as the Holy Grail of Learning-Based
Self-Adaptive Systems: Research Directions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Self-adaptive systems continuously adapt to changes in their execution
environment. Capturing all possible changes to define suitable behaviour
beforehand is unfeasible, or even impossible in the case of unknown changes,
hence human intervention may be required. We argue that adapting to unknown
situations is the ultimate challenge for self-adaptive systems. Learning-based
approaches are used to learn the suitable behaviour to exhibit in the case of
unknown situations, to minimize or fully remove human intervention. While such
approaches can, to a certain extent, generalize existing adaptations to new
situations, there is a number of breakthroughs that need to be achieved before
systems can adapt to general unknown and unforeseen situations. We posit the
research directions that need to be explored to achieve unanticipated
adaptation from the perspective of learning-based self-adaptive systems. At
minimum, systems need to define internal representations of previously unseen
situations on-the-fly, extrapolate the relationship to the previously
encountered situations to evolve existing adaptations, and reason about the
feasibility of achieving their intrinsic goals in the new set of conditions. We
close discussing whether, even when we can, we should indeed build systems that
define their own behaviour and adapt their goals, without involving a human
supervisor.
| [
{
"version": "v1",
"created": "Thu, 11 Mar 2021 19:07:02 GMT"
}
] | 1,615,766,400,000 | [
[
"Dusparic",
"Ivana",
""
],
[
"Cardozo",
"Nicolas",
""
]
] |
2103.07494 | Soumi Chattopadhyay | Soumi Chattopadhyay, Chandranath Adak, Ranjana Roy Chowdhury | FES: A Fast Efficient Scalable QoS Prediction Framework | 13 pages, 15 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Quality-of-Service prediction of web service is an integral part of services
computing due to its diverse applications in the various facets of a service
life cycle, such as service composition, service selection, service
recommendation. One of the primary objectives of designing a QoS prediction
algorithm is to achieve satisfactory prediction accuracy. However, accuracy is
not the only criteria to meet while developing a QoS prediction algorithm. The
algorithm has to be faster in terms of prediction time so that it can be
integrated into a real-time recommendation or composition system. The other
important factor to consider while designing the prediction algorithm is
scalability to ensure that the prediction algorithm can tackle large-scale
datasets. The existing algorithms on QoS prediction often compromise on one
goal while ensuring the others. In this paper, we propose a semi-offline QoS
prediction model to achieve three important goals simultaneously: higher
accuracy, faster prediction time, scalability. Here, we aim to predict the QoS
value of service that varies across users. Our framework consists of
multi-phase prediction algorithms: preprocessing-phase prediction, online
prediction, and prediction using the pre-trained model. In the preprocessing
phase, we first apply multi-level clustering on the dataset to obtain
correlated users and services. We then preprocess the clusters using
collaborative filtering to remove the sparsity of the given QoS invocation log
matrix. Finally, we create a two-staged, semi-offline regression model using
neural networks to predict the QoS value of service to be invoked by a user in
real-time. Our experimental results on four publicly available WS-DREAM
datasets show the efficiency in terms of accuracy, scalability, fast
responsiveness of our framework as compared to the state-of-the-art methods.
| [
{
"version": "v1",
"created": "Fri, 12 Mar 2021 19:28:17 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Mar 2021 04:11:46 GMT"
}
] | 1,615,939,200,000 | [
[
"Chattopadhyay",
"Soumi",
""
],
[
"Adak",
"Chandranath",
""
],
[
"Chowdhury",
"Ranjana Roy",
""
]
] |
2103.07512 | Francisco Baeta | Francisco Baeta, Jo\~ao Correia, Tiago Martins and Penousal Machado | TensorGP -- Genetic Programming Engine in TensorFlow | To be published in the 24th International Conference on the
Applications of Evolutionary Computation proceedings. 16 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we resort to the TensorFlow framework to investigate the
benefits of applying data vectorization and fitness caching methods to domain
evaluation in Genetic Programming. For this purpose, an independent engine was
developed, TensorGP, along with a testing suite to extract comparative timing
results across different architectures and amongst both iterative and
vectorized approaches. Our performance benchmarks demonstrate that by
exploiting the TensorFlow eager execution model, performance gains of up to two
orders of magnitude can be achieved on a parallel approach running on dedicated
hardware when compared to a standard iterative approach.
| [
{
"version": "v1",
"created": "Fri, 12 Mar 2021 20:19:37 GMT"
}
] | 1,615,852,800,000 | [
[
"Baeta",
"Francisco",
""
],
[
"Correia",
"João",
""
],
[
"Martins",
"Tiago",
""
],
[
"Machado",
"Penousal",
""
]
] |
2103.07789 | Yuval Shahar | Avner Hatsek and Yuval Shahar | A Methodology for Bi-Directional Knowledge-Based Assessment of
Compliance to Continuous Application of Clinical Guidelines | 25 pages; 13 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Clinicians often do not sufficiently adhere to evidence-based clinical
guidelines in a manner sensitive to the context of each patient. It is
important to detect such deviations, typically including redundant or missing
actions, even when the detection is performed retrospectively, so as to inform
both the attending clinician and policy makers. Furthermore, it would be
beneficial to detect such deviations in a manner proportional to the level of
the deviation, and not to simply use arbitrary cut-off values. In this study,
we introduce a new approach for automated guideline-based quality assessment of
the care process, the bidirectional knowledge-based assessment of compliance
(BiKBAC) method. Our BiKBAC methodology assesses the degree of compliance when
applying clinical guidelines, with respect to multiple different aspects of the
guideline (e.g., the guideline's process and outcome objectives). The
assessment is performed through a highly detailed, automated quality-assessment
retrospective analysis, which compares a formal representation of the guideline
and of its process and outcome intentions (we use the Asbru language for that
purpose) with the longitudinal electronic medical record of its continuous
application over a significant time period, using both a top-down and a
bottom-up approach, which we explain in detail. Partial matches of the data to
the process and to the outcome objectives are resolved using fuzzy temporal
logic. We also introduce the DiscovErr system, which implements the BiKBAC
approach, and present its detailed architecture. The DiscovErr system was
evaluated in a separate study in the type 2 diabetes management domain, by
comparing its performance to a panel of three clinicians, with highly
encouraging results with respect to the completeness and correctness of its
comments.
| [
{
"version": "v1",
"created": "Sat, 13 Mar 2021 20:43:45 GMT"
}
] | 1,615,852,800,000 | [
[
"Hatsek",
"Avner",
""
],
[
"Shahar",
"Yuval",
""
]
] |
2103.07877 | Xinliang Wu | Xinliang Wu and Mengying Jiang and Guizhong Liu | R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Heterogeneous graph is a kind of data structure widely existing in real life.
Nowadays, the research of graph neural network on heterogeneous graph has
become more and more popular. The existing heterogeneous graph neural network
algorithms mainly have two ideas, one is based on meta-path and the other is
not. The idea based on meta-path often requires a lot of manual preprocessing,
at the same time it is difficult to extend to large scale graphs. In this
paper, we proposed the general heterogeneous message passing paradigm and
designed R-GSN that does not need meta-path, which is much improved compared to
the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves
the state-of-the-art performance on the ogbn-mag large scale heterogeneous
graph dataset.
| [
{
"version": "v1",
"created": "Sun, 14 Mar 2021 09:25:36 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Jun 2021 17:40:24 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Jun 2021 09:36:05 GMT"
}
] | 1,624,838,400,000 | [
[
"Wu",
"Xinliang",
""
],
[
"Jiang",
"Mengying",
""
],
[
"Liu",
"Guizhong",
""
]
] |
2103.07903 | Mustafa Gunel | Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural,
Ferhat Yurdakul, Melih Dal, Nazim Kemal Ure | Investigating Value of Curriculum Reinforcement Learning in Autonomous
Driving Under Diverse Road and Weather Conditions | 6 pages, IV2021 Workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Applications of reinforcement learning (RL) are popular in autonomous driving
tasks. That being said, tuning the performance of an RL agent and guaranteeing
the generalization performance across variety of different driving scenarios is
still largely an open problem. In particular, getting good performance on
complex road and weather conditions require exhaustive tuning and computation
time. Curriculum RL, which focuses on solving simpler automation tasks in order
to transfer knowledge to complex tasks, is attracting attention in RL
community. The main contribution of this paper is a systematic study for
investigating the value of curriculum reinforcement learning in autonomous
driving applications. For this purpose, we setup several different driving
scenarios in a realistic driving simulator, with varying road complexity and
weather conditions. Next, we train and evaluate performance of RL agents on
different sequences of task combinations and curricula. Results show that
curriculum RL can yield significant gains in complex driving tasks, both in
terms of driving performance and sample complexity. Results also demonstrate
that different curricula might enable different benefits, which hints future
research directions for automated curriculum training.
| [
{
"version": "v1",
"created": "Sun, 14 Mar 2021 12:05:05 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Apr 2021 11:59:48 GMT"
},
{
"version": "v3",
"created": "Mon, 2 Aug 2021 07:49:27 GMT"
}
] | 1,627,948,800,000 | [
[
"Ozturk",
"Anil",
""
],
[
"Gunel",
"Mustafa Burak",
""
],
[
"Dagdanov",
"Resul",
""
],
[
"Vural",
"Mirac Ekim",
""
],
[
"Yurdakul",
"Ferhat",
""
],
[
"Dal",
"Melih",
""
],
[
"Ure",
"Nazim Kemal",
""
]
] |
2103.08155 | Kenny Chour | Kenny Chour, Sivakumar Rathinam, Ramamoorthi Ravi | S$^*$: A Heuristic Information-Based Approximation Framework for
Multi-Goal Path Finding | In Proceedings of the 31st International Conference on Automated
Planning and Scheduling (ICAPS 2021) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We combine ideas from uni-directional and bi-directional heuristic search,
and approximation algorithms for the Traveling Salesman Problem, to develop a
novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a
2-approximation guarantee. MGPF aims to find a least-cost path from an origin
to a destination such that each node in a given set of goals is visited at
least once along the path. We present numerical results to illustrate the
advantages of our framework over conventional alternates in terms of the number
of expanded nodes and run time.
| [
{
"version": "v1",
"created": "Mon, 15 Mar 2021 06:27:37 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Mar 2021 03:12:06 GMT"
}
] | 1,615,939,200,000 | [
[
"Chour",
"Kenny",
""
],
[
"Rathinam",
"Sivakumar",
""
],
[
"Ravi",
"Ramamoorthi",
""
]
] |
2103.08183 | Tadahiro Taniguchi | Tadahiro Taniguchi, Hiroshi Yamakawa, Takayuki Nagai, Kenji Doya,
Masamichi Sakagami, Masahiro Suzuki, Tomoaki Nakamura, Akira Taniguchi | A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive
Architectures for Developmental Robots | 62 pages, 9 figures, submitted to Neural Networks | Neural Networks, 2022, Volume 150, 293-312 | 10.1016/j.neunet.2022.02.026 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Building a humanlike integrative artificial cognitive system, that is, an
artificial general intelligence (AGI), is the holy grail of the artificial
intelligence (AI) field. Furthermore, a computational model that enables an
artificial system to achieve cognitive development will be an excellent
reference for brain and cognitive science. This paper describes an approach to
develop a cognitive architecture by integrating elemental cognitive modules to
enable the training of the modules as a whole. This approach is based on two
ideas: (1) brain-inspired AI, learning human brain architecture to build
human-level intelligence, and (2) a probabilistic generative model(PGM)-based
cognitive system to develop a cognitive system for developmental robots by
integrating PGMs. The development framework is called a whole brain PGM
(WB-PGM), which differs fundamentally from existing cognitive architectures in
that it can learn continuously through a system based on sensory-motor
information. In this study, we describe the rationale of WB-PGM, the current
status of PGM-based elemental cognitive modules, their relationship with the
human brain, the approach to the integration of the cognitive modules, and
future challenges. Our findings can serve as a reference for brain studies. As
PGMs describe explicit informational relationships between variables, this
description provides interpretable guidance from computational sciences to
brain science. By providing such information, researchers in neuroscience can
provide feedback to researchers in AI and robotics on what the current models
lack with reference to the brain. Further, it can facilitate collaboration
among researchers in neuro-cognitive sciences as well as AI and robotics.
| [
{
"version": "v1",
"created": "Mon, 15 Mar 2021 07:42:04 GMT"
},
{
"version": "v2",
"created": "Sun, 9 Jan 2022 23:38:27 GMT"
}
] | 1,674,000,000,000 | [
[
"Taniguchi",
"Tadahiro",
""
],
[
"Yamakawa",
"Hiroshi",
""
],
[
"Nagai",
"Takayuki",
""
],
[
"Doya",
"Kenji",
""
],
[
"Sakagami",
"Masamichi",
""
],
[
"Suzuki",
"Masahiro",
""
],
[
"Nakamura",
"Tomoaki",
""
],
[
"Taniguchi",
"Akira",
""
]
] |
2103.08228 | Zhihao Ma | Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li,
Wulong Liu, Jianye Hao | Learning Symbolic Rules for Interpretable Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent progress in deep reinforcement learning (DRL) can be largely
attributed to the use of neural networks. However, this black-box approach
fails to explain the learned policy in a human understandable way. To address
this challenge and improve the transparency, we propose a Neural Symbolic
Reinforcement Learning framework by introducing symbolic logic into DRL. This
framework features a fertilization of reasoning and learning modules, enabling
end-to-end learning with prior symbolic knowledge. Moreover, interpretability
is achieved by extracting the logical rules learned by the reasoning module in
a symbolic rule space. The experimental results show that our framework has
better interpretability, along with competing performance in comparison to
state-of-the-art approaches.
| [
{
"version": "v1",
"created": "Mon, 15 Mar 2021 09:26:00 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Mar 2021 05:32:42 GMT"
}
] | 1,615,939,200,000 | [
[
"Ma",
"Zhihao",
""
],
[
"Zhuang",
"Yuzheng",
""
],
[
"Weng",
"Paul",
""
],
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Li",
"Dong",
""
],
[
"Liu",
"Wulong",
""
],
[
"Hao",
"Jianye",
""
]
] |
2103.08249 | Zhaoyang Hai | Zhaoyang Hai, Xiabi Liu | Evolving parametrized Loss for Image Classification Learning on Small
Datasets | This article has been abandoned for publication, and the researcher
will no longer participate in related research | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a meta-learning approach to evolving a parametrized loss
function, which is called Meta-Loss Network (MLN), for training the image
classification learning on small datasets. In our approach, the MLN is embedded
in the framework of classification learning as a differentiable objective
function. The MLN is evolved with the Evolutionary Strategy algorithm (ES) to
an optimized loss function, such that a classifier, which optimized to minimize
this loss, will achieve a good generalization effect. A classifier learns on a
small training dataset to minimize MLN with Stochastic Gradient Descent (SGD),
and then the MLN is evolved with the precision of the small-dataset-updated
classifier on a large validation dataset. In order to evaluate our approach,
the MLN is trained with a large number of small sample learning tasks sampled
from FashionMNIST and tested on validation tasks sampled from FashionMNIST and
CIFAR10. Experiment results demonstrate that the MLN effectively improved
generalization compared to classical cross-entropy error and mean squared
error.
| [
{
"version": "v1",
"created": "Mon, 15 Mar 2021 10:00:18 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Oct 2023 07:27:01 GMT"
}
] | 1,698,710,400,000 | [
[
"Hai",
"Zhaoyang",
""
],
[
"Liu",
"Xiabi",
""
]
] |
2103.08391 | Blai Bonet | Ivan D. Rodriguez and Blai Bonet and Sebastian Sardina and Hector
Geffner | Flexible FOND Planning with Explicit Fairness Assumptions | Extended version of ICAPS-21 paper | Journal of Artificial Intelligence Research 2022 | 10.1613/jair.1.13599 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We consider the problem of reaching a propositional goal condition in
fully-observable non-deterministic (FOND) planning under a general class of
fairness assumptions that are given explicitly. The fairness assumptions are of
the form A/B and say that state trajectories that contain infinite occurrences
of an action a from A in a state s and finite occurrence of actions from B,
must also contain infinite occurrences of action a in s followed by each one of
its possible outcomes. The infinite trajectories that violate this condition
are deemed as unfair, and the solutions are policies for which all the fair
trajectories reach a goal state. We show that strong and strong-cyclic FOND
planning, as well as QNP planning, a planning model introduced recently for
generalized planning, are all special cases of FOND planning with fairness
assumptions of this form which can also be combined. FOND+ planning, as this
form of planning is called, combines the syntax of FOND planning with some of
the versatility of LTL for expressing fairness constraints. A new planner is
implemented by reducing FOND+ planning to answer set programs, and the
performance of the planner is evaluated in comparison with FOND and QNP
planners, and LTL synthesis tools.
| [
{
"version": "v1",
"created": "Mon, 15 Mar 2021 13:57:07 GMT"
}
] | 1,656,460,800,000 | [
[
"Rodriguez",
"Ivan D.",
""
],
[
"Bonet",
"Blai",
""
],
[
"Sardina",
"Sebastian",
""
],
[
"Geffner",
"Hector",
""
]
] |
2103.08673 | Mingyue Zhang | Mingyue Zhang | System Component-Level Self-Adaptations for Security via Bayesian Games | Published in International Conference on Software Engineering,
Companion Volume | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Security attacks present unique challenges to self-adaptive system design due
to the adversarial nature of the environment. However, modeling the system as a
single player, as done in prior works in security domain, is insufficient for
the system under partial compromise and for the design of fine-grained
defensive strategies where the rest of the system with autonomy can cooperate
to mitigate the impact of attacks. To deal with such issues, we propose a new
self-adaptive framework incorporating Bayesian game and model the defender
(i.e., the system) at the granularity of components in system architecture. The
system architecture model is translated into a Bayesian multi-player game,
where each component is modeled as an independent player while security attacks
are encoded as variant types for the components. The defensive strategy for the
system is dynamically computed by solving the pure equilibrium to achieve the
best possible system utility, improving the resiliency of the system against
security attacks.
| [
{
"version": "v1",
"created": "Fri, 12 Mar 2021 16:20:59 GMT"
}
] | 1,615,939,200,000 | [
[
"Zhang",
"Mingyue",
""
]
] |
2103.09031 | Yuval Shahar | Avner Hatsek, Irit Hochberg, Deeb Daoud Naccache, Aya Biderman, and
Yuval Shahar | Evaluation of a Bi-Directional Methodology for Automated Assessment of
Compliance to Continuous Application of Clinical Guidelines, in the Type 2
Diabetes-Management Domain | 25 pages; 4 figures, 6 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We evaluated the DiscovErr system, in which we had previously implemented a
new methodology for assessment of compliance to continuous application of
evidence-based clinical guidelines, based on a bidirectional search from the
guideline objectives to the patient's longitudinal data, and vice versa. We
compared the system comments on 1584 transactions regarding the management,
over a mean of 5.23 years, of 10 randomly selected Type 2 diabetes patients, to
those of two diabetes experts and a senior family practitioner. After providing
their own comments, the experts assessed both the correctness (precision) and
the importance of each of the DiscovErr system comments. The completeness
(recall or coverage) of the system was computed by comparing its comments to
those made by the experts. The system made 279 comments. The experts made 181
unique comments. The completeness of the system was 91% compared to comments
made by at least two experts, and 98% when compared to comments made by all
three. 172 comments were evaluated by the experts for correctness and
importance: All 114 medication-related comments, and a random 35% of the 165
monitoring-related comments. The system's correctness was 81% compared to
comments judged as correct by both diabetes experts, and 91% compared to
comments judged as correct by a diabetes expert and at least as partially
correct by the other. 89% of the comments were judged as important by both
diabetes experts, 8% were judged as important by one expert, 3% were judged as
less important by both experts. The completeness scores of the three experts
(compared to the comments of all experts plus the validated system comments)
were 75%, 60%, and 55%; the experts' correctness scores (compared to their
majority) were respectively 99%, 91%, and 88%. Conclusion: Systems such as
DiscovErr can assess the quality of continuous guideline-based care.
| [
{
"version": "v1",
"created": "Tue, 16 Mar 2021 13:02:07 GMT"
}
] | 1,615,939,200,000 | [
[
"Hatsek",
"Avner",
""
],
[
"Hochberg",
"Irit",
""
],
[
"Naccache",
"Deeb Daoud",
""
],
[
"Biderman",
"Aya",
""
],
[
"Shahar",
"Yuval",
""
]
] |
2103.09173 | Chang Liu | Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee
Seng Chan, Qiang Yang | Ternary Hashing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a novel ternary hash encoding for learning to hash
methods, which provides a principled more efficient coding scheme with
performances better than those of the state-of-the-art binary hashing
counterparts. Two kinds of axiomatic ternary logic, Kleene logic and
{\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance
(THD) for both the learning/encoding and testing/querying phases. Our work
demonstrates that, with an efficient implementation of ternary logic on
standard binary machines, the proposed ternary hashing is compared favorably to
the binary hashing methods with consistent improvements of retrieval mean
average precision (mAP) ranging from 1\% to 5.9\% as shown in CIFAR10, NUS-WIDE
and ImageNet100 datasets.
| [
{
"version": "v1",
"created": "Tue, 16 Mar 2021 16:20:54 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Mar 2021 12:39:32 GMT"
}
] | 1,616,371,200,000 | [
[
"Liu",
"Chang",
""
],
[
"Fan",
"Lixin",
""
],
[
"Ng",
"Kam Woh",
""
],
[
"Jin",
"Yilun",
""
],
[
"Ju",
"Ce",
""
],
[
"Zhang",
"Tianyu",
""
],
[
"Chan",
"Chee Seng",
""
],
[
"Yang",
"Qiang",
""
]
] |
2103.09627 | Keisuke Fujii | Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro, Keisuke Fujii | Evaluation of soccer team defense based on prediction models of ball
recovery and being attacked: A pilot study | 15 pages, 5 figures | PLoS One, 17(1) e0263051, 2022 | 10.1371/journal.pone.0263051 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | With the development of measurement technology, data on the movements of
actual games in various sports can be obtained and used for planning and
evaluating the tactics and strategy. Defense in team sports is generally
difficult to be evaluated because of the lack of statistical data. Conventional
evaluation methods based on predictions of scores are considered unreliable
because they predict rare events throughout the game. Besides, it is difficult
to evaluate various plays leading up to a score. In this study, we propose a
method to evaluate team defense from a comprehensive perspective related to
team performance by predicting ball recovery and being attacked, which occur
more frequently than goals, using player actions and positional data of all
players and the ball. Using data from 45 soccer matches, we examined the
relationship between the proposed index and team performance in actual matches
and throughout a season. Results show that the proposed classifiers predicted
the true events (mean F1 score $>$ 0.483) better than the existing classifiers
which were based on rare events or goals (mean F1 score $<$ 0.201). Also, the
proposed index had a moderate correlation with the long-term outcomes of the
season ($r =$ 0.397). These results suggest that the proposed index might be a
more reliable indicator rather than winning or losing with the inclusion of
accidental factors.
| [
{
"version": "v1",
"created": "Wed, 17 Mar 2021 13:15:41 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Mar 2021 00:42:56 GMT"
},
{
"version": "v3",
"created": "Sat, 7 May 2022 06:27:09 GMT"
}
] | 1,652,140,800,000 | [
[
"Toda",
"Kosuke",
""
],
[
"Teranishi",
"Masakiyo",
""
],
[
"Kushiro",
"Keisuke",
""
],
[
"Fujii",
"Keisuke",
""
]
] |
2103.09990 | Zahra Zahedi | Zahra Zahedi and Subbarao Kambhampati | Human-AI Symbiosis: A Survey of Current Approaches | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we aim at providing a comprehensive outline of the different
threads of work in human-AI collaboration. By highlighting various aspects of
works on the human-AI team such as the flow of complementing, task horizon,
model representation, knowledge level, and teaming goal, we make a taxonomy of
recent works according to these dimensions. We hope that the survey will
provide a more clear connection between the works in the human-AI team and
guidance to new researchers in this area.
| [
{
"version": "v1",
"created": "Thu, 18 Mar 2021 02:39:28 GMT"
}
] | 1,616,112,000,000 | [
[
"Zahedi",
"Zahra",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2103.10213 | Jianhua He | Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas,
Shjian Lu, and C.V. Jawahar | ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction | null | null | 10.1109/ICDAR.2019.00244 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Scanned receipts OCR and key information extraction (SROIE) represent the
processeses of recognizing text from scanned receipts and extracting key texts
from them and save the extracted tests to structured documents. SROIE plays
critical roles for many document analysis applications and holds great
commercial potentials, but very little research works and advances have been
published in this area. In recognition of the technical challenges, importance
and huge commercial potentials of SROIE, we organized the ICDAR 2019
competition on SROIE. In this competition, we set up three tasks, namely,
Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and
Key Information Extraction from Scanned Receipts (Task 3). A new dataset with
1000 whole scanned receipt images and annotations is created for the
competition. In this report we will presents the motivation, competition
datasets, task definition, evaluation protocol, submission statistics,
performance of submitted methods and results analysis.
| [
{
"version": "v1",
"created": "Thu, 18 Mar 2021 12:33:41 GMT"
}
] | 1,616,112,000,000 | [
[
"Huang",
"Zheng",
""
],
[
"Chen",
"Kai",
""
],
[
"He",
"Jianhua",
""
],
[
"Bai",
"Xiang",
""
],
[
"Karatzas",
"Dimosthenis",
""
],
[
"Lu",
"Shjian",
""
],
[
"Jawahar",
"C. V.",
""
]
] |
2103.10453 | Olivier Goudet Dr | Olivier Goudet and Jin-Kao Hao | A massively parallel evolutionary algorithm for the partial Latin square
extension problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The partial Latin square extension problem is to fill as many as possible
empty cells of a partially filled Latin square. This problem is a useful model
for a wide range of applications in diverse domains. This paper presents the
first massively parallel evolutionary algorithm algorithm for this
computationally challenging problem based on a transformation of the problem to
partial graph coloring. The algorithm features the following original elements.
Based on a very large population (with more than $10^4$ individuals) and modern
graphical processing units, the algorithm performs many local searches in
parallel to ensure an intensive exploitation of the search space. The algorithm
employs a dedicated crossover with a specific parent matching strategy to
create a large number of diversified and information-preserving offspring at
each generation. Extensive experiments on 1800 benchmark instances show a high
competitiveness of the algorithm compared to the current best performing
methods. Competitive results are also reported on the related Latin square
completion problem. Analyses are performed to shed lights on the roles of the
main algorithmic components. The code of the algorithm will be made publicly
available.
| [
{
"version": "v1",
"created": "Thu, 18 Mar 2021 18:09:50 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Sep 2021 16:25:53 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Jan 2022 17:31:08 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Feb 2022 10:50:02 GMT"
}
] | 1,644,537,600,000 | [
[
"Goudet",
"Olivier",
""
],
[
"Hao",
"Jin-Kao",
""
]
] |
2103.10507 | Alessandro Gianola | Paolo Felli and Alessandro Gianola and Marco Montali and Andrey Rivkin
and Sarah Winkler | CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
(Extended Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conformance checking is a key process mining task for comparing the expected
behavior captured in a process model and the actual behavior recorded in a log.
While this problem has been extensively studied for pure control-flow
processes, conformance checking with multi-perspective processes is still at
its infancy. In this paper, we attack this challenging problem by considering
processes that combine the data and control-flow dimensions. In particular, we
adopt data Petri nets (DPNs) as the underlying reference formalism, and show
how solid, well-established automated reasoning techniques can be effectively
employed for computing conformance metrics and data-aware alignments. We do so
by introducing the CoCoMoT (Computing Conformance Modulo Theories) framework,
with a fourfold contribution. First, we show how SAT-based encodings studied in
the pure control-flow setting can be lifted to our data-aware case, using SMT
as the underlying formal and algorithmic framework. Second, we introduce a
novel preprocessing technique based on a notion of property-preserving
clustering, to speed up the computation of conformance checking outputs. Third,
we provide a proof-of-concept implementation that uses a state-of-the-art SMT
solver and report on preliminary experiments. Finally, we discuss how CoCoMoT
directly lends itself to a number of further tasks, like multi- and
anti-alignments, log analysis by clustering, and model repair.
| [
{
"version": "v1",
"created": "Thu, 18 Mar 2021 20:22:50 GMT"
},
{
"version": "v2",
"created": "Mon, 19 Apr 2021 12:26:50 GMT"
}
] | 1,618,876,800,000 | [
[
"Felli",
"Paolo",
""
],
[
"Gianola",
"Alessandro",
""
],
[
"Montali",
"Marco",
""
],
[
"Rivkin",
"Andrey",
""
],
[
"Winkler",
"Sarah",
""
]
] |
2103.10694 | Amb Mis | Sarika Jain and Archana Patel | Semantic Contextual Reasoning to Provide Human Behavior | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, the world has witnessed various primitives pertaining to the
complexity of human behavior. Identifying an event in the presence of
insufficient, incomplete, or tentative premises along with the constraints on
resources such as time, data and memory is a vital aspect of an intelligent
system. Data explosion presents one of the most challenging research issues for
intelligent systems; to optimally represent and store this heterogeneous and
voluminous data semantically to provide human behavior. There is a requirement
of intelligent but personalized human behavior subject to constraints on
resources and priority of the user. Knowledge, when represented in the form of
an ontology, procures an intelligent response to a query posed by users; but it
does not offer content in accordance with the user context. To this aim, we
propose a model to quantify the user context and provide semantic contextual
reasoning. A diagnostic belief algorithm (DBA) is also presented that
identifies a given event and also computes the confidence of the decision as a
function of available resources, premises, exceptions, and desired specificity.
We conduct an empirical study in the domain of day-to-day routine queries and
the experimental results show that the answer to queries and also its
confidence varies with user context.
| [
{
"version": "v1",
"created": "Fri, 19 Mar 2021 09:02:38 GMT"
}
] | 1,616,371,200,000 | [
[
"Jain",
"Sarika",
""
],
[
"Patel",
"Archana",
""
]
] |
2103.10844 | David Fernandez-Llorca | David Fern\'andez Llorca | From driving automation systems to autonomous vehicles: clarifying the
terminology | 6 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The terminological landscape is rather cluttered when referring to autonomous
driving or vehicles. A plethora of terms are used interchangeably, leading to
misuse and confusion. With its technological, social and legal progress, it is
increasingly imperative to establish a clear terminology that allows each
concept to be placed in its corresponding place.
| [
{
"version": "v1",
"created": "Fri, 19 Mar 2021 14:53:15 GMT"
}
] | 1,616,371,200,000 | [
[
"Llorca",
"David Fernández",
""
]
] |
2103.11218 | Amin Jalali | Amin Jalali | Evaluating Perceived Usefulness and Ease of Use of CMMN and DCR | null | null | 10.1007/978-3-030-79186-5_10 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Case Management has been gradually evolving to support Knowledge-intensive
business process management, which resulted in developing different modeling
languages, e.g., Declare, Dynamic Condition Response (DCR), and Case Management
Model and Notation (CMMN). A language will die if users do not accept and use
it in practice - similar to extinct human languages. Thus, it is important to
evaluate how users perceive languages to determine if there is a need for
improvement. Although some studies have investigated how the process designers
perceived Declare and DCR, there is a lack of research on how they perceive
CMMN. Therefore, this study investigates how the process designers perceive the
usefulness and ease of use of CMMN and DCR based on the Technology Acceptance
Model. DCR is included to enable comparing the study result with previous ones.
The study is performed by educating master level students with these languages
over eight weeks by giving feedback on their assignments to reduce perceptions
biases. The students' perceptions are collected through questionnaires before
and after sending feedback on their final practice in the exam. Thus, the
result shows how the perception of participants can change by receiving
feedback - despite being well trained. The reliability of responses is tested
using Cronbach's alpha, and the result indicates that both languages have an
acceptable level for both perceived usefulness and ease of use.
| [
{
"version": "v1",
"created": "Sat, 20 Mar 2021 17:57:19 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Mar 2021 19:41:14 GMT"
},
{
"version": "v3",
"created": "Mon, 3 May 2021 10:22:36 GMT"
}
] | 1,644,451,200,000 | [
[
"Jalali",
"Amin",
""
]
] |
2103.11345 | Vincent Thomas | Vincent Thomas, G\'er\'emy Hutin, Olivier Buffet | Monte Carlo Information-Oriented Planning | 9 pages, revised version of ECAI 2020 paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we discuss how to solve information-gathering problems
expressed as rho-POMDPs, an extension of Partially Observable Markov Decision
Processes (POMDPs) whose reward rho depends on the belief state. Point-based
approaches used for solving POMDPs have been extended to solving rho-POMDPs as
belief MDPs when its reward rho is convex in B or when it is
Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to
propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient
on-line planner which can be used for any rho function. Adaptations are
required due to the belief-dependent rewards to (i) propagate more than one
state at a time, and (ii) prevent biases in value estimates. An asymptotic
convergence proof to epsilon-optimal values is given when rho is continuous.
Experiments are conducted to analyze the algorithms at hand and show that they
outperform myopic approaches.
| [
{
"version": "v1",
"created": "Sun, 21 Mar 2021 09:09:27 GMT"
}
] | 1,616,457,600,000 | [
[
"Thomas",
"Vincent",
""
],
[
"Hutin",
"Gérémy",
""
],
[
"Buffet",
"Olivier",
""
]
] |
2103.11692 | Ramon Fraga Pereira | Ramon Fraga Pereira, Francesco Fuggitti, and Giuseppe De Giacomo | Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic
Domain Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Goal Recognition is the task of discerning the correct intended goal that an
agent aims to achieve, given a set of possible goals, a domain model, and a
sequence of observations as a sample of the plan being executed in the
environment. Existing approaches assume that the possible goals are formalized
as a conjunction in deterministic settings. In this paper, we develop a novel
approach that is capable of recognizing temporally extended goals in Fully
Observable Non-Deterministic (FOND) planning domain models, focusing on goals
on finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past
Linear Temporal Logic (PLTLf). We empirically evaluate our goal recognition
approach using different LTLf and PLTLf goals over six common FOND planning
domain models, and show that our approach is accurate to recognize temporally
extended goals at several levels of observability.
| [
{
"version": "v1",
"created": "Mon, 22 Mar 2021 09:46:03 GMT"
}
] | 1,616,457,600,000 | [
[
"Pereira",
"Ramon Fraga",
""
],
[
"Fuggitti",
"Francesco",
""
],
[
"De Giacomo",
"Giuseppe",
""
]
] |
2103.11961 | Noah Klarmann | Noah Klarmann | Artificial Intelligence Narratives: An Objective Perspective on Current
Developments | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This work provides a starting point for researchers interested in gaining a
deeper understanding of the big picture of artificial intelligence (AI). To
this end, a narrative is conveyed that allows the reader to develop an
objective view on current developments that is free from false promises that
dominate public communication. An essential takeaway for the reader is that AI
must be understood as an umbrella term encompassing a plethora of different
methods, schools of thought, and their respective historical movements.
Consequently, a bottom-up strategy is pursued in which the field of AI is
introduced by presenting various aspects that are characteristic of the
subject. This paper is structured in three parts: (i) Discussion of current
trends revealing false public narratives, (ii) an introduction to the history
of AI focusing on recurring patterns and main characteristics, and (iii) a
critical discussion on the limitations of current methods in the context of the
potential emergence of a strong(er) AI. It should be noted that this work does
not cover any of these aspects holistically; rather, the content addressed is a
selection made by the author and subject to a didactic strategy.
| [
{
"version": "v1",
"created": "Thu, 18 Mar 2021 17:33:00 GMT"
}
] | 1,616,457,600,000 | [
[
"Klarmann",
"Noah",
""
]
] |
2103.12701 | Zhaoxing Bu | Zhaoxing Bu and Richard E. Korf | A*+BFHS: A Hybrid Heuristic Search Algorithm | 8 pages, 5 figures, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new algorithm A*+BFHS for solving problems with unit-cost
operators where A* and IDA* fail due to memory limitations and/or the existence
of many distinct paths between the same pair of nodes. A*+BFHS is based on A*
and breadth-first heuristic search (BFHS). A*+BFHS combines advantages from
both algorithms, namely A*'s node ordering, BFHS's memory savings, and both
algorithms' duplicate detection. On easy problems, A*+BFHS behaves the same as
A*. On hard problems, it is slower than A* but saves a large amount of memory.
Compared to BFIDA*, A*+BFHS reduces the search time and/or memory requirement
by several times on a variety of planning domains.
| [
{
"version": "v1",
"created": "Tue, 23 Mar 2021 17:22:03 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Dec 2021 09:16:57 GMT"
}
] | 1,639,699,200,000 | [
[
"Bu",
"Zhaoxing",
""
],
[
"Korf",
"Richard E.",
""
]
] |
2103.12854 | Jo\v{z}e Ro\v{z}anec | Jo\v{z}e M. Ro\v{z}anec, Jinzhi Lu, Jan Rupnik, Maja \v{S}krjanc,
Dunja Mladeni\'c, Bla\v{z} Fortuna, Xiaochen Zheng, Dimitris Kiritsis | Actionable Cognitive Twins for Decision Making in Manufacturing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Actionable Cognitive Twins are the next generation Digital Twins enhanced
with cognitive capabilities through a knowledge graph and artificial
intelligence models that provide insights and decision-making options to the
users. The knowledge graph describes the domain-specific knowledge regarding
entities and interrelationships related to a manufacturing setting. It also
contains information on possible decision-making options that can assist
decision-makers, such as planners or logisticians. In this paper, we propose a
knowledge graph modeling approach to construct actionable cognitive twins for
capturing specific knowledge related to demand forecasting and production
planning in a manufacturing plant. The knowledge graph provides semantic
descriptions and contextualization of the production lines and processes,
including data identification and simulation or artificial intelligence
algorithms and forecasts used to support them. Such semantics provide ground
for inferencing, relating different knowledge types: creative, deductive,
definitional, and inductive. To develop the knowledge graph models for
describing the use case completely, systems thinking approach is proposed to
design and verify the ontology, develop a knowledge graph and build an
actionable cognitive twin. Finally, we evaluate our approach in two use cases
developed for a European original equipment manufacturer related to the
automotive industry as part of the European Horizon 2020 project FACTLOG.
| [
{
"version": "v1",
"created": "Tue, 23 Mar 2021 21:32:07 GMT"
}
] | 1,616,630,400,000 | [
[
"Rožanec",
"Jože M.",
""
],
[
"Lu",
"Jinzhi",
""
],
[
"Rupnik",
"Jan",
""
],
[
"Škrjanc",
"Maja",
""
],
[
"Mladenić",
"Dunja",
""
],
[
"Fortuna",
"Blaž",
""
],
[
"Zheng",
"Xiaochen",
""
],
[
"Kiritsis",
"Dimitris",
""
]
] |
2103.13496 | Jordan Meadows | Jordan Meadows, Andr\'e Freitas | Similarity-Based Equational Inference in Physics | null | null | 10.1103/PhysRevResearch.3.L042010 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automating the derivation of published results is a challenge, in part due to
the informal use of mathematics by physicists, compared to that of
mathematicians. Following demand, we describe a method for converting informal
hand-written derivations into datasets, and present an example dataset crafted
from a contemporary result in condensed matter. We define an equation
reconstruction task completed by rederiving an unknown intermediate equation
posed as a state, taken from three consecutive equational states within a
derivation. Derivation automation is achieved by applying string-based
CAS-reliant actions to states, which mimic mathematical operations and induce
state transitions. We implement a symbolic similarity-based heuristic search to
solve the equation reconstruction task as an early step towards multi-hop
equational inference in physics.
| [
{
"version": "v1",
"created": "Wed, 24 Mar 2021 21:36:39 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Jun 2021 02:09:15 GMT"
}
] | 1,635,465,600,000 | [
[
"Meadows",
"Jordan",
""
],
[
"Freitas",
"André",
""
]
] |
2103.13520 | Amit Sheth | Amit Sheth and Krishnaprasad Thirunarayan | The Duality of Data and Knowledge Across the Three Waves of AI | A version of this will appear as (cite as): IT Professional Magazine
(special section to commemorate the 75th Anniversary of IEEE Computer
Society), 23 (3) April-May 2021 | IT Professional, 23 (3), April-May 2021 | 10.1109/MITP.2021.3070985 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)
systems that focused only on data have been handicapped, and how knowledge has
been critical in developing smarter, intelligent, and more effective systems.
In fact, the vast progress in AI can be viewed in terms of the three waves of
AI as identified by DARPA. During the first wave, handcrafted knowledge has
been at the center-piece, while during the second wave, the data-driven
approaches supplanted knowledge. Now we see a strong role and resurgence of
knowledge fueling major breakthroughs in the third wave of AI underpinning
future intelligent systems as they attempt human-like decision making, and seek
to become trusted assistants and companions for humans. We find a wider
availability of knowledge created from diverse sources, using manual to
automated means both by repurposing as well as by extraction. Using knowledge
with statistical learning is becoming increasingly indispensable to help make
AI systems more transparent and auditable. We will draw a parallel with the
role of knowledge and experience in human intelligence based on cognitive
science, and discuss emerging neuro-symbolic or hybrid AI systems in which
knowledge is the critical enabler for combining capabilities of the
data-intensive statistical AI systems with those of symbolic AI systems,
resulting in more capable AI systems that support more human-like intelligence.
| [
{
"version": "v1",
"created": "Wed, 24 Mar 2021 23:07:47 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Apr 2021 19:57:57 GMT"
}
] | 1,618,531,200,000 | [
[
"Sheth",
"Amit",
""
],
[
"Thirunarayan",
"Krishnaprasad",
""
]
] |
2103.13901 | Pedro Zuidberg Dos Martires | Ivan Miosic, Pedro Zuidberg Dos Martires | Measure Theoretic Weighted Model Integration | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Weighted model counting (WMC) is a popular framework to perform probabilistic
inference with discrete random variables. Recently, WMC has been extended to
weighted model integration (WMI) in order to additionally handle continuous
variables. At their core, WMI problems consist of computing integrals and sums
over weighted logical formulas. From a theoretical standpoint, WMI has been
formulated by patching the sum over weighted formulas, which is already present
in WMC, with Riemann integration. A more principled approach to integration,
which is rooted in measure theory, is Lebesgue integration. Lebesgue
integration allows one to treat discrete and continuous variables on equal
footing in a principled fashion. We propose a theoretically sound measure
theoretic formulation of weighted model integration, which naturally reduces to
weighted model counting in the absence of continuous variables. Instead of
regarding weighted model integration as an extension of weighted model
counting, WMC emerges as a special case of WMI in our formulation.
| [
{
"version": "v1",
"created": "Thu, 25 Mar 2021 15:11:11 GMT"
}
] | 1,616,716,800,000 | [
[
"Miosic",
"Ivan",
""
],
[
"Martires",
"Pedro Zuidberg Dos",
""
]
] |
2103.14434 | Javier Segovia Aguas | Javier Segovia-Aguas, Sergio Jim\'enez and Anders Jonsson | Generalized Planning as Heuristic Search | Accepted at ICAPS-21 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although heuristic search is one of the most successful approaches to
classical planning, this planning paradigm does not apply straightforwardly to
Generalized Planning (GP). Planning as heuristic search traditionally addresses
the computation of sequential plans by searching in a grounded state-space. On
the other hand GP aims at computing algorithm-like plans, that can branch and
loop, and that generalize to a (possibly infinite) set of classical planning
instances. This paper adapts the planning as heuristic search paradigm to the
particularities of GP, and presents the first native heuristic search approach
to GP. First, the paper defines a novel GP solution space that is independent
of the number of planning instances in a GP problem, and the size of these
instances. Second, the paper defines different evaluation and heuristic
functions for guiding a combinatorial search in our GP solution space. Lastly
the paper defines a GP algorithm, called Best-First Generalized Planning
(BFGP), that implements a best-first search in the solution space guided by our
evaluation/heuristic functions.
| [
{
"version": "v1",
"created": "Fri, 26 Mar 2021 12:35:10 GMT"
}
] | 1,616,976,000,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
]
] |
2103.14930 | Kai Wang | Kai Wang, Yu Liu, Dan Lin, Quan Z. Sheng | Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models
for Low-Dimensional Knowledge Graph Embeddings | Accepted for publication at the Findings of EMNLP 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent knowledge graph embedding (KGE) models based on hyperbolic geometry
have shown great potential in a low-dimensional embedding space. However, the
necessity of hyperbolic space in KGE is still questionable, because the
calculation based on hyperbolic geometry is much more complicated than
Euclidean operations. In this paper, based on the state-of-the-art
hyperbolic-based model RotH, we develop two lightweight Euclidean-based models,
called RotL and Rot2L. The RotL model simplifies the hyperbolic operations
while keeping the flexible normalization effect. Utilizing a novel two-layer
stacked transformation and based on RotL, the Rot2L model obtains an improved
representation capability, yet costs fewer parameters and calculations than
RotH. The experiments on link prediction show that Rot2L achieves the
state-of-the-art performance on two widely-used datasets in low-dimensional
knowledge graph embeddings. Furthermore, RotL achieves similar performance as
RotH but only requires half of the training time.
| [
{
"version": "v1",
"created": "Sat, 27 Mar 2021 15:34:32 GMT"
},
{
"version": "v2",
"created": "Sun, 24 Oct 2021 13:50:45 GMT"
}
] | 1,635,206,400,000 | [
[
"Wang",
"Kai",
""
],
[
"Liu",
"Yu",
""
],
[
"Lin",
"Dan",
""
],
[
"Sheng",
"Quan Z.",
""
]
] |
2103.14950 | Michael Green | Christoph Salge, Michael Cerny Green, Rodrigo Canaan, Filip Skwarski,
Rafael Fritsch, Adrian Brightmoore, Shaofang Ye, Changxing Cao and Julian
Togelius | The AI Settlement Generation Challenge in Minecraft: First Year Report | 14 pages, 9 figures, published in KI-K\"unstliche Intelligenz | KI-K\"unstliche Intelligenz 2020 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article outlines what we learned from the first year of the AI
Settlement Generation Competition in Minecraft, a competition about producing
AI programs that can generate interesting settlements in Minecraft for an
unseen map. This challenge seeks to focus research into adaptive and holistic
procedural content generation. Generating Minecraft towns and villages given
existing maps is a suitable task for this, as it requires the generated content
to be adaptive, functional, evocative and aesthetic at the same time. Here, we
present the results from the first iteration of the competition. We discuss the
evaluation methodology, present the different technical approaches by the
competitors, and outline the open problems.
| [
{
"version": "v1",
"created": "Sat, 27 Mar 2021 17:27:05 GMT"
}
] | 1,617,062,400,000 | [
[
"Salge",
"Christoph",
""
],
[
"Green",
"Michael Cerny",
""
],
[
"Canaan",
"Rodrigo",
""
],
[
"Skwarski",
"Filip",
""
],
[
"Fritsch",
"Rafael",
""
],
[
"Brightmoore",
"Adrian",
""
],
[
"Ye",
"Shaofang",
""
],
[
"Cao",
"Changxing",
""
],
[
"Togelius",
"Julian",
""
]
] |
2103.14986 | Ildar Batyrshin Z. | Ildar Batyrshin, Luis Alfonso Villa-Vargas, Marco Antonio
Ramirez-Salinas, Moises Salinas-Rosales, Nailya Kubysheva | Generating Negations of Probability Distributions | 10 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently it was introduced a negation of a probability distribution. The need
for such negation arises when a knowledge-based system can use the terms like
NOT HIGH, where HIGH is represented by a probability distribution (pd). For
example, HIGH PROFIT or HIGH PRICE can be considered. The application of this
negation in Dempster-Shafer theory was considered in many works. Although
several negations of probability distributions have been proposed, it was not
clear how to construct other negations. In this paper, we consider negations of
probability distributions as point-by-point transformations of pd using
decreasing functions defined on [0,1] called negators. We propose the general
method of generation of negators and corresponding negations of pd, and study
their properties. We give a characterization of linear negators as a convex
combination of Yager and uniform negators.
| [
{
"version": "v1",
"created": "Sat, 27 Mar 2021 20:24:10 GMT"
}
] | 1,617,062,400,000 | [
[
"Batyrshin",
"Ildar",
""
],
[
"Villa-Vargas",
"Luis Alfonso",
""
],
[
"Ramirez-Salinas",
"Marco Antonio",
""
],
[
"Salinas-Rosales",
"Moises",
""
],
[
"Kubysheva",
"Nailya",
""
]
] |
2103.15059 | Miao Li | Rui Zhang, Bayu Distiawan Trisedy, Miao Li, Yong Jiang, Jianzhong Qi | A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment
via Representation Learning | to appear in VLDB Journal, 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the last few years, the interest in knowledge bases has grown
exponentially in both the research community and the industry due to their
essential role in AI applications. Entity alignment is an important task for
enriching knowledge bases. This paper provides a comprehensive tutorial-type
survey on representative entity alignment techniques that use the new approach
of representation learning. We present a framework for capturing the key
characteristics of these techniques, propose two datasets to address the
limitation of existing benchmark datasets, and conduct extensive experiments
using the proposed datasets. The framework gives a clear picture of how the
techniques work. The experiments yield important results about the empirical
performance of the techniques and how various factors affect the performance.
One important observation not stressed by previous work is that techniques
making good use of attribute triples and relation predicates as features stand
out as winners.
| [
{
"version": "v1",
"created": "Sun, 28 Mar 2021 06:23:48 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Jan 2022 15:29:36 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Mar 2022 02:02:27 GMT"
},
{
"version": "v4",
"created": "Sat, 2 Apr 2022 12:36:39 GMT"
},
{
"version": "v5",
"created": "Fri, 6 May 2022 03:51:22 GMT"
}
] | 1,652,054,400,000 | [
[
"Zhang",
"Rui",
""
],
[
"Trisedy",
"Bayu Distiawan",
""
],
[
"Li",
"Miao",
""
],
[
"Jiang",
"Yong",
""
],
[
"Qi",
"Jianzhong",
""
]
] |
2103.15100 | Benjamin Goertzel | Ben Goertzel | The General Theory of General Intelligence: A Pragmatic Patternist
Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A multi-decade exploration into the theoretical foundations of artificial and
natural general intelligence, which has been expressed in a series of books and
papers and used to guide a series of practical and research-prototype software
systems, is reviewed at a moderate level of detail. The review covers
underlying philosophies (patternist philosophy of mind, foundational
phenomenological and logical ontology), formalizations of the concept of
intelligence, and a proposed high level architecture for AGI systems partly
driven by these formalizations and philosophies. The implementation of specific
cognitive processes such as logical reasoning, program learning, clustering and
attention allocation in the context and language of this high level
architecture is considered, as is the importance of a common (e.g. typed
metagraph based) knowledge representation for enabling "cognitive synergy"
between the various processes. The specifics of human-like cognitive
architecture are presented as manifestations of these general principles, and
key aspects of machine consciousness and machine ethics are also treated in
this context. Lessons for practical implementation of advanced AGI in
frameworks such as OpenCog Hyperon are briefly considered.
| [
{
"version": "v1",
"created": "Sun, 28 Mar 2021 10:11:25 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Apr 2021 01:30:34 GMT"
},
{
"version": "v3",
"created": "Sun, 4 Apr 2021 04:30:42 GMT"
}
] | 1,617,667,200,000 | [
[
"Goertzel",
"Ben",
""
]
] |
2103.15171 | Ramya Ramakrishnan | Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar, Julie Shah | A Bayesian Approach to Identifying Representational Errors | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Trained AI systems and expert decision makers can make errors that are often
difficult to identify and understand. Determining the root cause for these
errors can improve future decisions. This work presents Generative Error Model
(GEM), a generative model for inferring representational errors based on
observations of an actor's behavior (either simulated agent, robot, or human).
The model considers two sources of error: those that occur due to
representational limitations -- "blind spots" -- and non-representational
errors, such as those caused by noise in execution or systematic errors present
in the actor's policy. Disambiguating these two error types allows for targeted
refinement of the actor's policy (i.e., representational errors require
perceptual augmentation, while other errors can be reduced through methods such
as improved training or attention support). We present a Bayesian inference
algorithm for GEM and evaluate its utility in recovering representational
errors on multiple domains. Results show that our approach can recover blind
spots of both reinforcement learning agents as well as human users.
| [
{
"version": "v1",
"created": "Sun, 28 Mar 2021 16:43:01 GMT"
}
] | 1,617,062,400,000 | [
[
"Ramakrishnan",
"Ramya",
""
],
[
"Unhelkar",
"Vaibhav",
""
],
[
"Kamar",
"Ece",
""
],
[
"Shah",
"Julie",
""
]
] |
2103.15294 | Bin Liu | Bin Liu | "Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest
Value for us? | 7 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | AI has surpassed humans across a variety of tasks such as image
classification, playing games (e.g., go, "Starcraft" and poker), and protein
structure prediction. However, at the same time, AI is also bearing serious
controversies. Many researchers argue that little substantial progress has been
made for AI in recent decades. In this paper, the author (1) explains why
controversies about AI exist; (2) discriminates two paradigms of AI research,
termed "weak AI" and "strong AI" (a.k.a. artificial general intelligence); (3)
clarifies how to judge which paradigm a research work should be classified
into; (4) discusses what is the greatest value of "weak AI" if it has no chance
to develop into "strong AI".
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 02:57:48 GMT"
}
] | 1,617,062,400,000 | [
[
"Liu",
"Bin",
""
]
] |
2103.15452 | Xin Mao | Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan | Boosting the Speed of Entity Alignment 10*: Dual Attention Matching
Network with Normalized Hard Sample Mining | 12 pages; Accepted by TheWebConf(WWW) 2021 | null | 10.1145/3442381.3449897 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is
the pivotal step to KGs integration, also known as \emph{entity alignment}
(EA). However, most existing EA methods are inefficient and poor in
scalability. A recent summary points out that some of them even require several
days to deal with a dataset containing 200,000 nodes (DWY100K). We believe
over-complex graph encoder and inefficient negative sampling strategy are the
two main reasons. In this paper, we propose a novel KG encoder -- Dual
Attention Matching Network (Dual-AMN), which not only models both intra-graph
and cross-graph information smartly, but also greatly reduces computational
complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to
smoothly select hard negative samples with reduced loss shift. The experimental
results on widely used public datasets indicate that our method achieves both
high accuracy and high efficiency. On DWY100K, the whole running process of our
method could be finished in 1,100 seconds, at least 10* faster than previous
work. The performances of our method also outperform previous works across all
datasets, where Hits@1 and MRR have been improved from 6% to 13%.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 09:35:07 GMT"
}
] | 1,617,062,400,000 | [
[
"Mao",
"Xin",
""
],
[
"Wang",
"Wenting",
""
],
[
"Wu",
"Yuanbin",
""
],
[
"Lan",
"Man",
""
]
] |
2103.15551 | A. M. Khalili | Abdullah Khalili and Abdelhamid Bouchachia | Toward Building Science Discovery Machines | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dream of building machines that can do science has inspired scientists
for decades. Remarkable advances have been made recently; however, we are still
far from achieving this goal. In this paper, we focus on the scientific
discovery process where a high level of reasoning and remarkable
problem-solving ability are required. We review different machine learning
techniques used in scientific discovery with their limitations. We survey and
discuss the main principles driving the scientific discovery process. These
principles are used in different fields and by different scientists to solve
problems and discover new knowledge. We provide many examples of the use of
these principles in different fields such as physics, mathematics, and biology.
We also review AI systems that attempt to implement some of these principles.
We argue that building science discovery machines should be guided by these
principles as an alternative to the dominant approach of current AI systems
that focuses on narrow objectives. Building machines that fully incorporate
these principles in an automated way might open the doors for many
advancements.
| [
{
"version": "v1",
"created": "Wed, 24 Mar 2021 14:04:03 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Apr 2021 15:24:48 GMT"
},
{
"version": "v3",
"created": "Tue, 1 Jun 2021 20:02:36 GMT"
},
{
"version": "v4",
"created": "Thu, 12 Aug 2021 14:37:52 GMT"
},
{
"version": "v5",
"created": "Mon, 28 Feb 2022 22:54:13 GMT"
},
{
"version": "v6",
"created": "Thu, 3 Mar 2022 14:12:22 GMT"
},
{
"version": "v7",
"created": "Mon, 14 Mar 2022 17:33:21 GMT"
}
] | 1,647,302,400,000 | [
[
"Khalili",
"Abdullah",
""
],
[
"Bouchachia",
"Abdelhamid",
""
]
] |
2103.15558 | Huansheng Ning Prof | Wenxi Wang, Huansheng Ning, Feifei Shi, Sahraoui Dhelim, Weishan
Zhang, Liming Chen | A Survey of Hybrid Human-Artificial Intelligence for Social Computing | null | IEEE Transactions on Human-Machine Systems 2021 | 10.1109/THMS.2021.3131683 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Along with the development of modern computing technology and social
sciences, both theoretical research and practical applications of social
computing have been continuously extended. In particular with the boom of
artificial intelligence (AI), social computing is significantly influenced by
AI. However, the conventional technologies of AI have drawbacks in dealing with
more complicated and dynamic problems. Such deficiency can be rectified by
hybrid human-artificial intelligence (H-AI) which integrates both human
intelligence and AI into one unity, forming a new enhanced intelligence. H-AI
in dealing with social problems shows the advantages that AI can not surpass.
This paper firstly introduces the concept of H-AI. AI is the intelligence in
the transition stage of H-AI, so the latest research progresses of AI in social
computing are reviewed. Secondly, it summarizes typical challenges faced by AI
in social computing, and makes it possible to introduce H-AI to solve these
challenges. Finally, the paper proposes a holistic framework of social
computing combining with H-AI, which consists of four layers: object layer,
base layer, analysis layer, and application layer. It represents H-AI has
significant advantages over AI in solving social problems.
| [
{
"version": "v1",
"created": "Wed, 17 Mar 2021 08:39:44 GMT"
}
] | 1,646,006,400,000 | [
[
"Wang",
"Wenxi",
""
],
[
"Ning",
"Huansheng",
""
],
[
"Shi",
"Feifei",
""
],
[
"Dhelim",
"Sahraoui",
""
],
[
"Zhang",
"Weishan",
""
],
[
"Chen",
"Liming",
""
]
] |
2103.15571 | Xiaosen Wang | Xiaosen Wang, Kun He | Enhancing the Transferability of Adversarial Attacks through Variance
Tuning | Accepted by CVPR 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks are vulnerable to adversarial examples that mislead the
models with imperceptible perturbations. Though adversarial attacks have
achieved incredible success rates in the white-box setting, most existing
adversaries often exhibit weak transferability in the black-box setting,
especially under the scenario of attacking models with defense mechanisms. In
this work, we propose a new method called variance tuning to enhance the class
of iterative gradient based attack methods and improve their attack
transferability. Specifically, at each iteration for the gradient calculation,
instead of directly using the current gradient for the momentum accumulation,
we further consider the gradient variance of the previous iteration to tune the
current gradient so as to stabilize the update direction and escape from poor
local optima. Empirical results on the standard ImageNet dataset demonstrate
that our method could significantly improve the transferability of
gradient-based adversarial attacks. Besides, our method could be used to attack
ensemble models or be integrated with various input transformations.
Incorporating variance tuning with input transformations on iterative
gradient-based attacks in the multi-model setting, the integrated method could
achieve an average success rate of 90.1% against nine advanced defense methods,
improving the current best attack performance significantly by 85.1% . Code is
available at https://github.com/JHL-HUST/VT.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 12:41:55 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Jul 2021 03:30:39 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Aug 2021 07:52:52 GMT"
}
] | 1,629,072,000,000 | [
[
"Wang",
"Xiaosen",
""
],
[
"He",
"Kun",
""
]
] |
2103.15575 | Benjamin Krarup | Benjamin Krarup and Senka Krivic and Daniele Magazzeni and Derek Long
and Michael Cashmore and David E. Smith | Contrastive Explanations of Plans Through Model Restrictions | 80 pages, 32 figures, 7 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In automated planning, the need for explanations arises when there is a
mismatch between a proposed plan and the user's expectation. We frame
Explainable AI Planning in the context of the plan negotiation problem, in
which a succession of hypothetical planning problems are generated and solved.
The object of the negotiation is for the user to understand and ultimately
arrive at a satisfactory plan. We present the results of a user study that
demonstrates that when users ask questions about plans, those questions are
contrastive, i.e. "why A rather than B?". We use the data from this study to
construct a taxonomy of user questions that often arise during plan
negotiation. We formally define our approach to plan negotiation through model
restriction as an iterative process. This approach generates hypothetical
problems and contrastive plans by restricting the model through constraints
implied by user questions. We formally define model-based compilations in
PDDL2.1 of each constraint derived from a user question in the taxonomy, and
empirically evaluate the compilations in terms of computational complexity. The
compilations were implemented as part of an explanation framework that employs
iterative model restriction. We demonstrate its benefits in a second user
study.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 12:47:15 GMT"
}
] | 1,617,062,400,000 | [
[
"Krarup",
"Benjamin",
""
],
[
"Krivic",
"Senka",
""
],
[
"Magazzeni",
"Daniele",
""
],
[
"Long",
"Derek",
""
],
[
"Cashmore",
"Michael",
""
],
[
"Smith",
"David E.",
""
]
] |
2103.15592 | Vladimir Ivanov | V. K. Ivanov, N .V. Vinogradova, B. V. Palyukh, A. N. Sotnikov | Current Trends and Applications of Dempster-Shafer Theory (Review) | 11 pages, in Russian. Artificial intelligence and decision making.
2018. N 4 | null | 10.14357/20718594180403 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The article provides a review of the publications on the current trends and
developments in Dempster-Shafer theory and its different applications in
science, engineering, and technologies. The review took account of the
following provisions with a focus on some specific aspects of the theory.
Firstly, the article considers the research directions whose results are known
not only in scientific and academic community but understood by a wide circle
of potential designers and developers of advanced engineering solutions and
technologies. Secondly, the article shows the theory applications in some
important areas of human activity such as manufacturing systems, diagnostics of
technological processes, materials and products, building and construction,
product quality control, economic and social systems. The particular attention
is paid to the current state of research in the domains under consideration
and, thus, the papers published, as a rule, in recent years and presenting the
achievements of modern research on Dempster-Shafer theory and its application
are selected and analyzed.
| [
{
"version": "v1",
"created": "Fri, 26 Mar 2021 09:37:28 GMT"
}
] | 1,617,062,400,000 | [
[
"Ivanov",
"V. K.",
""
],
[
"Vinogradova",
"N . V.",
""
],
[
"Palyukh",
"B. V.",
""
],
[
"Sotnikov",
"A. N.",
""
]
] |
2103.15739 | Vivek Nallur | Vivek Nallur and Martin Lloyd and Siani Pearson | Automation: An Essential Component Of Ethical AI? | 4 pages, 15th Multi Conference on Computer Science and Information
Systems, 20-23 July 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Ethics is sometimes considered to be too abstract to be meaningfully
implemented in artificial intelligence (AI). In this paper, we reflect on other
aspects of computing that were previously considered to be very abstract. Yet,
these are now accepted as being done very well by computers. These tasks have
ranged from multiple aspects of software engineering to mathematics to
conversation in natural language with humans. This was done by automating the
simplest possible step and then building on it to perform more complex tasks.
We wonder if ethical AI might be similarly achieved and advocate the process of
automation as key step in making AI take ethical decisions. The key
contribution of this paper is to reflect on how automation was introduced into
domains previously considered too abstract for computers.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 16:25:58 GMT"
}
] | 1,617,062,400,000 | [
[
"Nallur",
"Vivek",
""
],
[
"Lloyd",
"Martin",
""
],
[
"Pearson",
"Siani",
""
]
] |
2103.15746 | Vivek Nallur | Siani Pearson and Martin Lloyd and Vivek Nallur | Towards An Ethics-Audit Bot | 5 pages, short paper, 15th Multi Conference on Computer Science and
Information Systems, 20-23 July 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper we focus on artificial intelligence (AI) for governance, not
governance for AI, and on just one aspect of governance, namely ethics audit.
Different kinds of ethical audit bots are possible, but who makes the choices
and what are the implications? In this paper, we do not provide
ethical/philosophical solutions, but rather focus on the technical aspects of
what an AI-based solution for validating the ethical soundness of a target
system would be like. We propose a system that is able to conduct an ethical
audit of a target system, given certain socio-technical conditions. To be more
specific, we propose the creation of a bot that is able to support
organisations in ensuring that their software development lifecycles contain
processes that meet certain ethical standards.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 16:33:22 GMT"
}
] | 1,617,062,400,000 | [
[
"Pearson",
"Siani",
""
],
[
"Lloyd",
"Martin",
""
],
[
"Nallur",
"Vivek",
""
]
] |
2103.15764 | Usha Lokala | Usha Lokala, Francois Lamy, Triyasha Ghosh Dastidar, Kaushik Roy,
Raminta Daniulaityte, Srinivasan Parthasarathy, Amit Sheth | eDarkTrends: Harnessing Social Media Trends in Substance use disorders
for Opioid Listings on Cryptomarket | 6 pages, ICLR AI for Public Health Workshop 2021 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the opioid crisis. The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being substance misuse causes poor mental health. However, the lack of evidence
on the relationship has resulted in opioids being largely inaccessible through
legal means. This study analyzes the substance misuse posts on social media
with the opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and BERT-based models to
generate sentiment and emotion for the social media posts to understand user
perception on social media by investigating questions such as, which synthetic
opioids people are optimistic, neutral, or negative about or what kind of drugs
induced fear and sorrow or what kind of drugs people love or thankful about or
which drug people think negatively about or which opioids cause little to no
sentimental reaction. We also perform topic analysis associated with the
generated sentiments and emotions to understand which topics correlate with
people's responses to various drugs. Our findings can help shape policy to help
isolate opioid use cases where timely intervention may be required to prevent
adverse consequences, prevent overdose-related deaths, and worsen the epidemic.
| [
{
"version": "v1",
"created": "Mon, 29 Mar 2021 16:58:26 GMT"
}
] | 1,617,062,400,000 | [
[
"Lokala",
"Usha",
""
],
[
"Lamy",
"Francois",
""
],
[
"Dastidar",
"Triyasha Ghosh",
""
],
[
"Roy",
"Kaushik",
""
],
[
"Daniulaityte",
"Raminta",
""
],
[
"Parthasarathy",
"Srinivasan",
""
],
[
"Sheth",
"Amit",
""
]
] |
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