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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2007.12989 | Svetlana Yanushkevich | Shawn C. Eastwood and Svetlana N. Yanushkevich | Information Fusion on Belief Networks | 25 pages, pages of Appendix, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper will focus on the process of 'fusing' several observations or
models of uncertainty into a single resultant model. Many existing approaches
to fusion use subjective quantities such as 'strengths of belief' and process
these quantities with heuristic algorithms. This paper argues in favor of
quantities that can be objectively measured, as opposed to the subjective
'strength of belief' values. This paper will focus on probability
distributions, and more importantly, structures that denote sets of probability
distributions known as 'credal sets'. The novel aspect of this paper will be a
taxonomy of models of fusion that use specific types of credal sets, namely
probability interval distributions and Dempster-Shafer models. An objective
requirement for information fusion algorithms is provided, and is satisfied by
all models of fusion presented in this paper. Dempster's rule of combination is
shown to not satisfy this requirement. This paper will also assess the
computational challenges involved for the proposed fusion approaches.
| [
{
"version": "v1",
"created": "Sat, 25 Jul 2020 18:10:45 GMT"
}
] | 1,595,894,400,000 | [
[
"Eastwood",
"Shawn C.",
""
],
[
"Yanushkevich",
"Svetlana N.",
""
]
] |
2007.13257 | Yara Rizk | Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf, Yasaman
Khazaeni, Vinod Muthusamy, Yara Rizk, Merve Unuvar | From Robotic Process Automation to Intelligent Process Automation:
Emerging Trends | Internation Conference on Business Process Management 2020 RPA Forum | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this survey, we study how recent advances in machine intelligence are
disrupting the world of business processes. Over the last decade, there has
been steady progress towards the automation of business processes under the
umbrella of ``robotic process automation'' (RPA). However, we are currently at
an inflection point in this evolution, as a new paradigm called ``Intelligent
Process Automation'' (IPA) emerges, bringing machine learning (ML) and
artificial intelligence (AI) technologies to bear in order to improve business
process outcomes. The purpose of this paper is to provide a survey of this
emerging theme and identify key open research challenges at the intersection of
AI and business processes. We hope that this emerging theme will spark engaging
conversations at the RPA Forum.
| [
{
"version": "v1",
"created": "Mon, 27 Jul 2020 00:43:08 GMT"
}
] | 1,595,894,400,000 | [
[
"Chakraborti",
"Tathagata",
""
],
[
"Isahagian",
"Vatche",
""
],
[
"Khalaf",
"Rania",
""
],
[
"Khazaeni",
"Yasaman",
""
],
[
"Muthusamy",
"Vinod",
""
],
[
"Rizk",
"Yara",
""
],
[
"Unuvar",
"Merve",
""
]
] |
2007.13363 | Nicolas Perrin-Gilbert | Thomas Pierrot, Nicolas Perrin, Feryal Behbahani, Alexandre Laterre,
Olivier Sigaud, Karim Beguir, Nando de Freitas | Learning Compositional Neural Programs for Continuous Control | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel solution to challenging sparse-reward, continuous control
problems that require hierarchical planning at multiple levels of abstraction.
Our solution, dubbed AlphaNPI-X, involves three separate stages of learning.
First, we use off-policy reinforcement learning algorithms with experience
replay to learn a set of atomic goal-conditioned policies, which can be easily
repurposed for many tasks. Second, we learn self-models describing the effect
of the atomic policies on the environment. Third, the self-models are harnessed
to learn recursive compositional programs with multiple levels of abstraction.
The key insight is that the self-models enable planning by imagination,
obviating the need for interaction with the world when learning higher-level
compositional programs. To accomplish the third stage of learning, we extend
the AlphaNPI algorithm, which applies AlphaZero to learn recursive neural
programmer-interpreters. We empirically show that AlphaNPI-X can effectively
learn to tackle challenging sparse manipulation tasks, such as stacking
multiple blocks, where powerful model-free baselines fail.
| [
{
"version": "v1",
"created": "Mon, 27 Jul 2020 08:27:14 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Apr 2021 12:08:39 GMT"
}
] | 1,618,358,400,000 | [
[
"Pierrot",
"Thomas",
""
],
[
"Perrin",
"Nicolas",
""
],
[
"Behbahani",
"Feryal",
""
],
[
"Laterre",
"Alexandre",
""
],
[
"Sigaud",
"Olivier",
""
],
[
"Beguir",
"Karim",
""
],
[
"de Freitas",
"Nando",
""
]
] |
2007.13475 | Beatrice Bouchou Markhoff | Mathieu Lirzin (BDTLN), B\'eatrice Markhoff (BDTLN) | Towards an ontology of HTTP interactions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Enterprise information systems have adopted Web-based foundations for
exchanges between heterogeneous programmes. These programs provide and consume
via Web APIs some resources identified by URIs, whose representations are
transmitted via HTTP. Furthermore HTTP remains at the heart of all Web
developments (Semantic Web, linked data, IoT...). Thus, situations where a
program must be able to reason about HTTP interactions (request-response) are
multiplying. This requires an explicit formal specification of a shared
conceptualization of those interactions. A proposal for an RDF vocabulary
exists, developed with a view to carrying out web application conformity tests
and record the tests outputs. This vocabulary has already been reused. In this
paper we propose to adapt and extend it for making it more reusable.
| [
{
"version": "v1",
"created": "Mon, 20 Jul 2020 08:38:36 GMT"
}
] | 1,595,894,400,000 | [
[
"Lirzin",
"Mathieu",
"",
"BDTLN"
],
[
"Markhoff",
"Béatrice",
"",
"BDTLN"
]
] |
2007.14778 | Nadjet Bourdache | Nadjet Bourdache, Patrice Perny and Olivier Spanjaard | Bayesian preference elicitation for multiobjective combinatorial
optimization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new incremental preference elicitation procedure able to deal
with noisy responses of a Decision Maker (DM). The originality of the
contribution is to propose a Bayesian approach for determining a preferred
solution in a multiobjective decision problem involving a combinatorial set of
alternatives. We assume that the preferences of the DM are represented by an
aggregation function whose parameters are unknown and that the uncertainty
about them is represented by a density function on the parameter space.
Pairwise comparison queries are used to reduce this uncertainty (by Bayesian
revision). The query selection strategy is based on the solution of a mixed
integer linear program with a combinatorial set of variables and constraints,
which requires to use columns and constraints generation methods. Numerical
tests are provided to show the practicability of the approach.
| [
{
"version": "v1",
"created": "Wed, 29 Jul 2020 12:28:37 GMT"
}
] | 1,596,067,200,000 | [
[
"Bourdache",
"Nadjet",
""
],
[
"Perny",
"Patrice",
""
],
[
"Spanjaard",
"Olivier",
""
]
] |
2007.15185 | Huimin Fu | Huimin Fu, Yang Xu, Jun Liu, Guanfeng Wu, Sutcliffe Geoff | Improving probability selecting based weights for Satisfiability Problem | null | null | null | arXiv:2007.15185 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Boolean Satisfiability problem (SAT) is important on artificial
intelligence community and the impact of its solving on complex problems.
Recently, great breakthroughs have been made respectively on stochastic local
search (SLS) algorithms for uniform random k-SAT resulting in several
state-of-the-art SLS algorithms Score2SAT, YalSAT, ProbSAT, CScoreSAT and on a
hybrid algorithm for hard random SAT (HRS) resulting in one state-of-the-art
hybrid algorithm SparrowToRiss. However, there is no an algorithm which can
effectively solve both uniform random k-SAT and HRS. In this paper, we present
a new SLS algorithm named SelectNTS for uniform random k-SAT and HRS. SelectNTS
is an improved probability selecting based local search algorithm for SAT
problem. The core of SelectNTS relies on new clause and variable selection
heuristics. The new clause selection heuristic uses a new clause weighting
scheme and a biased random walk. The new variable selection heuristic uses a
probability selecting strategy with the variation of CC strategy based on a new
variable weighting scheme. Extensive experimental results on the well-known
random benchmarks instances from the SAT Competitions in 2017 and 2018, and on
randomly generated problems, show that our algorithm outperforms
state-of-the-art random SAT algorithms, and our SelectNTS can effectively solve
both uniform random k-SAT and HRS.
| [
{
"version": "v1",
"created": "Thu, 30 Jul 2020 02:23:07 GMT"
}
] | 1,596,585,600,000 | [
[
"Fu",
"Huimin",
""
],
[
"Xu",
"Yang",
""
],
[
"Liu",
"Jun",
""
],
[
"Wu",
"Guanfeng",
""
],
[
"Geoff",
"Sutcliffe",
""
]
] |
2007.15393 | Andres Garcia-Camino PhD | Andr\'es Garc\'ia-Camino | Towards a new Social Choice Theory | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Social choice is the theory about collective decision towards social welfare
starting from individual opinions, preferences, interests or welfare. The field
of Computational Social Welfare is somewhat recent and it is gaining impact in
the Artificial Intelligence Community. Classical literature makes the
assumption of single-peaked preferences, i.e. there exist a order in the
preferences and there is a global maximum in this order. This year some
theoretical results were published about Two-stage Approval Voting Systems
(TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular
Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to
introduced Social Choice Optimisation as a generalisation of TAVs where there
is a max stage and a min stage implementing thus a Minimax, well-known
Artificial Intelligence decision-making rule to minimize hindering towards a
(Social) Goal. Secondly, I want to introduce, following my Open Standardization
and Open Integration Theory (in refinement process) put in practice in my
dissertation, the Open Standardization of Social Inclusion, as a global social
goal of Social Choice Optimization.
| [
{
"version": "v1",
"created": "Thu, 30 Jul 2020 11:36:36 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Aug 2020 21:05:47 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Jul 2023 05:06:41 GMT"
}
] | 1,690,243,200,000 | [
[
"García-Camino",
"Andrés",
""
]
] |
2007.15703 | Desmond Ong | Terence X. Lim, Sidney Tio, Desmond C. Ong | Improving Multi-Agent Cooperation using Theory of Mind | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in Artificial Intelligence have produced agents that can beat
human world champions at games like Go, Starcraft, and Dota2. However, most of
these models do not seem to play in a human-like manner: People infer others'
intentions from their behaviour, and use these inferences in scheming and
strategizing. Here, using a Bayesian Theory of Mind (ToM) approach, we
investigated how much an explicit representation of others' intentions improves
performance in a cooperative game. We compared the performance of humans
playing with optimal-planning agents with and without ToM, in a cooperative
game where players have to flexibly cooperate to achieve joint goals. We find
that teams with ToM agents significantly outperform non-ToM agents when
collaborating with all types of partners: non-ToM, ToM, as well as human
players, and that the benefit of ToM increases the more ToM agents there are.
These findings have implications for designing better cooperative agents.
| [
{
"version": "v1",
"created": "Thu, 30 Jul 2020 19:31:31 GMT"
}
] | 1,596,412,800,000 | [
[
"Lim",
"Terence X.",
""
],
[
"Tio",
"Sidney",
""
],
[
"Ong",
"Desmond C.",
""
]
] |
2008.00463 | Alessandro Antonucci | Marco Zaffalon and Alessandro Antonucci and Rafael Caba\~nas | Structural Causal Models Are (Solvable by) Credal Networks | To appear in the proceedings of the 10th International Conference on
Probabilistic Graphical Models (PGM 2020) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A structural causal model is made of endogenous (manifest) and exogenous
(latent) variables. We show that endogenous observations induce linear
constraints on the probabilities of the exogenous variables. This allows to
exactly map a causal model into a credal network. Causal inferences, such as
interventions and counterfactuals, can consequently be obtained by standard
algorithms for the updating of credal nets. These natively return sharp values
in the identifiable case, while intervals corresponding to the exact bounds are
produced for unidentifiable queries. A characterization of the causal models
that allow the map above to be compactly derived is given, along with a
discussion about the scalability for general models. This contribution should
be regarded as a systematic approach to represent structural causal models by
credal networks and hence to systematically compute causal inferences. A number
of demonstrative examples is presented to clarify our methodology. Extensive
experiments show that approximate algorithms for credal networks can
immediately be used to do causal inference in real-size problems.
| [
{
"version": "v1",
"created": "Sun, 2 Aug 2020 11:19:36 GMT"
}
] | 1,596,499,200,000 | [
[
"Zaffalon",
"Marco",
""
],
[
"Antonucci",
"Alessandro",
""
],
[
"Cabañas",
"Rafael",
""
]
] |
2008.01188 | Quentin Cohen-Solal | Quentin Cohen-Solal | Learning to Play Two-Player Perfect-Information Games without Knowledge | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, several techniques for learning game state evaluation
functions by reinforcement are proposed. The first is a generalization of tree
bootstrapping (tree learning): it is adapted to the context of reinforcement
learning without knowledge based on non-linear functions. With this technique,
no information is lost during the reinforcement learning process. The second is
a modification of minimax with unbounded depth extending the best sequences of
actions to the terminal states. This modified search is intended to be used
during the learning process. The third is to replace the classic gain of a game
(+1 / -1) with a reinforcement heuristic. We study particular reinforcement
heuristics such as: quick wins and slow defeats ; scoring ; mobility or
presence. The four is another variant of unbounded minimax, which plays the
safest action instead of playing the best action. This modified search is
intended to be used after the learning process. The five is a new action
selection distribution. The conducted experiments suggest that these techniques
improve the level of play. Finally, we apply these different techniques to
design program-players to the game of Hex (size 11 and 13) surpassing the level
of Mohex 3HNN with reinforcement learning from self-play without knowledge.
| [
{
"version": "v1",
"created": "Mon, 3 Aug 2020 21:01:22 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Dec 2020 17:50:39 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Oct 2021 17:37:35 GMT"
}
] | 1,634,083,200,000 | [
[
"Cohen-Solal",
"Quentin",
""
]
] |
2008.01253 | Botros Hanna | B. N. Hanna, L. T. Trieu, T. C. Son, and N. T. Dinh | An Application of ASP in Nuclear Engineering: Explaining the Three Mile
Island Nuclear Accident Scenario | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2019), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper describes an ongoing effort in developing a declarative system for
supporting operators in the Nuclear Power Plant (NPP) control room. The focus
is on two modules: diagnosis and explanation of events that happened in NPPs.
We describe an Answer Set Programming (ASP) representation of an NPP, which
consists of declarations of state variables, components, their connections, and
rules encoding the plant behavior. We then show how the ASP program can be used
to explain the series of events that occurred in the Three Mile Island, Unit 2
(TMI-2) NPP accident, the most severe accident in the USA nuclear power plant
operating history. We also describe an explanation module aimed at addressing
answers to questions such as ``why an event occurs?'' or ``what should be
done?'' given the collected data.
This paper is *under consideration* for acceptance in TPLP Journal.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 00:21:27 GMT"
}
] | 1,596,585,600,000 | [
[
"Hanna",
"B. N.",
""
],
[
"Trieu",
"L. T.",
""
],
[
"Son",
"T. C.",
""
],
[
"Dinh",
"N. T.",
""
]
] |
2008.01415 | Pierre Talbot | Pierre Talbot, \'Eric Monfroy and Charlotte Truchet | Modular Constraint Solver Cooperation via Abstract Interpretation | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 17 pages. v2: Fix an example in Section 3.2 (improved closure) | Theory and Practice of Logic Programming 20 (2020) 848-863 | 10.1017/S1471068420000162 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cooperation among constraint solvers is difficult because different solving
paradigms have different theoretical foundations. Recent works have shown that
abstract interpretation can provide a unifying theory for various constraint
solvers. In particular, it relies on abstract domains which capture constraint
languages as ordered structures. The key insight of this paper is viewing
cooperation schemes as abstract domains combinations. We propose a modular
framework in which solvers and cooperation schemes can be seamlessly added and
combined. This differs from existing approaches such as SMT where the
cooperation scheme is usually fixed (e.g., Nelson-Oppen). We contribute to two
new cooperation schemes: (i) interval propagators completion that allows
abstract domains to exchange bound constraints, and (ii) delayed product which
exchanges over-approximations of constraints between two abstract domains.
Moreover, the delayed product is based on delayed goal of logic programming,
and it shows that abstract domains can also capture control aspects of
constraint solving. Finally, to achieve modularity, we propose the shared
product to combine abstract domains and cooperation schemes. Our approach has
been fully implemented, and we provide various examples on the flexible job
shop scheduling problem. Under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 08:52:19 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Sep 2020 10:01:14 GMT"
}
] | 1,600,819,200,000 | [
[
"Talbot",
"Pierre",
""
],
[
"Monfroy",
"Éric",
""
],
[
"Truchet",
"Charlotte",
""
]
] |
2008.01499 | Zhen Zhang Dr. | Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao,
Cong-Cong Li, Yucheng Dong and Francisco Herrera | Distributed Linguistic Representations in Decision Making: Taxonomy, Key
Elements and Applications, and Challenges in Data Science and Explainable
Artificial Intelligence | 37 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Distributed linguistic representations are powerful tools for modelling the
uncertainty and complexity of preference information in linguistic decision
making. To provide a comprehensive perspective on the development of
distributed linguistic representations in decision making, we present the
taxonomy of existing distributed linguistic representations. Then, we review
the key elements of distributed linguistic information processing in decision
making, including the distance measurement, aggregation methods, distributed
linguistic preference relations, and distributed linguistic multiple attribute
decision making models. Next, we provide a discussion on ongoing challenges and
future research directions from the perspective of data science and explainable
artificial intelligence.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 13:13:59 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Aug 2020 06:03:43 GMT"
}
] | 1,597,017,600,000 | [
[
"Wu",
"Yuzhu",
""
],
[
"Zhang",
"Zhen",
""
],
[
"Kou",
"Gang",
""
],
[
"Zhang",
"Hengjie",
""
],
[
"Chao",
"Xiangrui",
""
],
[
"Li",
"Cong-Cong",
""
],
[
"Dong",
"Yucheng",
""
],
[
"Herrera",
"Francisco",
""
]
] |
2008.01508 | Xinzhi Wang Dr. | Xinzhi Wang, Huao Li, Hui Zhang, Michael Lewis, Katia Sycara | Explanation of Reinforcement Learning Model in Dynamic Multi-Agent
System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, there has been increasing interest in transparency and
interpretability in Deep Reinforcement Learning (DRL) systems. Verbal
explanations, as the most natural way of communication in our daily life,
deserve more attention, since they allow users to gain a better understanding
of the system which ultimately could lead to a high level of trust and smooth
collaboration. This paper reports a novel work in generating verbal
explanations for DRL behaviors agent. A rule-based model is designed to
construct explanations using a series of rules which are predefined with prior
knowledge. A learning model is then proposed to expand the implicit logic of
generating verbal explanation to general situations by employing rule-based
explanations as training data. The learning model is shown to have better
flexibility and generalizability than the static rule-based model. The
performance of both models is evaluated quantitatively through objective
metrics. The results show that verbal explanation generated by both models
improve subjective satisfaction of users towards the interpretability of DRL
systems. Additionally, seven variants of the learning model are designed to
illustrate the contribution of input channels, attention mechanism, and
proposed encoder in improving the quality of verbal explanation.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 13:21:19 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Dec 2020 13:24:37 GMT"
}
] | 1,608,854,400,000 | [
[
"Wang",
"Xinzhi",
""
],
[
"Li",
"Huao",
""
],
[
"Zhang",
"Hui",
""
],
[
"Lewis",
"Michael",
""
],
[
"Sycara",
"Katia",
""
]
] |
2008.01519 | George Baryannis | George Baryannis, Ilias Tachmazidis, Sotiris Batsakis, Grigoris
Antoniou, Mario Alviano, Emmanuel Papadakis | A Generalised Approach for Encoding and Reasoning with Qualitative
Theories in Answer Set Programming | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 18 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative reasoning involves expressing and deriving knowledge based on
qualitative terms such as natural language expressions, rather than strict
mathematical quantities. Well over 40 qualitative calculi have been proposed so
far, mostly in the spatial and temporal domains, with several practical
applications such as naval traffic monitoring, warehouse process optimisation
and robot manipulation. Even if a number of specialised qualitative reasoning
tools have been developed so far, an important barrier to the wider adoption of
these tools is that only qualitative reasoning is supported natively, when
real-world problems most often require a combination of qualitative and other
forms of reasoning. In this work, we propose to overcome this barrier by using
ASP as a unifying formalism to tackle problems that require qualitative
reasoning in addition to non-qualitative reasoning. A family of ASP encodings
is proposed which can handle any qualitative calculus with binary relations.
These encodings are experimentally evaluated using a real-world dataset based
on a case study of determining optimal coverage of telecommunication antennas,
and compared with the performance of two well-known dedicated reasoners.
Experimental results show that the proposed encodings outperform one of the two
reasoners, but fall behind the other, an acceptable trade-off given the added
benefits of handling any type of reasoning as well as the interpretability of
logic programs. This paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 13:31:25 GMT"
}
] | 1,596,585,600,000 | [
[
"Baryannis",
"George",
""
],
[
"Tachmazidis",
"Ilias",
""
],
[
"Batsakis",
"Sotiris",
""
],
[
"Antoniou",
"Grigoris",
""
],
[
"Alviano",
"Mario",
""
],
[
"Papadakis",
"Emmanuel",
""
]
] |
2008.01700 | Athirai A. Irissappane | Neil Hulbert, Sam Spillers, Brandon Francis, James Haines-Temons, Ken
Gil Romero, Benjamin De Jager, Sam Wong, Kevin Flora, Bowei Huang, Athirai A.
Irissappane | EasyRL: A Simple and Extensible Reinforcement Learning Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, Reinforcement Learning (RL), has become a popular field of
study as well as a tool for enterprises working on cutting-edge artificial
intelligence research. To this end, many researchers have built RL frameworks
such as openAI Gym and KerasRL for ease of use. While these works have made
great strides towards bringing down the barrier of entry for those new to RL,
we propose a much simpler framework called EasyRL, by providing an interactive
graphical user interface for users to train and evaluate RL agents. As it is
entirely graphical, EasyRL does not require programming knowledge for training
and testing simple built-in RL agents. EasyRL also supports custom RL agents
and environments, which can be highly beneficial for RL researchers in
evaluating and comparing their RL models.
| [
{
"version": "v1",
"created": "Tue, 4 Aug 2020 17:02:56 GMT"
},
{
"version": "v2",
"created": "Thu, 5 Nov 2020 20:35:33 GMT"
}
] | 1,604,880,000,000 | [
[
"Hulbert",
"Neil",
""
],
[
"Spillers",
"Sam",
""
],
[
"Francis",
"Brandon",
""
],
[
"Haines-Temons",
"James",
""
],
[
"Romero",
"Ken Gil",
""
],
[
"De Jager",
"Benjamin",
""
],
[
"Wong",
"Sam",
""
],
[
"Flora",
"Kevin",
""
],
[
"Huang",
"Bowei",
""
],
[
"Irissappane",
"Athirai A.",
""
]
] |
2008.02708 | Hrithwik Shalu | Joseph Stember, Hrithwik Shalu | Deep reinforcement learning to detect brain lesions on MRI: a
proof-of-concept application of reinforcement learning to medical images | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated
data sets. 2) Non-generalizability that limits deployment to new scanners /
institutions. And 3) Inadequate explainability and interpretability. We believe
that reinforcement learning can address all three shortcomings, with robust and
intuitive algorithms trainable on small datasets. To the best of our knowledge,
reinforcement learning has not been directly applied to computer vision tasks
for radiological images. In this proof-of-principle work, we train a deep
reinforcement learning network to predict brain tumor location.
Materials and Methods: Using the BraTS brain tumor imaging database, we
trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We
did so in concert with image exploration, with rewards and punishments designed
to localize lesions. To compare with supervised deep learning, we trained a
keypoint detection convolutional neural network on the same 70 images. We
applied both approaches to a separate 30 image testing set.
Results: Reinforcement learning predictions consistently improved during
training, whereas those of supervised deep learning quickly diverged.
Reinforcement learning predicted testing set lesion locations with 85%
accuracy, compared to roughly 7% accuracy for the supervised deep network.
Conclusion: Reinforcement learning predicted lesions with high accuracy,
which is unprecedented for such a small training set. We believe that
reinforcement learning can propel radiology AI well past the inherent
limitations of supervised deep learning, with more clinician-driven research
and finally toward true clinical applicability.
| [
{
"version": "v1",
"created": "Thu, 6 Aug 2020 15:26:28 GMT"
}
] | 1,596,758,400,000 | [
[
"Stember",
"Joseph",
""
],
[
"Shalu",
"Hrithwik",
""
]
] |
2008.02735 | Kenneth Skiba | Kenneth Skiba and Matthias Thimm | Towards Ranking-based Semantics for Abstract Argumentation using
Conditional Logic Semantics | arXiv admin note: substantial text overlap with arXiv:2006.12020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel ranking-based semantics for Dung-style argumentation
frameworks with the help of conditional logics. Using an intuitive translation
for an argumentation framework to generate conditionals, we can apply
nonmonotonic inference systems to generate a ranking on possible worlds. With
this ranking we construct a ranking for our arguments. With a small extension
to this ranking-based semantics we already satisfy some desirable properties
for a ranking over arguments.
| [
{
"version": "v1",
"created": "Wed, 5 Aug 2020 08:34:16 GMT"
}
] | 1,596,758,400,000 | [
[
"Skiba",
"Kenneth",
""
],
[
"Thimm",
"Matthias",
""
]
] |
2008.03007 | Agostino Dovier | Alessandro Burigana, Francesco Fabiano, Agostino Dovier, Enrico
Pontelli | Modelling Multi-Agent Epistemic Planning in ASP | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2019), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages | Theory and Practice of Logic Programming 20 (2020) 593-608 | 10.1017/S1471068420000289 | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Designing agents that reason and act upon the world has always been one of
the main objectives of the Artificial Intelligence community. While for
planning in "simple" domains the agents can solely rely on facts about the
world, in several contexts, e.g., economy, security, justice and politics, the
mere knowledge of the world could be insufficient to reach a desired goal. In
these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs
about themselves and about other agents' beliefs, is essential to design
winning strategies.
This paper addresses the problem of reasoning in multi-agent epistemic
settings exploiting declarative programming techniques. In particular, the
paper presents an actual implementation of a multi-shot Answer Set
Programming-based planner that can reason in multi-agent epistemic settings,
called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP
paradigm enables a concise and elegant design of the planner, w.r.t. other
imperative implementations, facilitating the development of formal verification
of correctness.
The paper shows how the planner, exploiting an ad-hoc epistemic state
representation and the efficiency of ASP solvers, has competitive performance
results on benchmarks collected from the literature. It is under consideration
for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Fri, 7 Aug 2020 06:35:56 GMT"
}
] | 1,600,819,200,000 | [
[
"Burigana",
"Alessandro",
""
],
[
"Fabiano",
"Francesco",
""
],
[
"Dovier",
"Agostino",
""
],
[
"Pontelli",
"Enrico",
""
]
] |
2008.03100 | Richard Taupe | Richard Taupe, Antonius Weinzierl, Gerhard Friedrich | Conflict Generalisation in ASP: Learning Correct and Effective
Non-Ground Constraints | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalising and re-using knowledge learned while solving one problem
instance has been neglected by state-of-the-art answer set solvers. We suggest
a new approach that generalises learned nogoods for re-use to speed-up the
solving of future problem instances. Our solution combines well-known ASP
solving techniques with deductive logic-based machine learning. Solving
performance can be improved by adding learned non-ground constraints to the
original program. We demonstrate the effects of our method by means of
realistic examples, showing that our approach requires low computational cost
to learn constraints that yield significant performance benefits in our test
cases. These benefits can be seen with ground-and-solve systems as well as
lazy-grounding systems. However, ground-and-solve systems suffer from
additional grounding overheads, induced by the additional constraints in some
cases. By means of conflict minimization, non-minimal learned constraints can
be reduced. This can result in significant reductions of grounding and solving
efforts, as our experiments show. (Under consideration for acceptance in TPLP.)
| [
{
"version": "v1",
"created": "Fri, 7 Aug 2020 12:02:32 GMT"
}
] | 1,597,017,600,000 | [
[
"Taupe",
"Richard",
""
],
[
"Weinzierl",
"Antonius",
""
],
[
"Friedrich",
"Gerhard",
""
]
] |
2008.03212 | Konstantin Schekotihin | Carmine Dodaro, Thomas Eiter, Paul Ogris, Konstantin Schekotihin | Managing caching strategies for stream reasoning with reinforcement
learning | Paper presented at the 36th International Conference on Logic
Programming (ICLP 2019), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficient decision-making over continuously changing data is essential for
many application domains such as cyber-physical systems, industry
digitalization, etc. Modern stream reasoning frameworks allow one to model and
solve various real-world problems using incremental and continuous evaluation
of programs as new data arrives in the stream. Applied techniques use, e.g.,
Datalog-like materialization or truth maintenance algorithms to avoid costly
re-computations, thus ensuring low latency and high throughput of a stream
reasoner. However, the expressiveness of existing approaches is quite limited
and, e.g., they cannot be used to encode problems with constraints, which often
appear in practice. In this paper, we suggest a novel approach that uses the
Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy
solutions by using intelligent management of learned constraints. In
particular, we study the applicability of reinforcement learning to
continuously assess the utility of learned constraints computed in previous
invocations of the solving algorithm for the current one. Evaluations conducted
on real-world reconfiguration problems show that providing a CDCL algorithm
with relevant learned constraints from previous iterations results in
significant performance improvements of the algorithm in stream reasoning
scenarios.
Under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Fri, 7 Aug 2020 15:01:41 GMT"
}
] | 1,597,017,600,000 | [
[
"Dodaro",
"Carmine",
""
],
[
"Eiter",
"Thomas",
""
],
[
"Ogris",
"Paul",
""
],
[
"Schekotihin",
"Konstantin",
""
]
] |
2008.03444 | Xinyi Xu Mr | Xinyi Xu and Tiancheng Huang and Pengfei Wei and Akshay Narayan and
Tze-Yun Leong | Hierarchical Reinforcement Learning in StarCraft II with Human Expertise
in Subgoals Selection | In Submission to AAMAS 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work is inspired by recent advances in hierarchical reinforcement
learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in
learning efficiency from heuristic-based subgoal selection, experience replay
(Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning
(Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to
integrate HRL, experience replay and effective subgoal selection through an
implicit curriculum design based on human expertise to support sample-efficient
learning and enhance interpretability of the agent's behavior. Human expertise
remains indispensable in many areas such as medicine (Buch, Ahmed, and
Maruthappu 2018) and law (Cath 2018), where interpretability, explainability
and transparency are crucial in the decision making process, for ethical and
legal reasons. Our method simplifies the complex task sets for achieving the
overall objectives by decomposing them into subgoals at different levels of
abstraction. Incorporating relevant subjective knowledge also significantly
reduces the computational resources spent in exploration for RL, especially in
high speed, changing, and complex environments where the transition dynamics
cannot be effectively learned and modelled in a short time. Experimental
results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate
that our method can achieve better sample efficiency than flat and end-to-end
RL methods, and provides an effective method for explaining the agent's
performance.
| [
{
"version": "v1",
"created": "Sat, 8 Aug 2020 04:56:30 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Sep 2020 00:15:12 GMT"
},
{
"version": "v3",
"created": "Tue, 29 Sep 2020 01:15:05 GMT"
}
] | 1,601,424,000,000 | [
[
"Xu",
"Xinyi",
""
],
[
"Huang",
"Tiancheng",
""
],
[
"Wei",
"Pengfei",
""
],
[
"Narayan",
"Akshay",
""
],
[
"Leong",
"Tze-Yun",
""
]
] |
2008.03518 | Joshua Bertram | Joshua R Bertram, Peng Wei, Joseph Zambreno | Scalable FastMDP for Pre-departure Airspace Reservation and Strategic
De-conflict | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo
delivery drones will require on-demand scheduling of large numbers of aircraft.
We examine the scalability of an algorithm known as FastMDP which was shown to
perform well in deconflicting many dozens of aircraft in a dense airspace
environment with terrain. We show that the algorithm can adapted to perform
first-come-first-served pre-departure flight plan scheduling where conflict
free flight plans are generated on demand. We demonstrate a parallelized
implementation of the algorithm on a Graphics Processor Unit (GPU) which we
term FastMDP-GPU and show the level of performance and scaling that can be
achieved. Our results show that on commodity GPU hardware we can perform flight
plan scheduling against 2000-3000 known flight plans and with server-class
hardware the performance can be higher. We believe the results show promise for
implementing a large scale UAM scheduler capable of performing on-demand flight
scheduling that would be suitable for both a centralized or distributed flight
planning system
| [
{
"version": "v1",
"created": "Sat, 8 Aug 2020 13:25:09 GMT"
}
] | 1,597,104,000,000 | [
[
"Bertram",
"Joshua R",
""
],
[
"Wei",
"Peng",
""
],
[
"Zambreno",
"Joseph",
""
]
] |
2008.03900 | David Martin | David L. Martin, Peter F. Patel-Schneider | Wikidata Constraints on MARS (Extended Technical Report) | 22 pages, no figures. V2 includes a title change, revision of the
abstract, and a handful of minor changes in the body of the paper and the
appendix | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wikidata constraints, albeit useful, are represented and processed in an
incomplete, ad hoc fashion. Constraint declarations do not fully express their
meaning, and thus do not provide a precise, unambiguous basis for constraint
specification, or a logical foundation for constraint-checking implementations.
In prior work we have proposed a logical framework for Wikidata as a whole,
based on multi-attributed relational structures (MARS) and related logical
languages. In this paper we explain how constraints are handled in the proposed
framework, and show that nearly all of Wikidata's existing property constraints
can be completely characterized in it, in a natural and economical fashion. We
also give characterizations for several proposed property constraints, and show
that a variety of non-property constraints can be handled in the same
framework.
| [
{
"version": "v1",
"created": "Mon, 10 Aug 2020 04:49:02 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Aug 2020 02:57:00 GMT"
}
] | 1,597,708,800,000 | [
[
"Martin",
"David L.",
""
],
[
"Patel-Schneider",
"Peter F.",
""
]
] |
2008.04600 | Nir Lipovetzky | Gang Chen, Yi Ding, Hugo Edwards, Chong Hin Chau, Sai Hou, Grace
Johnson, Mohammed Sharukh Syed, Haoyuan Tang, Yue Wu, Ye Yan, Gil Tidhar and
Nir Lipovetzky | Planimation | Best ICAPS 19 - Systen Demo Award - technical report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planimation is a modular and extensible open source framework to visualise
sequential solutions of planning problems specified in PDDL. We introduce a
preliminary declarative PDDL-like animation profile specification, expressive
enough to synthesise animations of arbitrary initial states and goals of a
benchmark with just a single profile.
| [
{
"version": "v1",
"created": "Tue, 11 Aug 2020 09:32:24 GMT"
}
] | 1,597,190,400,000 | [
[
"Chen",
"Gang",
""
],
[
"Ding",
"Yi",
""
],
[
"Edwards",
"Hugo",
""
],
[
"Chau",
"Chong Hin",
""
],
[
"Hou",
"Sai",
""
],
[
"Johnson",
"Grace",
""
],
[
"Syed",
"Mohammed Sharukh",
""
],
[
"Tang",
"Haoyuan",
""
],
[
"Wu",
"Yue",
""
],
[
"Yan",
"Ye",
""
],
[
"Tidhar",
"Gil",
""
],
[
"Lipovetzky",
"Nir",
""
]
] |
2008.04793 | Andrzej Cichocki | Andrzej Cichocki and Alexander P. Kuleshov | Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles | 19 Figures, 27 pages | Computational Intelligence and Neuroscience (2020) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article discusses some trends and concepts in developing new generation
of future Artificial General Intelligence (AGI) systems which relate to complex
facets and different types of human intelligence, especially social, emotional,
attentional and ethical intelligence. We describe various aspects of multiple
human intelligences and learning styles, which may impact on a variety of AI
problem domains. Using the concept of 'multiple intelligences' rather than a
single type of intelligence, we categorize and provide working definitions of
various AGI depending on their cognitive skills or capacities. Future AI
systems will be able not only to communicate with human users and each other,
but also to efficiently exchange knowledge and wisdom with abilities of
cooperation, collaboration and even co-creating something new and valuable and
have meta-learning capacities. Multi-agent systems such as these can be used to
solve problems that would be difficult to solve by any individual intelligent
agent.
Key words: Artificial General Intelligence (AGI), multiple intelligences,
learning styles, physical intelligence, emotional intelligence, social
intelligence, attentional intelligence, moral-ethical intelligence, responsible
decision making, creative-innovative intelligence, cognitive functions,
meta-learning of AI systems.
| [
{
"version": "v1",
"created": "Fri, 7 Aug 2020 21:00:13 GMT"
},
{
"version": "v2",
"created": "Sun, 30 Aug 2020 22:46:43 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Dec 2020 23:08:57 GMT"
},
{
"version": "v4",
"created": "Fri, 11 Dec 2020 10:38:05 GMT"
}
] | 1,607,904,000,000 | [
[
"Cichocki",
"Andrzej",
""
],
[
"Kuleshov",
"Alexander P.",
""
]
] |
2008.04875 | Andrew W. E. McDonald | Andrew W.E. McDonald, Sean Grimes, David E. Breen | Ortus: an Emotion-Driven Approach to (artificial) Biological
Intelligence | \c{opyright} 2017 Massachusetts Institute of Technology Published
under a Creative Commons Attribution 4.0 International | European Conference on Artificial Life 2017 | 10.7551/ecal_a_086 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ortus is a simple virtual organism that also serves as an initial framework
for investigating and developing biologically-based artificial intelligence.
Born from a goal to create complex virtual intelligence and an initial attempt
to model C. elegans, Ortus implements a number of mechanisms observed in
organic nervous systems, and attempts to fill in unknowns based upon plausible
biological implementations and psychological observations. Implemented
mechanisms include excitatory and inhibitory chemical synapses, bidirectional
gap junctions, and Hebbian learning with its Stentian extension. We present an
initial experiment that showcases Ortus' fundamental principles; specifically,
a cyclic respiratory circuit, and emotionally-driven associative learning with
respect to an input stimulus. Finally, we discuss the implications and future
directions for Ortus and similar systems.
| [
{
"version": "v1",
"created": "Tue, 11 Aug 2020 17:29:10 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Feb 2021 22:39:06 GMT"
}
] | 1,613,606,400,000 | [
[
"McDonald",
"Andrew W. E.",
""
],
[
"Grimes",
"Sean",
""
],
[
"Breen",
"David E.",
""
]
] |
2008.05297 | Umberto Straccia | Franco Alberto Cardillo and Umberto Straccia | Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued
Boosting | null | null | 10.1016/j.fss.2021.07.002 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | OWL ontologies are nowadays a quite popular way to describe structured
knowledge in terms of classes, relations among classes and class instances. In
this paper, given a target class T of an OWL ontology, we address the problem
of learning fuzzy concept inclusion axioms that describe sufficient conditions
for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST
that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL
case. We illustrate its effectiveness by means of an experimentation. An
interesting feature is that the learned rules can be represented directly into
Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to
automatically determine/classify (and to which degree) whether an individual
belongs to the target class T.
| [
{
"version": "v1",
"created": "Mon, 3 Aug 2020 15:19:31 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Mar 2021 07:10:04 GMT"
}
] | 1,646,870,400,000 | [
[
"Cardillo",
"Franco Alberto",
""
],
[
"Straccia",
"Umberto",
""
]
] |
2008.05585 | Zhili Zhang | Zhili Zhang and Quanyan Zhu | Deceptive Kernel Function on Observations of Discrete POMDP | 22 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the deception applied on agent in a partially observable
Markov decision process. We introduce deceptive kernel function (the kernel)
applied to agent's observations in a discrete POMDP. Based on value iteration,
value function approximation and POMCP three characteristic algorithms used by
agent, we analyze its belief being misled by falsified observations as the
kernel's outputs and anticipate its probable threat on agent's reward and
potentially other performance. We validate our expectation and explore more
detrimental effects of the deception by experimenting on two POMDP problems.
The result shows that the kernel applied on agent's observation can affect its
belief and substantially lower its resulting rewards; meantime certain
implementation of the kernel could induce other abnormal behaviors by the
agent.
| [
{
"version": "v1",
"created": "Wed, 12 Aug 2020 21:59:42 GMT"
}
] | 1,597,363,200,000 | [
[
"Zhang",
"Zhili",
""
],
[
"Zhu",
"Quanyan",
""
]
] |
2008.06313 | Zelong Yang | Zelong Yang, Zhufeng Pan, Yan Wang, Deng Cai, Xiaojiang Liu, Shuming
Shi, Shao-Lun Huang | Interpretable Real-Time Win Prediction for Honor of Kings, a Popular
Mobile MOBA Esport | 10 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid prevalence and explosive development of MOBA esports
(Multiplayer Online Battle Arena electronic sports), much research effort has
been devoted to automatically predicting game results (win predictions). While
this task has great potential in various applications, such as esports live
streaming and game commentator AI systems, previous studies fail to investigate
the methods to interpret these win predictions. To mitigate this issue, we
collected a large-scale dataset that contains real-time game records with rich
input features of the popular MOBA game Honor of Kings. For interpretable
predictions, we proposed a Two-Stage Spatial-Temporal Network (TSSTN) that can
not only provide accurate real-time win predictions but also attribute the
ultimate prediction results to the contributions of different features for
interpretability. Experiment results and applications in real-world live
streaming scenarios showed that the proposed TSSTN model is effective both in
prediction accuracy and interpretability.
| [
{
"version": "v1",
"created": "Fri, 14 Aug 2020 12:00:58 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Sep 2020 03:38:28 GMT"
},
{
"version": "v3",
"created": "Fri, 16 Apr 2021 11:29:02 GMT"
}
] | 1,618,790,400,000 | [
[
"Yang",
"Zelong",
""
],
[
"Pan",
"Zhufeng",
""
],
[
"Wang",
"Yan",
""
],
[
"Cai",
"Deng",
""
],
[
"Liu",
"Xiaojiang",
""
],
[
"Shi",
"Shuming",
""
],
[
"Huang",
"Shao-Lun",
""
]
] |
2008.06599 | Peter Patel-Schneider | Peter F. Patel-Schneider and David Martin | Wikidata on MARS | arXiv admin note: text overlap with arXiv:2008.03900 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-attributed relational structures (MARSs) have been proposed as a formal
data model for generalized property graphs, along with multi-attributed
rule-based predicate logic (MARPL) as a useful rule-based logic in which to
write inference rules over property graphs. Wikidata can be modelled in an
extended MARS that adds the (imprecise) datatypes of Wikidata. The rules of
inference for the Wikidata ontology can be modelled as a MARPL ontology, with
extensions to handle the Wikidata datatypes and functions over these datatypes.
Because many Wikidata qualifiers should participate in most inference rules in
Wikidata a method of implicitly handling qualifier values on a per-qualifier
basis is needed to make this modelling useful. The meaning of Wikidata is then
the extended MARS that is the closure of running these rules on the Wikidata
data model. Wikidata constraints can be modelled as multi-attributed predicate
logic (MAPL) formulae, again extended with datatypes, that are evaluated over
this extended MARS. The result models Wikidata in a way that fixes several of
its major problems.
| [
{
"version": "v1",
"created": "Fri, 14 Aug 2020 22:58:04 GMT"
}
] | 1,597,708,800,000 | [
[
"Patel-Schneider",
"Peter F.",
""
],
[
"Martin",
"David",
""
]
] |
2008.06693 | Alexandre Heuillet | Alexandre Heuillet, Fabien Couthouis and Natalia D\'iaz-Rodr\'iguez | Explainability in Deep Reinforcement Learning | Article accepted at Knowledge-Based Systems | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A large set of the explainable Artificial Intelligence (XAI) literature is
emerging on feature relevance techniques to explain a deep neural network (DNN)
output or explaining models that ingest image source data. However, assessing
how XAI techniques can help understand models beyond classification tasks, e.g.
for reinforcement learning (RL), has not been extensively studied. We review
recent works in the direction to attain Explainable Reinforcement Learning
(XRL), a relatively new subfield of Explainable Artificial Intelligence,
intended to be used in general public applications, with diverse audiences,
requiring ethical, responsible and trustable algorithms. In critical situations
where it is essential to justify and explain the agent's behaviour, better
explainability and interpretability of RL models could help gain scientific
insight on the inner workings of what is still considered a black box. We
evaluate mainly studies directly linking explainability to RL, and split these
into two categories according to the way the explanations are generated:
transparent algorithms and post-hoc explainaility. We also review the most
prominent XAI works from the lenses of how they could potentially enlighten the
further deployment of the latest advances in RL, in the demanding present and
future of everyday problems.
| [
{
"version": "v1",
"created": "Sat, 15 Aug 2020 10:11:42 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Aug 2020 09:15:07 GMT"
},
{
"version": "v3",
"created": "Fri, 11 Dec 2020 17:14:08 GMT"
},
{
"version": "v4",
"created": "Fri, 18 Dec 2020 10:08:51 GMT"
}
] | 1,608,508,800,000 | [
[
"Heuillet",
"Alexandre",
""
],
[
"Couthouis",
"Fabien",
""
],
[
"Díaz-Rodríguez",
"Natalia",
""
]
] |
2008.07463 | Alessandro Artale | Sabiha Tahrat, German Braun, Alessandro Artale, Marco Gario, and Ana
Ozaki | Automated Reasoning in Temporal DL-Lite | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the feasibility of automated reasoning over temporal
DL-Lite (TDL-Lite) knowledge bases (KBs). We test the usage of off-the-shelf
LTL reasoners to check satisfiability of TDL-Lite KBs. In particular, we test
the robustness and the scalability of reasoners when dealing with TDL-Lite
TBoxes paired with a temporal ABox. We conduct various experiments to analyse
the performance of different reasoners by randomly generating TDL-Lite KBs and
then measuring the running time and the size of the translations. Furthermore,
in an effort to make the usage of TDL-Lite KBs a reality, we present a fully
fledged tool with a graphical interface to design them. Our interface is based
on conceptual modelling principles and it is integrated with our translation
tool and a temporal reasoner.
| [
{
"version": "v1",
"created": "Mon, 17 Aug 2020 16:40:27 GMT"
}
] | 1,597,708,800,000 | [
[
"Tahrat",
"Sabiha",
""
],
[
"Braun",
"German",
""
],
[
"Artale",
"Alessandro",
""
],
[
"Gario",
"Marco",
""
],
[
"Ozaki",
"Ana",
""
]
] |
2008.07667 | Weichao Zhou | Weichao Zhou, Ruihan Gao, BaekGyu Kim, Eunsuk Kang, Wenchao Li | Runtime-Safety-Guided Policy Repair | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of policy repair for learning-based control policies in
safety-critical settings. We consider an architecture where a high-performance
learning-based control policy (e.g. one trained as a neural network) is paired
with a model-based safety controller. The safety controller is endowed with the
abilities to predict whether the trained policy will lead the system to an
unsafe state, and take over control when necessary. While this architecture can
provide added safety assurances, intermittent and frequent switching between
the trained policy and the safety controller can result in undesirable
behaviors and reduced performance. We propose to reduce or even eliminate
control switching by `repairing' the trained policy based on runtime data
produced by the safety controller in a way that deviates minimally from the
original policy. The key idea behind our approach is the formulation of a
trajectory optimization problem that allows the joint reasoning of policy
update and safety constraints. Experimental results demonstrate that our
approach is effective even when the system model in the safety controller is
unknown and only approximated.
| [
{
"version": "v1",
"created": "Mon, 17 Aug 2020 23:31:48 GMT"
}
] | 1,597,795,200,000 | [
[
"Zhou",
"Weichao",
""
],
[
"Gao",
"Ruihan",
""
],
[
"Kim",
"BaekGyu",
""
],
[
"Kang",
"Eunsuk",
""
],
[
"Li",
"Wenchao",
""
]
] |
2008.08114 | Filip Ilievski | Filip Ilievski, Pedro Szekely, and Daniel Schwabe | Commonsense Knowledge in Wikidata | WikiData Workshop at ISWC 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Wikidata and Wikipedia have been proven useful for reason-ing in natural
language applications, like question answering or entitylinking. Yet, no
existing work has studied the potential of Wikidata for commonsense reasoning.
This paper investigates whether Wikidata con-tains commonsense knowledge which
is complementary to existing commonsense sources. Starting from a definition of
common sense, we devise three guiding principles, and apply them to generate a
commonsense subgraph of Wikidata (Wikidata-CS). Within our approach, we map the
relations of Wikidata to ConceptNet, which we also leverage to integrate
Wikidata-CS into an existing consolidated commonsense graph. Our experiments
reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it
is an indicator that Wikidata contains relevant commonsense knowledge, which
can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS
and other commonsense sources is low, motivating the value of knowledge
integration; 3) Wikidata-CS has been evolving over time at a slightly slower
rate compared to the overall Wikidata, indicating a possible lack of focus on
commonsense knowledge. Based on these findings, we propose three recommended
actions to improve the coverage and quality of Wikidata-CS further.
| [
{
"version": "v1",
"created": "Tue, 18 Aug 2020 18:23:06 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Oct 2020 23:04:31 GMT"
}
] | 1,603,065,600,000 | [
[
"Ilievski",
"Filip",
""
],
[
"Szekely",
"Pedro",
""
],
[
"Schwabe",
"Daniel",
""
]
] |
2008.08524 | Lilith Mattei | Lilith Mattei, Alessandro Antonucci, Denis Deratani Mau\'a, Alessandro
Facchini, Julissa Villanueva Llerena | Tractable Inference in Credal Sentential Decision Diagrams | To appear in the International Journal of Approximate Reasoning (IJAR
Volume 125) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic sentential decision diagrams are logic circuits where the
inputs of disjunctive gates are annotated by probability values. They allow for
a compact representation of joint probability mass functions defined over sets
of Boolean variables, that are also consistent with the logical constraints
defined by the circuit. The probabilities in such a model are usually learned
from a set of observations. This leads to overconfident and prior-dependent
inferences when data are scarce, unreliable or conflicting. In this work, we
develop the credal sentential decision diagrams, a generalisation of their
probabilistic counterpart that allows for replacing the local probabilities
with (so-called credal) sets of mass functions. These models induce a joint
credal set over the set of Boolean variables, that sharply assigns probability
zero to states inconsistent with the logical constraints. Three inference
algorithms are derived for these models, these allow to compute: (i) the lower
and upper probabilities of an observation for an arbitrary number of variables;
(ii) the lower and upper conditional probabilities for the state of a single
variable given an observation; (iii) whether or not all the probabilistic
sentential decision diagrams compatible with the credal specification have the
same most probable explanation of a given set of variables given an observation
of the other variables. These inferences are tractable, as all the three
algorithms, based on bottom-up traversal with local linear programming tasks on
the disjunctive gates, can be solved in polynomial time with respect to the
circuit size. For a first empirical validation, we consider a simple
application based on noisy seven-segment display images. The credal models are
observed to properly distinguish between easy and hard-to-detect instances and
outperform other generative models not able to cope with logical constraints.
| [
{
"version": "v1",
"created": "Wed, 19 Aug 2020 16:04:34 GMT"
}
] | 1,597,881,600,000 | [
[
"Mattei",
"Lilith",
""
],
[
"Antonucci",
"Alessandro",
""
],
[
"Mauá",
"Denis Deratani",
""
],
[
"Facchini",
"Alessandro",
""
],
[
"Llerena",
"Julissa Villanueva",
""
]
] |
2008.08548 | Shiqi Zhang | Shiqi Zhang and Mohan Sridharan | A Survey of Knowledge-based Sequential Decision Making under Uncertainty | AI Magazine, Volume 43, Issue 2, Pages 249-266, 2022 | null | 10.1002/aaai.12053 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason
with declarative domain knowledge, including commonsense knowledge, that is
either provided a priori or acquired over time, while SDM methods
(probabilistic planning and reinforcement learning) seek to compute action
policies that maximize the expected cumulative utility over a time horizon;
both classes of methods reason in the presence of uncertainty. Despite the rich
literature in these two areas, researchers have not fully explored their
complementary strengths. In this paper, we survey algorithms that leverage RDK
methods while making sequential decisions under uncertainty. We discuss
significant developments, open problems, and directions for future work.
| [
{
"version": "v1",
"created": "Wed, 19 Aug 2020 16:48:03 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Sep 2020 15:54:38 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Jun 2022 05:38:53 GMT"
}
] | 1,656,633,600,000 | [
[
"Zhang",
"Shiqi",
""
],
[
"Sridharan",
"Mohan",
""
]
] |
2008.09067 | Toby Walsh | Toby Walsh | Adventures in Mathematical Reasoning | To appear in "DReaM On: 45 years of Automated Reasoning", a
festschrift for Alan Bundy published by Springer-Verlag | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Mathematics is not a careful march down a well-cleared highway, but a
journey into a strange wilderness, where the explorers often get lost. Rigour
should be a signal to the historian that the maps have been made, and the real
explorers have gone elsewhere." W.S. Anglin, the Mathematical Intelligencer, 4
(4), 1982.
| [
{
"version": "v1",
"created": "Thu, 20 Aug 2020 16:41:18 GMT"
}
] | 1,597,968,000,000 | [
[
"Walsh",
"Toby",
""
]
] |
2008.09982 | Liangwei Li | Liangwei Li, Liucheng Sun, Chenwei Weng, Chengfu Huo, Weijun Ren | Spending Money Wisely: Online Electronic Coupon Allocation based on
Real-Time User Intent Detection | null | null | 10.1145/3340531.3412745 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce
platforms to attract users to place orders. E-coupons are the digital
equivalent of traditional paper coupons which provide customers with discounts
or gifts. One of the fundamental problems related is how to deliver e-coupons
with minimal cost while users' willingness to place an order is maximized. We
call this problem the coupon allocation problem. This is a non-trivial problem
since the number of regular users on a mature e-platform often reaches hundreds
of millions and the types of e-coupons to be allocated are often multiple. The
policy space is extremely large and the online allocation has to satisfy a
budget constraint. Besides, one can never observe the responses of one user
under different policies which increases the uncertainty of the policy making
process. Previous work fails to deal with these challenges. In this paper, we
decompose the coupon allocation task into two subtasks: the user intent
detection task and the allocation task. Accordingly, we propose a two-stage
solution: at the first stage (detection stage), we put forward a novel
Instantaneous Intent Detection Network (IIDN) which takes the user-coupon
features as input and predicts user real-time intents; at the second stage
(allocation stage), we model the allocation problem as a Multiple-Choice
Knapsack Problem (MCKP) and provide a computational efficient allocation method
using the intents predicted at the detection stage. We conduct extensive online
and offline experiments and the results show the superiority of our proposed
framework, which has brought great profits to the platform and continues to
function online.
| [
{
"version": "v1",
"created": "Sun, 23 Aug 2020 07:19:25 GMT"
}
] | 1,598,313,600,000 | [
[
"Li",
"Liangwei",
""
],
[
"Sun",
"Liucheng",
""
],
[
"Weng",
"Chenwei",
""
],
[
"Huo",
"Chengfu",
""
],
[
"Ren",
"Weijun",
""
]
] |
2008.10080 | Tristan Cazenave | Tristan Cazenave | Mobile Networks for Computer Go | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The architecture of the neural networks used in Deep Reinforcement Learning
programs such as Alpha Zero or Polygames has been shown to have a great impact
on the performances of the resulting playing engines. For example the use of
residual networks gave a 600 ELO increase in the strength of Alpha Go. This
paper proposes to evaluate the interest of Mobile Network for the game of Go
using supervised learning as well as the use of a policy head and a value head
different from the Alpha Zero heads. The accuracy of the policy, the mean
squared error of the value, the efficiency of the networks with the number of
parameters, the playing speed and strength of the trained networks are
evaluated.
| [
{
"version": "v1",
"created": "Sun, 23 Aug 2020 17:57:33 GMT"
}
] | 1,598,313,600,000 | [
[
"Cazenave",
"Tristan",
""
]
] |
2008.10114 | Roohallah Alizadehsani | Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain, Abbas
Khosravi, Afsaneh Koohestani, Mohammad Hossein Zangooei, Moloud Abdar, Adham
Beykikhoshk, Afshin Shoeibi, Assef Zare, Maryam Panahiazar, Saeid Nahavandi,
Dipti Srinivasan, Amir F. Atiya, U. Rajendra Acharya | Handling of uncertainty in medical data using machine learning and
probability theory techniques: A review of 30 years (1991-2020) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding data and reaching valid conclusions are of paramount importance
in the present era of big data. Machine learning and probability theory methods
have widespread application for this purpose in different fields. One
critically important yet less explored aspect is how data and model
uncertainties are captured and analyzed. Proper quantification of uncertainty
provides valuable information for optimal decision making. This paper reviewed
related studies conducted in the last 30 years (from 1991 to 2020) in handling
uncertainties in medical data using probability theory and machine learning
techniques. Medical data is more prone to uncertainty due to the presence of
noise in the data. So, it is very important to have clean medical data without
any noise to get accurate diagnosis. The sources of noise in the medical data
need to be known to address this issue. Based on the medical data obtained by
the physician, diagnosis of disease, and treatment plan are prescribed. Hence,
the uncertainty is growing in healthcare and there is limited knowledge to
address these problems. We have little knowledge about the optimal treatment
methods as there are many sources of uncertainty in medical science. Our
findings indicate that there are few challenges to be addressed in handling the
uncertainty in medical raw data and new models. In this work, we have
summarized various methods employed to overcome this problem. Nowadays,
application of novel deep learning techniques to deal such uncertainties have
significantly increased.
| [
{
"version": "v1",
"created": "Sun, 23 Aug 2020 21:54:27 GMT"
}
] | 1,598,313,600,000 | [
[
"Alizadehsani",
"Roohallah",
""
],
[
"Roshanzamir",
"Mohamad",
""
],
[
"Hussain",
"Sadiq",
""
],
[
"Khosravi",
"Abbas",
""
],
[
"Koohestani",
"Afsaneh",
""
],
[
"Zangooei",
"Mohammad Hossein",
""
],
[
"Abdar",
"Moloud",
""
],
[
"Beykikhoshk",
"Adham",
""
],
[
"Shoeibi",
"Afshin",
""
],
[
"Zare",
"Assef",
""
],
[
"Panahiazar",
"Maryam",
""
],
[
"Nahavandi",
"Saeid",
""
],
[
"Srinivasan",
"Dipti",
""
],
[
"Atiya",
"Amir F.",
""
],
[
"Acharya",
"U. Rajendra",
""
]
] |
2008.10386 | Evgenii Safronov | Evgenii Safronov, Michele Colledanchise and Lorenzo Natale | Compact Belief State Representation for Task Planning | Accepted to CASE 2020 16th IEEE International Conference on
Automation Science and Engineering | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Task planning in a probabilistic belief state domains allows generating
complex and robust execution policies in those domains affected by state
uncertainty. The performance of a task planner relies on the belief state
representation. However, current belief state representation becomes easily
intractable as the number of variables and execution time grows. To address
this problem, we developed a novel belief state representation based on
cartesian product and union operations over belief substates. These two
operations and single variable assignment nodes form And-Or directed acyclic
graph of Belief State (AOBS). We show how to apply actions with probabilistic
outcomes and measure the probability of conditions holding over belief state.
We evaluated AOBS performance in simulated forward state space exploration. We
compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that
were previously used to represent belief state. We show that AOBS
representation is not only much more compact than a full belief state but it
also scales better than BDD for most of the cases.
| [
{
"version": "v1",
"created": "Fri, 21 Aug 2020 09:38:36 GMT"
}
] | 1,598,313,600,000 | [
[
"Safronov",
"Evgenii",
""
],
[
"Colledanchise",
"Michele",
""
],
[
"Natale",
"Lorenzo",
""
]
] |
2008.10401 | Mark Dukes Dr | Mark Dukes, Anthony A. Casey | Combinatorial diversity metrics for the analysis of policy processes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present several completely general diversity metrics to quantify the
problem-solving capacity of any public policy decision making process. This is
performed by modelling the policy process using a declarative process paradigm
in conjunction with constraints modelled by expressions in linear temporal
logic. We introduce a class of traces, called first-passage traces, to
represent the different executions of the declarative processes. Heuristics of
what properties a diversity measure of such processes ought to satisfy are used
to derive two different metrics for these processes in terms of the set of
first-passage traces. These metrics turn out to have formulations in terms of
the entropies of two different random variables on the set of traces of the
processes. In addition, we introduce a measure of `goodness' whereby a trace is
termed {\it good} if it satisfies some prescribed linear temporal logic
expression. This allows for comparisons of policy processes with respect to the
prescribed notion of `goodness'.
| [
{
"version": "v1",
"created": "Wed, 19 Aug 2020 19:46:29 GMT"
}
] | 1,598,313,600,000 | [
[
"Dukes",
"Mark",
""
],
[
"Casey",
"Anthony A.",
""
]
] |
2008.11258 | Megan Charity | Megan Charity, Dipika Rajesh, Rachel Ombok, L. B. Soros | Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI | 7 pages, Accepted as poster to AIIDE 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes environment design in the life simulation game The Sims
as a novel platform and challenge for testing divergent search algorithms. In
this domain, which includes a minimal viability criterion, the goal is to
furnish a house with objects that satisfy the physical needs of a simulated
agent. Importantly, the large number of objects available to the player
(whether human or automated) affords a wide variety of solutions to the
underlying design problem. Empirical studies in a novel open source simulator
called SimSim investigate the ability of novelty-based evolutionary algorithms
to effectively generate viable environment designs.
| [
{
"version": "v1",
"created": "Tue, 25 Aug 2020 20:31:26 GMT"
}
] | 1,598,486,400,000 | [
[
"Charity",
"Megan",
""
],
[
"Rajesh",
"Dipika",
""
],
[
"Ombok",
"Rachel",
""
],
[
"Soros",
"L. B.",
""
]
] |
2008.12879 | Ozkan Kilic | Hugo Latapie and Ozkan Kilic | A Metamodel and Framework for AGI | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Can artificial intelligence systems exhibit superhuman performance, but in
critical ways, lack the intelligence of even a single-celled organism? The
answer is clearly 'yes' for narrow AI systems. Animals, plants, and even
single-celled organisms learn to reliably avoid danger and move towards food.
This is accomplished via a physical knowledge preserving metamodel that
autonomously generates useful models of the world. We posit that preserving the
structure of knowledge is critical for higher intelligences that manage
increasingly higher levels of abstraction, be they human or artificial. This is
the key lesson learned from applying AGI subsystems to complex real-world
problems that require continuous learning and adaptation. In this paper, we
introduce the Deep Fusion Reasoning Engine (DFRE), which implements a
knowledge-preserving metamodel and framework for constructing applied AGI
systems. The DFRE metamodel exhibits some important fundamental knowledge
preserving properties such as clear distinctions between symmetric and
antisymmetric relations, and the ability to create a hierarchical knowledge
representation that clearly delineates between levels of abstraction. The DFRE
metamodel, which incorporates these capabilities, demonstrates how this
approach benefits AGI in specific ways such as managing combinatorial explosion
and enabling cumulative, distributed and federated learning. Our experiments
show that the proposed framework achieves 94% accuracy on average on
unsupervised object detection and recognition. This work is inspired by the
state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular
computing community, as well as Alfred Korzybski's general semantics.
| [
{
"version": "v1",
"created": "Fri, 28 Aug 2020 23:34:21 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Sep 2020 23:36:32 GMT"
}
] | 1,599,523,200,000 | [
[
"Latapie",
"Hugo",
""
],
[
"Kilic",
"Ozkan",
""
]
] |
2008.12937 | Shaghayegh Roohi | Shaghayegh Roohi (1), Asko Relas (2), Jari Takatalo (2), Henri
Heiskanen (2), Perttu H\"am\"al\"ainen (1) ((1) Aalto University, Espoo,
Finland, (2) Rovio Entertainment, Espoo, Finland) | Predicting Game Difficulty and Churn Without Players | 9 pages, 9 figures, In Proceedings of the Annual Symposium on
Computer-Human Interaction in Play (CHI PLAY '20) | null | 10.1145/3410404.3414235 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel simulation model that is able to predict the per-level
churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play
game. Our primary contribution is to combine AI gameplay using Deep
Reinforcement Learning (DRL) with a simulation of how the player population
evolves over the levels. The AI players predict level difficulty, which is used
to drive a player population model with simulated skill, persistence, and
boredom. This allows us to model, e.g., how less persistent and skilled players
are more sensitive to high difficulty, and how such players churn early, which
makes the player population and the relation between difficulty and churn
evolve level by level. Our work demonstrates that player behavior predictions
produced by DRL gameplay can be significantly improved by even a very simple
population-level simulation of individual player differences, without requiring
costly retraining of agents or collecting new DRL gameplay data for each
simulated player.
| [
{
"version": "v1",
"created": "Sat, 29 Aug 2020 08:37:47 GMT"
}
] | 1,598,918,400,000 | [
[
"Roohi",
"Shaghayegh",
""
],
[
"Relas",
"Asko",
""
],
[
"Takatalo",
"Jari",
""
],
[
"Heiskanen",
"Henri",
""
],
[
"Hämäläinen",
"Perttu",
""
]
] |
2008.13146 | Arvind Kiwelekar | Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik
B Nikam | Deep Learning Techniques for Geospatial Data Analysis | This is a pre-print of the following chapter: Arvind W. Kiwelekar,
Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning
Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning
Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher
Springer, Cham reproduced with permission of publisher Springer, Cham | In Machine Learning Paradigms, pp. 63-81. Springer, Cham, 2020 | 10.1007/978-3-030-49724-8_3 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consumer electronic devices such as mobile handsets, goods tagged with RFID
labels, location and position sensors are continuously generating a vast amount
of location enriched data called geospatial data. Conventionally such
geospatial data is used for military applications. In recent times, many useful
civilian applications have been designed and deployed around such geospatial
data. For example, a recommendation system to suggest restaurants or places of
attraction to a tourist visiting a particular locality. At the same time, civic
bodies are harnessing geospatial data generated through remote sensing devices
to provide better services to citizens such as traffic monitoring, pothole
identification, and weather reporting. Typically such applications are
leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes
Classifiers, Support Vector Machines, and decision trees. Recent advances in
the field of deep-learning showed that Neural Network-based techniques
outperform conventional techniques and provide effective solutions for many
geospatial data analysis tasks such as object recognition, image
classification, and scene understanding. The chapter presents a survey on the
current state of the applications of deep learning techniques for analyzing
geospatial data.
The chapter is organized as below: (i) A brief overview of deep learning
algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii)
Deep-learning techniques for Remote Sensing data analytics tasks (iv)
Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques
for RFID data analytics.
| [
{
"version": "v1",
"created": "Sun, 30 Aug 2020 11:51:10 GMT"
}
] | 1,598,918,400,000 | [
[
"Kiwelekar",
"Arvind W.",
""
],
[
"Mahamunkar",
"Geetanjali S.",
""
],
[
"Netak",
"Laxman D.",
""
],
[
"Nikam",
"Valmik B",
""
]
] |
2008.13618 | Zhenyu A. Liao | Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek | Learning All Credible Bayesian Network Structures for Model Averaging | under review by JMLR. arXiv admin note: substantial text overlap with
arXiv:1811.05039 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Bayesian network is a widely used probabilistic graphical model with
applications in knowledge discovery and prediction. Learning a Bayesian network
(BN) from data can be cast as an optimization problem using the well-known
score-and-search approach. However, selecting a single model (i.e., the best
scoring BN) can be misleading or may not achieve the best possible accuracy. An
alternative to committing to a single model is to perform some form of Bayesian
or frequentist model averaging, where the space of possible BNs is sampled or
enumerated in some fashion. Unfortunately, existing approaches for model
averaging either severely restrict the structure of the Bayesian network or
have only been shown to scale to networks with fewer than 30 random variables.
In this paper, we propose a novel approach to model averaging inspired by
performance guarantees in approximation algorithms. Our approach has two
primary advantages. First, our approach only considers credible models in that
they are optimal or near-optimal in score. Second, our approach is more
efficient and scales to significantly larger Bayesian networks than existing
approaches.
| [
{
"version": "v1",
"created": "Thu, 27 Aug 2020 19:56:27 GMT"
}
] | 1,598,918,400,000 | [
[
"Liao",
"Zhenyu A.",
""
],
[
"Sharma",
"Charupriya",
""
],
[
"Cussens",
"James",
""
],
[
"van Beek",
"Peter",
""
]
] |
2009.00318 | Heiko Paulheim | Andreea Iana and Heiko Paulheim | More is not Always Better: The Negative Impact of A-box Materialization
on RDF2vec Knowledge Graph Embeddings | Accepted at the Workshop on Combining Symbolic and Sub-symbolic
methods and their Applications (CSSA 2020) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | RDF2vec is an embedding technique for representing knowledge graph entities
in a continuous vector space. In this paper, we investigate the effect of
materializing implicit A-box axioms induced by subproperties, as well as
symmetric and transitive properties. While it might be a reasonable assumption
that such a materialization before computing embeddings might lead to better
embeddings, we conduct a set of experiments on DBpedia which demonstrate that
the materialization actually has a negative effect on the performance of
RDF2vec. In our analysis, we argue that despite the huge body of work devoted
on completing missing information in knowledge graphs, such missing implicit
information is actually a signal, not a defect, and we show examples
illustrating that assumption.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 09:52:33 GMT"
}
] | 1,599,004,800,000 | [
[
"Iana",
"Andreea",
""
],
[
"Paulheim",
"Heiko",
""
]
] |
2009.00326 | Christophe Lecoutre | Christophe Lecoutre and Nicolas Szczepanski | PyCSP3: Modeling Combinatorial Constrained Problems in Python | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this document, we introduce PyCSP$3$, a Python library that allows us to
write models of combinatorial constrained problems in a declarative manner.
Currently, with PyCSP$3$, you can write models of constraint satisfaction and
optimization problems. More specifically, you can build CSP (Constraint
Satisfaction Problem) and COP (Constraint Optimization Problem) models.
Importantly, there is a complete separation between the modeling and solving
phases: you write a model, you compile it (while providing some data) in order
to generate an XCSP$3$ instance (file), and you solve that problem instance by
means of a constraint solver. You can also directly pilot the solving procedure
in PyCSP$3$, possibly conducting an incremental solving strategy. In this
document, you will find all that you need to know about PyCSP$3$, with more
than 50 illustrative models.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 10:11:31 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Jun 2021 16:29:31 GMT"
},
{
"version": "v3",
"created": "Sat, 18 Dec 2021 12:48:14 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Nov 2022 10:04:07 GMT"
},
{
"version": "v5",
"created": "Sun, 10 Dec 2023 12:46:50 GMT"
}
] | 1,702,339,200,000 | [
[
"Lecoutre",
"Christophe",
""
],
[
"Szczepanski",
"Nicolas",
""
]
] |
2009.00335 | Vivek Nallur | Vivek Nallur | Landscape of Machine Implemented Ethics | 25 pages | Science and Engineering Ethics (2020) | 10.1007/s11948-020-00236-y | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper surveys the state-of-the-art in machine ethics, that is,
considerations of how to implement ethical behaviour in robots, unmanned
autonomous vehicles, or software systems. The emphasis is on covering the
breadth of ethical theories being considered by implementors, as well as the
implementation techniques being used. There is no consensus on which ethical
theory is best suited for any particular domain, nor is there any agreement on
which technique is best placed to implement a particular theory. Another
unresolved problem in these implementations of ethical theories is how to
objectively validate the implementations. The paper discusses the dilemmas
being used as validating 'whetstones' and whether any alternative validation
mechanism exists. Finally, it speculates that an intermediate step of creating
domain-specific ethics might be a possible stepping stone towards creating
machines that exhibit ethical behaviour.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 10:34:59 GMT"
}
] | 1,599,004,800,000 | [
[
"Nallur",
"Vivek",
""
]
] |
2009.00514 | Christophe Lecoutre | Fr\'ed\'eric Boussemart and Christophe Lecoutre and Gilles Audemard
and C\'edric Piette | XCSP3-core: A Format for Representing Constraint
Satisfaction/Optimization Problems | arXiv admin note: substantial text overlap with arXiv:1611.03398 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us
to represent constraint satisfaction/optimization problems. The interest of
XCSP3-core is multiple: (i) focusing on the most popular frameworks (CSP and
COP) and constraints, (ii) facilitating the parsing process by means of
dedicated XCSP3-core parsers written in Java and C++ (using callback
functions), (iii) and defining a core format for comparisons (competitions) of
constraint solvers.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 15:24:49 GMT"
},
{
"version": "v2",
"created": "Sat, 16 Jan 2021 12:00:45 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Nov 2022 11:05:36 GMT"
}
] | 1,667,865,600,000 | [
[
"Boussemart",
"Frédéric",
""
],
[
"Lecoutre",
"Christophe",
""
],
[
"Audemard",
"Gilles",
""
],
[
"Piette",
"Cédric",
""
]
] |
2009.00541 | Mikhail Jacob | Mikhail Jacob, Sam Devlin, Katja Hofmann | "It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities
for Creating Agents in Commercial Games | 7 pages, 3 figures, to be published in the 16th AAAI Conference on
Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game agents such as opponents, non-player characters, and teammates are
central to player experiences in many modern games. As the landscape of AI
techniques used in the games industry evolves to adopt machine learning (ML)
more widely, it is vital that the research community learn from the best
practices cultivated within the industry over decades creating agents. However,
although commercial game agent creation pipelines are more mature than those
based on ML, opportunities for improvement still abound. As a foundation for
shared progress identifying research opportunities between researchers and
practitioners, we interviewed seventeen game agent creators from AAA studios,
indie studios, and industrial research labs about the challenges they
experienced with their professional workflows. Our study revealed several open
challenges ranging from design to implementation and evaluation. We compare
with literature from the research community that address the challenges
identified and conclude by highlighting promising directions for future
research supporting agent creation in the games industry.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 16:21:19 GMT"
}
] | 1,599,004,800,000 | [
[
"Jacob",
"Mikhail",
""
],
[
"Devlin",
"Sam",
""
],
[
"Hofmann",
"Katja",
""
]
] |
2009.00655 | Henry Ward | Henry N. Ward, Daniel J. Brooks, Dan Troha, Bobby Mills, Arseny S.
Khakhalin | AI solutions for drafting in Magic: the Gathering | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drafting in Magic the Gathering is a sub-game within a larger trading card
game, where several players progressively build decks by picking cards from a
common pool. Drafting poses an interesting problem for game and AI research due
to its large search space, mechanical complexity, multiplayer nature, and
hidden information. Despite this, drafting remains understudied, in part due to
a lack of high-quality, public datasets. To rectify this problem, we present a
dataset of over 100,000 simulated, anonymized human drafts collected from
Draftsim.com. We also propose four diverse strategies for drafting agents,
including a primitive heuristic agent, an expert-tuned complex heuristic agent,
a Naive Bayes agent, and a deep neural network agent. We benchmark their
ability to emulate human drafting, and show that the deep neural network agent
outperforms other agents, while the Naive Bayes and expert-tuned agents
outperform simple heuristics. We analyze the accuracy of AI agents across the
timeline of a draft, and describe unique strengths and weaknesses for each
approach. This work helps to identify next steps in the creation of humanlike
drafting agents, and can serve as a benchmark for the next generation of
drafting bots.
| [
{
"version": "v1",
"created": "Tue, 1 Sep 2020 18:44:10 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Sep 2020 00:51:54 GMT"
},
{
"version": "v3",
"created": "Sun, 4 Apr 2021 19:13:53 GMT"
}
] | 1,617,667,200,000 | [
[
"Ward",
"Henry N.",
""
],
[
"Brooks",
"Daniel J.",
""
],
[
"Troha",
"Dan",
""
],
[
"Mills",
"Bobby",
""
],
[
"Khakhalin",
"Arseny S.",
""
]
] |
2009.00822 | Vikas Singh | Vikas Singh, Homanga Bharadhwaj, Nishchal K Verma | A Bayesian Approach with Type-2 Student-tMembership Function for T-S
Model Identification | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno
(T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering
based on type-2 fuzzyset has been shown the remarkable results on non-sparse
databut their performance degraded on sparse data. In this paper, aninnovative
architecture for fuzzyc-regression model is presentedand a novel
student-tdistribution based membership functionis designed for sparse data
modelling. To avoid the overfitting,we have adopted a Bayesian approach for
incorporating aGaussian prior on the regression coefficients. Additional
noveltyof our approach lies in type-reduction where the final output iscomputed
using Karnik Mendel algorithm and the consequentparameters of the model are
optimized using Stochastic GradientDescent method. As detailed experimentation,
the result showsthat proposed approach outperforms on standard datasets
incomparison of various state-of-the-art methods.
| [
{
"version": "v1",
"created": "Wed, 2 Sep 2020 05:10:13 GMT"
}
] | 1,599,091,200,000 | [
[
"Singh",
"Vikas",
""
],
[
"Bharadhwaj",
"Homanga",
""
],
[
"Verma",
"Nishchal K",
""
]
] |
2009.00964 | Laura Giordano | Laura Giordano, Daniele Theseider Dupr\'e | A framework for a modular multi-concept lexicographic closure semantics | 18 pages. Accepted for presentation at NMR2020 (18th International
Workshop on Non-Monotonic Reasoning, September 12th - 14th - Rhodes, Greece | null | null | TR-INF-2020-09-03-UNIPMN | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define a modular multi-concept extension of the lexicographic closure
semantics for defeasible description logics with typicality. The idea is that
of distributing the defeasible properties of concepts into different modules,
according to their subject, and of defining a notion of preference for each
module based on the lexicographic closure semantics. The preferential semantics
of the knowledge base can then be defined as a combination of the preferences
of the single modules. The range of possibilities, from fine grained to coarse
grained modules, provides a spectrum of alternative semantics.
| [
{
"version": "v1",
"created": "Wed, 2 Sep 2020 11:41:38 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Sep 2020 05:19:53 GMT"
}
] | 1,599,436,800,000 | [
[
"Giordano",
"Laura",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
] |
2009.01442 | Srinivasan Ravichandran | Srinivasan Ravichandran, Drona Khurana, Bharath Venkatesh, Narayanan
Unny Edakunni | FairXGBoost: Fairness-aware Classification in XGBoost | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Highly regulated domains such as finance have long favoured the use of
machine learning algorithms that are scalable, transparent, robust and yield
better performance. One of the most prominent examples of such an algorithm is
XGBoost. Meanwhile, there is also a growing interest in building fair and
unbiased models in these regulated domains and numerous bias-mitigation
algorithms have been proposed to this end. However, most of these
bias-mitigation methods are restricted to specific model families such as
logistic regression or support vector machine models, thus leaving modelers
with a difficult decision of choosing between fairness from the bias-mitigation
algorithms and scalability, transparency, performance from algorithms such as
XGBoost. We aim to leverage the best of both worlds by proposing a fair variant
of XGBoost that enjoys all the advantages of XGBoost, while also matching the
levels of fairness from the state-of-the-art bias-mitigation algorithms.
Furthermore, the proposed solution requires very little in terms of changes to
the original XGBoost library, thus making it easy for adoption. We provide an
empirical analysis of our proposed method on standard benchmark datasets used
in the fairness community.
| [
{
"version": "v1",
"created": "Thu, 3 Sep 2020 04:08:23 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Oct 2020 05:14:38 GMT"
}
] | 1,602,115,200,000 | [
[
"Ravichandran",
"Srinivasan",
""
],
[
"Khurana",
"Drona",
""
],
[
"Venkatesh",
"Bharath",
""
],
[
"Edakunni",
"Narayanan Unny",
""
]
] |
2009.01453 | Zhaoqing Peng | Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang,
Weinan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu,
Kun Gai | Learning to Infer User Hidden States for Online Sequential Advertising | to be published in CIKM 2020 | null | 10.1145/3340531.3412721 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To drive purchase in online advertising, it is of the advertiser's great
interest to optimize the sequential advertising strategy whose performance and
interpretability are both important. The lack of interpretability in existing
deep reinforcement learning methods makes it not easy to understand, diagnose
and further optimize the strategy. In this paper, we propose our Deep Intents
Sequential Advertising (DISA) method to address these issues. The key part of
interpretability is to understand a consumer's purchase intent which is,
however, unobservable (called hidden states). In this paper, we model this
intention as a latent variable and formulate the problem as a Partially
Observable Markov Decision Process (POMDP) where the underlying intents are
inferred based on the observable behaviors. Large-scale industrial offline and
online experiments demonstrate our method's superior performance over several
baselines. The inferred hidden states are analyzed, and the results prove the
rationality of our inference.
| [
{
"version": "v1",
"created": "Thu, 3 Sep 2020 05:12:26 GMT"
}
] | 1,599,177,600,000 | [
[
"Peng",
"Zhaoqing",
""
],
[
"Jin",
"Junqi",
""
],
[
"Luo",
"Lan",
""
],
[
"Yang",
"Yaodong",
""
],
[
"Luo",
"Rui",
""
],
[
"Wang",
"Jun",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Xu",
"Haiyang",
""
],
[
"Xu",
"Miao",
""
],
[
"Yu",
"Chuan",
""
],
[
"Luo",
"Tiejian",
""
],
[
"Li",
"Han",
""
],
[
"Xu",
"Jian",
""
],
[
"Gai",
"Kun",
""
]
] |
2009.01509 | Junrui Tian | Junrui Tian, Zhiying Tu, Zhongjie Wang, Xiaofei Xu, Min Liu | User Intention Recognition and Requirement Elicitation Method for
Conversational AI Services | accepted as a full paper at IEEE ICWS 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, chat-bot has become a new type of intelligent terminal to
guide users to consume services. However, it is criticized most that the
services it provides are not what users expect or most expect. This defect
mostly dues to two problems, one is that the incompleteness and uncertainty of
user's requirement expression caused by the information asymmetry, the other is
that the diversity of service resources leads to the difficulty of service
selection. Conversational bot is a typical mesh device, so the guided
multi-rounds Q$\&$A is the most effective way to elicit user requirements.
Obviously, complex Q$\&$A with too many rounds is boring and always leads to
bad user experience. Therefore, we aim to obtain user requirements as
accurately as possible in as few rounds as possible. To achieve this, a user
intention recognition method based on Knowledge Graph (KG) was developed for
fuzzy requirement inference, and a requirement elicitation method based on
Granular Computing was proposed for dialog policy generation. Experimental
results show that these two methods can effectively reduce the number of
conversation rounds, and can quickly and accurately identify the user
intention.
| [
{
"version": "v1",
"created": "Thu, 3 Sep 2020 08:26:39 GMT"
}
] | 1,599,177,600,000 | [
[
"Tian",
"Junrui",
""
],
[
"Tu",
"Zhiying",
""
],
[
"Wang",
"Zhongjie",
""
],
[
"Xu",
"Xiaofei",
""
],
[
"Liu",
"Min",
""
]
] |
2009.01606 | Attila Egri-Nagy | Attila Egri-Nagy and Antti T\"orm\"anen | Derived metrics for the game of Go -- intrinsic network strength
assessment and cheat-detection | 16 pages, 12 figures, accepted for CANDAR 2020, The Eighth
International Symposium on Computing and Networking, Naha, Okinawa, Japan,
November 24-27, 2020; final version will be published in IEEE Xplore | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The widespread availability of superhuman AI engines is changing how we play
the ancient game of Go. The open-source software packages developed after the
AlphaGo series shifted focus from producing strong playing entities to
providing tools for analyzing games. Here we describe two ways of how the
innovations of the second generation engines (e.g.~score estimates, variable
komi) can be used for defining new metrics that help deepen our understanding
of the game. First, we study how much information the search component
contributes in addition to the raw neural network policy output. This gives an
intrinsic strength measurement for the neural network. Second, we define the
effect of a move by the difference in score estimates. This gives a
fine-grained, move-by-move performance evaluation of a player. We use this in
combating the new challenge of detecting online cheating.
| [
{
"version": "v1",
"created": "Thu, 3 Sep 2020 12:25:02 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Sep 2020 05:15:19 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Nov 2020 12:11:36 GMT"
}
] | 1,605,484,800,000 | [
[
"Egri-Nagy",
"Attila",
""
],
[
"Törmänen",
"Antti",
""
]
] |
2009.01810 | Deokgun Park | Aishwarya Pothula, Md Ashaduzzaman Rubel Mondol, Sanath Narasimhan, Sm
Mazharul Islam, Deokgun Park | SEDRo: A Simulated Environment for Developmental Robotics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even with impressive advances in application-specific models, we still lack
knowledge about how to build a model that can learn in a human-like way and do
multiple tasks. To learn in a human-like way, we need to provide a diverse
experience that is comparable to humans. In this paper, we introduce our
ongoing effort to build a simulated environment for developmental robotics
(SEDRo). SEDRo provides diverse human experiences ranging from those of a fetus
to a 12th-month-old. A series of simulated tests based on developmental
psychology will be used to evaluate the progress of a learning model. We
anticipate SEDRo to lower the cost of entry and facilitate research in the
developmental robotics community.
| [
{
"version": "v1",
"created": "Thu, 3 Sep 2020 17:16:54 GMT"
}
] | 1,599,177,600,000 | [
[
"Pothula",
"Aishwarya",
""
],
[
"Mondol",
"Md Ashaduzzaman Rubel",
""
],
[
"Narasimhan",
"Sanath",
""
],
[
"Islam",
"Sm Mazharul",
""
],
[
"Park",
"Deokgun",
""
]
] |
2009.02083 | Seiji Ishihara | Seiji Ishihara and Harukazu Igarashi | Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy
Rules: Application to Simulations of Speed Control of an Automobile | null | Journal of Japan Society for Fuzzy Theory and Intelligent
Informatics, Vol. 32, No. 4, pp. 801-810, 2020 (in Japanese) | 10.3156/jsoft.32.4_801 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A method of a fusion of fuzzy inference and policy gradient reinforcement
learning has been proposed that directly learns, as maximizes the expected
value of the reward per episode, parameters in a policy function represented by
fuzzy rules with weights. A study has applied this method to a task of speed
control of an automobile and has obtained correct policies, some of which
control speed of the automobile appropriately but many others generate
inappropriate vibration of speed. In general, the policy is not desirable that
causes sudden time change or vibration in the output value, and there would be
many cases where the policy giving smooth time change in the output value is
desirable. In this paper, we propose a fusion method using the objective
function, that introduces defuzzification with the center of gravity model
weighted stochastically and a constraint term for smoothness of time change, as
an improvement measure in order to suppress sudden change of the output value
of the fuzzy controller. Then we show the learning rule in the fusion, and also
consider the effect by reward functions on the fluctuation of the output value.
As experimental results of an application of our method on speed control of an
automobile, it was confirmed that the proposed method has the effect of
suppressing the undesirable fluctuation in time-series of the output value.
Moreover, it was also showed that the difference between reward functions might
adversely affect the results of learning.
| [
{
"version": "v1",
"created": "Fri, 4 Sep 2020 09:30:13 GMT"
}
] | 1,599,436,800,000 | [
[
"Ishihara",
"Seiji",
""
],
[
"Igarashi",
"Harukazu",
""
]
] |
2009.02164 | Joni Pajarinen | Joni Pajarinen | Technical Report: The Policy Graph Improvement Algorithm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimizing a partially observable Markov decision process (POMDP) policy is
challenging. The policy graph improvement (PGI) algorithm for POMDPs represents
the policy as a fixed size policy graph and improves the policy monotonically.
Due to the fixed policy size, computation time for each improvement iteration
is known in advance. Moreover, the method allows for compact understandable
policies. This report describes the technical details of the PGI [1] and
particle based PGI [2] algorithms for POMDPs in a more accessible way than [1]
or [2] allowing practitioners and students to understand and implement the
algorithms.
| [
{
"version": "v1",
"created": "Fri, 4 Sep 2020 13:00:37 GMT"
}
] | 1,599,436,800,000 | [
[
"Pajarinen",
"Joni",
""
]
] |
2009.03420 | Federico Cerutti | Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani
Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti | A Hybrid Neuro-Symbolic Approach for Complex Event Processing | Accepted as extended abstract at ICLP2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training a model to detect patterns of interrelated events that form
situations of interest can be a complex problem: such situations tend to be
uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic
architecture based on Event Calculus that can perform Complex Event Processing
(CEP). It leverages both a neural network to interpret inputs and logical rules
that express the pattern of the complex event. Our approach is capable of
training with much fewer labelled data than a pure neural network approach, and
to learn to classify individual events even when training in an end-to-end
manner. We demonstrate this comparing our approach against a pure neural
network approach on a dataset based on Urban Sounds 8K.
| [
{
"version": "v1",
"created": "Mon, 7 Sep 2020 21:05:51 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Sep 2020 09:56:50 GMT"
},
{
"version": "v3",
"created": "Tue, 13 Oct 2020 21:08:17 GMT"
}
] | 1,602,720,000,000 | [
[
"Vilamala",
"Marc Roig",
""
],
[
"Taylor",
"Harrison",
""
],
[
"Xing",
"Tianwei",
""
],
[
"Garcia",
"Luis",
""
],
[
"Srivastava",
"Mani",
""
],
[
"Kaplan",
"Lance",
""
],
[
"Preece",
"Alun",
""
],
[
"Kimmig",
"Angelika",
""
],
[
"Cerutti",
"Federico",
""
]
] |
2009.03793 | Amirhoshang Hoseinpour Dehkordi | Amirhoshang Hoseinpour Dehkordi, Majid Alizadeh, Ali Movaghar | Linear Temporal Public Announcement Logic: a new perspective for
reasoning about the knowledge of multi-classifiers | 27 pages, 1 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this note, a formal transition system model called LTPAL to extract
knowledge in a classification process is suggested. The model combines the
Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL). In the
model, first, we consider classifiers, which capture single-framed data. Next,
we took classifiers for data-stream data input into consideration. Finally, we
formalize natural language properties in LTPAL with a video-stream object
detection sample.
| [
{
"version": "v1",
"created": "Tue, 8 Sep 2020 14:38:59 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Sep 2020 17:19:40 GMT"
},
{
"version": "v3",
"created": "Tue, 24 May 2022 09:39:40 GMT"
}
] | 1,653,436,800,000 | [
[
"Dehkordi",
"Amirhoshang Hoseinpour",
""
],
[
"Alizadeh",
"Majid",
""
],
[
"Movaghar",
"Ali",
""
]
] |
2009.04589 | Andrey Rivkin | Marco Montali, Andrey Rivkin, Daniel Ritter | Formalizing Integration Patterns with Multimedia Data (Extended Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The previous works on formalizing enterprise application integration (EAI)
scenarios showed an emerging need for setting up formal foundations for
integration patterns, the EAI building blocks, in order to facilitate the
model-driven development and ensure its correctness. So far, the formalization
requirements were focusing on more "conventional" integration scenarios, in
which control-flow, transactional persistent data and time aspects were
considered. However, none of these works took into consideration another
arising EAI trend that covers social and multimedia computing. In this work we
propose a Petri net-based formalism that addresses requirements arising from
the multimedia domain. We also demonstrate realizations of one of the most
frequently used multimedia patterns and discuss which implications our formal
proposal may bring into the area of the multimedia EAI development.
| [
{
"version": "v1",
"created": "Wed, 9 Sep 2020 22:00:41 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Apr 2021 17:23:36 GMT"
}
] | 1,617,926,400,000 | [
[
"Montali",
"Marco",
""
],
[
"Rivkin",
"Andrey",
""
],
[
"Ritter",
"Daniel",
""
]
] |
2009.04743 | Tom Bewley | Tom Bewley, Jonathan Lawry | TripleTree: A Versatile Interpretable Representation of Black Box Agents
and their Environments | 12 pages (incl. references and appendices), 15 figures. Pre-print,
under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In explainable artificial intelligence, there is increasing interest in
understanding the behaviour of autonomous agents to build trust and validate
performance. Modern agent architectures, such as those trained by deep
reinforcement learning, are currently so lacking in interpretable structure as
to effectively be black boxes, but insights may still be gained from an
external, behaviourist perspective. Inspired by conceptual spaces theory, we
suggest that a versatile first step towards general understanding is to
discretise the state space into convex regions, jointly capturing similarities
over the agent's action, value function and temporal dynamics within a dataset
of observations. We create such a representation using a novel variant of the
CART decision tree algorithm, and demonstrate how it facilitates practical
understanding of black box agents through prediction, visualisation and
rule-based explanation.
| [
{
"version": "v1",
"created": "Thu, 10 Sep 2020 09:22:27 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Sep 2020 16:06:19 GMT"
}
] | 1,600,732,800,000 | [
[
"Bewley",
"Tom",
""
],
[
"Lawry",
"Jonathan",
""
]
] |
2009.04869 | Jean-Guy Mailly | Jean-Guy Mailly | A Note on Rich Incomplete Argumentation Frameworks | Technical report, 12 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, qualitative uncertainty in abstract argumentation has received much
attention. The first works on this topic introduced uncertainty about the
presence of attacks, then about the presence of arguments, and finally combined
both kinds of uncertainty. This results in the Incomplete Argumentation
Framework (IAFs). But another kind of uncertainty was introduced in the context
of Control Argumentation Frameworks (CAFs): it consists in a conflict relation
with uncertain orientation, i.e. we are sure that there is an attack between
two arguments, but the actual direction of the attack is unknown. Here, we
formally define Rich IAFs, that combine the three different kinds of
uncertainty that were previously introduced in IAFs and CAFs. We show that this
new model, although strictly more expressive than IAFs, does not suffer from a
blow up of computational complexity. Also, the existing computational approach
based on SAT can be easily adapted to the new framework.
| [
{
"version": "v1",
"created": "Thu, 10 Sep 2020 14:11:02 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Oct 2020 04:35:56 GMT"
},
{
"version": "v3",
"created": "Thu, 19 Nov 2020 14:33:35 GMT"
}
] | 1,605,830,400,000 | [
[
"Mailly",
"Jean-Guy",
""
]
] |
2009.04903 | Jean-Guy Mailly | Jean-Guy Mailly | Possible Controllability of Control Argumentation Frameworks -- Extended
Version | Extended version of a paper accepted at the 8th International
Conference on Computational Models of Argument, 15 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent Control Argumentation Framework (CAF) is a generalization of
Dung's Argumentation Framework which handles argumentation dynamics under
uncertainty; especially it can be used to model the behavior of an agent which
can anticipate future changes in the environment. Here we provide new insights
on this model by defining the notion of possible controllability of a CAF. We
study the complexity of this new form of reasoning for the four classical
semantics, and we provide a logical encoding for reasoning with this framework.
| [
{
"version": "v1",
"created": "Thu, 10 Sep 2020 14:50:53 GMT"
}
] | 1,599,782,400,000 | [
[
"Mailly",
"Jean-Guy",
""
]
] |
2009.04978 | Iliana M. Petrova | Piero A. Bonatti, Iliana M. Petrova, Luigi Sauro | Defeasible reasoning in Description Logics: an overview on DL^N | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | DL^N is a recent approach that extends description logics with defeasible
reasoning capabilities. In this paper we provide an overview on DL^N,
illustrating the underlying knowledge engineering requirements as well as the
characteristic features that preserve DL^N from some recurrent semantic and
computational drawbacks. We also compare DL^N with some alternative
nonmonotonic semantics, enlightening the relationships between the KLM
postulates and DL^N.
| [
{
"version": "v1",
"created": "Thu, 10 Sep 2020 16:30:30 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Sep 2020 14:37:31 GMT"
}
] | 1,600,387,200,000 | [
[
"Bonatti",
"Piero A.",
""
],
[
"Petrova",
"Iliana M.",
""
],
[
"Sauro",
"Luigi",
""
]
] |
2009.05161 | Pavel Surynek | Pavel Surynek | Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal
Vertex Ordering | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce multi-goal multi agent path finding (MAPF$^{MG}$) which
generalizes the standard discrete multi-agent path finding (MAPF) problem.
While the task in MAPF is to navigate agents in an undirected graph from their
starting vertices to one individual goal vertex per agent, MAPF$^{MG}$ assigns
each agent multiple goal vertices and the task is to visit each of them at
least once. Solving MAPF$^{MG}$ not only requires finding collision free paths
for individual agents but also determining the order of visiting agent's goal
vertices so that common objectives like the sum-of-costs are optimized. We
suggest two novel algorithms using different paradigms to address MAPF$^{MG}$:
a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a
compilation-based algorithm built using the SMT paradigm, called
SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of
compilation-based approach.
| [
{
"version": "v1",
"created": "Thu, 10 Sep 2020 22:27:18 GMT"
}
] | 1,600,041,600,000 | [
[
"Surynek",
"Pavel",
""
]
] |
2009.05186 | Mariela Morveli-Espinoza | Mariela Morveli-Espinoza, Juan Carlos Nieves, Ayslan Possebom, Josep
Puyol-Gruart, and Cesar Augusto Tacla | An Argumentation-based Approach for Identifying and Dealing with
Incompatibilities among Procedural Goals | 31 pages, 9 figures, Accepted in the International Journal of
Approximate Reasoning (2019) | International Journal of Approximate Reasoning, year 2019, vol.
105, pp. 1-26 | 10.4114/intartif.vol22iss64pp47-62 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | During the first step of practical reasoning, i.e. deliberation, an
intelligent agent generates a set of pursuable goals and then selects which of
them he commits to achieve. An intelligent agent may in general generate
multiple pursuable goals, which may be incompatible among them. In this paper,
we focus on the definition, identification and resolution of these
incompatibilities. The suggested approach considers the three forms of
incompatibility introduced by Castelfranchi and Paglieri, namely the terminal
incompatibility, the instrumental or resources incompatibility and the
superfluity. We characterise computationally these forms of incompatibility by
means of arguments that represent the plans that allow an agent to achieve his
goals. Thus, the incompatibility among goals is defined based on the conflicts
among their plans, which are represented by means of attacks in an
argumentation framework. We also work on the problem of goals selection; we
propose to use abstract argumentation theory to deal with this problem, i.e. by
applying argumentation semantics. We use a modified version of the "cleaner
world" scenario in order to illustrate the performance of our proposal.
| [
{
"version": "v1",
"created": "Fri, 11 Sep 2020 01:01:34 GMT"
}
] | 1,600,041,600,000 | [
[
"Morveli-Espinoza",
"Mariela",
""
],
[
"Nieves",
"Juan Carlos",
""
],
[
"Possebom",
"Ayslan",
""
],
[
"Puyol-Gruart",
"Josep",
""
],
[
"Tacla",
"Cesar Augusto",
""
]
] |
2009.05643 | Diego Perez Liebana Dr. | Diego Perez-Liebana, Alexander Dockhorn, Jorge Hurtado Grueso, Dominik
Jeurissen | The Design Of "Stratega": A General Strategy Games Framework | 7 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stratega, a general strategy games framework, has been designed to foster
research on computational intelligence for strategy games. In contrast to other
strategy game frameworks, Stratega allows to create a wide variety of
turn-based and real-time strategy games using a common API for agent
development. While the current version supports the development of turn-based
strategy games and agents, we will add support for real-time strategy games in
future updates. Flexibility is achieved by utilising YAML-files to configure
tiles, units, actions, and levels. Therefore, the user can design and run a
variety of games to test developed agents without specifically adjusting it to
the game being generated. The framework has been built with a focus of
statistical forward planning (SFP) agents. For this purpose, agents can access
and modify game-states and use the forward model to simulate the outcome of
their actions. While SFP agents have shown great flexibility in general
game-playing, their performance is limited in case of complex state and
action-spaces. Finally, we hope that the development of this framework and its
respective agents helps to better understand the complex decision-making
process in strategy games. Stratega can be downloaded at:
https://github.research.its.qmul.ac.uk/eecsgameai/Stratega
| [
{
"version": "v1",
"created": "Fri, 11 Sep 2020 20:02:00 GMT"
}
] | 1,600,128,000,000 | [
[
"Perez-Liebana",
"Diego",
""
],
[
"Dockhorn",
"Alexander",
""
],
[
"Grueso",
"Jorge Hurtado",
""
],
[
"Jeurissen",
"Dominik",
""
]
] |
2009.05678 | Jingan Yang | Jingan Yang, Yang Peng | To Root Artificial Intelligence Deeply in Basic Science for a New
Generation of AI | 13 pages; 7 figures; 23 references | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the ambitions of artificial intelligence is to root artificial
intelligence deeply in basic science while developing brain-inspired artificial
intelligence platforms that will promote new scientific discoveries. The
challenges are essential to push artificial intelligence theory and applied
technologies research forward. This paper presents the grand challenges of
artificial intelligence research for the next 20 years which include:~(i) to
explore the working mechanism of the human brain on the basis of understanding
brain science, neuroscience, cognitive science, psychology and data science;
(ii) how is the electrical signal transmitted by the human brain? What is the
coordination mechanism between brain neural electrical signals and human
activities? (iii)~to root brain-computer interface~(BCI) and brain-muscle
interface~(BMI) technologies deeply in science on human behaviour; (iv)~making
research on knowledge-driven visual commonsense reasoning~(VCR), develop a new
inference engine for cognitive network recognition~(CNR); (v)~to develop
high-precision, multi-modal intelligent perceptrons; (vi)~investigating
intelligent reasoning and fast decision-making systems based on knowledge
graph~(KG). We believe that the frontier theory innovation of AI,
knowledge-driven modeling methodologies for commonsense reasoning,
revolutionary innovation and breakthroughs of the novel algorithms and new
technologies in AI, and developing responsible AI should be the main research
strategies of AI scientists in the future.
| [
{
"version": "v1",
"created": "Fri, 11 Sep 2020 22:38:38 GMT"
}
] | 1,600,128,000,000 | [
[
"Yang",
"Jingan",
""
],
[
"Peng",
"Yang",
""
]
] |
2009.05777 | Can Li | Can Li, Lei Bai, Wei Liu, Lina Yao, S Travis Waller | Knowledge Adaption for Demand Prediction based on Multi-task Memory
Neural Network | 10 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate demand forecasting of different public transport modes(e.g., buses
and light rails) is essential for public service operation.However, the
development level of various modes often varies sig-nificantly, which makes it
hard to predict the demand of the modeswith insufficient knowledge and sparse
station distribution (i.e.,station-sparse mode). Intuitively, different public
transit modes mayexhibit shared demand patterns temporally and spatially in a
city.As such, we propose to enhance the demand prediction of station-sparse
modes with the data from station-intensive mode and designaMemory-Augmented
Multi-taskRecurrent Network (MATURE)to derive the transferable demand patterns
from each mode andboost the prediction of station-sparse modes through
adaptingthe relevant patterns from the station-intensive mode.
Specifically,MATUREcomprises three components: 1) a memory-augmentedrecurrent
network for strengthening the ability to capture the long-short term
information and storing temporal knowledge of eachtransit mode; 2) a knowledge
adaption module to adapt the rele-vant knowledge from a station-intensive
source to station-sparsesources; 3) a multi-task learning framework to
incorporate all theinformation and forecast the demand of multiple modes
jointly.The experimental results on a real-world dataset covering four pub-lic
transport modes demonstrate that our model can promote thedemand forecasting
performance for the station-sparse modes.
| [
{
"version": "v1",
"created": "Sat, 12 Sep 2020 12:21:09 GMT"
}
] | 1,600,128,000,000 | [
[
"Li",
"Can",
""
],
[
"Bai",
"Lei",
""
],
[
"Liu",
"Wei",
""
],
[
"Yao",
"Lina",
""
],
[
"Waller",
"S Travis",
""
]
] |
2009.05815 | Inga Ibs | Inga Ibs and Nico Potyka | Explainable Automated Reasoning in Law using Probabilistic Epistemic
Argumentation | 9 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Applying automated reasoning tools for decision support and analysis in law
has the potential to make court decisions more transparent and objective. Since
there is often uncertainty about the accuracy and relevance of evidence,
non-classical reasoning approaches are required. Here, we investigate
probabilistic epistemic argumentation as a tool for automated reasoning about
legal cases. We introduce a general scheme to model legal cases as
probabilistic epistemic argumentation problems, explain how evidence can be
modeled and sketch how explanations for legal decisions can be generated
automatically. Our framework is easily interpretable, can deal with cyclic
structures and imprecise probabilities and guarantees polynomial-time
probabilistic reasoning in the worst-case.
| [
{
"version": "v1",
"created": "Sat, 12 Sep 2020 15:40:42 GMT"
}
] | 1,600,128,000,000 | [
[
"Ibs",
"Inga",
""
],
[
"Potyka",
"Nico",
""
]
] |
2009.05897 | Mariela Morveli-Espinoza | Mariela Morveli-Espinoza, Ayslan Possebom, and Cesar Augusto Tacla | Argumentation-based Agents that Explain their Decisions | 9 pages, accepted in the 7th Brazilian Conference on Intelligent
Systems, 2019 | null | 10.1109/BRACIS.2019.00088 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Explainable Artificial Intelligence (XAI) systems, including intelligent
agents, must be able to explain their internal decisions, behaviours and
reasoning that produce their choices to the humans (or other systems) with
which they interact. In this paper, we focus on how an extended model of BDI
(Beliefs-Desires-Intentions) agents can be able to generate explanations about
their reasoning, specifically, about the goals he decides to commit to. Our
proposal is based on argumentation theory, we use arguments to represent the
reasons that lead an agent to make a decision and use argumentation semantics
to determine acceptable arguments (reasons). We propose two types of
explanations: the partial one and the complete one. We apply our proposal to a
scenario of rescue robots.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 02:08:10 GMT"
}
] | 1,600,128,000,000 | [
[
"Morveli-Espinoza",
"Mariela",
""
],
[
"Possebom",
"Ayslan",
""
],
[
"Tacla",
"Cesar Augusto",
""
]
] |
2009.05898 | Mariela Morveli-Espinoza | Mariela Morveli-Espinoza, Ayslan Possebom, and Cesar Augusto Tacla | Resolving Resource Incompatibilities in Intelligent Agents | 9 pages, 2 figures, accepted in the 6th Brazilian Conference on
Intelligent Systems, 2017 | null | 10.1109/BRACIS.2017.28 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | An intelligent agent may in general pursue multiple procedural goals
simultaneously, which may lead to arise some conflicts (incompatibilities)
among them. In this paper, we focus on the incompatibilities that emerge due to
resources limitations. Thus, the contribution of this article is twofold. On
one hand, we give an algorithm for identifying resource incompatibilities from
a set of pursued goals and, on the other hand, we propose two ways for
selecting those goals that will continue to be pursued: (i) the first is based
on abstract argumentation theory, and (ii) the second based on two algorithms
developed by us. We illustrate our proposal using examples throughout the
article.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 02:09:04 GMT"
}
] | 1,600,128,000,000 | [
[
"Morveli-Espinoza",
"Mariela",
""
],
[
"Possebom",
"Ayslan",
""
],
[
"Tacla",
"Cesar Augusto",
""
]
] |
2009.05912 | Yushan Zhu | Yushan Zhu (1), Wen Zhang (1), Mingyang Chen (1), Hui Chen (2), Xu
Cheng (3), Wei Zhang (2), Huajun Chen (1) ((1) Zhejiang University, (2)
Alibaba Group, (3) CETC Big Data Research Institute) | DualDE: Dually Distilling Knowledge Graph Embedding for Faster and
Cheaper Reasoning | Accepted at WSDM 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and
training KGEs with higher dimension are usually preferred since they have
better reasoning capability. However, high-dimensional KGEs pose huge
challenges to storage and computing resources and are not suitable for
resource-limited or time-constrained applications, for which faster and cheaper
reasoning is necessary. To address this problem, we propose DualDE, a knowledge
distillation method to build low-dimensional student KGE from pre-trained
high-dimensional teacher KGE. DualDE considers the dual-influence between the
teacher and the student. In DualDE, we propose a soft label evaluation
mechanism to adaptively assign different soft label and hard label weights to
different triples, and a two-stage distillation approach to improve the
student's acceptance of the teacher. Our DualDE is general enough to be applied
to various KGEs. Experimental results show that our method can successfully
reduce the embedding parameters of a high-dimensional KGE by 7 times - 15 times
and increase the inference speed by 2 times - 6 times while retaining a high
performance. We also experimentally prove the effectiveness of our soft label
evaluation mechanism and two-stage distillation approach via ablation study.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 04:03:10 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Dec 2021 11:35:38 GMT"
}
] | 1,639,440,000,000 | [
[
"Zhu",
"Yushan",
""
],
[
"Zhang",
"Wen",
""
],
[
"Chen",
"Mingyang",
""
],
[
"Chen",
"Hui",
""
],
[
"Cheng",
"Xu",
""
],
[
"Zhang",
"Wei",
""
],
[
"Chen",
"Huajun",
""
]
] |
2009.05977 | Duyen Le Nguyen Thanh | Duyen N.T. Le, Hieu X. Le, Lua T. Ngo, Hoan T. Ngo | Transfer learning with class-weighted and focal loss function for
automatic skin cancer classification | 7 pages, 8 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Skin cancer is by far in top-3 of the world's most common cancer. Among
different skin cancer types, melanoma is particularly dangerous because of its
ability to metastasize. Early detection is the key to success in skin cancer
treatment. However, skin cancer diagnosis is still a challenge, even for
experienced dermatologists, due to strong resemblances between benign and
malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed
a deep learning system that can effectively and automatically classify skin
lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell
Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6)
Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the
system. An end-to-end deep learning process, transfer learning technique,
utilizing multiple pre-trained models, combining with class-weighted and focal
loss were applied for the classification process. The result was that our
ensemble of modified ResNet50 models can classify skin lesions into one of the
seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%,
respectively. This deep learning system can potentially be integrated into
computer-aided diagnosis systems that support dermatologists in skin cancer
diagnosis.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 10:59:51 GMT"
}
] | 1,600,128,000,000 | [
[
"Le",
"Duyen N. T.",
""
],
[
"Le",
"Hieu X.",
""
],
[
"Ngo",
"Lua T.",
""
],
[
"Ngo",
"Hoan T.",
""
]
] |
2009.05991 | Yang Yang | Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu,
Weinan Zhang and Yong Yu | GIKT: A Graph-based Interaction Model for Knowledge Tracing | 16 pages,2 figures, ECMLPKDD2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development in online education, knowledge tracing (KT) has
become a fundamental problem which traces students' knowledge status and
predicts their performance on new questions. Questions are often numerous in
online education systems, and are always associated with much fewer skills.
However, the previous literature fails to involve question information together
with high-order question-skill correlations, which is mostly limited by data
sparsity and multi-skill problems. From the model perspective, previous models
can hardly capture the long-term dependency of student exercise history, and
cannot model the interactions between student-questions, and student-skills in
a consistent way. In this paper, we propose a Graph-based Interaction model for
Knowledge Tracing (GIKT) to tackle the above probems. More specifically, GIKT
utilizes graph convolutional network (GCN) to substantially incorporate
question-skill correlations via embedding propagation. Besides, considering
that relevant questions are usually scattered throughout the exercise history,
and that question and skill are just different instantiations of knowledge,
GIKT generalizes the degree of students' master of the question to the
interactions between the student's current state, the student's history related
exercises, the target question, and related skills. Experiments on three
datasets demonstrate that GIKT achieves the new state-of-the-art performance,
with at least 1% absolute AUC improvement.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 12:50:32 GMT"
}
] | 1,600,128,000,000 | [
[
"Yang",
"Yang",
""
],
[
"Shen",
"Jian",
""
],
[
"Qu",
"Yanru",
""
],
[
"Liu",
"Yunfei",
""
],
[
"Wang",
"Kerong",
""
],
[
"Zhu",
"Yaoming",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Yu",
"Yong",
""
]
] |
2009.06051 | Meghna Lowalekar | Meghna Lowalekar, Pradeep Varakantham and Patrick Jaillet | Zone pAth Construction (ZAC) based Approaches for Effective Real-Time
Ridesharing | 48 pages, 22 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have
become hugely popular as they reduce the costs for customers, improve per trip
revenue for drivers and reduce traffic on the roads by grouping customers with
similar itineraries. The key challenge in these systems is to group the "right"
requests to travel together in the "right" available vehicles in real-time, so
that the objective (e.g., requests served, revenue or delay) is optimized. This
challenge has been addressed in existing work by: (i) generating as many
relevant feasible (with respect to the available delay for customers)
combinations of requests as possible in real-time; and then (ii) optimizing
assignment of the feasible request combinations to vehicles. Since the number
of request combinations increases exponentially with the increase in vehicle
capacity and number of requests, unfortunately, such approaches have to employ
ad hoc heuristics to identify a subset of request combinations for assignment.
Our key contribution is in developing approaches that employ zone (abstraction
of individual locations) paths instead of request combinations. Zone paths
allow for generation of significantly more "relevant" combinations (in
comparison to ad hoc heuristics) in real-time than competing approaches due to
two reasons: (i) Each zone path can typically represent multiple request
combinations; (ii) Zone paths are generated using a combination of offline and
online methods. Specifically, we contribute both myopic (ridesharing assignment
focussed on current requests only) and non-myopic (ridesharing assignment
considers impact on expected future requests) approaches that employ zone
paths. In our experimental results, we demonstrate that our myopic approach
outperforms (with respect to both objective and runtime) the current best
myopic approach for ridesharing on both real-world and synthetic datasets.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 17:57:15 GMT"
}
] | 1,600,128,000,000 | [
[
"Lowalekar",
"Meghna",
""
],
[
"Varakantham",
"Pradeep",
""
],
[
"Jaillet",
"Patrick",
""
]
] |
2009.06082 | Karina Kanjaria | Karina Kanjaria, Anup Pillai, Chaitanya Shivade, Marina Bendersky,
Ashutosh Jadhav, Vandana Mukherjee, Tanveer Syeda-Mahmood | Receptivity of an AI Cognitive Assistant by the Radiology Community: A
Report on Data Collected at RSNA | null | Proceedings of the 13th International Joint Conference on
Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN
978-989-758-398-8, pages 178-186. 2020 | 10.5220/0008984901780186 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to advances in machine learning and artificial intelligence (AI), a new
role is emerging for machines as intelligent assistants to radiologists in
their clinical workflows. But what systematic clinical thought processes are
these machines using? Are they similar enough to those of radiologists to be
trusted as assistants? A live demonstration of such a technology was conducted
at the 2016 Scientific Assembly and Annual Meeting of the Radiological Society
of North America (RSNA). The demonstration was presented in the form of a
question-answering system that took a radiology multiple choice question and a
medical image as inputs. The AI system then demonstrated a cognitive workflow,
involving text analysis, image analysis, and reasoning, to process the question
and generate the most probable answer. A post demonstration survey was made
available to the participants who experienced the demo and tested the question
answering system. Of the reported 54,037 meeting registrants, 2,927 visited the
demonstration booth, 1,991 experienced the demo, and 1,025 completed a
post-demonstration survey. In this paper, the methodology of the survey is
shown and a summary of its results are presented. The results of the survey
show a very high level of receptiveness to cognitive computing technology and
artificial intelligence among radiologists.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 20:40:30 GMT"
}
] | 1,600,128,000,000 | [
[
"Kanjaria",
"Karina",
""
],
[
"Pillai",
"Anup",
""
],
[
"Shivade",
"Chaitanya",
""
],
[
"Bendersky",
"Marina",
""
],
[
"Jadhav",
"Ashutosh",
""
],
[
"Mukherjee",
"Vandana",
""
],
[
"Syeda-Mahmood",
"Tanveer",
""
]
] |
2009.06103 | Jay Yu Ph.D. | Jay Yu, Kevin McCluskey, Saikat Mukherjee | Tax Knowledge Graph for a Smarter and More Personalized TurboTax | KDD2020 International Workshop on Knowledge Graph: Mining Knowledge
Graph for Deep Insights. See:
https://suitclub.ischool.utexas.edu/IWKG_KDD2020/index.html. 6 pages, 9
figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most knowledge graph use cases are data-centric, focusing on representing
data entities and their semantic relationships. There are no published success
stories to represent large-scale complicated business logic with knowledge
graph technologies. In this paper, we will share our innovative and practical
approach to representing complicated U.S. and Canadian income tax compliance
logic (calculations and rules) via a large-scale knowledge graph. We will cover
how the Tax Knowledge Graph is constructed and automated, how it is used to
calculate tax refunds, reasoned to find missing info, and navigated to explain
the calculated results. The Tax Knowledge Graph has helped transform Intuit's
flagship TurboTax product into a smart and personalized experience,
accelerating and automating the tax preparation process while instilling
confidence for millions of customers.
| [
{
"version": "v1",
"created": "Sun, 13 Sep 2020 22:41:01 GMT"
}
] | 1,600,128,000,000 | [
[
"Yu",
"Jay",
""
],
[
"McCluskey",
"Kevin",
""
],
[
"Mukherjee",
"Saikat",
""
]
] |
2009.06131 | Mariela Morveli-Espinoza | Mariela Morveli-Espinoza, Cesar Augusto Tacla, and Henrique Jasinski | An Argumentation-based Approach for Explaining Goal Selection in
Intelligent Agents | 11 pages, 3 figures, accepted in the 9th Brazilian Conference on
Intelligent Systems, 2020 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | During the first step of practical reasoning, i.e. deliberation or goals
selection, an intelligent agent generates a set of pursuable goals and then
selects which of them he commits to achieve. Explainable Artificial
Intelligence (XAI) systems, including intelligent agents, must be able to
explain their internal decisions. In the context of goals selection, agents
should be able to explain the reasoning path that leads them to select (or not)
a certain goal. In this article, we use an argumentation-based approach for
generating explanations about that reasoning path. Besides, we aim to enrich
the explanations with information about emerging conflicts during the selection
process and how such conflicts were resolved. We propose two types of
explanations: the partial one and the complete one and a set of explanatory
schemes to generate pseudo-natural explanations. Finally, we apply our proposal
to the cleaner world scenario.
| [
{
"version": "v1",
"created": "Mon, 14 Sep 2020 01:10:13 GMT"
}
] | 1,600,128,000,000 | [
[
"Morveli-Espinoza",
"Mariela",
""
],
[
"Tacla",
"Cesar Augusto",
""
],
[
"Jasinski",
"Henrique",
""
]
] |
2009.06245 | Chao Qian Mr | Chao Qian, Wenjing Ye | Accelerating gradient-based topology optimization design with dual-model
neural networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Topology optimization (TO) is a common technique used in free-form designs.
However, conventional TO-based design approaches suffer from high computational
cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as
Finite Element Analysis (FEA). In this work, neural networks are used as
efficient surrogate models for forward and sensitivity calculations in order to
greatly accelerate the design process of topology optimization. To improve the
accuracy of sensitivity analyses, dual-model neural networks that are trained
with both forward and sensitivity data are constructed and are integrated into
the Solid Isotropic Material with Penalization (SIMP) method to replace FEA.
The performance of the accelerated SIMP method is demonstrated on two benchmark
design problems namely minimum compliance design and metamaterial design. The
efficiency gained in the problem with size of 64x64 is 137 times in forward
calculation and 74 times in sensitivity analysis. In addition, effective data
generation methods suitable for TO designs are investigated and developed,
which lead to a great saving in training time. In both benchmark design
problems, a design accuracy of 95% can be achieved with only around 2000
training data.
| [
{
"version": "v1",
"created": "Mon, 14 Sep 2020 07:52:55 GMT"
}
] | 1,600,128,000,000 | [
[
"Qian",
"Chao",
""
],
[
"Ye",
"Wenjing",
""
]
] |
2009.06251 | Boris Ruf | Boris Ruf and Marcin Detyniecki | Active Fairness Instead of Unawareness | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The possible risk that AI systems could promote discrimination by reproducing
and enforcing unwanted bias in data has been broadly discussed in research and
society. Many current legal standards demand to remove sensitive attributes
from data in order to achieve "fairness through unawareness". We argue that
this approach is obsolete in the era of big data where large datasets with
highly correlated attributes are common. In the contrary, we propose the active
use of sensitive attributes with the purpose of observing and controlling any
kind of discrimination, and thus leading to fair results.
| [
{
"version": "v1",
"created": "Mon, 14 Sep 2020 08:14:17 GMT"
}
] | 1,600,128,000,000 | [
[
"Ruf",
"Boris",
""
],
[
"Detyniecki",
"Marcin",
""
]
] |
2009.06370 | J. G. Wolff | J Gerard Wolff | Transparency and granularity in the SP Theory of Intelligence and its
realisation in the SP Computer Model | Published in the book {\em Interpretable Artificial Intelligence: A
Perspective of Granular Computing}, Witold Pedrycz and Shyi-Ming Chen
(editors), Springer: Heidelberg, 2021, ISBN 978-3-030-64948-7, DOI:
10.1007/978-3-030-64949-4 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter describes how the SP System, meaning the SP Theory of
Intelligence, and its realisation as the SP Computer Model, may promote
transparency and granularity in AI, and some other areas of application. The
chapter describes how transparency in the workings and output of the SP
Computer Model may be achieved via three routes: 1) the program provides a very
full audit trail for such processes as recognition, reasoning, analysis of
language, and so on. There is also an explicit audit trail for the unsupervised
learning of new knowledge; 2) knowledge from the system is likely to be
granular and easy for people to understand; and 3) there are seven principles
for the organisation of knowledge which are central in the workings of the SP
System and also very familiar to people (eg chunking-with-codes, part-whole
hierarchies, and class-inclusion hierarchies), and that kind of familiarity in
the way knowledge is structured by the system, is likely to be important in the
interpretability, explainability, and transparency of that knowledge. Examples
from the SP Computer Model are shown throughout the chapter.
| [
{
"version": "v1",
"created": "Mon, 7 Sep 2020 18:31:12 GMT"
},
{
"version": "v2",
"created": "Sun, 9 May 2021 13:32:31 GMT"
}
] | 1,620,691,200,000 | [
[
"Wolff",
"J Gerard",
""
]
] |
2009.06756 | Justin Harris | Justin D. Harris | Analysis of Models for Decentralized and Collaborative AI on Blockchain | Accepted to ICBC 2020 | null | 10.1007/978-3-030-59638-5_10 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning has recently enabled large advances in artificial
intelligence, but these results can be highly centralized. The large datasets
required are generally proprietary; predictions are often sold on a per-query
basis; and published models can quickly become out of date without effort to
acquire more data and maintain them. Published proposals to provide models and
data for free for certain tasks include Microsoft Research's Decentralized and
Collaborative AI on Blockchain. The framework allows participants to
collaboratively build a dataset and use smart contracts to share a continuously
updated model on a public blockchain. The initial proposal gave an overview of
the framework omitting many details of the models used and the incentive
mechanisms in real world scenarios. In this work, we evaluate the use of
several models and configurations in order to propose best practices when using
the Self-Assessment incentive mechanism so that models can remain accurate and
well-intended participants that submit correct data have the chance to profit.
We have analyzed simulations for each of three models: Perceptron, Na\"ive
Bayes, and a Nearest Centroid Classifier, with three different datasets:
predicting a sport with user activity from Endomondo, sentiment analysis on
movie reviews from IMDB, and determining if a news article is fake. We compare
several factors for each dataset when models are hosted in smart contracts on a
public blockchain: their accuracy over time, balances of a good and bad user,
and transaction costs (or gas) for deploying, updating, collecting refunds, and
collecting rewards. A free and open source implementation for the Ethereum
blockchain and simulations written in Python is provided at
https://github.com/microsoft/0xDeCA10B. This version has updated gas costs
using newer optimizations written after the original publication.
| [
{
"version": "v1",
"created": "Mon, 14 Sep 2020 21:38:55 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Sep 2020 03:14:47 GMT"
}
] | 1,600,819,200,000 | [
[
"Harris",
"Justin D.",
""
]
] |
2009.06981 | Martin Plajner | Martin Plajner and Ji\v{r}\'i Vomlel | Monotonicity in practice of adaptive testing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In our previous work we have shown how Bayesian networks can be used for
adaptive testing of student skills. Later, we have taken the advantage of
monotonicity restrictions in order to learn models fitting data better. This
article provides a synergy between these two phases as it evaluates Bayesian
network models used for computerized adaptive testing and learned with a
recently proposed monotonicity gradient algorithm. This learning method is
compared with another monotone method, the isotonic regression EM algorithm.
The quality of methods is empirically evaluated on a large data set of the
Czech National Mathematics Exam. Besides advantages of adaptive testing
approach we observed also advantageous behavior of monotonic methods,
especially for small learning data set sizes. Another novelty of this work is
the use of the reliability interval of the score distribution, which is used to
predict student's final score and grade. In the experiments we have clearly
shown we can shorten the test while keeping its reliability. We have also shown
that the monotonicity increases the prediction quality with limited training
data sets. The monotone model learned by the gradient method has a lower
question prediction quality than unrestricted models but it is better in the
main target of this application, which is the student score prediction. It is
an important observation that a mere optimization of the model likelihood or
the prediction accuracy do not necessarily lead to a model that describes best
the student.
| [
{
"version": "v1",
"created": "Tue, 15 Sep 2020 10:55:41 GMT"
}
] | 1,600,214,400,000 | [
[
"Plajner",
"Martin",
""
],
[
"Vomlel",
"Jiří",
""
]
] |
2009.07362 | Mayssa Kahla | Mayssa Ben Kahla and Dalel Kanzari and Ahmed Maalel | General DeepLCP model for disease prediction : Case of Lung Cancer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to GHO (Global Health Observatory (GHO), the high prevalence of a
large variety of diseases such as Ischaemic heart disease, stroke, lung cancer
disease and lower respiratory infections have remained the top killers during
the past decade.
The growth in the number of mortalities caused by these disease is due to the
very delayed symptoms'detection. Since in the early stages, the symptoms are
insignificant and similar to those of benign diseases (e.g. the flu ), and we
can only detect the disease at an advanced stage.
In addition, The high frequency of improper practices that are harmful to
health, the hereditary factors, and the stressful living conditions can
increase the death rates.
Many researches dealt with these fatal disease, and most of them applied
advantage machine learning models to deal with image diagnosis. However the
drawback is that imagery permit only to detect disease at a very delayed stage
and then patient can hardly be saved.
In this Paper we present our new approach "DeepLCP" to predict fatal diseases
that threaten people's lives. It's mainly based on raw and heterogeneous data
of the concerned (or under-tested) person. "DeepLCP" results of a combination
combination of the Natural Language Processing (NLP) and the deep learning
paradigm.The experimental results of the proposed model in the case of Lung
cancer prediction have approved high accuracy and a low loss data rate during
the validation of the disease prediction.
| [
{
"version": "v1",
"created": "Tue, 15 Sep 2020 21:43:48 GMT"
}
] | 1,600,300,800,000 | [
[
"Kahla",
"Mayssa Ben",
""
],
[
"Kanzari",
"Dalel",
""
],
[
"Maalel",
"Ahmed",
""
]
] |
2009.07405 | Mariela Morveli-Espinoza | Mariela Morveli-Espinoza, Juan Carlos Nieves, and Cesar Augusto Tacla | An Imprecise Probability Approach for Abstract Argumentation based on
Credal Sets | 8 pages, 2 figures, Accepted in The 15th European Conference on
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU
2019) | null | 10.1007/978-3-030-29765-7_4 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Some abstract argumentation approaches consider that arguments have a degree
of uncertainty, which impacts on the degree of uncertainty of the extensions
obtained from a abstract argumentation framework (AAF) under a semantics. In
these approaches, both the uncertainty of the arguments and of the extensions
are modeled by means of precise probability values. However, in many real life
situations the exact probabilities values are unknown and sometimes there is a
need for aggregating the probability values of different sources. In this
paper, we tackle the problem of calculating the degree of uncertainty of the
extensions considering that the probability values of the arguments are
imprecise. We use credal sets to model the uncertainty values of arguments and
from these credal sets, we calculate the lower and upper bounds of the
extensions. We study some properties of the suggested approach and illustrate
it with an scenario of decision making.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 00:52:18 GMT"
}
] | 1,600,646,400,000 | [
[
"Morveli-Espinoza",
"Mariela",
""
],
[
"Nieves",
"Juan Carlos",
""
],
[
"Tacla",
"Cesar Augusto",
""
]
] |
2009.07429 | Denghui Zhang | Denghui Zhang, Junming Liu, Hengshu Zhu, Yanchi Liu, Lichen Wang,
Pengyang Wang, Hui Xiong | Job2Vec: Job Title Benchmarking with Collective Multi-View
Representation Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Job Title Benchmarking (JTB) aims at matching job titles with similar
expertise levels across various companies. JTB could provide precise guidance
and considerable convenience for both talent recruitment and job seekers for
position and salary calibration/prediction. Traditional JTB approaches mainly
rely on manual market surveys, which is expensive and labor-intensive.
Recently, the rapid development of Online Professional Graph has accumulated a
large number of talent career records, which provides a promising trend for
data-driven solutions. However, it is still a challenging task since (1) the
job title and job transition (job-hopping) data is messy which contains a lot
of subjective and non-standard naming conventions for the same position (e.g.,
Programmer, Software Development Engineer, SDE, Implementation Engineer), (2)
there is a large amount of missing title/transition information, and (3) one
talent only seeks limited numbers of jobs which brings the incompleteness and
randomness modeling job transition patterns. To overcome these challenges, we
aggregate all the records to construct a large-scale Job Title Benchmarking
Graph (Job-Graph), where nodes denote job titles affiliated with specific
companies and links denote the correlations between jobs. We reformulate the
JTB as the task of link prediction over the Job-Graph that matched job titles
should have links. Along this line, we propose a collective multi-view
representation learning method (Job2Vec) by examining the Job-Graph jointly in
(1) graph topology view, (2)semantic view, (3) job transition balance view, and
(4) job transition duration view. We fuse the multi-view representations in the
encode-decode paradigm to obtain a unified optimal representation for the task
of link prediction. Finally, we conduct extensive experiments to validate the
effectiveness of our proposed method.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 02:33:32 GMT"
}
] | 1,600,300,800,000 | [
[
"Zhang",
"Denghui",
""
],
[
"Liu",
"Junming",
""
],
[
"Zhu",
"Hengshu",
""
],
[
"Liu",
"Yanchi",
""
],
[
"Wang",
"Lichen",
""
],
[
"Wang",
"Pengyang",
""
],
[
"Xiong",
"Hui",
""
]
] |
2009.07445 | Dung Nguyen | Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen, Truyen Tran | Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement
Learning | Accepted for publication at ACML 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Guilt aversion induces experience of a utility loss in people if they believe
they have disappointed others, and this promotes cooperative behaviour in
human. In psychological game theory, guilt aversion necessitates modelling of
agents that have theory about what other agents think, also known as Theory of
Mind (ToM). We aim to build a new kind of affective reinforcement learning
agents, called Theory of Mind Agents with Guilt Aversion (ToMAGA), which are
equipped with an ability to think about the wellbeing of others instead of just
self-interest. To validate the agent design, we use a general-sum game known as
Stag Hunt as a test bed. As standard reinforcement learning agents could learn
suboptimal policies in social dilemmas like Stag Hunt, we propose to use
belief-based guilt aversion as a reward shaping mechanism. We show that our
belief-based guilt averse agents can efficiently learn cooperative behaviours
in Stag Hunt Games.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 03:15:46 GMT"
}
] | 1,600,300,800,000 | [
[
"Nguyen",
"Dung",
""
],
[
"Venkatesh",
"Svetha",
""
],
[
"Nguyen",
"Phuoc",
""
],
[
"Tran",
"Truyen",
""
]
] |
2009.07448 | Xingyi Cheng | Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le
Song, Taifeng Wang, Yuan Qi, Wei Chu | Question Directed Graph Attention Network for Numerical Reasoning over
Text | Accepted at EMNLP 2020 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerical reasoning over texts, such as addition, subtraction, sorting and
counting, is a challenging machine reading comprehension task, since it
requires both natural language understanding and arithmetic computation. To
address this challenge, we propose a heterogeneous graph representation for the
context of the passage and question needed for such reasoning, and design a
question directed graph attention network to drive multi-step numerical
reasoning over this context graph. The code link is at:
https://github.com/emnlp2020qdgat/QDGAT
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 03:37:54 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Nov 2023 10:47:04 GMT"
}
] | 1,700,524,800,000 | [
[
"Chen",
"Kunlong",
""
],
[
"Xu",
"Weidi",
""
],
[
"Cheng",
"Xingyi",
""
],
[
"Xiaochuan",
"Zou",
""
],
[
"Zhang",
"Yuyu",
""
],
[
"Song",
"Le",
""
],
[
"Wang",
"Taifeng",
""
],
[
"Qi",
"Yuan",
""
],
[
"Chu",
"Wei",
""
]
] |
2009.07497 | Paolo Liberatore | Paolo Liberatore | One head is better than two: a polynomial restriction for propositional
definite Horn forgetting | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical forgetting is \np-complete even in the simple case of propositional
Horn formulae, and may exponentially increase their size. A way to forget is to
replace each variable to forget with the body of each clause whose head is the
variable. It takes polynomial time in the single-head case: each variable is at
most the head of a clause. Some formulae are not single-head but can be made so
to simplify forgetting. They are single-head equivalent. The first contribution
of this article is the study of a semantical characterization of single-head
equivalence. Two necessary conditions are given. They are sufficient when the
formula is inequivalent: it makes two sets of variables equivalent only if they
are also equivalent to their intersection. All acyclic formulae are
inequivalent. The second contribution of this article is an incomplete
algorithm for turning a formula single-head. In case of success, forgetting
becomes possible in polynomial time and produces a polynomial-size formula,
none of which is otherwise guaranteed. The algorithm is complete on
inequivalent formulae.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 06:49:08 GMT"
},
{
"version": "v2",
"created": "Mon, 22 Mar 2021 08:59:42 GMT"
},
{
"version": "v3",
"created": "Sun, 28 Jan 2024 12:17:24 GMT"
}
] | 1,706,572,800,000 | [
[
"Liberatore",
"Paolo",
""
]
] |
2009.07916 | Noud de Kroon | Arnoud A.W.M. de Kroon, Danielle Belgrave, Joris M. Mooij | Causal Bandits without prior knowledge using separating sets | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Causal Bandit is a variant of the classic Bandit problem where an agent
must identify the best action in a sequential decision-making process, where
the reward distribution of the actions displays a non-trivial dependence
structure that is governed by a causal model. Methods proposed for this problem
thus far in the literature rely on exact prior knowledge of the full causal
graph. We formulate new causal bandit algorithms that no longer necessarily
rely on prior causal knowledge. Instead, they utilize an estimator based on
separating sets, which we can find using simple conditional independence tests
or causal discovery methods. We show that, given a true separating set, for
discrete i.i.d. data, this estimator is unbiased, and has variance which is
upper bounded by that of the sample mean. We develop algorithms based on
Thompson Sampling and UCB for discrete and Gaussian models respectively and
show increased performance on simulation data as well as on a bandit drawing
from real-world protein signaling data.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 20:08:03 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Sep 2022 12:33:53 GMT"
}
] | 1,664,496,000,000 | [
[
"de Kroon",
"Arnoud A. W. M.",
""
],
[
"Belgrave",
"Danielle",
""
],
[
"Mooij",
"Joris M.",
""
]
] |
2009.07963 | Akash Gupta | Akash Gupta, Michael T. Lash, Senthil K. Nachimuthu | Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial
Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Sepsis is one of the leading causes of death in Intensive Care Units (ICU).
The strategy for treating sepsis involves the infusion of intravenous (IV)
fluids and administration of antibiotics. Determining the optimal quantity of
IV fluids is a challenging problem due to the complexity of a patient's
physiology. In this study, we develop a data-driven optimization solution that
derives the optimal quantity of IV fluids for individual patients. The proposed
method minimizes the probability of severe outcomes by controlling the
prescribed quantity of IV fluids and utilizes human-in-the-loop artificial
intelligence. We demonstrate the performance of our model on 1122 ICU patients
with sepsis diagnosis extracted from the MIMIC-III dataset. The results show
that, on average, our model can reduce mortality by 22%. This study has the
potential to help physicians synthesize optimal, patient-specific treatment
strategies.
| [
{
"version": "v1",
"created": "Wed, 16 Sep 2020 22:34:43 GMT"
}
] | 1,600,387,200,000 | [
[
"Gupta",
"Akash",
""
],
[
"Lash",
"Michael T.",
""
],
[
"Nachimuthu",
"Senthil K.",
""
]
] |
2009.08087 | Ya Zhang | Ya Zhang, Mingming Lu, Haifeng Li | Urban Traffic Flow Forecast Based on FastGCRNN | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic forecasting is an important prerequisite for the application of
intelligent transportation systems in urban traffic networks. The existing
works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to
characterize the temporal and spatial correlation of traffic flows. However, it
is hard to apply GCRN to the large scale road networks due to high
computational complexity. To address this problem, we propose to abstract the
road network into a geometric graph and build a Fast Graph Convolution
Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies
of traffic flow. Specifically, We use FastGCN unit to efficiently capture the
topological relationship between the roads and the surrounding roads in the
graph with reducing the computational complexity through importance sampling,
combine GRU unit to capture the temporal dependency of traffic flow, and embed
the spatiotemporal features into Seq2Seq based on the Encoder-Decoder
framework. Experiments on large-scale traffic data sets illustrate that the
proposed method can greatly reduce computational complexity and memory
consumption while maintaining relatively high accuracy.
| [
{
"version": "v1",
"created": "Thu, 17 Sep 2020 06:05:05 GMT"
}
] | 1,600,387,200,000 | [
[
"Zhang",
"Ya",
""
],
[
"Lu",
"Mingming",
""
],
[
"Li",
"Haifeng",
""
]
] |
2009.08438 | Szymon Brych | Szymon Brych and Antoine Cully | Competitiveness of MAP-Elites against Proximal Policy Optimization on
locomotion tasks in deterministic simulations | Quality-Diversity optimization, Reinforcement Learning, Proximal
Policy Optimization, MAP-Elites | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increasing importance of robots and automation creates a demand for
learnable controllers which can be obtained through various approaches such as
Evolutionary Algorithms (EAs) or Reinforcement Learning (RL). Unfortunately,
these two families of algorithms have mainly developed independently and there
are only a few works comparing modern EAs with deep RL algorithms. We show that
Multidimensional Archive of Phenotypic Elites (MAP-Elites), which is a modern
EA, can deliver better-performing solutions than one of the state-of-the-art RL
methods, Proximal Policy Optimization (PPO) in the generation of locomotion
controllers for a simulated hexapod robot. Additionally, extensive
hyper-parameter tuning shows that MAP-Elites displays greater robustness across
seeds and hyper-parameter sets. Generally, this paper demonstrates that EAs
combined with modern computational resources display promising characteristics
and have the potential to contribute to the state-of-the-art in controller
learning.
| [
{
"version": "v1",
"created": "Thu, 17 Sep 2020 17:41:46 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Sep 2020 08:33:45 GMT"
}
] | 1,600,732,800,000 | [
[
"Brych",
"Szymon",
""
],
[
"Cully",
"Antoine",
""
]
] |
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