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1701.08832 | Francois Belletti | Francois Belletti, Daniel Haziza, Gabriel Gomes, Alexandre M. Bayen | Expert Level control of Ramp Metering based on Multi-task Deep
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article shows how the recent breakthroughs in Reinforcement Learning
(RL) that have enabled robots to learn to play arcade video games, walk or
assemble colored bricks, can be used to perform other tasks that are currently
at the core of engineering cyberphysical systems. We present the first use of
RL for the control of systems modeled by discretized non-linear Partial
Differential Equations (PDEs) and devise a novel algorithm to use
non-parametric control techniques for large multi-agent systems. We show how
neural network based RL enables the control of discretized PDEs whose
parameters are unknown, random, and time-varying. We introduce an algorithm of
Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality
of multi-agent control schemes by sharing experience between agents while
giving each agent the opportunity to specialize its action policy so as to
tailor it to the local parameters of the part of the system it is located in.
| [
{
"version": "v1",
"created": "Mon, 30 Jan 2017 21:27:14 GMT"
}
] | 1,485,907,200,000 | [
[
"Belletti",
"Francois",
""
],
[
"Haziza",
"Daniel",
""
],
[
"Gomes",
"Gabriel",
""
],
[
"Bayen",
"Alexandre M.",
""
]
] |
1701.08868 | AmirEmad Ghassami | AmirEmad Ghassami and Negar Kiyavash | Interaction Information for Causal Inference: The Case of Directed
Triangle | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interaction information is one of the multivariate generalizations of mutual
information, which expresses the amount information shared among a set of
variables, beyond the information, which is shared in any proper subset of
those variables. Unlike (conditional) mutual information, which is always
non-negative, interaction information can be negative. We utilize this property
to find the direction of causal influences among variables in a triangle
topology under some mild assumptions.
| [
{
"version": "v1",
"created": "Mon, 30 Jan 2017 23:01:15 GMT"
}
] | 1,485,907,200,000 | [
[
"Ghassami",
"AmirEmad",
""
],
[
"Kiyavash",
"Negar",
""
]
] |
1701.09000 | Fabio Cozman | Fabio Gagliardi Cozman, Denis Deratani Mau\'a | On the Semantics and Complexity of Probabilistic Logic Programs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the meaning and the complexity of probabilistic logic programs
that consist of a set of rules and a set of independent probabilistic facts
(that is, programs based on Sato's distribution semantics). We focus on two
semantics, respectively based on stable and on well-founded models. We show
that the semantics based on stable models (referred to as the "credal
semantics") produces sets of probability models that dominate infinitely
monotone Choquet capacities, we describe several useful consequences of this
result. We then examine the complexity of inference with probabilistic logic
programs. We distinguish between the complexity of inference when a
probabilistic program and a query are given (the inferential complexity), and
the complexity of inference when the probabilistic program is fixed and the
query is given (the query complexity, akin to data complexity as used in
database theory). We obtain results on the inferential and query complexity for
acyclic, stratified, and cyclic propositional and relational programs,
complexity reaches various levels of the counting hierarchy and even
exponential levels.
| [
{
"version": "v1",
"created": "Tue, 31 Jan 2017 11:54:15 GMT"
}
] | 1,485,907,200,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
],
[
"Mauá",
"Denis Deratani",
""
]
] |
1702.00318 | Christian Blum | Christian Blum and Maria J. Blesa | A Hybrid Evolutionary Algorithm Based on Solution Merging for the
Longest Arc-Preserving Common Subsequence Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The longest arc-preserving common subsequence problem is an NP-hard
combinatorial optimization problem from the field of computational biology.
This problem finds applications, in particular, in the comparison of
arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a
simple, hybrid evolutionary algorithm to tackle this problem. The most
important feature of this algorithm concerns a crossover operator based on
solution merging. In solution merging, two or more solutions to the problem are
merged, and an exact technique is used to find the best solution within this
union. It is experimentally shown that the proposed algorithm outperforms a
heuristic from the literature.
| [
{
"version": "v1",
"created": "Wed, 1 Feb 2017 15:34:27 GMT"
}
] | 1,485,993,600,000 | [
[
"Blum",
"Christian",
""
],
[
"Blesa",
"Maria J.",
""
]
] |
1702.00539 | Adam Summerville | Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer
Holmg{\aa}rd, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius | Procedural Content Generation via Machine Learning (PCGML) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This survey explores Procedural Content Generation via Machine Learning
(PCGML), defined as the generation of game content using machine learning
models trained on existing content. As the importance of PCG for game
development increases, researchers explore new avenues for generating
high-quality content with or without human involvement; this paper addresses
the relatively new paradigm of using machine learning (in contrast with
search-based, solver-based, and constructive methods). We focus on what is most
often considered functional game content such as platformer levels, game maps,
interactive fiction stories, and cards in collectible card games, as opposed to
cosmetic content such as sprites and sound effects. In addition to using PCG
for autonomous generation, co-creativity, mixed-initiative design, and
compression, PCGML is suited for repair, critique, and content analysis because
of its focus on modeling existing content. We discuss various data sources and
representations that affect the resulting generated content. Multiple PCGML
methods are covered, including neural networks, long short-term memory (LSTM)
networks, autoencoders, and deep convolutional networks; Markov models,
$n$-grams, and multi-dimensional Markov chains; clustering; and matrix
factorization. Finally, we discuss open problems in the application of PCGML,
including learning from small datasets, lack of training data, multi-layered
learning, style-transfer, parameter tuning, and PCG as a game mechanic.
| [
{
"version": "v1",
"created": "Thu, 2 Feb 2017 04:49:22 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Aug 2017 11:50:28 GMT"
},
{
"version": "v3",
"created": "Mon, 7 May 2018 17:30:42 GMT"
}
] | 1,525,737,600,000 | [
[
"Summerville",
"Adam",
""
],
[
"Snodgrass",
"Sam",
""
],
[
"Guzdial",
"Matthew",
""
],
[
"Holmgård",
"Christoffer",
""
],
[
"Hoover",
"Amy K.",
""
],
[
"Isaksen",
"Aaron",
""
],
[
"Nealen",
"Andy",
""
],
[
"Togelius",
"Julian",
""
]
] |
1702.00858 | Zachary Sunberg | Zachary Sunberg, Christopher Ho, and Mykel Kochenderfer | The Value of Inferring the Internal State of Traffic Participants for
Autonomous Freeway Driving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Safe interaction with human drivers is one of the primary challenges for
autonomous vehicles. In order to plan driving maneuvers effectively, the
vehicle's control system must infer and predict how humans will behave based on
their latent internal state (e.g., intentions and aggressiveness). This
research uses a simple model for human behavior with unknown parameters that
make up the internal states of the traffic participants and presents a method
for quantifying the value of estimating these states and planning with their
uncertainty explicitly modeled. An upper performance bound is established by an
omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of
the internal states. A baseline lower bound is established by planning with
MCTS assuming that all drivers have the same internal state. MCTS variants are
then used to solve a partially observable Markov decision process (POMDP) that
models the internal state uncertainty to determine whether inferring the
internal state offers an advantage over the baseline. Applying this method to a
freeway lane changing scenario reveals that there is a significant performance
gap between the upper bound and baseline. POMDP planning techniques come close
to closing this gap, especially when important hidden model parameters are
correlated with measurable parameters.
| [
{
"version": "v1",
"created": "Thu, 2 Feb 2017 22:38:10 GMT"
}
] | 1,486,339,200,000 | [
[
"Sunberg",
"Zachary",
""
],
[
"Ho",
"Christopher",
""
],
[
"Kochenderfer",
"Mykel",
""
]
] |
1702.01510 | Yong Wang | Zi Jian Yang, Yong Wang | Survey of modern Fault Diagnosis methods in networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advent of modern computer networks, fault diagnosis has been a focus
of research activity. This paper reviews the history of fault diagnosis in
networks and discusses the main methods in information gathering section,
information analyzing section and diagnosing and revolving section of fault
diagnosis in networks. Emphasis will be placed upon knowledge-based methods
with discussing the advantages and shortcomings of the different methods. The
survey is concluded with a description of some open problems.
| [
{
"version": "v1",
"created": "Mon, 6 Feb 2017 06:43:16 GMT"
}
] | 1,486,425,600,000 | [
[
"Yang",
"Zi Jian",
""
],
[
"Wang",
"Yong",
""
]
] |
1702.01795 | Peter Patel-Schneider | Peter F. Patel-Schneider | ASHACL: Alternative Shapes Constraint Language | 18 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | ASHACL, a variant of the W3C Shapes Constraint Language, is designed to
determine whether an RDF graph meets some conditions. These conditions are
grouped into shapes, which validate whether particular RDF terms each meet the
constraints of the shape. Shapes are themselves expressed as RDF triples in an
RDF graph, called a shapes graph.
| [
{
"version": "v1",
"created": "Mon, 6 Feb 2017 21:13:43 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Mar 2017 13:10:02 GMT"
}
] | 1,489,104,000,000 | [
[
"Patel-Schneider",
"Peter F.",
""
]
] |
1702.01886 | Sara Bernardini | Sara Bernardini, Fabio Fagnani, David E. Smith | Extracting Lifted Mutual Exclusion Invariants from Temporal Planning
Domains | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a technique for automatically extracting mutual exclusion
invariants from temporal planning instances. It first identifies a set of
invariant templates by inspecting the lifted representation of the domain and
then checks these templates against properties that assure invariance. Our
technique builds on other approaches to invariant synthesis presented in the
literature, but departs from their limited focus on instantaneous actions by
addressing temporal domains. To deal with time, we formulate invariance
conditions that account for the entire structure of the actions and the
possible concurrent interactions between them. As a result, we construct a
significantly more comprehensive technique than previous methods, which is able
to find not only invariants for temporal domains, but also a broader set of
invariants for non-temporal domains. The experimental results reported in this
paper provide evidence that identifying a broader set of invariants results in
the generation of fewer multi-valued state variables with larger domains. We
show that, in turn, this reduction in the number of variables reflects
positively on the performance of a number of temporal planners that use a
variable/value representation by significantly reducing their running time.
| [
{
"version": "v1",
"created": "Tue, 7 Feb 2017 06:02:50 GMT"
}
] | 1,486,512,000,000 | [
[
"Bernardini",
"Sara",
""
],
[
"Fagnani",
"Fabio",
""
],
[
"Smith",
"David E.",
""
]
] |
1702.02302 | Hyunmin Chae | Hyunmin Chae, Chang Mook Kang, ByeoungDo Kim, Jaekyum Kim, Chung Choo
Chung and Jun Won Choi | Autonomous Braking System via Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new autonomous braking system based on deep
reinforcement learning. The proposed autonomous braking system automatically
decides whether to apply the brake at each time step when confronting the risk
of collision using the information on the obstacle obtained by the sensors. The
problem of designing brake control is formulated as searching for the optimal
policy in Markov decision process (MDP) model where the state is given by the
relative position of the obstacle and the vehicle's speed, and the action space
is defined as whether brake is stepped or not. The policy used for brake
control is learned through computer simulations using the deep reinforcement
learning method called deep Q-network (DQN). In order to derive desirable
braking policy, we propose the reward function which balances the damage
imposed to the obstacle in case of accident and the reward achieved when the
vehicle runs out of risk as soon as possible. DQN is trained for the scenario
where a vehicle is encountered with a pedestrian crossing the urban road.
Experiments show that the control agent exhibits desirable control behavior and
avoids collision without any mistake in various uncertain environments.
| [
{
"version": "v1",
"created": "Wed, 8 Feb 2017 06:51:33 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Apr 2017 12:43:36 GMT"
}
] | 1,493,078,400,000 | [
[
"Chae",
"Hyunmin",
""
],
[
"Kang",
"Chang Mook",
""
],
[
"Kim",
"ByeoungDo",
""
],
[
"Kim",
"Jaekyum",
""
],
[
"Chung",
"Chung Choo",
""
],
[
"Choi",
"Jun Won",
""
]
] |
1702.02470 | Emmanuel Hebrard | Cl\'ement Carbonnel (LAAS-ROC), Emmanuel H\'ebrard (LAAS-ROC) | Propagation via Kernelization: The Vertex Cover Constraint | null | Michel Rueher. The 22nd International Conference on Principles and
Practice of Constraint Programming, Sep 2016, Toulouse, France. Lecture Notes
in Computer Science, 9892, pp.147 - 156, 2016, Principles and Practice of
Constraint Programming | 10.1007/978-3-319-44953-1_10 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The technique of kernelization consists in extracting, from an instance of a
problem, an essentially equivalent instance whose size is bounded in a
parameter k. Besides being the basis for efficient param-eterized algorithms,
this method also provides a wealth of information to reason about in the
context of constraint programming. We study the use of kernelization for
designing propagators through the example of the Vertex Cover constraint. Since
the classic kernelization rules often correspond to dominance rather than
consistency, we introduce the notion of "loss-less" kernel. While our
preliminary experimental results show the potential of the approach, they also
show some of its limits. In particular, this method is more effective for
vertex covers of large and sparse graphs, as they tend to have, relatively,
smaller kernels.
| [
{
"version": "v1",
"created": "Tue, 7 Feb 2017 15:45:39 GMT"
}
] | 1,486,598,400,000 | [
[
"Carbonnel",
"Clément",
"",
"LAAS-ROC"
],
[
"Hébrard",
"Emmanuel",
"",
"LAAS-ROC"
]
] |
1702.03401 | Aske Plaat | Aske Plaat, Jonathan Schaeffer, Wim Pijls, Arie de Bruin | A Minimax Algorithm Better Than Alpha-beta?: No and Yes | Report version of AI Journal article Best-first fixed-depth minimax
algorithms 1996. arXiv admin note: text overlap with arXiv:1404.1517 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper has three main contributions to our understanding of fixed-depth
minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on
Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*,
finally transforming it into a practical algorithm. In effect, we show that
SSS* = alpha-beta + ransposition tables. The crucial step is the realization
that transposition tables contain so-called solution trees, structures that are
used in best-first search algorithms like SSS*. Having created a practical
version, we present performance measurements with tournament game-playing
programs for three different minimax games, yielding results that contradict a
number of publications. (B) Based on the insights gained in our attempts at
understanding SSS*, we present a framework that facilitates the construction of
several best-first fixed- depth game-tree search algorithms, known and new. The
framework is based on depth-first null-window Alpha-Beta search, enhanced with
storage to allow for the refining of previous search results. It focuses
attention on the essential differences between algorithms. (C) We present a new
instance of the framework, MTD(f). It is well-suited for use with iterative
deepening, and performs better than algorithms that are currently used in most
state-of-the-art game-playing programs. We provide experimental evidence to
explain why MTD(f) performs better than the other fixed-depth minimax
algorithms.
| [
{
"version": "v1",
"created": "Sat, 11 Feb 2017 09:48:12 GMT"
}
] | 1,487,548,800,000 | [
[
"Plaat",
"Aske",
""
],
[
"Schaeffer",
"Jonathan",
""
],
[
"Pijls",
"Wim",
""
],
[
"de Bruin",
"Arie",
""
]
] |
1702.03592 | Benedikt B\"unz | Benedikt B\"unz and Matthew Lamm | Graph Neural Networks and Boolean Satisfiability | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we explore whether or not deep neural architectures can learn
to classify Boolean satisfiability (SAT). We devote considerable time to
discussing the theoretical properties of SAT. Then, we define a graph
representation for Boolean formulas in conjunctive normal form, and train
neural classifiers over general graph structures called Graph Neural Networks,
or GNNs, to recognize features of satisfiability. To the best of our knowledge
this has never been tried before. Our preliminary findings are potentially
profound. In a weakly-supervised setting, that is, without problem specific
feature engineering, Graph Neural Networks can learn features of
satisfiability.
| [
{
"version": "v1",
"created": "Sun, 12 Feb 2017 23:12:01 GMT"
}
] | 1,487,030,400,000 | [
[
"Bünz",
"Benedikt",
""
],
[
"Lamm",
"Matthew",
""
]
] |
1702.03594 | Andr\'es Herrera-Poyatos | Andr\'es Herrera-Poyatos and Francisco Herrera | Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy
Diversification | 27 pages, 5 figures, 11 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The lack of diversity in a genetic algorithm's population may lead to a bad
performance of the genetic operators since there is not an equilibrium between
exploration and exploitation. In those cases, genetic algorithms present a fast
and unsuitable convergence.
In this paper we develop a novel hybrid genetic algorithm which attempts to
obtain a balance between exploration and exploitation. It confronts the
diversity problem using the named greedy diversification operator. Furthermore,
the proposed algorithm applies a competition between parent and children so as
to exploit the high quality visited solutions. These operators are complemented
by a simple selection mechanism designed to preserve and take advantage of the
population diversity.
Additionally, we extend our proposal to the field of memetic algorithms,
obtaining an improved model with outstanding results in practice.
The experimental study shows the validity of the approach as well as how
important is taking into account the exploration and exploitation concepts when
designing an evolutionary algorithm.
| [
{
"version": "v1",
"created": "Sun, 12 Feb 2017 23:23:17 GMT"
}
] | 1,487,030,400,000 | [
[
"Herrera-Poyatos",
"Andrés",
""
],
[
"Herrera",
"Francisco",
""
]
] |
1702.03724 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | On Seeking Consensus Between Document Similarity Measures | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the application of consensus clustering and
meta-clustering to the set of all possible partitions of a data set. We show
that when using a "complement" of Rand Index as a measure of cluster
similarity, the total-separation partition, putting each element in a separate
set, is chosen.
| [
{
"version": "v1",
"created": "Mon, 13 Feb 2017 11:46:04 GMT"
}
] | 1,487,030,400,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1702.04047 | Marcello Balduccini | Marcello Balduccini, Yuliya Lierler | Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter | Under consideration in Theory and Practice of Logic Programming
(TPLP) | TPLP 17(4) 462-515 (2017) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Researchers in answer set programming and constraint programming have spent
significant efforts in the development of hybrid languages and solving
algorithms combining the strengths of these traditionally separate fields.
These efforts resulted in a new research area: constraint answer set
programming. Constraint answer set programming languages and systems proved to
be successful at providing declarative, yet efficient solutions to problems
involving hybrid reasoning tasks. One of the main contributions of this paper
is the first comprehensive account of the constraint answer set language and
solver EZCSP, a mainstream representative of this research area that has been
used in various successful applications. We also develop an extension of the
transition systems proposed by Nieuwenhuis et al. in 2006 to capture Boolean
satisfiability solvers. We use this extension to describe the EZCSP algorithm
and prove formal claims about it. The design and algorithmic details behind
EZCSP clearly demonstrate that the development of the hybrid systems of this
kind is challenging. Many questions arise when one faces various design choices
in an attempt to maximize system's benefits. One of the key decisions that a
developer of a hybrid solver makes is settling on a particular integration
schema within its implementation. Thus, another important contribution of this
paper is a thorough case study based on EZCSP, focused on the various
integration schemas that it provides.
Under consideration in Theory and Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2017 02:29:29 GMT"
},
{
"version": "v2",
"created": "Mon, 15 May 2017 22:43:03 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Dec 2017 00:25:53 GMT"
}
] | 1,512,345,600,000 | [
[
"Balduccini",
"Marcello",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
1702.04282 | Yan Karklin | Chaitanya Ekanadham, Yan Karklin | T-SKIRT: Online Estimation of Student Proficiency in an Adaptive
Learning System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop T-SKIRT: a temporal, structured-knowledge, IRT-based method for
predicting student responses online. By explicitly accounting for student
learning and employing a structured, multidimensional representation of student
proficiencies, the model outperforms standard IRT-based methods on an online
response prediction task when applied to real responses collected from students
interacting with diverse pools of educational content.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2017 16:42:49 GMT"
}
] | 1,487,116,800,000 | [
[
"Ekanadham",
"Chaitanya",
""
],
[
"Karklin",
"Yan",
""
]
] |
1702.04389 | Norbert B\'atfai Ph.D. | Norbert B\'atfai and Ren\'at\'o Besenczi and Gerg\H{o} Bogacsovics and
Fanny Monori | Entropy Non-increasing Games for the Improvement of Dataflow Programming | 15 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we introduce a new conception of a family of esport games
called Samu Entropy to try to improve dataflow program graphs like the ones
that are based on Google's TensorFlow. Currently, the Samu Entropy project
specifies only requirements for new esport games to be developed with
particular attention to the investigation of the relationship between esport
and artificial intelligence. It is quite obvious that there is a very close and
natural relationship between esport games and artificial intelligence.
Furthermore, the project Samu Entropy focuses not only on using artificial
intelligence, but on creating AI in a new way. We present a reference game
called Face Battle that implements the Samu Entropy requirements.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2017 21:18:17 GMT"
}
] | 1,487,203,200,000 | [
[
"Bátfai",
"Norbert",
""
],
[
"Besenczi",
"Renátó",
""
],
[
"Bogacsovics",
"Gergő",
""
],
[
"Monori",
"Fanny",
""
]
] |
1702.04594 | Minghao Yin | Yiyuan Wang, Shaowei Cai, Minghao Yin | Local Search for Minimum Weight Dominating Set with Two-Level
Configuration Checking and Frequency Based Scoring Function | 29 pages, 1 figure | JAIR 58 (2017) 267-295 | 10.1613/jair.5205 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Minimum Weight Dominating Set (MWDS) problem is an important
generalization of the Minimum Dominating Set (MDS) problem with extensive
applications. This paper proposes a new local search algorithm for the MWDS
problem, which is based on two new ideas. The first idea is a heuristic called
two-level configuration checking (CC2), which is a new variant of a recent
powerful configuration checking strategy (CC) for effectively avoiding the
recent search paths. The second idea is a novel scoring function based on the
frequency of being uncovered of vertices. Our algorithm is called CC2FS,
according to the names of the two ideas. The experimental results show that,
CC2FS performs much better than some state-of-the-art algorithms in terms of
solution quality on a broad range of MWDS benchmarks.
| [
{
"version": "v1",
"created": "Wed, 15 Feb 2017 13:22:57 GMT"
}
] | 1,487,203,200,000 | [
[
"Wang",
"Yiyuan",
""
],
[
"Cai",
"Shaowei",
""
],
[
"Yin",
"Minghao",
""
]
] |
1702.05383 | Kumar Sankar Ray | Kumar S. Ray and Mandrita Mondal | Theorem Proving Based on Semantics of DNA Strand Graph | 25 pages,12 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because of several technological limitations of traditional silicon based
computing, for past few years a paradigm shift, from silicon to carbon, is
occurring in computational world. DNA computing has been considered to be quite
promising in solving computational and reasoning problems by using DNA strands.
Resolution, an important aspect of automated theorem proving and mathematical
logic, is a rule of inference which leads to proof by contradiction technique
for sentences in propositional logic and first-order logic. This can also be
called refutation theorem-proving. In this paper we have shown how the theorem
proving with resolution refutation by DNA computation can be represented by the
semantics of process calculus and strand graph.
| [
{
"version": "v1",
"created": "Wed, 15 Feb 2017 11:12:34 GMT"
}
] | 1,487,548,800,000 | [
[
"Ray",
"Kumar S.",
""
],
[
"Mondal",
"Mandrita",
""
]
] |
1702.06199 | Quan Nguyen | Quan Nguyen | The Dialog State Tracking Challenge with Bayesian Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative model has been one of the most common approaches for solving the
Dialog State Tracking Problem with the capabilities to model the dialog
hypotheses in an explicit manner. The most important task in such Bayesian
networks models is constructing the most reliable user models by learning and
reflecting the training data into the probability distribution of user actions
conditional on networks states. This paper provides an overall picture of the
learning process in a Bayesian framework with an emphasize on the
state-of-the-art theoretical analyses of the Expectation Maximization learning
algorithm.
| [
{
"version": "v1",
"created": "Mon, 20 Feb 2017 22:43:54 GMT"
}
] | 1,487,721,600,000 | [
[
"Nguyen",
"Quan",
""
]
] |
1702.06662 | Davoud Mougouei | Davoud Mougouei, David M. W. Powers, Asghar Moeini | An Integer Programming Model for Binary Knapsack Problem with
Value-Related Dependencies among Elements | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binary Knapsack Problem (BKP) is to select a subset of an element (item) set
with the highest value while keeping the total weight within the capacity of
the knapsack. This paper presents an integer programming model for a variation
of BKP where the value of each element may depend on selecting or ignoring
other elements. Strengths of such Value-Related Dependencies are assumed to be
imprecise and hard to specify. To capture this imprecision, we have proposed
modeling value-related dependencies using fuzzy graphs and their algebraic
structure.
| [
{
"version": "v1",
"created": "Wed, 22 Feb 2017 03:14:05 GMT"
}
] | 1,487,808,000,000 | [
[
"Mougouei",
"Davoud",
""
],
[
"Powers",
"David M. W.",
""
],
[
"Moeini",
"Asghar",
""
]
] |
1702.06915 | Ferdinando Fioretto Ferdinando Fioretto | Ferdinando Fioretto and Agostino Dovier and Enrico Pontelli and
William Yeoh and Roie Zivan | Solving DCOPs with Distributed Large Neighborhood Search | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The field of Distributed Constraint Optimization has gained momentum in
recent years, thanks to its ability to address various applications related to
multi-agent cooperation. Nevertheless, solving Distributed Constraint
Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale,
complex applications, incomplete DCOP algorithms are necessary. Current
incomplete DCOP algorithms suffer of one or more of the following limitations:
they (a) find local minima without providing quality guarantees; (b) provide
loose quality assessment; or (c) are unable to benefit from the structure of
the problem, such as domain-dependent knowledge and hard constraints.
Therefore, capitalizing on strategies from the centralized constraint solving
community, we propose a Distributed Large Neighborhood Search (D-LNS) framework
to solve DCOPs. The proposed framework (with its novel repair phase) provides
guarantees on solution quality, refining upper and lower bounds during the
iterative process, and can exploit domain-dependent structures. Our
experimental results show that D-LNS outperforms other incomplete DCOP
algorithms on both structured and unstructured problem instances.
| [
{
"version": "v1",
"created": "Wed, 22 Feb 2017 17:54:23 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Feb 2017 01:21:38 GMT"
}
] | 1,487,894,400,000 | [
[
"Fioretto",
"Ferdinando",
""
],
[
"Dovier",
"Agostino",
""
],
[
"Pontelli",
"Enrico",
""
],
[
"Yeoh",
"William",
""
],
[
"Zivan",
"Roie",
""
]
] |
1702.07001 | Doron Zarchy | Doron Zarchy | Theoretical and Experimental Analysis of the Canadian Traveler Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Devising an optimal strategy for navigation in a partially observable
environment is one of the key objectives in AI. One of the problem in this
context is the Canadian Traveler Problem (CTP). CTP is a navigation problem
where an agent is tasked to travel from source to target in a partially
observable weighted graph, whose edge might be blocked with a certain
probability and observing such blockage occurs only when reaching upon one of
the edges end points. The goal is to find a strategy that minimizes the
expected travel cost. The problem is known to be P$\#$ hard. In this work we
study the CTP theoretically and empirically. First, we study the Dep-CTP, a CTP
variant we introduce which assumes dependencies between the edges status. We
show that Dep-CTP is intractable, and further we analyze two of its subclasses
on disjoint paths graph. Second, we develop a general algorithm Gen-PAO that
optimally solve the CTP. Gen-PAO is capable of solving two other types of CTP
called Sensing-CTP and Expensive-Edges CTP. Since the CTP is intractable,
Gen-PAO use some pruning methods to reduce the space search for the optimal
solution. We also define some variants of Gen-PAO, compare their performance
and show some benefits of Gen-PAO over existing work.
| [
{
"version": "v1",
"created": "Wed, 22 Feb 2017 20:57:29 GMT"
}
] | 1,487,894,400,000 | [
[
"Zarchy",
"Doron",
""
]
] |
1702.07168 | Amit Mishra | Amit Kumar Mishra | A DIKW Paradigm to Cognitive Engineering | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Though the word cognitive has a wide range of meanings we define cognitive
engineering as learning from brain to bolster engineering solutions. However,
giving an achievable framework to the process towards this has been a difficult
task. In this work we take the classic data information knowledge wisdom (DIKW)
framework to set some achievable goals and sub-goals towards cognitive
engineering. A layered framework like DIKW aligns nicely with the layered
structure of pre-frontal cortex. And breaking the task into sub-tasks based on
the layers also makes it easier to start developmental endeavours towards
achieving the final goal of a brain-inspired system.
| [
{
"version": "v1",
"created": "Thu, 23 Feb 2017 10:51:32 GMT"
}
] | 1,487,894,400,000 | [
[
"Mishra",
"Amit Kumar",
""
]
] |
1702.07543 | Mengya Wang | Mengya Wang, Hankui Zhuo, Huiling Zhu | Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of
Rules | This paper has been withdrawn by the authors due to a crucial sign
error in equations | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Representation learning of knowledge graphs encodes entities and relation
types into a continuous low-dimensional vector space, learns embeddings of
entities and relation types. Most existing methods only concentrate on
knowledge triples, ignoring logic rules which contain rich background
knowledge. Although there has been some work aiming at leveraging both
knowledge triples and logic rules, they ignore the transitivity and
antisymmetry of logic rules. In this paper, we propose a novel approach to
learn knowledge representations with entities and ordered relations in
knowledges and logic rules. The key idea is to integrate knowledge triples and
logic rules, and approximately order the relation types in logic rules to
utilize the transitivity and antisymmetry of logic rules. All entries of the
embeddings of relation types are constrained to be non-negative. We translate
the general constrained optimization problem into an unconstrained optimization
problem to solve the non-negative matrix factorization. Experimental results
show that our model significantly outperforms other baselines on knowledge
graph completion task. It indicates that our model is capable of capturing the
transitivity and antisymmetry information, which is significant when learning
embeddings of knowledge graphs.
| [
{
"version": "v1",
"created": "Fri, 24 Feb 2017 11:28:02 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Apr 2017 07:52:39 GMT"
}
] | 1,492,646,400,000 | [
[
"Wang",
"Mengya",
""
],
[
"Zhuo",
"Hankui",
""
],
[
"Zhu",
"Huiling",
""
]
] |
1702.08222 | Ewa Andrejczuk Ms. | Ewa Andrejczuk, Juan A. Rodriguez-Aguilar, Carme Roig, Carles Sierra | Synergistic Team Composition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective teams are crucial for organisations, especially in environments
that require teams to be constantly created and dismantled, such as software
development, scientific experiments, crowd-sourcing, or the classroom. Key
factors influencing team performance are competences and personality of team
members. Hence, we present a computational model to compose proficient and
congenial teams based on individuals' personalities and their competences to
perform tasks of different nature. With this purpose, we extend Wilde's
post-Jungian method for team composition, which solely employs individuals'
personalities. The aim of this study is to create a model to partition agents
into teams that are balanced in competences, personality and gender. Finally,
we present some preliminary empirical results that we obtained when analysing
student performance. Results show the benefits of a more informed team
composition that exploits individuals' competences besides information about
their personalities.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2017 10:36:36 GMT"
}
] | 1,488,240,000,000 | [
[
"Andrejczuk",
"Ewa",
""
],
[
"Rodriguez-Aguilar",
"Juan A.",
""
],
[
"Roig",
"Carme",
""
],
[
"Sierra",
"Carles",
""
]
] |
1702.08367 | Fan Yang | Fan Yang, Zhilin Yang, William W. Cohen | Differentiable Learning of Logical Rules for Knowledge Base Reasoning | Accepted at NIPS 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of learning probabilistic first-order logical rules for
knowledge base reasoning. This learning problem is difficult because it
requires learning the parameters in a continuous space as well as the structure
in a discrete space. We propose a framework, Neural Logic Programming, that
combines the parameter and structure learning of first-order logical rules in
an end-to-end differentiable model. This approach is inspired by a
recently-developed differentiable logic called TensorLog, where inference tasks
can be compiled into sequences of differentiable operations. We design a neural
controller system that learns to compose these operations. Empirically, our
method outperforms prior work on multiple knowledge base benchmark datasets,
including Freebase and WikiMovies.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2017 16:44:38 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Jun 2017 04:17:58 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Nov 2017 17:50:15 GMT"
}
] | 1,511,827,200,000 | [
[
"Yang",
"Fan",
""
],
[
"Yang",
"Zhilin",
""
],
[
"Cohen",
"William W.",
""
]
] |
1702.08495 | Sebastian Benthall | Sebastian Benthall | Don't Fear the Reaper: Refuting Bostrom's Superintelligence Argument | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years prominent intellectuals have raised ethical concerns about
the consequences of artificial intelligence. One concern is that an autonomous
agent might modify itself to become "superintelligent" and, in supremely
effective pursuit of poorly specified goals, destroy all of humanity. This
paper considers and rejects the possibility of this outcome. We argue that this
scenario depends on an agent's ability to rapidly improve its ability to
predict its environment through self-modification. Using a Bayesian model of a
reasoning agent, we show that there are important limitations to how an agent
may improve its predictive ability through self-modification alone. We conclude
that concern about this artificial intelligence outcome is misplaced and better
directed at policy questions around data access and storage.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2017 19:57:17 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Mar 2017 20:43:32 GMT"
}
] | 1,488,844,800,000 | [
[
"Benthall",
"Sebastian",
""
]
] |
1703.00426 | Francois Chollet | Cezary Kaliszyk, Fran\c{c}ois Chollet, Christian Szegedy | HolStep: A Machine Learning Dataset for Higher-order Logic Theorem
Proving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large computer-understandable proofs consist of millions of intermediate
logical steps. The vast majority of such steps originate from manually selected
and manually guided heuristics applied to intermediate goals. So far, machine
learning has generally not been used to filter or generate these steps. In this
paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for
the purpose of developing new machine learning-based theorem-proving
strategies. We make this dataset publicly available under the BSD license. We
propose various machine learning tasks that can be performed on this dataset,
and discuss their significance for theorem proving. We also benchmark a set of
simple baseline machine learning models suited for the tasks (including
logistic regression, convolutional neural networks and recurrent neural
networks). The results of our baseline models show the promise of applying
machine learning to HOL theorem proving.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2017 18:20:19 GMT"
}
] | 1,488,412,800,000 | [
[
"Kaliszyk",
"Cezary",
""
],
[
"Chollet",
"François",
""
],
[
"Szegedy",
"Christian",
""
]
] |
1703.00760 | Pierre Roy | Pierre Roy, Alexandre Papadopoulos, Fran\c{c}ois Pachet | Sampling Variations of Lead Sheets | 16 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine-learning techniques have been recently used with spectacular results
to generate artefacts such as music or text. However, these techniques are
still unable to capture and generate artefacts that are convincingly
structured. In this paper we present an approach to generate structured musical
sequences. We introduce a mechanism for sampling efficiently variations of
musical sequences. Given a input sequence and a statistical model, this
mechanism samples a set of sequences whose distance to the input sequence is
approximately within specified bounds. This mechanism is implemented as an
extension of belief propagation, and uses local fields to bias the generation.
We show experimentally that sampled sequences are indeed closely correlated to
the standard musical similarity measure defined by Mongeau and Sankoff. We then
show how this mechanism can used to implement composition strategies that
enforce arbitrary structure on a musical lead sheet generation problem.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2017 12:33:28 GMT"
}
] | 1,488,499,200,000 | [
[
"Roy",
"Pierre",
""
],
[
"Papadopoulos",
"Alexandre",
""
],
[
"Pachet",
"François",
""
]
] |
1703.00838 | Reuth Mirsky | Retuh Mirsky and Ya'akov (Kobi) Gal | SLIM: Semi-Lazy Inference Mechanism for Plan Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plan Recognition algorithms require to recognize a complete hierarchy
explaining the agent's actions and goals. While the output of such algorithms
is informative to the recognizer, the cost of its calculation is high in
run-time, space, and completeness. Moreover, performing plan recognition online
requires the observing agent to reason about future actions that have not yet
been seen and maintain a set of hypotheses to support all possible options.
This paper presents a new and efficient algorithm for online plan recognition
called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and
top-down parsing processes, which allow it to commit only to the minimum
necessary actions in real-time, but still provide complete hypotheses post
factum. We show both theoretically and empirically that although the
computational cost of this process is still exponential, there is a significant
improvement in run-time when compared to a state of the art of plan recognition
algorithm.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2017 15:53:19 GMT"
}
] | 1,488,499,200,000 | [
[
"Mirsky",
"Retuh",
"",
"Kobi"
],
[
"Ya'akov",
"",
"",
"Kobi"
],
[
"Gal",
"",
""
]
] |
1703.01083 | Reuth Mirsky | Reuth Mirsky and Roni Stern and Ya'akov (Kobi) Gal and Meir Kalech | Sequential Plan Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plan recognition algorithms infer agents' plans from their observed actions.
Due to imperfect knowledge about the agent's behavior and the environment, it
is often the case that there are multiple hypotheses about an agent's plans
that are consistent with the observations, though only one of these hypotheses
is correct. This paper addresses the problem of how to disambiguate between
hypotheses, by querying the acting agent about whether a candidate plan in one
of the hypotheses matches its intentions. This process is performed
sequentially and used to update the set of possible hypotheses during the
recognition process. The paper defines the sequential plan recognition process
(SPRP), which seeks to reduce the number of hypotheses using a minimal number
of queries. We propose a number of policies for the SPRP which use maximum
likelihood and information gain to choose which plan to query. We show this
approach works well in practice on two domains from the literature,
significantly reducing the number of hypotheses using fewer queries than a
baseline approach. Our results can inform the design of future plan recognition
systems that interleave the recognition process with intelligent interventions
of their users.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2017 09:03:46 GMT"
}
] | 1,488,758,400,000 | [
[
"Mirsky",
"Reuth",
"",
"Kobi"
],
[
"Stern",
"Roni",
"",
"Kobi"
],
[
"Ya'akov",
"",
"",
"Kobi"
],
[
"Gal",
"",
""
],
[
"Kalech",
"Meir",
""
]
] |
1703.01161 | Alexander Vezhnevets | Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess,
Max Jaderberg, David Silver, Koray Kavukcuoglu | FeUdal Networks for Hierarchical Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement
learning proposal of Dayan and Hinton, and gains power and efficacy by
decoupling end-to-end learning across multiple levels -- allowing it to utilise
different resolutions of time. Our framework employs a Manager module and a
Worker module. The Manager operates at a lower temporal resolution and sets
abstract goals which are conveyed to and enacted by the Worker. The Worker
generates primitive actions at every tick of the environment. The decoupled
structure of FuN conveys several benefits -- in addition to facilitating very
long timescale credit assignment it also encourages the emergence of
sub-policies associated with different goals set by the Manager. These
properties allow FuN to dramatically outperform a strong baseline agent on
tasks that involve long-term credit assignment or memorisation. We demonstrate
the performance of our proposed system on a range of tasks from the ATARI suite
and also from a 3D DeepMind Lab environment.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2017 14:05:11 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Mar 2017 18:17:18 GMT"
}
] | 1,488,844,800,000 | [
[
"Vezhnevets",
"Alexander Sasha",
""
],
[
"Osindero",
"Simon",
""
],
[
"Schaul",
"Tom",
""
],
[
"Heess",
"Nicolas",
""
],
[
"Jaderberg",
"Max",
""
],
[
"Silver",
"David",
""
],
[
"Kavukcuoglu",
"Koray",
""
]
] |
1703.01310 | Georg Ostrovski | Georg Ostrovski, Marc G. Bellemare, Aaron van den Oord, Remi Munos | Count-Based Exploration with Neural Density Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
a density model, to generalize count-based exploration to non-tabular
reinforcement learning. This pseudo-count was used to generate an exploration
bonus for a DQN agent and combined with a mixed Monte Carlo update was
sufficient to achieve state of the art on the Atari 2600 game Montezuma's
Revenge. We consider two questions left open by their work: First, how
important is the quality of the density model for exploration? Second, what
role does the Monte Carlo update play in exploration? We answer the first
question by demonstrating the use of PixelCNN, an advanced neural density model
for images, to supply a pseudo-count. In particular, we examine the intrinsic
difficulties in adapting Bellemare et al.'s approach when assumptions about the
model are violated. The result is a more practical and general algorithm
requiring no special apparatus. We combine PixelCNN pseudo-counts with
different agent architectures to dramatically improve the state of the art on
several hard Atari games. One surprising finding is that the mixed Monte Carlo
update is a powerful facilitator of exploration in the sparsest of settings,
including Montezuma's Revenge.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2017 19:07:53 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Jun 2017 13:56:28 GMT"
}
] | 1,497,484,800,000 | [
[
"Ostrovski",
"Georg",
""
],
[
"Bellemare",
"Marc G.",
""
],
[
"Oord",
"Aaron van den",
""
],
[
"Munos",
"Remi",
""
]
] |
1703.01358 | Marcus Hutter | Sean Lamont and John Aslanides and Jan Leike and Marcus Hutter | Generalised Discount Functions applied to a Monte-Carlo AImu
Implementation | 12 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, work has been done to develop the theory of General
Reinforcement Learning (GRL). However, there are few examples demonstrating
these results in a concrete way. In particular, there are no examples
demonstrating the known results regarding gener- alised discounting. We have
added to the GRL simulation platform AIXIjs the functionality to assign an
agent arbitrary discount functions, and an environment which can be used to
determine the effect of discounting on an agent's policy. Using this, we
investigate how geometric, hyperbolic and power discounting affect an informed
agent in a simple MDP. We experimentally reproduce a number of theoretical
results, and discuss some related subtleties. It was found that the agent's
behaviour followed what is expected theoretically, assuming appropriate
parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning
algorithm.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2017 23:25:38 GMT"
}
] | 1,488,844,800,000 | [
[
"Lamont",
"Sean",
""
],
[
"Aslanides",
"John",
""
],
[
"Leike",
"Jan",
""
],
[
"Hutter",
"Marcus",
""
]
] |
1703.01893 | He Jiang | He Jiang, Shuwei Zhang, Zhilei Ren, Xiaochen Lai, Yong Piao | Approximate Muscle Guided Beam Search for Three-Index Assignment Problem | 9 pages, 3 figures, Proceedings of the Fifth International Conference
on Swarm Intelligence, 2014 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a well-known NP-hard problem, the Three-Index Assignment Problem (AP3) has
attracted lots of research efforts for developing heuristics. However, existing
heuristics either obtain less competitive solutions or consume too much time.
In this paper, a new heuristic named Approximate Muscle guided Beam Search
(AMBS) is developed to achieve a good trade-off between solution quality and
running time. By combining the approximate muscle with beam search, the
solution space size can be significantly decreased, thus the time for searching
the solution can be sharply reduced. Extensive experimental results on the
benchmark indicate that the new algorithm is able to obtain solutions with
competitive quality and it can be employed on instances with largescale. Work
of this paper not only proposes a new efficient heuristic, but also provides a
promising method to improve the efficiency of beam search.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 14:30:06 GMT"
}
] | 1,488,844,800,000 | [
[
"Jiang",
"He",
""
],
[
"Zhang",
"Shuwei",
""
],
[
"Ren",
"Zhilei",
""
],
[
"Lai",
"Xiaochen",
""
],
[
"Piao",
"Yong",
""
]
] |
1703.01908 | Christopher A. Tucker | Christopher A. Tucker | A proposal for ethically traceable artificial intelligence | 4 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the problem of a critique of robotic behavior in near-unanimous
agreement to human norms seems intractable, a starting point of such an
ambition is a framework of the collection of knowledge a priori and experience
a posteriori categorized as a set of synthetical judgments available to the
intelligence, translated into computer code. If such a proposal were
successful, an algorithm with ethically traceable behavior and cogent
equivalence to human cognition is established. This paper will propose the
application of Kant's critique of reason to current programming constructs of
an autonomous intelligent system.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 14:54:19 GMT"
},
{
"version": "v2",
"created": "Sun, 28 May 2017 09:15:49 GMT"
}
] | 1,496,102,400,000 | [
[
"Tucker",
"Christopher A.",
""
]
] |
1703.01924 | Arthur Van Camp | Arthur Van Camp, Gert de Cooman | Exchangeable choice functions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate how to model exchangeability with choice functions.
Exchangeability is a structural assessment on a sequence of uncertain
variables. We show how such assessments are a special indifference assessment,
and how that leads to a counterpart of de Finetti's Representation Theorem,
both in a finite and a countable context.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 15:34:54 GMT"
}
] | 1,488,844,800,000 | [
[
"Van Camp",
"Arthur",
""
],
[
"de Cooman",
"Gert",
""
]
] |
1703.01971 | Wen Jiang | Zichang He and Wen Jiang | Evidential supplier selection based on interval data fusion | 29 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Supplier selection is a typical multi-criteria decision making (MCDM) problem
and lots of uncertain information exist inevitably. To address this issue, a
new method was proposed based on interval data fusion. Our method follows the
original way to generate classical basic probability assignment(BPA) determined
by the distance among the evidences. However, the weights of criteria are kept
as interval numbers to generate interval BPAs and do the fusion of interval
BPAs. Finally, the order is ranked and the decision is made according to the
obtained interval BPAs. In this paper, a numerical example of supplier
selection is applied to verify the feasibility and validity of our method. The
new method is presented aiming at solving multiple-criteria decision-making
problems in which the weights of criteria or experts are described in fuzzy
data like linguistic terms or interval data.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 16:54:12 GMT"
}
] | 1,488,844,800,000 | [
[
"He",
"Zichang",
""
],
[
"Jiang",
"Wen",
""
]
] |
1703.02239 | Katsunari Shibata | Katsunari Shibata | Functions that Emerge through End-to-End Reinforcement Learning - The
Direction for Artificial General Intelligence - | The Multi-disciplinary Conference on Reinforcement Learning and
Decision Making (RLDM) 2017, 5 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, triggered by the impressive results in TV-games or game of Go by
Google DeepMind, end-to-end reinforcement learning (RL) is collecting
attentions. Although little is known, the author's group has propounded this
framework for around 20 years and already has shown various functions that
emerge in a neural network (NN) through RL. In this paper, they are introduced
again at this timing.
"Function Modularization" approach is deeply penetrated subconsciously. The
inputs and outputs for a learning system can be raw sensor signals and motor
commands. "State space" or "action space" generally used in RL show the
existence of functional modules. That has limited reinforcement learning to
learning only for the action-planning module. In order to extend reinforcement
learning to learning of the entire function on a huge degree of freedom of a
massively parallel learning system and to explain or develop human-like
intelligence, the author has believed that end-to-end RL from sensors to motors
using a recurrent NN (RNN) becomes an essential key. Especially in the higher
functions, this approach is very effective by being free from the need to
decide their inputs and outputs.
The functions that emerge, we have confirmed, through RL using a NN cover a
broad range from real robot learning with raw camera pixel inputs to
acquisition of dynamic functions in a RNN. Those are (1)image recognition,
(2)color constancy (optical illusion), (3)sensor motion (active recognition),
(4)hand-eye coordination and hand reaching movement, (5)explanation of brain
activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective
attention, (10)prediction, (11)exploration. The end-to-end RL enables the
emergence of very flexible comprehensive functions that consider many things in
parallel although it is difficult to give the boundary of each function
clearly.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 06:51:19 GMT"
},
{
"version": "v2",
"created": "Tue, 16 May 2017 07:22:07 GMT"
}
] | 1,494,979,200,000 | [
[
"Shibata",
"Katsunari",
""
]
] |
1703.03453 | Zhaohan Guo | Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill | Using Options and Covariance Testing for Long Horizon Off-Policy Policy
Evaluation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluating a policy by deploying it in the real world can be risky and
costly. Off-policy policy evaluation (OPE) algorithms use historical data
collected from running a previous policy to evaluate a new policy, which
provides a means for evaluating a policy without requiring it to ever be
deployed. Importance sampling is a popular OPE method because it is robust to
partial observability and works with continuous states and actions. However,
the amount of historical data required by importance sampling can scale
exponentially with the horizon of the problem: the number of sequential
decisions that are made. We propose using policies over temporally extended
actions, called options, and show that combining these policies with importance
sampling can significantly improve performance for long-horizon problems. In
addition, we can take advantage of special cases that arise due to
options-based policies to further improve the performance of importance
sampling. We further generalize these special cases to a general covariance
testing rule that can be used to decide which weights to drop in an IS
estimate, and derive a new IS algorithm called Incremental Importance Sampling
that can provide significantly more accurate estimates for a broad class of
domains.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 20:21:36 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Dec 2017 23:47:59 GMT"
}
] | 1,512,604,800,000 | [
[
"Guo",
"Zhaohan Daniel",
""
],
[
"Thomas",
"Philip S.",
""
],
[
"Brunskill",
"Emma",
""
]
] |
1703.03543 | Katsunari Shibata | Katsunari Shibata | Communications that Emerge through Reinforcement Learning Using a
(Recurrent) Neural Network | The Multi-disciplinary Conference on Reinforcement Learning and
Decision Making (RLDM) 2017, 5 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Communication is not only an action of choosing a signal, but needs to
consider the context and sensor signals. It also needs to decide what
information is communicated and how it is represented in or understood from
signals. Therefore, communication should be realized comprehensively together
with its purpose and other functions.
The recent successful results in end-to-end reinforcement learning (RL) show
the importance of comprehensive learning and the usefulness of end-to-end RL.
Although little is known, we have shown that a variety of communications emerge
through RL using a (recurrent) neural network (NN). Here, three of them are
introduced.
In the 1st one, negotiation to avoid conflicts among 4 randomly-picked agents
was learned. Each agent generates a binary signal from the output of its
recurrent NN (RNN), and receives 4 signals from the agents three times. After
learning, each agent made an appropriate final decision after negotiation for
any combination of 4 agents. Differentiation of individuality among the agents
also could be seen.
The 2nd one focused on discretization of communication signal. A sender agent
perceives the receiver's location and generates a continuous signal twice by
its RNN. A receiver agent receives them sequentially, and moves according to
its RNN's output to reach the sender's location. When noises were added to the
signal, it was binarized through learning and 2-bit communication was
established.
The 3rd one focused on end-to-end comprehensive communication. A sender
receives 1,785-pixel real camera image on which a real robot can be seen, and
sends two sounds whose frequencies are computed by its NN. A receiver receives
them, and two motion commands for the robot are generated by its NN. After
learning, though some preliminary learning was necessary for the sender, the
robot could reach the goal from any initial location.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 04:41:29 GMT"
},
{
"version": "v2",
"created": "Tue, 16 May 2017 07:27:12 GMT"
}
] | 1,494,979,200,000 | [
[
"Shibata",
"Katsunari",
""
]
] |
1703.03693 | Subhash Kak | Subhash Kak | On Quantum Decision Trees | 9 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantum decision systems are being increasingly considered for use in
artificial intelligence applications. Classical and quantum nodes can be
distinguished based on certain correlations in their states. This paper
investigates some properties of the states obtained in a decision tree
structure. How these correlations may be mapped to the decision tree is
considered. Classical tree representations and approximations to quantum states
are provided.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 21:39:52 GMT"
}
] | 1,489,363,200,000 | [
[
"Kak",
"Subhash",
""
]
] |
1703.03868 | Nathan Sturtevant | Jingwei Chen, Robert C. Holte, Sandra Zilles, Nathan R. Sturtevant | Front-to-End Bidirectional Heuristic Search with Near-Optimal Node
Expansions | Accepted to IJCAI 2017. Camera ready version with new timing results | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well-known that any admissible unidirectional heuristic search
algorithm must expand all states whose $f$-value is smaller than the optimal
solution cost when using a consistent heuristic. Such states are called "surely
expanded" (s.e.). A recent study characterized s.e. pairs of states for
bidirectional search with consistent heuristics: if a pair of states is s.e.
then at least one of the two states must be expanded. This paper derives a
lower bound, VC, on the minimum number of expansions required to cover all s.e.
pairs, and present a new admissible front-to-end bidirectional heuristic search
algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no
more than 2VC expansions. We further prove that no admissible front-to-end
algorithm has a worst case better than 2VC. Experimental results show that NBS
competes with or outperforms existing bidirectional search algorithms, and
often outperforms A* as well.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 23:19:50 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 17:33:21 GMT"
}
] | 1,495,584,000,000 | [
[
"Chen",
"Jingwei",
""
],
[
"Holte",
"Robert C.",
""
],
[
"Zilles",
"Sandra",
""
],
[
"Sturtevant",
"Nathan R.",
""
]
] |
1703.03916 | Shuwa Miura | Shuwa Miura and Alex Fukunaga | Axioms in Model-based Planners | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Axioms can be used to model derived predicates in domain- independent
planning models. Formulating models which use axioms can sometimes result in
problems with much smaller search spaces and shorter plans than the original
model. Previous work on axiom-aware planners focused solely on state- space
search planners. We propose axiom-aware planners based on answer set
programming and integer programming. We evaluate them on PDDL domains with
axioms and show that they can exploit additional expressivity of axioms.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 06:37:09 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Jun 2017 02:48:15 GMT"
}
] | 1,496,880,000,000 | [
[
"Miura",
"Shuwa",
""
],
[
"Fukunaga",
"Alex",
""
]
] |
1703.03933 | Sungtae Lee | Sungtae Lee, Sang-Woo Lee, Jinyoung Choi, Dong-Hyun Kwak and
Byoung-Tak Zhang | Micro-Objective Learning : Accelerating Deep Reinforcement Learning
through the Discovery of Continuous Subgoals | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, reinforcement learning has been successfully applied to the logical
game of Go, various Atari games, and even a 3D game, Labyrinth, though it
continues to have problems in sparse reward settings. It is difficult to
explore, but also difficult to exploit, a small number of successes when
learning policy. To solve this issue, the subgoal and option framework have
been proposed. However, discovering subgoals online is too expensive to be used
to learn options in large state spaces. We propose Micro-objective learning
(MOL) to solve this problem. The main idea is to estimate how important a state
is while training and to give an additional reward proportional to its
importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge
and Seaquest. With three experiments to each game, MOL significantly improved
the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times
better results than the previous state-of-the-art model.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 09:08:48 GMT"
}
] | 1,489,449,600,000 | [
[
"Lee",
"Sungtae",
""
],
[
"Lee",
"Sang-Woo",
""
],
[
"Choi",
"Jinyoung",
""
],
[
"Kwak",
"Dong-Hyun",
""
],
[
"Zhang",
"Byoung-Tak",
""
]
] |
1703.04115 | Oliver Obst | Olivia Michael and Oliver Obst | BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning | A sketch for a new team for RoboCup 2D simulation league, currently
planned for 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RoboCup offers a set of benchmark problems for Artificial Intelligence in
form of official world championships since 1997. The most tactical advanced and
richest in terms of behavioural complexity of these is the 2D Soccer Simulation
League, a simulated robotic soccer competition. BetaRun is a new attempt
combining both machine learning and manual programming approaches, with the
ultimate goal to arrive at a team that is trained entirely from observing and
playing games, and a new development based on agent2D.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2017 13:17:08 GMT"
},
{
"version": "v2",
"created": "Sat, 19 Aug 2017 07:15:22 GMT"
}
] | 1,503,360,000,000 | [
[
"Michael",
"Olivia",
""
],
[
"Obst",
"Oliver",
""
]
] |
1703.04159 | Konstantin Yakovlev S | Konstantin Yakovlev and Anton Andreychuk | Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm | Final version as submitted to ICAPS-2017 (main track); 8 pages; 4
figures; 1 algorithm; 2 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of finding conflict-free trajectories for multiple agents of
identical circular shape, operating in shared 2D workspace, is addressed in the
paper and decoupled, e.g., prioritized, approach is used to solve this problem.
Agents' workspace is tessellated into the square grid on which any-angle moves
are allowed, e.g. each agent can move into an arbitrary direction as long as
this move follows the straight line segment whose endpoints are tied to the
distinct grid elements. A novel any-angle planner based on Safe Interval Path
Planning (SIPP) algorithm is proposed to find trajectories for an agent moving
amidst dynamic obstacles (other agents) on a grid. This algorithm is then used
as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical,
side we show that AA-SIPP(m) is complete under well-defined conditions. On the
experimental side, in simulation tests with up to 200 agents involved, we show
that our planner finds much better solutions in terms of cost (up to 20%)
compared to the planners relying on cardinal moves only.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2017 18:43:28 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Mar 2017 08:16:37 GMT"
}
] | 1,489,622,400,000 | [
[
"Yakovlev",
"Konstantin",
""
],
[
"Andreychuk",
"Anton",
""
]
] |
1703.04361 | Benjamin Goertzel | Ben Goertzel | Toward a Formal Model of Cognitive Synergy | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Cognitive synergy" refers to a dynamic in which multiple cognitive
processes, cooperating to control the same cognitive system, assist each other
in overcoming bottlenecks encountered during their internal processing.
Cognitive synergy has been posited as a key feature of real-world general
intelligence, and has been used explicitly in the design of the OpenCog
cognitive architecture. Here category theory and related concepts are used to
give a formalization of the cognitive synergy concept.
A series of formal models of intelligent agents is proposed, with increasing
specificity and complexity: simple reinforcement learning agents; "cognit"
agents with an abstract memory and processing model; hypergraph-based agents
(in which "cognit" operations are carried out via hypergraphs); hypergraph
agents with a rich language of nodes and hyperlinks (such as the OpenCog
framework provides); "PGMC" agents whose rich hypergraphs are endowed with
cognitive processes guided via Probabilistic Growth and Mining of Combinations;
and finally variations of the PrimeAGI design, which is currently being built
on top of OpenCog.
A notion of cognitive synergy is developed for cognitive processes acting
within PGMC agents, based on developing a formal notion of "stuckness," and
defining synergy as a relationship between cognitive processes in which they
can help each other out when they get stuck. It is proposed that cognitive
processes relating to each other synergetically, associate in a certain way
with functors that map into each other via natural transformations. Cognitive
synergy is proposed to correspond to a certain inequality regarding the
relative costs of different paths through certain commutation diagrams.
Applications of this notion of cognitive synergy to particular cognitive
phenomena, and specific cognitive processes in the PrimeAGI design, are
discussed.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 12:48:15 GMT"
}
] | 1,489,449,600,000 | [
[
"Goertzel",
"Ben",
""
]
] |
1703.04368 | Benjamin Goertzel | Ruiting Lian and Ben Goertzel and Linas Vepstas and David Hanson and
Changle Zhou | Symbol Grounding via Chaining of Morphisms | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new model of symbol grounding is presented, in which the structures of
natural language, logical semantics, perception and action are represented
categorically, and symbol grounding is modeled via the composition of morphisms
between the relevant categories. This model gives conceptual insight into the
fundamentally systematic nature of symbol grounding, and also connects
naturally to practical real-world AI systems in current research and commercial
use. Specifically, it is argued that the structure of linguistic syntax can be
modeled as a certain asymmetric monoidal category, as e.g. implicit in the link
grammar formalism; the structure of spatiotemporal relationships and action
plans can be modeled similarly using "image grammars" and "action grammars";
and common-sense logical semantic structure can be modeled using
dependently-typed lambda calculus with uncertain truth values. Given these
formalisms, the grounding of linguistic descriptions in spatiotemporal
perceptions and coordinated actions consists of following morphisms from
language to logic through to spacetime and body (for comprehension), and vice
versa (for generation). The mapping is indicated between the spatial
relationships in the Region Connection Calculus and Allen Interval Algebra and
corresponding entries in the link grammar syntax parsing dictionary. Further,
the abstractions introduced here are shown to naturally model the structures
and systems currently being deployed in the context of using the OpenCog
cognitive architecture to control Hanson Robotics humanoid robots.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 13:06:49 GMT"
}
] | 1,489,449,600,000 | [
[
"Lian",
"Ruiting",
""
],
[
"Goertzel",
"Ben",
""
],
[
"Vepstas",
"Linas",
""
],
[
"Hanson",
"David",
""
],
[
"Zhou",
"Changle",
""
]
] |
1703.04382 | Benjamin Goertzel | Ben Goertzel | Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs
and Theorems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel partial order is defined on the space of digraphs or hypergraphs,
based on assessing the cost of producing a graph via a sequence of elementary
transformations. Leveraging work by Knuth and Skilling on the foundations of
inference, and the structure of Heyting algebras on graph space, this partial
order is used to construct an intuitionistic probability measure that applies
to either digraphs or hypergraphs. As logical inference steps can be
represented as transformations on hypergraphs representing logical statements,
this also yields an intuitionistic probability measure on spaces of theorems.
The central result is also extended to yield intuitionistic probabilities based
on more general weighted rule systems defined over bicartesian closed
categories.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 13:32:46 GMT"
}
] | 1,489,449,600,000 | [
[
"Goertzel",
"Ben",
""
]
] |
1703.04587 | Qi Zhang | Qi Zhang, Satinder Singh, Edmund Durfee | Minimizing Maximum Regret in Commitment Constrained Sequential Decision
Making | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In cooperative multiagent planning, it can often be beneficial for an agent
to make commitments about aspects of its behavior to others, allowing them in
turn to plan their own behaviors without taking the agent's detailed behavior
into account. Extending previous work in the Bayesian setting, we consider
instead a worst-case setting in which the agent has a set of possible
environments (MDPs) it could be in, and develop a commitment semantics that
allows for probabilistic guarantees on the agent's behavior in any of the
environments it could end up facing. Crucially, an agent receives observations
(of reward and state transitions) that allow it to potentially eliminate
possible environments and thus obtain higher utility by adapting its policy to
the history of observations. We develop algorithms and provide theory and some
preliminary empirical results showing that they ensure an agent meets its
commitments with history-dependent policies while minimizing maximum regret
over the possible environments.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2017 17:15:42 GMT"
}
] | 1,489,622,400,000 | [
[
"Zhang",
"Qi",
""
],
[
"Singh",
"Satinder",
""
],
[
"Durfee",
"Edmund",
""
]
] |
1703.04741 | Marija Slavkovik | Vicky Charisi and Louise Dennis and Michael Fisher and Robert Lieck
and Andreas Matthias and Marija Slavkovik and Janina Sombetzki and Alan F. T.
Winfield and Roman Yampolskiy | Towards Moral Autonomous Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both the ethics of autonomous systems and the problems of their technical
implementation have by now been studied in some detail. Less attention has been
given to the areas in which these two separate concerns meet. This paper,
written by both philosophers and engineers of autonomous systems, addresses a
number of issues in machine ethics that are located at precisely the
intersection between ethics and engineering. We first discuss the main
challenges which, in our view, machine ethics posses to moral philosophy. We
them consider different approaches towards the conceptual design of autonomous
systems and their implications on the ethics implementation in such systems.
Then we examine problematic areas regarding the specification and verification
of ethical behavior in autonomous systems, particularly with a view towards the
requirements of future legislation. We discuss transparency and accountability
issues that will be crucial for any future wide deployment of autonomous
systems in society. Finally we consider the, often overlooked, possibility of
intentional misuse of AI systems and the possible dangers arising out of
deliberately unethical design, implementation, and use of autonomous robots.
| [
{
"version": "v1",
"created": "Tue, 14 Mar 2017 21:46:04 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Mar 2017 08:12:10 GMT"
},
{
"version": "v3",
"created": "Tue, 31 Oct 2017 13:12:16 GMT"
}
] | 1,509,494,400,000 | [
[
"Charisi",
"Vicky",
""
],
[
"Dennis",
"Louise",
""
],
[
"Fisher",
"Michael",
""
],
[
"Lieck",
"Robert",
""
],
[
"Matthias",
"Andreas",
""
],
[
"Slavkovik",
"Marija",
""
],
[
"Sombetzki",
"Janina",
""
],
[
"Winfield",
"Alan F. T.",
""
],
[
"Yampolskiy",
"Roman",
""
]
] |
1703.04862 | Xinyang Deng | Xinyang Deng and Wen Jiang | Exploring the Combination Rules of D Numbers From a Perspective of
Conflict Redistribution | 6 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dempster-Shafer theory of evidence is widely applied to uncertainty modelling
and knowledge reasoning because of its advantages in dealing with uncertain
information. But some conditions or requirements, such as exclusiveness
hypothesis and completeness constraint, limit the development and application
of that theory to a large extend. To overcome the shortcomings and enhance its
capability of representing the uncertainty, a novel model, called D numbers,
has been proposed recently. However, many key issues, for example how to
implement the combination of D numbers, remain unsolved. In the paper, we have
explored the combination of D Numbers from a perspective of conflict
redistribution, and proposed two combination rules being suitable for different
situations for the fusion of two D numbers. The proposed combination rules can
reduce to the classical Dempster's rule in Dempster-Shafer theory under a
certain conditions. Numerical examples and discussion about the proposed rules
are also given in the paper.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 01:04:49 GMT"
}
] | 1,489,622,400,000 | [
[
"Deng",
"Xinyang",
""
],
[
"Jiang",
"Wen",
""
]
] |
1703.04912 | Sebastian Binnewies | Sebastian Binnewies, Zhiqiang Zhuang, Kewen Wang, Bela Stantic | Syntax-Preserving Belief Change Operators for Logic Programs | 44 pages, submitted to ACM Transactions on Computational Logic | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent methods have adapted the well-established AGM and belief base
frameworks for belief change to cover belief revision in logic programs. In
this study here, we present two new sets of belief change operators for logic
programs. They focus on preserving the explicit relationships expressed in the
rules of a program, a feature that is missing in purely semantic approaches
that consider programs only in their entirety. In particular, operators of the
latter class fail to satisfy preservation and support, two important properties
for belief change in logic programs required to ensure intuitive results.
We address this shortcoming of existing approaches by introducing partial
meet and ensconcement constructions for logic program belief change, which
allow us to define syntax-preserving operators that satisfy preservation and
support. Our work is novel in that our constructions not only preserve more
information from a logic program during a change operation than existing ones,
but they also facilitate natural definitions of contraction operators, the
first in the field to the best of our knowledge.
In order to evaluate the rationality of our operators, we translate the
revision and contraction postulates from the AGM and belief base frameworks to
the logic programming setting. We show that our operators fully comply with the
belief base framework and formally state the interdefinability between our
operators. We further propose an algorithm that is based on modularising a
logic program to reduce partial meet and ensconcement revisions or contractions
to performing the operation only on the relevant modules of that program.
Finally, we compare our approach to two state-of-the-art logic program revision
methods and demonstrate that our operators address the shortcomings of one and
generalise the other method.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 03:53:25 GMT"
},
{
"version": "v2",
"created": "Fri, 17 Mar 2017 00:56:18 GMT"
}
] | 1,489,968,000,000 | [
[
"Binnewies",
"Sebastian",
""
],
[
"Zhuang",
"Zhiqiang",
""
],
[
"Wang",
"Kewen",
""
],
[
"Stantic",
"Bela",
""
]
] |
1703.05201 | Mazurek Ji\v{r}\'i | Ji\v{r}\'i Mazurek | Fuzzy Rankings: Properties and Applications | 11 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In practice, a ranking of objects with respect to given set of criteria is of
considerable importance. However, due to lack of knowledge, information of time
pressure, decision makers might not be able to provide a (crisp) ranking of
objects from the top to the bottom. Instead, some objects might be ranked
equally, or better than other objects only to some degree. In such cases, a
generalization of crisp rankings to fuzzy rankings can be more useful. The aim
of the article is to introduce the notion of a fuzzy ranking and to discuss its
several properties, namely orderings, similarity and indecisiveness. The
proposed approach can be used both for group decision making or multiple
criteria decision making when uncertainty is involved.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 15:13:42 GMT"
}
] | 1,489,622,400,000 | [
[
"Mazurek",
"Jiří",
""
]
] |
1703.05204 | Mazurek Ji\v{r}\'i | Jiri Mazurek | On Inconsistency Indices and Inconsistency Axioms in Pairwise
Comparisons | 13 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pairwise comparisons are an important tool of modern (multiple criteria)
decision making. Since human judgments are often inconsistent, many studies
focused on the ways how to express and measure this inconsistency, and several
inconsistency indices were proposed as an alternative to Saaty inconsistency
index and inconsistency ratio for reciprocal pairwise comparisons matrices.
This paper aims to: firstly, introduce a new measure of inconsistency of
pairwise comparisons and to prove its basic properties; secondly, to postulate
an additional axiom, an upper boundary axiom, to an existing set of axioms; and
the last, but not least, the paper provides proofs of satisfaction of this
additional axiom by selected inconsistency indices as well as it provides their
numerical comparison.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 15:19:28 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Apr 2017 07:46:02 GMT"
}
] | 1,493,596,800,000 | [
[
"Mazurek",
"Jiri",
""
]
] |
1703.05376 | Gal Dalal | Gal Dalal, Balazs Szorenyi, Gugan Thoppe, Shie Mannor | Finite Sample Analysis of Two-Timescale Stochastic Approximation with
Applications to Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two-timescale Stochastic Approximation (SA) algorithms are widely used in
Reinforcement Learning (RL). Their iterates have two parts that are updated
using distinct stepsizes. In this work, we develop a novel recipe for their
finite sample analysis. Using this, we provide a concentration bound, which is
the first such result for a two-timescale SA. The type of bound we obtain is
known as `lock-in probability'. We also introduce a new projection scheme, in
which the time between successive projections increases exponentially. This
scheme allows one to elegantly transform a lock-in probability into a
convergence rate result for projected two-timescale SA. From this latter
result, we then extract key insights on stepsize selection. As an application,
we finally obtain convergence rates for the projected two-timescale RL
algorithms GTD(0), GTD2, and TDC.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 20:23:45 GMT"
},
{
"version": "v2",
"created": "Wed, 31 May 2017 16:35:17 GMT"
},
{
"version": "v3",
"created": "Thu, 7 Sep 2017 07:12:14 GMT"
},
{
"version": "v4",
"created": "Wed, 28 Feb 2018 12:13:00 GMT"
},
{
"version": "v5",
"created": "Mon, 4 Jun 2018 18:33:57 GMT"
}
] | 1,528,243,200,000 | [
[
"Dalal",
"Gal",
""
],
[
"Szorenyi",
"Balazs",
""
],
[
"Thoppe",
"Gugan",
""
],
[
"Mannor",
"Shie",
""
]
] |
1703.05614 | Xiao-Fan Niu | Xiao-Fan Niu, Wu-Jun Li | ParaGraphE: A Library for Parallel Knowledge Graph Embedding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph embedding aims at translating the knowledge graph into
numerical representations by transforming the entities and relations into
continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
been proposed to deal with this problem, but existing single-thread
implementations of them are time-consuming for large-scale knowledge graphs.
Here, we design a unified parallel framework to parallelize these methods,
which achieves a significant time reduction without influencing the accuracy.
We name our framework as ParaGraphE, which provides a library for parallel
knowledge graph embedding. The source code can be downloaded from
https://github.com/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE .
| [
{
"version": "v1",
"created": "Thu, 16 Mar 2017 13:36:41 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Mar 2017 06:15:48 GMT"
},
{
"version": "v3",
"created": "Wed, 5 Apr 2017 02:56:45 GMT"
}
] | 1,491,436,800,000 | [
[
"Niu",
"Xiao-Fan",
""
],
[
"Li",
"Wu-Jun",
""
]
] |
1703.06045 | Denis Deratani Mau\'a Dr. | Diarmaid Conaty, Denis D. Mau\'a and Cassio P. de Campos | Approximation Complexity of Maximum A Posteriori Inference in
Sum-Product Networks | 18 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss the computational complexity of approximating maximum a posteriori
inference in sum-product networks. We first show NP-hardness in trees of height
two by a reduction from maximum independent set; this implies
non-approximability within a sublinear factor. We show that this is a tight
bound, as we can find an approximation within a linear factor in networks of
height two. We then show that, in trees of height three, it is NP-hard to
approximate the problem within a factor $2^{f(n)}$ for any sublinear function
$f$ of the size of the input $n$. Again, this bound is tight, as we prove that
the usual max-product algorithm finds (in any network) approximations within
factor $2^{c \cdot n}$ for some constant $c < 1$. Last, we present a simple
algorithm, and show that it provably produces solutions at least as good as,
and potentially much better than, the max-product algorithm. We empirically
analyze the proposed algorithm against max-product using synthetic and
realistic networks.
| [
{
"version": "v1",
"created": "Fri, 17 Mar 2017 15:00:03 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Jun 2017 21:52:38 GMT"
},
{
"version": "v3",
"created": "Wed, 14 Jun 2017 17:30:18 GMT"
},
{
"version": "v4",
"created": "Wed, 23 Aug 2017 16:44:28 GMT"
},
{
"version": "v5",
"created": "Tue, 5 Sep 2017 14:15:44 GMT"
}
] | 1,504,656,000,000 | [
[
"Conaty",
"Diarmaid",
""
],
[
"Mauá",
"Denis D.",
""
],
[
"de Campos",
"Cassio P.",
""
]
] |
1703.06207 | Jacob Crandall | Jacob W. Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief
Abdallah, Jean-Fran\c{c}ois Bonnefon, Manuel Cebrian, Azim Shariff, Michael
A. Goodrich, and Iyad Rahwan | Cooperating with Machines | An updated version of this paper was published in Nature
Communications | Nature Communications, Vol. 9, Article No. 233, 2018 | 10.1038/s41467-017-02597-8 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major
driving force behind technical progress has been competition with human
cognition. Historical milestones have been frequently associated with computers
matching or outperforming humans in difficult cognitive tasks (e.g. face
recognition [2], personality classification [3], driving cars [4], or playing
video games [5]), or defeating humans in strategic zero-sum encounters (e.g.
Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast,
less attention has been given to developing autonomous machines that establish
mutually cooperative relationships with people who may not share the machine's
preferences. A main challenge has been that human cooperation does not require
sheer computational power, but rather relies on intuition [11], cultural norms
[12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions
toward cooperation [17], common-sense mechanisms that are difficult to encode
in machines for arbitrary contexts. Here, we combine a state-of-the-art
machine-learning algorithm with novel mechanisms for generating and acting on
signals to produce a new learning algorithm that cooperates with people and
other machines at levels that rival human cooperation in a variety of
two-player repeated stochastic games. This is the first general-purpose
algorithm that is capable, given a description of a previously unseen game
environment, of learning to cooperate with people within short timescales in
scenarios previously unanticipated by algorithm designers. This is achieved
without complex opponent modeling or higher-order theories of mind, thus
showing that flexible, fast, and general human-machine cooperation is
computationally achievable using a non-trivial, but ultimately simple, set of
algorithmic mechanisms.
| [
{
"version": "v1",
"created": "Fri, 17 Mar 2017 21:50:16 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Mar 2017 14:26:33 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Oct 2017 01:04:09 GMT"
},
{
"version": "v4",
"created": "Tue, 16 Jan 2018 15:17:33 GMT"
},
{
"version": "v5",
"created": "Wed, 21 Feb 2018 15:50:19 GMT"
}
] | 1,519,257,600,000 | [
[
"Crandall",
"Jacob W.",
""
],
[
"Oudah",
"Mayada",
""
],
[
"Tennom",
"",
""
],
[
"Ishowo-Oloko",
"Fatimah",
""
],
[
"Abdallah",
"Sherief",
""
],
[
"Bonnefon",
"Jean-François",
""
],
[
"Cebrian",
"Manuel",
""
],
[
"Shariff",
"Azim",
""
],
[
"Goodrich",
"Michael A.",
""
],
[
"Rahwan",
"Iyad",
""
]
] |
1703.06275 | Jialin Liu Ph.D | Jialin Liu, Julian Togelius, Diego Perez-Liebana, Simon M. Lucas | Evolving Game Skill-Depth using General Video Game AI Agents | 9 pages, 17 figures, CEC2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most games have, or can be generalised to have, a number of parameters that
may be varied in order to provide instances of games that lead to very
different player experiences. The space of possible parameter settings can be
seen as a search space, and we can therefore use a Random Mutation Hill
Climbing algorithm or other search methods to find the parameter settings that
induce the best games. One of the hardest parts of this approach is defining a
suitable fitness function. In this paper we explore the possibility of using
one of a growing set of General Video Game AI agents to perform automatic
play-testing. This enables a very general approach to game evaluation based on
estimating the skill-depth of a game. Agent-based play-testing is
computationally expensive, so we compare two simple but efficient optimisation
algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random
Mutation Hill-Climber. For the test game we use a space-battle game in order to
provide a suitable balance between simulation speed and potential skill-depth.
Results show that both algorithms are able to rapidly evolve game versions with
significant skill-depth, but that choosing a suitable resampling number is
essential in order to combat the effects of noise.
| [
{
"version": "v1",
"created": "Sat, 18 Mar 2017 09:04:05 GMT"
}
] | 1,490,054,400,000 | [
[
"Liu",
"Jialin",
""
],
[
"Togelius",
"Julian",
""
],
[
"Perez-Liebana",
"Diego",
""
],
[
"Lucas",
"Simon M.",
""
]
] |
1703.06321 | Ji\v{r}\'i Vomlel | Ji\v{r}\'i Vomlel and V\'aclav Kratochv\'il | Solving the Goddard problem by an influence diagram | 10 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influence diagrams are a decision-theoretic extension of probabilistic
graphical models. In this paper we show how they can be used to solve the
Goddard problem. We present results of numerical experiments with this problem
and compare the solutions provided by influence diagrams with the optimal
solution.
| [
{
"version": "v1",
"created": "Sat, 18 Mar 2017 17:25:55 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Mar 2017 08:11:19 GMT"
}
] | 1,490,140,800,000 | [
[
"Vomlel",
"Jiří",
""
],
[
"Kratochvíl",
"Václav",
""
]
] |
1703.06354 | Mark Muraven | Mark Muraven | Goal Conflict in Designing an Autonomous Artificial System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research on human self-regulation has shown that people hold many goals
simultaneously and have complex self-regulation mechanisms to deal with this
goal conflict. Artificial autonomous systems may also need to find ways to cope
with conflicting goals. Indeed, the intricate interplay among different goals
may be critical to the design as well as long-term safety and stability of
artificial autonomous systems. I discuss some of the critical features of the
human self-regulation system and how it might be applied to an artificial
system. Furthermore, the implications of goal conflict for the reliability and
stability of artificial autonomous systems and ensuring their alignment with
human goals and ethics is examined.
| [
{
"version": "v1",
"created": "Sat, 18 Mar 2017 21:25:29 GMT"
}
] | 1,490,054,400,000 | [
[
"Muraven",
"Mark",
""
]
] |
1703.06471 | Peeyush Kumar | Peeyush Kumar and Doina Precup | Multi-Timescale, Gradient Descent, Temporal Difference Learning with
Linear Options | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deliberating on large or continuous state spaces have been long standing
challenges in reinforcement learning. Temporal Abstraction have somewhat made
this possible, but efficiently planing using temporal abstraction still remains
an issue. Moreover using spatial abstractions to learn policies for various
situations at once while using temporal abstraction models is an open problem.
We propose here an efficient algorithm which is convergent under linear
function approximation while planning using temporally abstract actions. We
show how this algorithm can be used along with randomly generated option models
over multiple time scales to plan agents which need to act real time. Using
these randomly generated option models over multiple time scales are shown to
reduce number of decision epochs required to solve the given task, hence
effectively reducing the time needed for deliberation.
| [
{
"version": "v1",
"created": "Sun, 19 Mar 2017 17:31:13 GMT"
}
] | 1,490,054,400,000 | [
[
"Kumar",
"Peeyush",
""
],
[
"Precup",
"Doina",
""
]
] |
1703.06565 | Thanuka Wickramarathne | Thanuka Wickramarathne | Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning
in Soft and Hard Fusion Environments | The 20th IEEE International Conference on Information Fusion
(Fusion'17) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust belief revision methods are crucial in streaming data situations for
updating existing knowledge or beliefs with new incoming evidence. Bayes
conditioning is the primary mechanism in use for belief revision in data fusion
systems that use probabilistic inference. However, traditional conditioning
methods face several challenges due to inherent data/source imperfections in
big-data environments that harness soft (i.e., human or human-based) sources in
addition to hard (i.e., physics-based) sensors. The objective of this paper is
to investigate the most natural extension of Bayes conditioning that is
suitable for evidence updating in the presence of such uncertainties. By
viewing the evidence updating process as a thought experiment, an elegant
strategy is derived for robust evidence updating in the presence of extreme
uncertainties that are characteristic of big-data environments. In particular,
utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes
conditioning is derived for evidence that takes the form of a general belief
function. The presented work differs fundamentally from the Conditional Update
Equation (CUE) and authors own extensions of it. An overview of this
development is provided via illustrative examples. Furthermore, insights into
parameter selection under various fusion contexts are also provided.
| [
{
"version": "v1",
"created": "Mon, 20 Mar 2017 02:29:53 GMT"
},
{
"version": "v2",
"created": "Sat, 10 Jun 2017 12:28:43 GMT"
}
] | 1,497,312,000,000 | [
[
"Wickramarathne",
"Thanuka",
""
]
] |
1703.06597 | Tshilidzi Marwala | Tshilidzi Marwala and Evan Hurwitz | Artificial Intelligence and Economic Theories | Marwala, T. and Hurwitz, E. (2017) Artificial Intelligence and
Economic Theory: Skynet in the Market. Springer. (Accepted) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of artificial intelligence has changed many disciplines such as
engineering, social science and economics. Artificial intelligence is a
computational technique which is inspired by natural intelligence such as the
swarming of birds, the working of the brain and the pathfinding of the ants.
These techniques have impact on economic theories. This book studies the impact
of artificial intelligence on economic theories, a subject that has not been
extensively studied. The theories that are considered are: demand and supply,
asymmetrical information, pricing, rational choice, rational expectation, game
theory, efficient market hypotheses, mechanism design, prospect, bounded
rationality, portfolio theory, rational counterfactual and causality. The
benefit of this book is that it evaluates existing theories of economics and
update them based on the developments in artificial intelligence field.
| [
{
"version": "v1",
"created": "Mon, 20 Mar 2017 04:47:14 GMT"
}
] | 1,490,054,400,000 | [
[
"Marwala",
"Tshilidzi",
""
],
[
"Hurwitz",
"Evan",
""
]
] |
1703.06815 | Fabio Aurelio D'Asaro | Fabio Aurelio D'Asaro, Antonis Bikakis, Luke Dickens, Rob Miller | Foundations for a Probabilistic Event Calculus | Technical report | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present PEC, an Event Calculus (EC) style action language for reasoning
about probabilistic causal and narrative information. It has an action language
style syntax similar to that of the EC variant Modular-E. Its semantics is
given in terms of possible worlds which constitute possible evolutions of the
domain, and builds on that of EFEC, an epistemic extension of EC. We also
describe an ASP implementation of PEC and show the sense in which this is sound
and complete.
| [
{
"version": "v1",
"created": "Mon, 20 Mar 2017 16:03:36 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Jun 2017 16:17:11 GMT"
}
] | 1,499,040,000,000 | [
[
"D'Asaro",
"Fabio Aurelio",
""
],
[
"Bikakis",
"Antonis",
""
],
[
"Dickens",
"Luke",
""
],
[
"Miller",
"Rob",
""
]
] |
1703.06939 | Julien Savaux | Julien Savaux, Julien Vion, Sylvain Piechowiak, Ren\'e Mandiau,
Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius
Silaghi | Distributed Constraint Problems for Utilitarian Agents with Privacy
Concerns, Recast as POMDPs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Privacy has traditionally been a major motivation for distributed problem
solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as
Distributed Constraint Optimization Problem (DCOP) are fundamental models used
to solve various families of distributed problems. Even though several
approaches have been proposed to quantify and preserve privacy in such
problems, none of them is exempt from limitations. Here we approach the problem
by assuming that computation is performed among utilitarian agents. We
introduce a utilitarian approach where the utility of each state is estimated
as the difference between the reward for reaching an agreement on assignments
of shared variables and the cost of privacy loss. We investigate extensions to
solvers where agents integrate the utility function to guide their search and
decide which action to perform, defining thereby their policy. We show that
these extended solvers succeed in significantly reducing privacy loss without
significant degradation of the solution quality.
| [
{
"version": "v1",
"created": "Mon, 20 Mar 2017 19:32:40 GMT"
}
] | 1,490,140,800,000 | [
[
"Savaux",
"Julien",
""
],
[
"Vion",
"Julien",
""
],
[
"Piechowiak",
"Sylvain",
""
],
[
"Mandiau",
"René",
""
],
[
"Matsui",
"Toshihiro",
""
],
[
"Hirayama",
"Katsutoshi",
""
],
[
"Yokoo",
"Makoto",
""
],
[
"Elmane",
"Shakre",
""
],
[
"Silaghi",
"Marius",
""
]
] |
1703.07075 | Manuel Mazzara | Vladimir Marochko, Leonard Johard, Manuel Mazzara | Pseudorehearsal in value function approximation | null | 11th International Conference on Agents and Multi-agent Systems
Technologies and Applications, 2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Catastrophic forgetting is of special importance in reinforcement learning,
as the data distribution is generally non-stationary over time. We study and
compare several pseudorehearsal approaches for Q-learning with function
approximation in a pole balancing task. We have found that pseudorehearsal
seems to assist learning even in such very simple problems, given proper
initialization of the rehearsal parameters.
| [
{
"version": "v1",
"created": "Tue, 21 Mar 2017 07:09:27 GMT"
}
] | 1,490,140,800,000 | [
[
"Marochko",
"Vladimir",
""
],
[
"Johard",
"Leonard",
""
],
[
"Mazzara",
"Manuel",
""
]
] |
1703.07469 | Jacob Devlin | Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh,
Abdel-rahman Mohamed, Pushmeet Kohli | RobustFill: Neural Program Learning under Noisy I/O | 8 pages + 9 pages of supplementary material | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of automatically generating a computer program from some
specification has been studied since the early days of AI. Recently, two
competing approaches for automatic program learning have received significant
attention: (1) neural program synthesis, where a neural network is conditioned
on input/output (I/O) examples and learns to generate a program, and (2) neural
program induction, where a neural network generates new outputs directly using
a latent program representation.
Here, for the first time, we directly compare both approaches on a
large-scale, real-world learning task. We additionally contrast to rule-based
program synthesis, which uses hand-crafted semantics to guide the program
generation. Our neural models use a modified attention RNN to allow encoding of
variable-sized sets of I/O pairs. Our best synthesis model achieves 92%
accuracy on a real-world test set, compared to the 34% accuracy of the previous
best neural synthesis approach. The synthesis model also outperforms a
comparable induction model on this task, but we more importantly demonstrate
that the strength of each approach is highly dependent on the evaluation metric
and end-user application. Finally, we show that we can train our neural models
to remain very robust to the type of noise expected in real-world data (e.g.,
typos), while a highly-engineered rule-based system fails entirely.
| [
{
"version": "v1",
"created": "Tue, 21 Mar 2017 23:29:47 GMT"
}
] | 1,490,227,200,000 | [
[
"Devlin",
"Jacob",
""
],
[
"Uesato",
"Jonathan",
""
],
[
"Bhupatiraju",
"Surya",
""
],
[
"Singh",
"Rishabh",
""
],
[
"Mohamed",
"Abdel-rahman",
""
],
[
"Kohli",
"Pushmeet",
""
]
] |
1703.07929 | Fred Glover | Fred Glover and Jin-Kao Hao | Diversification-Based Learning in Computing and Optimization | 17 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diversification-Based Learning (DBL) derives from a collection of principles
and methods introduced in the field of metaheuristics that have broad
applications in computing and optimization. We show that the DBL framework goes
significantly beyond that of the more recent Opposition-based learning (OBL)
framework introduced in Tizhoosh (2005), which has become the focus of numerous
research initiatives in machine learning and metaheuristic optimization. We
unify and extend earlier proposals in metaheuristic search (Glover, 1997,
Glover and Laguna, 1997) to give a collection of approaches that are more
flexible and comprehensive than OBL for creating intensification and
diversification strategies in metaheuristic search. We also describe potential
applications of DBL to various subfields of machine learning and optimization.
| [
{
"version": "v1",
"created": "Thu, 23 Mar 2017 04:26:46 GMT"
}
] | 1,490,313,600,000 | [
[
"Glover",
"Fred",
""
],
[
"Hao",
"Jin-Kao",
""
]
] |
1703.08397 | Christian Stra{\ss}er | Mathieu Beirlaen and Jesse Heyninck and Christian Stra{\ss}er | Reasoning by Cases in Structured Argumentation | Proceedings of SAC/KRR 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend the $ASPIC^+$ framework for structured argumentation so as to allow
applications of the reasoning by cases inference scheme for defeasible
arguments. Given an argument with conclusion `$A$ or $B$', an argument based on
$A$ with conclusion $C$, and an argument based on $B$ with conclusion $C$, we
allow the construction of an argument with conclusion $C$. We show how our
framework leads to different results than other approaches in non-monotonic
logic for dealing with disjunctive information, such as disjunctive default
theory or approaches based on the OR-rule (which allows to derive a defeasible
rule `If ($A$ or $B$) then $C$', given two defeasible rules `If $A$ then $C$'
and `If $B$ then $C$'). We raise new questions regarding the subtleties of
reasoning defeasibly with disjunctive information, and show that its
formalization is more intricate than one would presume.
| [
{
"version": "v1",
"created": "Fri, 24 Mar 2017 13:00:52 GMT"
}
] | 1,490,572,800,000 | [
[
"Beirlaen",
"Mathieu",
""
],
[
"Heyninck",
"Jesse",
""
],
[
"Straßer",
"Christian",
""
]
] |
1703.08762 | Sanaz Bahargam Sanaz Bahargam | Sanaz Bahargam, D\'ora Erdos, Azer Bestavros, Evimaria Terzi | Team Formation for Scheduling Educational Material in Massive Online
Classes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whether teaching in a classroom or a Massive Online Open Course it is crucial
to present the material in a way that benefits the audience as a whole. We
identify two important tasks to solve towards this objective, 1 group students
so that they can maximally benefit from peer interaction and 2 find an optimal
schedule of the educational material for each group. Thus, in this paper, we
solve the problem of team formation and content scheduling for education. Given
a time frame d, a set of students S with their required need to learn different
activities T and given k as the number of desired groups, we study the problem
of finding k group of students. The goal is to teach students within time frame
d such that their potential for learning is maximized and find the best
schedule for each group. We show this problem to be NP-hard and develop a
polynomial algorithm for it. We show our algorithm to be effective both on
synthetic as well as a real data set. For our experiments, we use real data on
students' grades in a Computer Science department. As part of our contribution,
we release a semi-synthetic dataset that mimics the properties of the real
data.
| [
{
"version": "v1",
"created": "Sun, 26 Mar 2017 03:47:54 GMT"
}
] | 1,490,659,200,000 | [
[
"Bahargam",
"Sanaz",
""
],
[
"Erdos",
"Dóra",
""
],
[
"Bestavros",
"Azer",
""
],
[
"Terzi",
"Evimaria",
""
]
] |
1703.09368 | Jingchi Jiang | Jingchi Jiang and Chao Zhao and Yi Guan and Qiubin Yu | Learning and inference in knowledge-based probabilistic model for
medical diagnosis | 32 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on a weighted knowledge graph to represent first-order knowledge and
combining it with a probabilistic model, we propose a methodology for the
creation of a medical knowledge network (MKN) in medical diagnosis. When a set
of symptoms is activated for a specific patient, we can generate a ground
medical knowledge network composed of symptom nodes and potential disease
nodes. By Incorporating a Boltzmann machine into the potential function of a
Markov network, we investigated the joint probability distribution of the MKN.
In order to deal with numerical symptoms, a multivariate inference model is
presented that uses conditional probability. In addition, the weights for the
knowledge graph were efficiently learned from manually annotated Chinese
Electronic Medical Records (CEMRs). In our experiments, we found numerically
that the optimum choice of the quality of disease node and the expression of
symptom variable can improve the effectiveness of medical diagnosis. Our
experimental results comparing a Markov logic network and the logistic
regression algorithm on an actual CEMR database indicate that our method holds
promise and that MKN can facilitate studies of intelligent diagnosis.
| [
{
"version": "v1",
"created": "Tue, 28 Mar 2017 01:51:34 GMT"
}
] | 1,490,745,600,000 | [
[
"Jiang",
"Jingchi",
""
],
[
"Zhao",
"Chao",
""
],
[
"Guan",
"Yi",
""
],
[
"Yu",
"Qiubin",
""
]
] |
1703.09513 | Aleksey Buzmakov | Aleksey Buzmakov and Sergei O. Kuznetsov and Amedeo Napoli | Mining Best Closed Itemsets for Projection-antimonotonic Constraints in
Polynomial Time | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The exponential explosion of the set of patterns is one of the main
challenges in pattern mining. This challenge is approached by introducing a
constraint for pattern selection. One of the first constraints proposed in
pattern mining is support (frequency) of a pattern in a dataset. Frequency is
an anti-monotonic function, i.e., given an infrequent pattern, all its
superpatterns are not frequent. However, many other constraints for pattern
selection are neither monotonic nor anti-monotonic, which makes it difficult to
generate patterns satisfying these constraints.
In order to deal with nonmonotonic constraints we introduce the notion of
"projection antimonotonicity" and SOFIA algorithm that allow generating best
patterns for a class of nonmonotonic constraints. Cosine interest, robustness,
stability of closed itemsets, and the associated delta-measure are among these
constraints. SOFIA starts from light descriptions of transactions in dataset (a
small set of items in the case of itemset description) and then iteratively
adds more information to these descriptions (more items with indication of
tidsets they describe).
| [
{
"version": "v1",
"created": "Tue, 28 Mar 2017 11:40:44 GMT"
}
] | 1,490,745,600,000 | [
[
"Buzmakov",
"Aleksey",
""
],
[
"Kuznetsov",
"Sergei O.",
""
],
[
"Napoli",
"Amedeo",
""
]
] |
1703.09620 | Christoph Benzm\"uller | Christoph Benzm\"uller | Universal Reasoning, Rational Argumentation and Human-Machine
Interaction | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classical higher-order logic, when utilized as a meta-logic in which various
other (classical and non-classical) logics can be shallowly embedded, is well
suited for realising a universal logic reasoning approach. Universal logic
reasoning in turn, as envisioned already by Leibniz, may support the rigorous
formalisation and deep logical analysis of rational arguments within machines.
A respective universal logic reasoning framework is described and a range of
exemplary applications are discussed. In the future, universal logic reasoning
in combination with appropriate, controlled forms of rational argumentation may
serve as a communication layer between humans and intelligent machines.
| [
{
"version": "v1",
"created": "Tue, 28 Mar 2017 15:00:57 GMT"
}
] | 1,490,745,600,000 | [
[
"Benzmüller",
"Christoph",
""
]
] |
1703.09923 | Deyu Meng | Zilu Ma and Shiqi Liu and Deyu Meng | On Convergence Property of Implicit Self-paced Objective | 9 pages, 0 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-paced learning (SPL) is a new methodology that simulates the learning
principle of humans/animals to start learning easier aspects of a learning
task, and then gradually take more complex examples into training. This
new-coming learning regime has been empirically substantiated to be effective
in various computer vision and pattern recognition tasks. Recently, it has been
proved that the SPL regime has a close relationship to a implicit self-paced
objective function. While this implicit objective could provide helpful
interpretations to the effectiveness, especially the robustness, insights under
the SPL paradigms, there are still no theoretical results strictly proved to
verify such relationship. To this issue, in this paper, we provide some
convergence results on this implicit objective of SPL. Specifically, we prove
that the learning process of SPL always converges to critical points of this
implicit objective under some mild conditions. This result verifies the
intrinsic relationship between SPL and this implicit objective, and makes the
previous robustness analysis on SPL complete and theoretically rational.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2017 07:53:43 GMT"
}
] | 1,490,832,000,000 | [
[
"Ma",
"Zilu",
""
],
[
"Liu",
"Shiqi",
""
],
[
"Meng",
"Deyu",
""
]
] |
1703.09962 | Mitra Baratchi Mitra Baratchi | Mitra Baratchi, Geert Heijenk, Maarten van Steen | Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we address the problem of how automated situation-awareness
can be achieved by learning real-world situations from ubiquitously generated
mobility data. Without semantic input about the time and space where situations
take place, this turns out to be a fundamental challenging problem.
Uncertainties also introduce technical challenges when data is generated in
irregular time intervals, being mixed with noise, and errors. Purely relying on
temporal patterns observable in mobility data, in this paper, we propose
Spaceprint, a fully automated algorithm for finding the repetitive pattern of
similar situations in spaces. We evaluate this technique by showing how the
latent variables describing the category, and the actual identity of a space
can be discovered from the extracted situation patterns. Doing so, we use
different real-world mobility datasets with data about the presence of mobile
entities in a variety of spaces. We also evaluate the performance of this
technique by showing its robustness against uncertainties.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2017 10:31:04 GMT"
}
] | 1,490,832,000,000 | [
[
"Baratchi",
"Mitra",
""
],
[
"Heijenk",
"Geert",
""
],
[
"van Steen",
"Maarten",
""
]
] |
1703.10316 | Yantao Jia | Denghui Zhang, Manling Li, Yantao Jia, Yuanzhuo Wang, Xueqi Cheng | Efficient Parallel Translating Embedding For Knowledge Graphs | WI 2017: 460-468 | null | 10.1145/3106426.3106447 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Knowledge graph embedding aims to embed entities and relations of knowledge
graphs into low-dimensional vector spaces. Translating embedding methods regard
relations as the translation from head entities to tail entities, which achieve
the state-of-the-art results among knowledge graph embedding methods. However,
a major limitation of these methods is the time consuming training process,
which may take several days or even weeks for large knowledge graphs, and
result in great difficulty in practical applications. In this paper, we propose
an efficient parallel framework for translating embedding methods, called
ParTrans-X, which enables the methods to be paralleled without locks by
utilizing the distinguished structures of knowledge graphs. Experiments on two
datasets with three typical translating embedding methods, i.e., TransE [3],
TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that
ParTrans-X can speed up the training process by more than an order of
magnitude.
| [
{
"version": "v1",
"created": "Thu, 30 Mar 2017 05:20:18 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Aug 2017 10:52:29 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Nov 2017 09:09:01 GMT"
},
{
"version": "v4",
"created": "Tue, 9 Jan 2018 02:40:30 GMT"
}
] | 1,515,542,400,000 | [
[
"Zhang",
"Denghui",
""
],
[
"Li",
"Manling",
""
],
[
"Jia",
"Yantao",
""
],
[
"Wang",
"Yuanzhuo",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
1703.10429 | Alejandro Ramos Soto | Alejandro Ramos-Soto, Jose M. Alonso, Ehud Reiter, Kees van Deemter,
Albert Gatt | An Empirical Approach for Modeling Fuzzy Geographical Descriptors | Conference paper: Accepted for FUZZIEEE-2017. One column version for
arXiv (8 pages) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel heuristic approach that defines fuzzy geographical
descriptors using data gathered from a survey with human subjects. The
participants were asked to provide graphical interpretations of the descriptors
`north' and `south' for the Galician region (Spain). Based on these
interpretations, our approach builds fuzzy descriptors that are able to compute
membership degrees for geographical locations. We evaluated our approach in
terms of efficiency and precision. The fuzzy descriptors are meant to be used
as the cornerstones of a geographical referring expression generation algorithm
that is able to linguistically characterize geographical locations and regions.
This work is also part of a general research effort that intends to establish a
methodology which reunites the empirical studies traditionally practiced in
data-to-text and the use of fuzzy sets to model imprecision and vagueness in
words and expressions for text generation purposes.
| [
{
"version": "v1",
"created": "Thu, 30 Mar 2017 12:06:15 GMT"
}
] | 1,490,918,400,000 | [
[
"Ramos-Soto",
"Alejandro",
""
],
[
"Alonso",
"Jose M.",
""
],
[
"Reiter",
"Ehud",
""
],
[
"van Deemter",
"Kees",
""
],
[
"Gatt",
"Albert",
""
]
] |
1704.00045 | Amir Ahooye Atashin | Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan | Comparison of ontology alignment systems across single matching task via
the McNemar's test | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontology alignment is widely-used to find the correspondences between
different ontologies in diverse fields.After discovering the alignments,several
performance scores are available to evaluate them.The scores typically require
the identified alignment and a reference containing the underlying actual
correspondences of the given ontologies.The current trend in the alignment
evaluation is to put forward a new score(e.g., precision, weighted precision,
etc.)and to compare various alignments by juxtaposing the obtained scores.
However,it is substantially provocative to select one measure among others for
comparison.On top of that, claiming if one system has a better performance than
one another cannot be substantiated solely by comparing two scalars.In this
paper,we propose the statistical procedures which enable us to theoretically
favor one system over one another.The McNemar's test is the statistical means
by which the comparison of two ontology alignment systems over one matching
task is drawn.The test applies to a 2x2 contingency table which can be
constructed in two different ways based on the alignments,each of which has
their own merits/pitfalls.The ways of the contingency table construction and
various apposite statistics from the McNemar's test are elaborated in minute
detail.In the case of having more than two alignment systems for comparison,
the family-wise error rate is expected to happen. Thus, the ways of preventing
such an error are also discussed.A directed graph visualizes the outcome of the
McNemar's test in the presence of multiple alignment systems.From this graph,
it is readily understood if one system is better than one another or if their
differences are imperceptible.The proposed statistical methodologies are
applied to the systems participated in the OAEI 2016 anatomy track, and also
compares several well-known similarity metrics for the same matching problem.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2017 15:20:01 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Apr 2018 12:58:37 GMT"
}
] | 1,524,441,600,000 | [
[
"Mohammadi",
"Majid",
""
],
[
"Atashin",
"Amir Ahooye",
""
],
[
"Hofman",
"Wout",
""
],
[
"Tan",
"Yao-Hua",
""
]
] |
1704.00325 | Sayyed Ali Mirsoleimani | S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik and Jos Vermaseren | Structured Parallel Programming for Monte Carlo Tree Search | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a new algorithm for parallel Monte Carlo tree
search (MCTS). It is based on the pipeline pattern and allows flexible
management of the control flow of the operations in parallel MCTS. The pipeline
pattern provides for the first structured parallel programming approach to
MCTS. Moreover, we propose a new lock-free tree data structure for parallel
MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel
MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores
when compared to the existing methods.
| [
{
"version": "v1",
"created": "Sun, 2 Apr 2017 16:22:31 GMT"
}
] | 1,491,264,000,000 | [
[
"Mirsoleimani",
"S. Ali",
""
],
[
"Plaat",
"Aske",
""
],
[
"Herik",
"Jaap van den",
""
],
[
"Vermaseren",
"Jos",
""
]
] |
1704.00853 | Fred Glover | Kenneth Sorensen, Marc Sevaux and Fred Glover | A History of Metaheuristics | 27 pages, to appear in: R. Marti, P. Pardalos, and M. Resende, eds.,
Handbook of Heuristics, Springer | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter describes the history of metaheuristics in five distinct
periods, starting long before the first use of the term and ending a long time
in the future.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2017 02:28:59 GMT"
}
] | 1,491,350,400,000 | [
[
"Sorensen",
"Kenneth",
""
],
[
"Sevaux",
"Marc",
""
],
[
"Glover",
"Fred",
""
]
] |
1704.01049 | Carina Geldhauser | Alexander Eckrot and Carina Geldhauser and Jan Jurczyk | A simulated annealing approach to optimal storing in a multi-level
warehouse | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simulated annealing algorithm specifically tailored to optimise
total retrieval times in a multi-level warehouse under complex pre-batched
picking constraints. Experiments on real data from a picker-to-parts order
picking process in the warehouse of a European manufacturer show that optimal
storage assignments do not necessarily display features presumed in heuristics,
such as clustering of positively correlated items or ordering of items by
picking frequency.
In an experiment run on more than 4000 batched orders with 1 to 150 items per
batch, the storage assignment suggested by the algorithm produces a 21\%
reduction in the total retrieval time with respect to a frequency-based storage
assignment.
| [
{
"version": "v1",
"created": "Sat, 25 Mar 2017 14:15:35 GMT"
}
] | 1,491,350,400,000 | [
[
"Eckrot",
"Alexander",
""
],
[
"Geldhauser",
"Carina",
""
],
[
"Jurczyk",
"Jan",
""
]
] |
1704.01161 | Gal Dalal | Gal Dalal, Bal\'azs Sz\"or\'enyi, Gugan Thoppe, Shie Mannor | Finite Sample Analyses for TD(0) with Function Approximation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | TD(0) is one of the most commonly used algorithms in reinforcement learning.
Despite this, there is no existing finite sample analysis for TD(0) with
function approximation, even for the linear case. Our work is the first to
provide such results. Existing convergence rates for Temporal Difference (TD)
methods apply only to somewhat modified versions, e.g., projected variants or
ones where stepsizes depend on unknown problem parameters. Our analyses obviate
these artificial alterations by exploiting strong properties of TD(0). We
provide convergence rates both in expectation and with high-probability. The
two are obtained via different approaches that use relatively unknown, recently
developed stochastic approximation techniques.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2017 19:47:52 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Jul 2017 10:28:28 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Nov 2017 18:24:15 GMT"
},
{
"version": "v4",
"created": "Mon, 11 Dec 2017 08:21:21 GMT"
}
] | 1,513,036,800,000 | [
[
"Dalal",
"Gal",
""
],
[
"Szörényi",
"Balázs",
""
],
[
"Thoppe",
"Gugan",
""
],
[
"Mannor",
"Shie",
""
]
] |
1704.01742 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | Transferrable Plausibility Model - A Probabilistic Interpretation of
Mathematical Theory of Evidence | Pre-publication version of: M.A. K{\l}opotek: Transferable
Plausibility Model - A Probabilistic Interpretation of Mathematical Theory of
Evidence O.Hryniewicz, J. Kacprzyk, J.Koronacki, S.Wierzcho\'{n}: Issues in
Intelligent Systems Paradigms Akademicka Oficyna Wydawnicza EXIT, Warszawa
2005 ISBN 83-87674-90-7, pp.107--118 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper suggests a new interpretation of the Dempster-Shafer theory in
terms of probabilistic interpretation of plausibility. A new rule of
combination of independent evidence is shown and its preservation of
interpretation is demonstrated.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2017 08:08:38 GMT"
}
] | 1,491,523,200,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1704.01944 | Paul Thaddeus Kazibudzki | Paul Thaddeus Kazibudzki | The quality of priority ratios estimation in relation to a selected
prioritization procedure and consistency measure for a Pairwise Comparison
Matrix | 30 pages, 11 tables, 3 figures | https://www.hindawi.com/journals/aor/2019/3574263/ | 10.1155/2019/3574263 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An overview of current debates and contemporary research devoted to the
modeling of decision making processes and their facilitation directs attention
to the Analytic Hierarchy Process (AHP). At the core of the AHP are various
prioritization procedures (PPs) and consistency measures (CMs) for a Pairwise
Comparison Matrix (PCM) which, in a sense, reflects preferences of decision
makers. Certainly, when judgments about these preferences are perfectly
consistent (cardinally transitive), all PPs coincide and the quality of the
priority ratios (PRs) estimation is exemplary. However, human judgments are
very rarely consistent, thus the quality of PRs estimation may significantly
vary. The scale of these variations depends on the applied PP and utilized CM
for a PCM. This is why it is important to find out which PPs and which CMs for
a PCM lead directly to an improvement of the PRs estimation accuracy. The main
goal of this research is realized through the properly designed, coded and
executed seminal and sophisticated simulation algorithms in Wolfram Mathematica
8.0. These research results convince that the embedded in the AHP and commonly
applied, both genuine PP and CM for PCM may significantly deteriorate the
quality of PRs estimation; however, solutions proposed in this paper can
significantly improve the methodology.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2017 17:25:39 GMT"
},
{
"version": "v2",
"created": "Mon, 19 Jun 2017 23:18:34 GMT"
}
] | 1,591,315,200,000 | [
[
"Kazibudzki",
"Paul Thaddeus",
""
]
] |
1704.02468 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek, S{\l}awomir T. Wierzcho\'n | Basic Formal Properties of A Relational Model of The Mathematical Theory
of Evidence | 23 pages | This is the preliminary version of the paper published in
Demonstratio Mathematica. Vol XXXI No 3,1998, pp. 669-688 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents a novel view of the Dempster-Shafer belief function as a
measure of diversity in relational data bases. It is demonstrated that under
the interpretation The Dempster rule of evidence combination corresponds to the
join operator of the relational database theory. This rough-set based
interpretation is qualitative in nature and can represent a number of belief
function operators.
The interpretation has the property that Given a definition of the belief
measure of objects in the interpretation domain we can perform operations in
this domain and the measure of the resulting object is derivable from measures
of component objects via belief operator. We demonstrated this property for
Dempster rule of combination, marginalization, Shafer's conditioning,
independent variables, Shenoy's notion of conditional independence of
variables.
The interpretation is based on rough sets (in connection with decision
tables), but differs from previous interpretations of this type in that it
counts the diversity rather than frequencies in a decision table.
| [
{
"version": "v1",
"created": "Sat, 8 Apr 2017 10:07:04 GMT"
}
] | 1,491,868,800,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
],
[
"Wierzchoń",
"Sławomir T.",
""
]
] |
1704.03048 | Luigi Troiano | Luigi Troiano and Irene D\'iaz and Ciro Gaglione | Matching Media Contents with User Profiles by means of the
Dempster-Shafer Theory | FUZZ-IEEE 2017. 6 pages, 3 figures, 4 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The media industry is increasingly personalizing the offering of contents in
attempt to better target the audience. This requires to analyze the
relationships that goes established between users and content they enjoy,
looking at one side to the content characteristics and on the other to the user
profile, in order to find the best match between the two. In this paper we
suggest to build that relationship using the Dempster-Shafer's Theory of
Evidence, proposing a reference model and illustrating its properties by means
of a toy example. Finally we suggest possible applications of the model for
tasks that are common in the modern media industry.
| [
{
"version": "v1",
"created": "Mon, 10 Apr 2017 20:34:37 GMT"
}
] | 1,491,955,200,000 | [
[
"Troiano",
"Luigi",
""
],
[
"Díaz",
"Irene",
""
],
[
"Gaglione",
"Ciro",
""
]
] |
1704.03342 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Beliefs and Probability in Bacchus' l.p. Logic: A~3-Valued Logic
Solution to Apparent Counter-intuition | Draft for the conference M.A. K{\l}opotek: Beliefs and Probability in
Bacchus' l.p. Logic: A 3-Valued Logic Solution to Apparent Counter-intuition.
[in:] R. Trappl Ed,: Cybernetics and Systems Research. Proc. 11 European
Meeting on Cybernetics and System Research EMCSR'92, Wien, Osterreich, 20.
April 1992. World Scientific Singapore, New Jersey, London, HongKong Vol. 1,
pp. 519-526 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fundamental discrepancy between first order logic and statistical inference
(global versus local properties of universe) is shown to be the obstacle for
integration of logic and probability in L.p. logic of Bacchus. To overcome the
counterintuitiveness of L.p. behaviour, a 3-valued logic is proposed.
| [
{
"version": "v1",
"created": "Tue, 11 Apr 2017 15:04:45 GMT"
}
] | 1,491,955,200,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1704.03402 | Sael Lee | Quoc Duy Vo, Jaya Thomas, Shinyoung Cho, Pradipta De, Bong Jun Choi,
Lee Sael | Next Generation Business Intelligence and Analytics: A Survey | 11 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Business Intelligence and Analytics (BI&A) is the process of extracting and
predicting business-critical insights from data. Traditional BI focused on data
collection, extraction, and organization to enable efficient query processing
for deriving insights from historical data. With the rise of big data and cloud
computing, there are many challenges and opportunities for the BI. Especially
with the growing number of data sources, traditional BI\&A are evolving to
provide intelligence at different scales and perspectives - operational BI,
situational BI, self-service BI. In this survey, we review the evolution of
business intelligence systems in full scale from back-end architecture to and
front-end applications. We focus on the changes in the back-end architecture
that deals with the collection and organization of the data. We also review the
changes in the front-end applications, where analytic services and
visualization are the core components. Using a uses case from BI in Healthcare,
which is one of the most complex enterprises, we show how BI\&A will play an
important role beyond the traditional usage. The survey provides a holistic
view of Business Intelligence and Analytics for anyone interested in getting a
complete picture of the different pieces in the emerging next generation BI\&A
solutions.
| [
{
"version": "v1",
"created": "Tue, 11 Apr 2017 16:31:51 GMT"
}
] | 1,491,955,200,000 | [
[
"Vo",
"Quoc Duy",
""
],
[
"Thomas",
"Jaya",
""
],
[
"Cho",
"Shinyoung",
""
],
[
"De",
"Pradipta",
""
],
[
"Choi",
"Bong Jun",
""
],
[
"Sael",
"Lee",
""
]
] |
1704.03574 | Marcello Balduccini | Marcello Balduccini, Daniele Magazzeni, Marco Maratea, Emily LeBlanc | CASP Solutions for Planning in Hybrid Domains | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.
| [
{
"version": "v1",
"created": "Wed, 12 Apr 2017 00:10:27 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jun 2018 13:46:34 GMT"
}
] | 1,529,971,200,000 | [
[
"Balduccini",
"Marcello",
""
],
[
"Magazzeni",
"Daniele",
""
],
[
"Maratea",
"Marco",
""
],
[
"LeBlanc",
"Emily",
""
]
] |
1704.03612 | Yang Wang | Yang Wang, Lin Wu | Finding Modes by Probabilistic Hypergraphs Shifting | Fixing some minor issues in PAKDD 2014 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we develop a novel paradigm, namely hypergraph shift, to find
robust graph modes by probabilistic voting strategy, which are semantically
sound besides the self-cohesiveness requirement in forming graph modes. Unlike
the existing techniques to seek graph modes by shifting vertices based on
pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting
high-order edges (hyperedges) to deliver graph modes. Specifically, we convert
the problem of seeking graph modes as the problem of seeking maximizers of a
novel objective function with the aim to generate good graph modes based on
sifting edges in hypergraphs. As a result, the generated graph modes based on
dense subhypergraphs may more accurately capture the object semantics besides
the self-cohesiveness requirement. We also formally prove that our technique is
always convergent. Extensive empirical studies on synthetic and real world data
sets are conducted on clustering and graph matching. They demonstrate that our
techniques significantly outperform the existing techniques.
| [
{
"version": "v1",
"created": "Wed, 12 Apr 2017 04:02:04 GMT"
}
] | 1,492,041,600,000 | [
[
"Wang",
"Yang",
""
],
[
"Wu",
"Lin",
""
]
] |
1704.03723 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw A. K{\l}opotek | Beliefs in Markov Trees - From Local Computations to Local Valuation | Preliminary versioin of conference paper: M.A. K{\l}opotek: Beliefs
in Markov Trees - From Local Computations to Local Valuation. [in:] R.
Trappl, Ed.: Cybernetics and Systems Research , Proc. 12th European Meeting
on Cybernetics and System Research, Vienna 5-8 April 1994, World Scientific
Publishers, Vol.1. pp. 351-358 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functions
| [
{
"version": "v1",
"created": "Wed, 12 Apr 2017 12:30:17 GMT"
}
] | 1,492,041,600,000 | [
[
"Kłopotek",
"Mieczysław A.",
""
]
] |
1704.04000 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | Dempster-Shafer Belief Function - A New Interpretation | 70 pages, an internat intermediate research report, dating back to
1993 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop our interpretation of the joint belief distribution and of
evidential updating that matches the following basic requirements:
* there must exist an efficient method for reasoning within this framework
* there must exist a clear correspondence between the contents of the
knowledge base and the real world
* there must be a clear correspondence between the reasoning method and some
real world process
* there must exist a clear correspondence between the results of the
reasoning process and the results of the real world process corresponding to
the reasoning process.
| [
{
"version": "v1",
"created": "Thu, 13 Apr 2017 06:00:00 GMT"
}
] | 1,492,128,000,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1704.04341 | Michael Littman | Michael L. Littman and Ufuk Topcu and Jie Fu and Charles Isbell and
Min Wen and James MacGlashan | Environment-Independent Task Specifications via GLTL | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new task-specification language for Markov decision processes
that is designed to be an improvement over reward functions by being
environment independent. The language is a variant of Linear Temporal Logic
(LTL) that is extended to probabilistic specifications in a way that permits
approximations to be learned in finite time. We provide several small
environments that demonstrate the advantages of our geometric LTL (GLTL)
language and illustrate how it can be used to specify standard
reinforcement-learning tasks straightforwardly.
| [
{
"version": "v1",
"created": "Fri, 14 Apr 2017 03:41:59 GMT"
}
] | 1,492,387,200,000 | [
[
"Littman",
"Michael L.",
""
],
[
"Topcu",
"Ufuk",
""
],
[
"Fu",
"Jie",
""
],
[
"Isbell",
"Charles",
""
],
[
"Wen",
"Min",
""
],
[
"MacGlashan",
"James",
""
]
] |
1704.04651 | Audrunas Gruslys | Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot,
Marc Bellemare, Remi Munos | The Reactor: A fast and sample-efficient Actor-Critic agent for
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we present a new agent architecture, called Reactor, which
combines multiple algorithmic and architectural contributions to produce an
agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al.,
2016) and Categorical DQN (Bellemare et al., 2017), while giving better
run-time performance than A3C (Mnih et al., 2016). Our first contribution is a
new policy evaluation algorithm called Distributional Retrace, which brings
multi-step off-policy updates to the distributional reinforcement learning
setting. The same approach can be used to convert several classes of multi-step
policy evaluation algorithms designed for expected value evaluation into
distributional ones. Next, we introduce the \b{eta}-leave-one-out policy
gradient algorithm which improves the trade-off between variance and bias by
using action values as a baseline. Our final algorithmic contribution is a new
prioritized replay algorithm for sequences, which exploits the temporal
locality of neighboring observations for more efficient replay prioritization.
Using the Atari 2600 benchmarks, we show that each of these innovations
contribute to both the sample efficiency and final agent performance. Finally,
we demonstrate that Reactor reaches state-of-the-art performance after 200
million frames and less than a day of training.
| [
{
"version": "v1",
"created": "Sat, 15 Apr 2017 15:38:23 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Jun 2018 15:32:15 GMT"
}
] | 1,529,452,800,000 | [
[
"Gruslys",
"Audrunas",
""
],
[
"Dabney",
"Will",
""
],
[
"Azar",
"Mohammad Gheshlaghi",
""
],
[
"Piot",
"Bilal",
""
],
[
"Bellemare",
"Marc",
""
],
[
"Munos",
"Remi",
""
]
] |
1704.04719 | Chang-Shing Lee | Chang-Shing Lee, Mei-Hui Wang, Chia-Hsiu Kao, Sheng-Chi Yang, Yusuke
Nojima, Ryosuke Saga, Nan Shuo, and Naoyuki Kubota | FML-based Prediction Agent and Its Application to Game of Go | 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy
Systems Association and 9th International Conference on Soft Computing and
Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 2017 | null | 10.1109/IFSA-SCIS.2017.8023311 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a robotic prediction agent including a darkforest
Go engine, a fuzzy markup language (FML) assessment engine, an FML-based
decision support engine, and a robot engine for game of Go application. The
knowledge base and rule base of FML assessment engine are constructed by
referring the information from the darkforest Go engine located in NUTN and
OPU, for example, the number of MCTS simulations and winning rate prediction.
The proposed robotic prediction agent first retrieves the database of Go
competition website, and then the FML assessment engine infers the winning
possibility based on the information generated by darkforest Go engine. The
FML-based decision support engine computes the winning possibility based on the
partial game situation inferred by FML assessment engine. Finally, the robot
engine combines with the human-friendly robot partner PALRO, produced by
Fujisoft incorporated, to report the game situation to human Go players.
Experimental results show that the FML-based prediction agent can work
effectively.
| [
{
"version": "v1",
"created": "Sun, 16 Apr 2017 04:19:36 GMT"
}
] | 1,555,286,400,000 | [
[
"Lee",
"Chang-Shing",
""
],
[
"Wang",
"Mei-Hui",
""
],
[
"Kao",
"Chia-Hsiu",
""
],
[
"Yang",
"Sheng-Chi",
""
],
[
"Nojima",
"Yusuke",
""
],
[
"Saga",
"Ryosuke",
""
],
[
"Shuo",
"Nan",
""
],
[
"Kubota",
"Naoyuki",
""
]
] |
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