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1704.04775 | He Jiang | He Jiang, Jifeng Xuan, Yan Hu | Approximating the Backbone in the Weighted Maximum Satisfiability
Problem | 14 pages, 1 figure, Proceedings of Advances in Knowledge Discovery
and Data Mining 2008 (PAKDD 2008), 2008 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The weighted Maximum Satisfiability problem (weighted MAX-SAT) is a NP-hard
problem with numerous applications arising in artificial intelligence. As an
efficient tool for heuristic design, the backbone has been applied to
heuristics design for many NP-hard problems. In this paper, we investigated the
computational complexity for retrieving the backbone in weighted MAX-SAT and
developed a new algorithm for solving this problem. We showed that it is
intractable to retrieve the full backbone under the assumption that . Moreover,
it is intractable to retrieve a fixed fraction of the backbone as well. And
then we presented a backbone guided local search (BGLS) with Walksat operator
for weighted MAX-SAT. BGLS consists of two phases: the first phase samples the
backbone information from local optima and the backbone phase conducts local
search under the guideline of backbone. Extensive experimental results on the
benchmark showed that BGLS outperforms the existing heuristics in both solution
quality and runtime.
| [
{
"version": "v1",
"created": "Sun, 16 Apr 2017 13:23:14 GMT"
}
] | 1,492,473,600,000 | [
[
"Jiang",
"He",
""
],
[
"Xuan",
"Jifeng",
""
],
[
"Hu",
"Yan",
""
]
] |
1704.04912 | Manuel Mazzara | Marochko Vladimir, Leonard Johard, Manuel Mazzara | Pseudorehearsal in actor-critic agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Catastrophic forgetting has a serious impact in reinforcement learning, as
the data distribution is generally sparse and non-stationary over time. The
purpose of this study is to investigate whether pseudorehearsal can increase
performance of an actor-critic agent with neural-network based policy selection
and function approximation in a pole balancing task and compare different
pseudorehearsal approaches. We expect that pseudorehearsal assists learning
even in such very simple problems, given proper initialization of the rehearsal
parameters.
| [
{
"version": "v1",
"created": "Mon, 17 Apr 2017 09:27:52 GMT"
}
] | 1,492,473,600,000 | [
[
"Vladimir",
"Marochko",
""
],
[
"Johard",
"Leonard",
""
],
[
"Mazzara",
"Manuel",
""
]
] |
1704.04977 | Marco Cusumano-Towner | Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K.
Mansinghka | Probabilistic programs for inferring the goals of autonomous agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intelligent systems sometimes need to infer the probable goals of people,
cars, and robots, based on partial observations of their motion. This paper
introduces a class of probabilistic programs for formulating and solving these
problems. The formulation uses randomized path planning algorithms as the basis
for probabilistic models of the process by which autonomous agents plan to
achieve their goals. Because these path planning algorithms do not have
tractable likelihood functions, new inference algorithms are needed. This paper
proposes two Monte Carlo techniques for these "likelihood-free" models, one of
which can use likelihood estimates from neural networks to accelerate
inference. The paper demonstrates efficacy on three simple examples, each using
under 50 lines of probabilistic code.
| [
{
"version": "v1",
"created": "Mon, 17 Apr 2017 14:34:02 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Apr 2017 14:40:03 GMT"
}
] | 1,492,560,000,000 | [
[
"Cusumano-Towner",
"Marco F.",
""
],
[
"Radul",
"Alexey",
""
],
[
"Wingate",
"David",
""
],
[
"Mansinghka",
"Vikash K.",
""
]
] |
1704.05325 | Pierre Parrend | Fabio Guigou (ICube), Pierre Collet (ICube, UNISTRA), Pierre Parrend
(ICube) | Anomaly detection and motif discovery in symbolic representations of
time series | null | null | 10.13140/RG.2.2.20158.69447 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of the Big Data hype and the consistent recollection of event logs
and real-time data from sensors, monitoring software and machine configuration
has generated a huge amount of time-varying data in about every sector of the
industry. Rule-based processing of such data has ceased to be relevant in many
scenarios where anomaly detection and pattern mining have to be entirely
accomplished by the machine. Since the early 2000s, the de-facto standard for
representing time series has been the Symbolic Aggregate approXimation (SAX).In
this document, we present a few algorithms using this representation for
anomaly detection and motif discovery, also known as pattern mining, in such
data. We propose a benchmark of anomaly detection algorithms using data from
Cloud monitoring software.
| [
{
"version": "v1",
"created": "Tue, 18 Apr 2017 13:19:50 GMT"
}
] | 1,492,560,000,000 | [
[
"Guigou",
"Fabio",
"",
"ICube"
],
[
"Collet",
"Pierre",
"",
"ICube, UNISTRA"
],
[
"Parrend",
"Pierre",
"",
"ICube"
]
] |
1704.05392 | Dmitry Demidov | Galina Rybina, Alexey Mozgachev, Dmitry Demidov | Synergy of all-purpose static solver and temporal reasoning tools in
dynamic integrated expert systems | 8 pages, 3 figures | "Informatsionno-izmeritelnye i upravlyayushchie sistemy"
(Information-measuring and Control Systems) no.8, vol.12, 2014. pp 27-33.
ISSN 2070-0814 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper discusses scientific and technological problems of dynamic
integrated expert systems development. Extensions of problem-oriented
methodology for dynamic integrated expert systems development are considered.
Attention is paid to the temporal knowledge representation and processing.
| [
{
"version": "v1",
"created": "Sun, 16 Apr 2017 21:50:23 GMT"
}
] | 1,492,560,000,000 | [
[
"Rybina",
"Galina",
""
],
[
"Mozgachev",
"Alexey",
""
],
[
"Demidov",
"Dmitry",
""
]
] |
1704.05539 | Russell Kaplan | Russell Kaplan, Christopher Sauer, Alexander Sosa | Beating Atari with Natural Language Guided Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the first deep reinforcement learning agent that learns to beat
Atari games with the aid of natural language instructions. The agent uses a
multimodal embedding between environment observations and natural language to
self-monitor progress through a list of English instructions, granting itself
reward for completing instructions in addition to increasing the game score.
Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous
Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym
on what is often considered the hardest Atari 2600 environment: Montezuma's
Revenge.
| [
{
"version": "v1",
"created": "Tue, 18 Apr 2017 21:31:29 GMT"
}
] | 1,492,646,400,000 | [
[
"Kaplan",
"Russell",
""
],
[
"Sauer",
"Christopher",
""
],
[
"Sosa",
"Alexander",
""
]
] |
1704.05569 | Mayank Kejriwal | Rahul Kapoor, Mayank Kejriwal and Pedro Szekely | Using Contexts and Constraints for Improved Geotagging of Human
Trafficking Webpages | 6 pages, GeoRich 2017 workshop at ACM SIGMOD conference | null | 10.1145/3080546.3080547 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extracting geographical tags from webpages is a well-motivated application in
many domains. In illicit domains with unusual language models, like human
trafficking, extracting geotags with both high precision and recall is a
challenging problem. In this paper, we describe a geotag extraction framework
in which context, constraints and the openly available Geonames knowledge base
work in tandem in an Integer Linear Programming (ILP) model to achieve good
performance. In preliminary empirical investigations, the framework improves
precision by 28.57% and F-measure by 36.9% on a difficult human trafficking
geotagging task compared to a machine learning-based baseline. The method is
already being integrated into an existing knowledge base construction system
widely used by US law enforcement agencies to combat human trafficking.
| [
{
"version": "v1",
"created": "Wed, 19 Apr 2017 00:52:02 GMT"
}
] | 1,492,646,400,000 | [
[
"Kapoor",
"Rahul",
""
],
[
"Kejriwal",
"Mayank",
""
],
[
"Szekely",
"Pedro",
""
]
] |
1704.06096 | Amos Korman | Amos Korman (GANG, IRIF), Yoav Rodeh | The Dependent Doors Problem: An Investigation into Sequential Decisions
without Feedback | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the dependent doors problem as an abstraction for situations in
which one must perform a sequence of possibly dependent decisions, without
receiving feedback information on the effectiveness of previously made actions.
Informally, the problem considers a set of $d$ doors that are initially closed,
and the aim is to open all of them as fast as possible. To open a door, the
algorithm knocks on it and it might open or not according to some probability
distribution. This distribution may depend on which other doors are currently
open, as well as on which other doors were open during each of the previous
knocks on that door. The algorithm aims to minimize the expected time until all
doors open. Crucially, it must act at any time without knowing whether or which
other doors have already opened. In this work, we focus on scenarios where
dependencies between doors are both positively correlated and acyclic.The
fundamental distribution of a door describes the probability it opens in the
best of conditions (with respect to other doors being open or closed). We show
that if in two configurations of $d$ doors corresponding doors share the same
fundamental distribution, then these configurations have the same optimal
running time up to a universal constant, no matter what are the dependencies
between doors and what are the distributions. We also identify algorithms that
are optimal up to a universal constant factor. For the case in which all doors
share the same fundamental distribution we additionally provide a simpler
algorithm, and a formula to calculate its running time. We furthermore analyse
the price of lacking feedback for several configurations governed by standard
fundamental distributions. In particular, we show that the price is logarithmic
in $d$ for memoryless doors, but can potentially grow to be linear in $d$ for
other distributions.We then turn our attention to investigate precise bounds.
Even for the case of two doors, identifying the optimal sequence is an
intriguing combinatorial question. Here, we study the case of two cascading
memoryless doors. That is, the first door opens on each knock independently
with probability $p\_1$. The second door can only open if the first door is
open, in which case it will open on each knock independently with probability
$p\_2$. We solve this problem almost completely by identifying algorithms that
are optimal up to an additive term of 1.
| [
{
"version": "v1",
"created": "Thu, 20 Apr 2017 11:35:44 GMT"
}
] | 1,492,732,800,000 | [
[
"Korman",
"Amos",
"",
"GANG, IRIF"
],
[
"Rodeh",
"Yoav",
""
]
] |
1704.06300 | Niranjani Prasad | Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis,
Barbara E Engelhardt | A Reinforcement Learning Approach to Weaning of Mechanical Ventilation
in Intensive Care Units | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The management of invasive mechanical ventilation, and the regulation of
sedation and analgesia during ventilation, constitutes a major part of the care
of patients admitted to intensive care units. Both prolonged dependence on
mechanical ventilation and premature extubation are associated with increased
risk of complications and higher hospital costs, but clinical opinion on the
best protocol for weaning patients off of a ventilator varies. This work aims
to develop a decision support tool that uses available patient information to
predict time-to-extubation readiness and to recommend a personalized regime of
sedation dosage and ventilator support. To this end, we use off-policy
reinforcement learning algorithms to determine the best action at a given
patient state from sub-optimal historical ICU data. We compare treatment
policies from fitted Q-iteration with extremely randomized trees and with
feedforward neural networks, and demonstrate that the policies learnt show
promise in recommending weaning protocols with improved outcomes, in terms of
minimizing rates of reintubation and regulating physiological stability.
| [
{
"version": "v1",
"created": "Thu, 20 Apr 2017 18:53:51 GMT"
}
] | 1,492,992,000,000 | [
[
"Prasad",
"Niranjani",
""
],
[
"Cheng",
"Li-Fang",
""
],
[
"Chivers",
"Corey",
""
],
[
"Draugelis",
"Michael",
""
],
[
"Engelhardt",
"Barbara E",
""
]
] |
1704.06616 | Siddharth Karamcheti | Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L.S. Wong,
and Stefanie Tellex | Accurately and Efficiently Interpreting Human-Robot Instructions of
Varying Granularities | Updated with final version - Published as Conference Paper in
Robotics: Science and Systems 2017 | null | 10.15607/RSS.2017.XIII.056 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans can ground natural language commands to tasks at both abstract and
fine-grained levels of specificity. For instance, a human forklift operator can
be instructed to perform a high-level action, like "grab a pallet" or a
low-level action like "tilt back a little bit." While robots are also capable
of grounding language commands to tasks, previous methods implicitly assume
that all commands and tasks reside at a single, fixed level of abstraction.
Additionally, methods that do not use multiple levels of abstraction encounter
inefficient planning and execution times as they solve tasks at a single level
of abstraction with large, intractable state-action spaces closely resembling
real world complexity. In this work, by grounding commands to all the tasks or
subtasks available in a hierarchical planning framework, we arrive at a model
capable of interpreting language at multiple levels of specificity ranging from
coarse to more granular. We show that the accuracy of the grounding procedure
is improved when simultaneously inferring the degree of abstraction in language
used to communicate the task. Leveraging hierarchy also improves efficiency:
our proposed approach enables a robot to respond to a command within one second
on 90% of our tasks, while baselines take over twenty seconds on half the
tasks. Finally, we demonstrate that a real, physical robot can ground commands
at multiple levels of abstraction allowing it to efficiently plan different
subtasks within the same planning hierarchy.
| [
{
"version": "v1",
"created": "Fri, 21 Apr 2017 16:15:19 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Jun 2018 17:19:29 GMT"
}
] | 1,529,452,800,000 | [
[
"Arumugam",
"Dilip",
""
],
[
"Karamcheti",
"Siddharth",
""
],
[
"Gopalan",
"Nakul",
""
],
[
"Wong",
"Lawson L. S.",
""
],
[
"Tellex",
"Stefanie",
""
]
] |
1704.06621 | Nasser Ghadiri | Amir Hossein Goudarzi, Nasser Ghadiri | A hybrid spatial data mining approach based on fuzzy topological
relations and MOSES evolutionary algorithm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Making high-quality decisions in strategic spatial planning is heavily
dependent on extracting knowledge from vast amounts of data. Although many
decision-making problems like developing urban areas require such perception
and reasoning, existing methods in this field usually neglect the deep
knowledge mined from geographic databases and are based on pure statistical
methods. Due to the large volume of data gathered in spatial databases, and the
uncertainty of spatial objects, mining association rules for high-level
knowledge representation is a challenging task. Few algorithms manage
geographical and non-geographical data using topological relations. In this
paper, a novel approach for spatial data mining based on the MOSES evolutionary
framework is presented which improves the classic genetic programming approach.
A hybrid architecture called GGeo is proposed to apply the MOSES mining rules
considering fuzzy topological relations from spatial data. The uncertainty and
fuzziness aspects are addressed using an enriched model of topological
relations by fuzzy region connection calculus. Moreover, to overcome the
problem of time-consuming fuzzy topological relationships calculations, this a
novel data pre-processing method is offered. GGeo analyses and learns from
geographical and non-geographical data and uses topological and distance
parameters, and returns a series of arithmetic-spatial formulas as
classification rules. The proposed approach is resistant to noisy data, and all
its stages run in parallel to increase speed. This approach may be used in
different spatial data classification problems as well as representing an
appropriate method of data analysis and economic policy making.
| [
{
"version": "v1",
"created": "Fri, 21 Apr 2017 16:30:10 GMT"
}
] | 1,492,992,000,000 | [
[
"Goudarzi",
"Amir Hossein",
""
],
[
"Ghadiri",
"Nasser",
""
]
] |
1704.06945 | Diego Perez Liebana Dr. | Kamolwan Kunanusont and Simon M. Lucas and Diego Perez-Liebana | General Video Game AI: Learning from Screen Capture | Proceedings of the IEEE Conference on Evolutionary Computation 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | General Video Game Artificial Intelligence is a general game playing
framework for Artificial General Intelligence research in the video-games
domain. In this paper, we propose for the first time a screen capture learning
agent for General Video Game AI framework. A Deep Q-Network algorithm was
applied and improved to develop an agent capable of learning to play different
games in the framework. After testing this algorithm using various games of
different categories and difficulty levels, the results suggest that our
proposed screen capture learning agent has the potential to learn many
different games using only a single learning algorithm.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2017 16:08:06 GMT"
}
] | 1,493,078,400,000 | [
[
"Kunanusont",
"Kamolwan",
""
],
[
"Lucas",
"Simon M.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
1704.07069 | Diego Perez Liebana Dr. | Joseph Walton-Rivers and Piers R. Williams and Richard Bartle and
Diego Perez-Liebana and Simon M. Lucas | Evaluating and Modelling Hanabi-Playing Agents | Proceedings of the IEEE Conference on Evolutionary Computation (2017) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addition to an Information Set Monte Carlo Tree Search
(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
predictor version that uses a model of the agents with which it is paired. We
observe a significant improvement in game-playing strength from this agent in
comparison to IS-MCTS, resulting from its consideration of what the other
agents in a game would do. In addition, we create a flawed rule-based agent to
highlight the predictor's capabilities with such an agent.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2017 07:44:10 GMT"
}
] | 1,493,078,400,000 | [
[
"Walton-Rivers",
"Joseph",
""
],
[
"Williams",
"Piers R.",
""
],
[
"Bartle",
"Richard",
""
],
[
"Perez-Liebana",
"Diego",
""
],
[
"Lucas",
"Simon M.",
""
]
] |
1704.07075 | Diego Perez Liebana Dr. | Raluca D. Gaina and Jialin Liu and Simon M. Lucas and Diego
Perez-Liebana | Analysis of Vanilla Rolling Horizon Evolution Parameters in General
Video Game Playing | null | Applications of Evolutionary Computation, EvoApplications, Lecture
Notes in Computer Science, vol. 10199, Springer, Cham., p. 418-434, 2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monte Carlo Tree Search techniques have generally dominated General Video
Game Playing, but recent research has started looking at Evolutionary
Algorithms and their potential at matching Tree Search level of play or even
outperforming these methods. Online or Rolling Horizon Evolution is one of the
options available to evolve sequences of actions for planning in General Video
Game Playing, but no research has been done up to date that explores the
capabilities of the vanilla version of this algorithm in multiple games. This
study aims to critically analyse the different configurations regarding
population size and individual length in a set of 20 games from the General
Video Game AI corpus. Distinctions are made between deterministic and
stochastic games, and the implications of using superior time budgets are
studied. Results show that there is scope for the use of these techniques,
which in some configurations outperform Monte Carlo Tree Search, and also
suggest that further research in these methods could boost their performance.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2017 08:01:39 GMT"
}
] | 1,493,078,400,000 | [
[
"Gaina",
"Raluca D.",
""
],
[
"Liu",
"Jialin",
""
],
[
"Lucas",
"Simon M.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
1704.07183 | Steven Prestwich D | Steven Prestwich and Roberto Rossi and Armagan Tarim | Stochastic Constraint Programming as Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic Constraint Programming (SCP) is an extension of Constraint
Programming (CP) used for modelling and solving problems involving constraints
and uncertainty. SCP inherits excellent modelling abilities and filtering
algorithms from CP, but so far it has not been applied to large problems.
Reinforcement Learning (RL) extends Dynamic Programming to large stochastic
problems, but is problem-specific and has no generic solvers. We propose a
hybrid combining the scalability of RL with the modelling and constraint
filtering methods of CP. We implement a prototype in a CP system and
demonstrate its usefulness on SCP problems.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2017 12:44:38 GMT"
}
] | 1,493,078,400,000 | [
[
"Prestwich",
"Steven",
""
],
[
"Rossi",
"Roberto",
""
],
[
"Tarim",
"Armagan",
""
]
] |
1704.07466 | Freddy Lecue | Freddy Lecue, Jiaoyan Chen, Jeff Pan, Huajun Chen | Learning from Ontology Streams with Semantic Concept Drift | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2017 21:12:13 GMT"
}
] | 1,493,164,800,000 | [
[
"Lecue",
"Freddy",
""
],
[
"Chen",
"Jiaoyan",
""
],
[
"Pan",
"Jeff",
""
],
[
"Chen",
"Huajun",
""
]
] |
1704.07555 | Marcus Olivecrona | Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen | Molecular De Novo Design through Deep Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn
to generate structures with certain specified desirable properties. We
demonstrate how this model can execute a range of tasks such as generating
analogues to a query structure and generating compounds predicted to be active
against a biological target. As a proof of principle, the model is first
trained to generate molecules that do not contain sulphur. As a second example,
the model is trained to generate analogues to the drug Celecoxib, a technique
that could be used for scaffold hopping or library expansion starting from a
single molecule. Finally, when tuning the model towards generating compounds
predicted to be active against the dopamine receptor type 2, the model
generates structures of which more than 95% are predicted to be active,
including experimentally confirmed actives that have not been included in
either the generative model nor the activity prediction model.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2017 06:41:21 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2017 12:31:19 GMT"
}
] | 1,504,051,200,000 | [
[
"Olivecrona",
"Marcus",
""
],
[
"Blaschke",
"Thomas",
""
],
[
"Engkvist",
"Ola",
""
],
[
"Chen",
"Hongming",
""
]
] |
1704.07899 | James Brusey | James Brusey, Diana Hintea, Elena Gaura, Neil Beloe | Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vehicle climate control systems aim to keep passengers thermally comfortable.
However, current systems control temperature rather than thermal comfort and
tend to be energy hungry, which is of particular concern when considering
electric vehicles. This paper poses energy-efficient vehicle comfort control as
a Markov Decision Process, which is then solved numerically using
Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of
the cabin. The resulting controller was tested in simulation using 200 randomly
selected scenarios and found to exceed the performance of bang-bang,
proportional, simple fuzzy logic, and commercial controllers with 23%, 43%,
40%, 56% increase, respectively. Compared to the next best performing
controller, energy consumption is reduced by 13% while the proportion of time
spent thermally comfortable is increased by 23%. These results indicate that
this is a viable approach that promises to translate into substantial comfort
and energy improvements in the car.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2017 20:24:17 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Sep 2017 11:02:03 GMT"
}
] | 1,504,656,000,000 | [
[
"Brusey",
"James",
""
],
[
"Hintea",
"Diana",
""
],
[
"Gaura",
"Elena",
""
],
[
"Beloe",
"Neil",
""
]
] |
1704.07950 | Yi Zhou Dr. | Yi Zhou | Structured Production System (extended abstract) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this extended abstract, we propose Structured Production Systems (SPS),
which extend traditional production systems with well-formed syntactic
structures. Due to the richness of structures, structured production systems
significantly enhance the expressive power as well as the flexibility of
production systems, for instance, to handle uncertainty. We show that different
rule application strategies can be reduced into the basic one by utilizing
structures. Also, many fundamental approaches in computer science, including
automata, grammar and logic, can be captured by structured production systems.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2017 02:39:07 GMT"
}
] | 1,493,251,200,000 | [
[
"Zhou",
"Yi",
""
]
] |
1704.08111 | Steven Meyer | Steven Meyer | A Popperian Falsification of Artificial Intelligence -- Lighthill
Defended | 12 pages. Version improves discussion of chess and adds sections on
when combinatorial explosion may not apply | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The area of computation called artificial intelligence (AI) is falsified by
describing a previous 1972 falsification of AI by British mathematical
physicist James Lighthill. How Lighthill's arguments continue to apply to
current AI is explained. It is argued that AI should use the Popperian
scientific method in which it is the duty of scientists to attempt to falsify
theories and if theories are falsified to replace or modify them. The paper
describes the Popperian method and discusses Paul Nurse's application of the
method to cell biology that also involves questions of mechanism and behavior.
It is shown how Lighthill's falsifying arguments especially combinatorial
explosion continue to apply to modern AI. Various skeptical arguments against
the assumptions of AI mostly by physicists especially against Hilbert's
philosophical programme that defined knowledge and truth as provable formal
sentences. John von Neumann's arguments from natural complexity against neural
networks and evolutionary algorithms are discussed. Next the game of chess is
discussed to show how modern chess experts have reacted to computer chess
programs. It is shown that currently chess masters can defeat any chess program
using Kasperov's arguments from his 1997 Deep Blue match and aftermath. The
game of 'go' and climate models are discussed to show computer applications
where combinatorial explosion may not apply. The paper concludes by advocating
studying computation as Peter Naur's Dataology.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2017 21:16:40 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Apr 2018 17:56:05 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Apr 2020 23:58:09 GMT"
}
] | 1,588,550,400,000 | [
[
"Meyer",
"Steven",
""
]
] |
1704.08350 | Vasanth Sarathy | Vasanth Sarathy and Matthias Scheutz | The MacGyver Test - A Framework for Evaluating Machine Resourcefulness
and Creative Problem Solving | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current measures of machine intelligence are either difficult to evaluate or
lack the ability to test a robot's problem-solving capacity in open worlds. We
propose a novel evaluation framework based on the formal notion of MacGyver
Test which provides a practical way for assessing the resilience and
resourcefulness of artificial agents.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2017 21:05:27 GMT"
}
] | 1,493,337,600,000 | [
[
"Sarathy",
"Vasanth",
""
],
[
"Scheutz",
"Matthias",
""
]
] |
1704.08464 | Zhiwei Lin | Zhiwei Lin, Yi Li, and Xiaolian Guo | Consensus measure of rankings | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A ranking is an ordered sequence of items, in which an item with higher
ranking score is more preferred than the items with lower ranking scores. In
many information systems, rankings are widely used to represent the preferences
over a set of items or candidates. The consensus measure of rankings is the
problem of how to evaluate the degree to which the rankings agree. The
consensus measure can be used to evaluate rankings in many information systems,
as quite often there is not ground truth available for evaluation.
This paper introduces a novel approach for consensus measure of rankings by
using graph representation, in which the vertices or nodes are the items and
the edges are the relationship of items in the rankings. Such representation
leads to various algorithms for consensus measure in terms of different aspects
of rankings, including the number of common patterns, the number of common
patterns with fixed length and the length of the longest common patterns. The
proposed measure can be adopted for various types of rankings, such as full
rankings, partial rankings and rankings with ties. This paper demonstrates how
the proposed approaches can be used to evaluate the quality of rank aggregation
and the quality of top-$k$ rankings from Google and Bing search engines.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2017 07:58:47 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Sep 2017 17:09:37 GMT"
}
] | 1,506,038,400,000 | [
[
"Lin",
"Zhiwei",
""
],
[
"Li",
"Yi",
""
],
[
"Guo",
"Xiaolian",
""
]
] |
1704.08950 | Amit Kumar | Amit Kumar, Rahul Dutta, Harbhajan Rai | Intelligent Personal Assistant with Knowledge Navigation | Converted O(N3) solution to viable O(N) solution | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An Intelligent Personal Agent (IPA) is an agent that has the purpose of
helping the user to gain information through reliable resources with the help
of knowledge navigation techniques and saving time to search the best content.
The agent is also responsible for responding to the chat-based queries with the
help of Conversation Corpus. We will be testing different methods for optimal
query generation. To felicitate the ease of usage of the application, the agent
will be able to accept the input through Text (Keyboard), Voice (Speech
Recognition) and Server (Facebook) and output responses using the same method.
Existing chat bots reply by making changes in the input, but we will give
responses based on multiple SRT files. The model will learn using the human
dialogs dataset and will be able respond human-like. Responses to queries about
famous things (places, people, and words) can be provided using web scraping
which will enable the bot to have knowledge navigation features. The agent will
even learn from its past experiences supporting semi-supervised learning.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2017 14:26:12 GMT"
}
] | 1,493,596,800,000 | [
[
"Kumar",
"Amit",
""
],
[
"Dutta",
"Rahul",
""
],
[
"Rai",
"Harbhajan",
""
]
] |
1705.00154 | Masataro Asai | Masataro Asai, Alex Fukunaga | Classical Planning in Deep Latent Space: Bridging the
Subsymbolic-Symbolic Boundary | This is an extended manuscript of the paper accepted in AAAI-18. The
contents of AAAI-18 paper itself is significantly extended from what has been
published in Arxiv or previous workshops. Over half of the paper describing
(2) is new. Additionally, this manuscript contains the supplemental materials
of AAAI-18 submission | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose LatPlan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), and a pair of images representing the
initial and the goal states (planning inputs), LatPlan finds a plan to the goal
state in a symbolic latent space and returns a visualized plan execution. The
contribution of this paper is twofold: (1) State Autoencoder, which finds a
propositional state representation of the environment using a Variational
Autoencoder. It generates a discrete latent vector from the images, based on
which a PDDL model can be constructed and then solved by an off-the-shelf
planner. (2) Action Autoencoder / Discriminator, a neural architecture which
jointly finds the action symbols and the implicit action models
(preconditions/effects), and provides a successor function for the implicit
graph search. We evaluate LatPlan using image-based versions of 3 planning
domains: 8-puzzle, Towers of Hanoi and LightsOut.
| [
{
"version": "v1",
"created": "Sat, 29 Apr 2017 08:22:29 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Nov 2017 04:09:36 GMT"
},
{
"version": "v3",
"created": "Sun, 3 Dec 2017 12:19:39 GMT"
}
] | 1,512,432,000,000 | [
[
"Asai",
"Masataro",
""
],
[
"Fukunaga",
"Alex",
""
]
] |
1705.00211 | Babatunde Akinkunmi | Babatunde Opeoluwa Akinkunmi and Adesoji A. Adegbola | Two Algorithms for Deciding Coincidence In Double Temporal Recurrence of
Eventuality Sequences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Let two sequences of eventualities x (signifying the sequence, x0,x1,
x2,...,xn-1) and y (signifying the sequence, y0, y1, y2,..,yn-1) both recur
over the same time interval and it is required to determine whether or not a
subinterval exists within the said interval which is a common subinterval of
the intervals of occurrence of xp and yq. This paper presents two algorithms
for solving the problem. the first explores an arbitrary cycle of the double
recurrence for the existence of such an interval. its worst case running time
is quadratic. The other algorithm is based on the novel notion of
gcd-partitions and has a linear worst case running time. If the eventuality
sequence pair (W,z) is a gcd-partition for the double recurrence (x, y),then,
from a certain property of gcd-partitions, within any cycle of the double
recurrence, there exists r and s such that intervals of occurrence of xp and yq
are non-disjoint with the interval of co-occurrence of wr and zs. As such, a
coincidence between xp and yq occurs within a cycle of the double recurrence if
and only if such r and s exist so that the interval of co-occurrence of wr and
zs shares a common interval with the common interval of occurrences of xp and
yq. The algorithm systematically reduces the number of wr and zs pairs to be
explored in the process of finding the existence of the coincidence.
| [
{
"version": "v1",
"created": "Sat, 29 Apr 2017 16:31:58 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Nov 2022 21:43:03 GMT"
}
] | 1,667,433,600,000 | [
[
"Akinkunmi",
"Babatunde Opeoluwa",
""
],
[
"Adegbola",
"Adesoji A.",
""
]
] |
1705.00303 | Beishui Liao | Beishui Liao and Leendert van der Torre | Defense semantics of argumentation: encoding reasons for accepting
arguments | 14 pages, first submitted on April 30, 2017; 16 pages, revised in
terms of the comments from MIREL2017 on August 03, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we show how the defense relation among abstract arguments can
be used to encode the reasons for accepting arguments. After introducing a
novel notion of defenses and defense graphs, we propose a defense semantics
together with a new notion of defense equivalence of argument graphs, and
compare defense equivalence with standard equivalence and strong equivalence,
respectively. Then, based on defense semantics, we define two kinds of reasons
for accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of argument graphs. Finally, we show how the notion of root
equivalence can be used in argumentation summarization.
| [
{
"version": "v1",
"created": "Sun, 30 Apr 2017 12:06:28 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Aug 2017 15:46:21 GMT"
}
] | 1,501,718,400,000 | [
[
"Liao",
"Beishui",
""
],
[
"van der Torre",
"Leendert",
""
]
] |
1705.00969 | Babatunde Akinkunmi | B.O. Akinkunmi | The Problem of Coincidence in A Theory of Temporal Multiple Recurrence | arXiv admin note: substantial text overlap with arXiv:1705.00211 | Journal of Applied Logic, 15:46-48, May 2016 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical theories have been developed which have allowed temporal reasoning
about eventualities (a la Galton) such as states, processes, actions, events,
processes and complex eventualities such as sequences and recurrences of other
eventualities. This paper presents the problem of coincidence within the
framework of a first order logical theory formalising temporal multiple
recurrence of two sequences of fixed duration eventualities and presents a
solution to it The coincidence problem is described as: if two complex
eventualities (or eventuality sequences) consisting respectively of component
eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k
and all eventualities are of fixed durations, is there a sub-interval of k over
which the incidence xt and yu for t between 0..r and s between 0..s coincide.
The solution presented here formalises the intuition that a solution can be
found by temporal projection over a cycle of the multiple recurrence of both
sequences.
| [
{
"version": "v1",
"created": "Sat, 29 Apr 2017 16:54:18 GMT"
}
] | 1,493,769,600,000 | [
[
"Akinkunmi",
"B. O.",
""
]
] |
1705.01076 | Rafa{\l} Skinderowicz | Rafa{\l} Skinderowicz | An improved Ant Colony System for the Sequential Ordering Problem | 30 pages, 8 tables, 11 figures | null | 10.1016/j.cor.2017.04.012 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.
| [
{
"version": "v1",
"created": "Tue, 2 May 2017 17:17:26 GMT"
}
] | 1,493,769,600,000 | [
[
"Skinderowicz",
"Rafał",
""
]
] |
1705.01080 | Jialin Liu Ph.D | Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu, Diego Perez-Liebana,
Simon M. Lucas | The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement | 8 pages, 9 figure, 2 tables, CEC2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new evolutionary algorithm that is especially well
suited to AI-Assisted Game Design. The approach adopted in this paper is to use
observations of AI agents playing the game to estimate the game's quality. Some
of best agents for this purpose are General Video Game AI agents, since they
can be deployed directly on a new game without game-specific tuning; these
agents tend to be based on stochastic algorithms which give robust but noisy
results and tend to be expensive to run. This motivates the main contribution
of the paper: the development of the novel N-Tuple Bandit Evolutionary
Algorithm, where a model is used to estimate the fitness of unsampled points
and a bandit approach is used to balance exploration and exploitation of the
search space. Initial results on optimising a Space Battle game variant suggest
that the algorithm offers far more robust results than the Random Mutation Hill
Climber and a Biased Mutation variant, which are themselves known to offer
competitive performance across a range of problems. Subjective observations are
also given by human players on the nature of the evolved games, which indicate
a preference towards games generated by the N-Tuple algorithm.
| [
{
"version": "v1",
"created": "Sat, 18 Mar 2017 09:10:09 GMT"
}
] | 1,493,769,600,000 | [
[
"Kunanusont",
"Kamolwan",
""
],
[
"Gaina",
"Raluca D.",
""
],
[
"Liu",
"Jialin",
""
],
[
"Perez-Liebana",
"Diego",
""
],
[
"Lucas",
"Simon M.",
""
]
] |
1705.01172 | Gavin Rens | Gavin Rens and Thomas Meyer | Imagining Probabilistic Belief Change as Imaging (Technical Report) | 21 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Imaging is a form of probabilistic belief change which could be employed for
both revision and update. In this paper, we propose a new framework for
probabilistic belief change based on imaging, called Expected Distance Imaging
(EDI). EDI is sufficiently general to define Bayesian conditioning and other
forms of imaging previously defined in the literature. We argue that, and
investigate how, EDI can be used for both revision and update. EDI's definition
depends crucially on a weight function whose properties are studied and whose
effect on belief change operations is analysed. Finally, four EDI
instantiations are proposed, two for revision and two for update, and
probabilistic rationality postulates are suggested for their analysis.
| [
{
"version": "v1",
"created": "Tue, 2 May 2017 20:50:59 GMT"
}
] | 1,493,856,000,000 | [
[
"Rens",
"Gavin",
""
],
[
"Meyer",
"Thomas",
""
]
] |
1705.01399 | Leonardo Anjoletto Ferreira | Leonardo A. Ferreira, Reinaldo A. C. Bianchi, Paulo E. Santos, Ramon
Lopez de Mantaras | Answer Set Programming for Non-Stationary Markov Decision Processes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains.
| [
{
"version": "v1",
"created": "Wed, 3 May 2017 13:13:51 GMT"
}
] | 1,493,856,000,000 | [
[
"Ferreira",
"Leonardo A.",
""
],
[
"Bianchi",
"Reinaldo A. C.",
""
],
[
"Santos",
"Paulo E.",
""
],
[
"de Mantaras",
"Ramon Lopez",
""
]
] |
1705.01681 | Francisco L\'opez-Ramos | Francisco L\'opez-Ramos, Armando Guarnaschelli, Jos\'e-Fernando
Camacho-Vallejo, Laura Hervert-Escobar, Rosa G. Gonz\'alez-Ram\'irez | Tramp Ship Scheduling Problem with Berth Allocation Considerations and
Time-dependent Constraints | 16 pages, 3 figures, 5 tables, proceedings paper of Mexican
International Conference on Artificial Intelligence (MICAI) 2016 | null | null | Accepted manuscript id 47 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work presents a model for the Tramp Ship Scheduling problem including
berth allocation considerations, motivated by a real case of a shipping
company. The aim is to determine the travel schedule for each vessel
considering multiple docking and multiple time windows at the berths. This work
is innovative due to the consideration of both spatial and temporal attributes
during the scheduling process. The resulting model is formulated as a
mixed-integer linear programming problem, and a heuristic method to deal with
multiple vessel schedules is also presented. Numerical experimentation is
performed to highlight the benefits of the proposed approach and the
applicability of the heuristic. Conclusions and recommendations for further
research are provided.
| [
{
"version": "v1",
"created": "Thu, 4 May 2017 02:49:26 GMT"
}
] | 1,493,942,400,000 | [
[
"López-Ramos",
"Francisco",
""
],
[
"Guarnaschelli",
"Armando",
""
],
[
"Camacho-Vallejo",
"José-Fernando",
""
],
[
"Hervert-Escobar",
"Laura",
""
],
[
"González-Ramírez",
"Rosa G.",
""
]
] |
1705.01817 | Christoph Schwering | Christoph Schwering | A Reasoning System for a First-Order Logic of Limited Belief | 22 pages, 0 figures, Twenty-sixth International Joint Conference on
Artificial Intelligence (IJCAI-17) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logics of limited belief aim at enabling computationally feasible reasoning
in highly expressive representation languages. These languages are often
dialects of first-order logic with a weaker form of logical entailment that
keeps reasoning decidable or even tractable. While a number of such logics have
been proposed in the past, they tend to remain for theoretical analysis only
and their practical relevance is very limited. In this paper, we aim to go
beyond the theory. Building on earlier work by Liu, Lakemeyer, and Levesque, we
develop a logic of limited belief that is highly expressive while remaining
decidable in the first-order and tractable in the propositional case and
exhibits some characteristics that make it attractive for an implementation. We
introduce a reasoning system that employs this logic as representation language
and present experimental results that showcase the benefit of limited belief.
| [
{
"version": "v1",
"created": "Thu, 4 May 2017 12:39:27 GMT"
}
] | 1,493,942,400,000 | [
[
"Schwering",
"Christoph",
""
]
] |
1705.02175 | Nikos Katzouris | Nikos Katzouris, Alexander Artikis, Georgios Paliouras | Distributed Online Learning of Event Definitions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logic-based event recognition systems infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
that learns event definitions in the form of Event Calculus theories, in a
single pass over a data stream. In this work we present a version of OLED that
allows for distributed, online learning. We evaluate our approach on a
benchmark activity recognition dataset and show that we can significantly
reduce training times, exchanging minimal information between processing nodes.
| [
{
"version": "v1",
"created": "Fri, 5 May 2017 11:40:11 GMT"
}
] | 1,494,201,600,000 | [
[
"Katzouris",
"Nikos",
""
],
[
"Artikis",
"Alexander",
""
],
[
"Paliouras",
"Georgios",
""
]
] |
1705.02476 | Mahardhika Pratama Dr | Mahardhika Pratama | PANFIS++: A Generalized Approach to Evolving Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The concept of evolving intelligent system (EIS) provides an effective avenue
for data stream mining because it is capable of coping with two prominent
issues: online learning and rapidly changing environments. We note at least
three uncharted territories of existing EISs: data uncertainty, temporal system
dynamic, redundant data streams. This book chapter aims at delivering a
concrete solution of this problem with the algorithmic development of a novel
learning algorithm, namely PANFIS++. PANFIS++ is a generalized version of the
PANFIS by putting forward three important components: 1) An online active
learning scenario is developed to overcome redundant data streams. This module
allows to actively select data streams for the training process, thereby
expediting execution time and enhancing generalization performance, 2) PANFIS++
is built upon an interval type-2 fuzzy system environment, which incorporates
the so-called footprint of uncertainty. This component provides a degree of
tolerance for data uncertainty. 3) PANFIS++ is structured under a recurrent
network architecture with a self-feedback loop. This is meant to tackle the
temporal system dynamic. The efficacy of the PANFIS++ has been numerically
validated through numerous real-world and synthetic case studies, where it
delivers the highest predictive accuracy while retaining the lowest complexity.
| [
{
"version": "v1",
"created": "Sat, 6 May 2017 12:02:15 GMT"
}
] | 1,494,288,000,000 | [
[
"Pratama",
"Mahardhika",
""
]
] |
1705.02477 | Mahardhika Pratama Dr | Mahardhika Pratama, Eric Dimla, Chow Yin Lai, Edwin Lughofer | Metacognitive Learning Approach for Online Tool Condition Monitoring | null | null | 10.1007/s10845-017-1348-9 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As manufacturing processes become increasingly automated, so should tool
condition monitoring (TCM) as it is impractical to have human workers monitor
the state of the tools continuously. Tool condition is crucial to ensure the
good quality of products: Worn tools affect not only the surface quality but
also the dimensional accuracy, which means higher reject rate of the products.
Therefore, there is an urgent need to identify tool failures before it occurs
on the fly. While various versions of intelligent tool condition monitoring
have been proposed, most of them suffer from a cognitive nature of traditional
machine learning algorithms. They focus on the how to learn process without
paying attention to other two crucial issues: what to learn, and when to learn.
The what to learn and the when to learn provide self regulating mechanisms to
select the training samples and to determine time instants to train a model. A
novel tool condition monitoring approach based on a psychologically plausible
concept, namely the metacognitive scaffolding theory, is proposed and built
upon a recently published algorithm, recurrent classifier (rClass). The
learning process consists of three phases: what to learn, how to learn, when to
learn and makes use of a generalized recurrent network structure as a cognitive
component. Experimental studies with real-world manufacturing data streams were
conducted where rClass demonstrated the highest accuracy while retaining the
lowest complexity over its counterparts.
| [
{
"version": "v1",
"created": "Sat, 6 May 2017 12:16:16 GMT"
}
] | 1,517,875,200,000 | [
[
"Pratama",
"Mahardhika",
""
],
[
"Dimla",
"Eric",
""
],
[
"Lai",
"Chow Yin",
""
],
[
"Lughofer",
"Edwin",
""
]
] |
1705.02620 | Wen Jiang | Dong Wu, Xiang Liu, Feng Xue, Hanqing Zheng, Yehang Shou, Wen Jiang | A New Medical Diagnosis Method Based on Z-Numbers | 24 pages, 9 figures, 13 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How to handle uncertainty in medical diagnosis is an open issue. In this
paper, a new decision making methodology based on Z-numbers is presented.
Firstly, the experts' opinions are represented by Z-numbers. Z-number is an
ordered pair of fuzzy numbers denoted as Z = (A, B). Then, a new method for
ranking fuzzy numbers is proposed. And based on the proposed fuzzy number
ranking method, a novel method is presented to transform the Z-numbers into
Basic Probability Assignment (BPA). As a result, the information from different
sources is combined by the Dempster' combination rule. The final decision
making is more reasonable due to the advantage of information fusion. Finally,
two experiments, risk analysis and medical diagnosis, are illustrated to show
the efficiency of the proposed methodology.
| [
{
"version": "v1",
"created": "Sun, 7 May 2017 13:29:53 GMT"
}
] | 1,494,288,000,000 | [
[
"Wu",
"Dong",
""
],
[
"Liu",
"Xiang",
""
],
[
"Xue",
"Feng",
""
],
[
"Zheng",
"Hanqing",
""
],
[
"Shou",
"Yehang",
""
],
[
"Jiang",
"Wen",
""
]
] |
1705.03078 | Toby Pereira | Toby Pereira | An Anthropic Argument against the Future Existence of Superintelligent
Artificial Intelligence | 11 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper uses anthropic reasoning to argue for a reduced likelihood that
superintelligent AI will come into existence in the future. To make this
argument, a new principle is introduced: the Super-Strong Self-Sampling
Assumption (SSSSA), building on the Self-Sampling Assumption (SSA) and the
Strong Self-Sampling Assumption (SSSA). SSA uses as its sample the relevant
observers, whereas SSSA goes further by using observer-moments. SSSSA goes
further still and weights each sample proportionally, according to the size of
a mind in cognitive terms. SSSSA is required for human observer-samples to be
typical, given by how much non-human animals outnumber humans. Given SSSSA, the
assumption that humans experience typical observer-samples relies on a future
where superintelligent AI does not dominate, which in turn reduces the
likelihood of it being created at all.
| [
{
"version": "v1",
"created": "Mon, 8 May 2017 20:37:45 GMT"
}
] | 1,494,374,400,000 | [
[
"Pereira",
"Toby",
""
]
] |
1705.03260 | Joshua Peterson | Joshua C. Peterson, Thomas L. Griffiths | Evidence for the size principle in semantic and perceptual domains | 6 pages, 4 figures, To appear in the Proceedings of the 39th Annual
Conference of the Cognitive Science Society | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shepard's Universal Law of Generalization offered a compelling case for the
first physics-like law in cognitive science that should hold for all
intelligent agents in the universe. Shepard's account is based on a rational
Bayesian model of generalization, providing an answer to the question of why
such a law should emerge. Extending this account to explain how humans use
multiple examples to make better generalizations requires an additional
assumption, called the size principle: hypotheses that pick out fewer objects
should make a larger contribution to generalization. The degree to which this
principle warrants similarly law-like status is far from conclusive. Typically,
evaluating this principle has not been straightforward, requiring additional
assumptions. We present a new method for evaluating the size principle that is
more direct, and apply this method to a diverse array of datasets. Our results
provide support for the broad applicability of the size principle.
| [
{
"version": "v1",
"created": "Tue, 9 May 2017 10:21:49 GMT"
}
] | 1,494,374,400,000 | [
[
"Peterson",
"Joshua C.",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
1705.03352 | Vaclav Kratochvil | Ji\v{r}ina Vejnarov\'a, V\'aclav Kratochv\'il | Composition of Credal Sets via Polyhedral Geometry | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently introduced composition operator for credal sets is an analogy of
such operators in probability, possibility, evidence and valuation-based
systems theories. It was designed to construct multidimensional models (in the
framework of credal sets) from a system of low- dimensional credal sets. In
this paper we study its potential from the computational point of view
utilizing methods of polyhedral geometry.
| [
{
"version": "v1",
"created": "Fri, 5 May 2017 14:46:44 GMT"
}
] | 1,494,374,400,000 | [
[
"Vejnarová",
"Jiřina",
""
],
[
"Kratochvíl",
"Václav",
""
]
] |
1705.03381 | Nicolas Maudet | Leila Amgoud, Elise Bonzon, Marco Correia, Jorge Cruz, J\'er\^ome
Delobelle, S\'ebastien Konieczny, Jo\~ao Leite, Alexis Martin, Nicolas
Maudet, Srdjan Vesic | A note on the uniqueness of models in social abstract argumentation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Social abstract argumentation is a principled way to assign values to
conflicting (weighted) arguments. In this note we discuss the important
property of the uniqueness of the model.
| [
{
"version": "v1",
"created": "Tue, 9 May 2017 15:18:13 GMT"
}
] | 1,494,374,400,000 | [
[
"Amgoud",
"Leila",
""
],
[
"Bonzon",
"Elise",
""
],
[
"Correia",
"Marco",
""
],
[
"Cruz",
"Jorge",
""
],
[
"Delobelle",
"Jérôme",
""
],
[
"Konieczny",
"Sébastien",
""
],
[
"Leite",
"João",
""
],
[
"Martin",
"Alexis",
""
],
[
"Maudet",
"Nicolas",
""
],
[
"Vesic",
"Srdjan",
""
]
] |
1705.03597 | Yan Li | Yan Li, Zhaohan Sun | Solving Multi-Objective MDP with Lexicographic Preference: An
application to stochastic planning with multiple quantile objective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most common settings of Markov Decision Process (MDP), an agent evaluate a
policy based on expectation of (discounted) sum of rewards. However in many
applications this criterion might not be suitable from two perspective: first,
in risk aversion situation expectation of accumulated rewards is not robust
enough, this is the case when distribution of accumulated reward is heavily
skewed; another issue is that many applications naturally take several
objective into consideration when evaluating a policy, for instance in
autonomous driving an agent needs to balance speed and safety when choosing
appropriate decision. In this paper, we consider evaluating a policy based on a
sequence of quantiles it induces on a set of target states, our idea is to
reformulate the original problem into a multi-objective MDP problem with
lexicographic preference naturally defined. For computation of finding an
optimal policy, we proposed an algorithm \textbf{FLMDP} that could solve
general multi-objective MDP with lexicographic reward preference.
| [
{
"version": "v1",
"created": "Wed, 10 May 2017 03:13:30 GMT"
}
] | 1,494,460,800,000 | [
[
"Li",
"Yan",
""
],
[
"Sun",
"Zhaohan",
""
]
] |
1705.04119 | Jin-Kao Hao | Yangming Zhou, Jin-Kao Hao, Fred Glover | Memetic search for identifying critical nodes in sparse graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Critical node problems involve identifying a subset of critical nodes from an
undirected graph whose removal results in optimizing a pre-defined measure over
the residual graph. As useful models for a variety of practical applications,
these problems are computational challenging. In this paper, we study the
classic critical node problem (CNP) and introduce an effective memetic
algorithm for solving CNP. The proposed algorithm combines a double
backbone-based crossover operator (to generate promising offspring solutions),
a component-based neighborhood search procedure (to find high-quality local
optima) and a rank-based pool updating strategy (to guarantee a healthy
population). Specially, the component-based neighborhood search integrates two
key techniques, i.e., two-phase node exchange strategy and node weighting
scheme. The double backbone-based crossover extends the idea of general
backbone-based crossovers. Extensive evaluations on 42 synthetic and real-world
benchmark instances show that the proposed algorithm discovers 21 new upper
bounds and matches 18 previous best-known upper bounds. We also demonstrate the
relevance of our algorithm for effectively solving a variant of the classic
CNP, called the cardinality-constrained critical node problem. Finally, we
investigate the usefulness of each key algorithmic component.
| [
{
"version": "v1",
"created": "Thu, 11 May 2017 11:43:30 GMT"
},
{
"version": "v2",
"created": "Sat, 7 Oct 2017 13:15:03 GMT"
}
] | 1,507,593,600,000 | [
[
"Zhou",
"Yangming",
""
],
[
"Hao",
"Jin-Kao",
""
],
[
"Glover",
"Fred",
""
]
] |
1705.04351 | Rachit Dubey | Rachit Dubey and Thomas L. Griffiths | A rational analysis of curiosity | Conference paper in CogSci 2017 | 39th Annual Conference of the Cognitive Science Society (CogSci),
2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a rational analysis of curiosity, proposing that people's
curiosity is driven by seeking stimuli that maximize their ability to make
appropriate responses in the future. This perspective offers a way to unify
previous theories of curiosity into a single framework. Experimental results
confirm our model's predictions, showing how the relationship between curiosity
and confidence can change significantly depending on the nature of the
environment. Please refer to https://psyarxiv.com/wg5m6/ for a more updated
version of this manuscript with a more detailed modeling section with extensive
experiments.
| [
{
"version": "v1",
"created": "Thu, 11 May 2017 18:54:10 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Aug 2020 05:15:12 GMT"
}
] | 1,596,499,200,000 | [
[
"Dubey",
"Rachit",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
1705.04530 | Arindam Bhattacharya | Arindam Bhattacharya | A Survey of Question Answering for Math and Science Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Turing test was long considered the measure for artificial intelligence. But
with the advances in AI, it has proved to be insufficient measure. We can now
aim to mea- sure machine intelligence like we measure human intelligence. One
of the widely accepted measure of intelligence is standardized math and science
test. In this paper, we explore the progress we have made towards the goal of
making a machine smart enough to pass the standardized test. We see the
challenges and opportunities posed by the domain, and note that we are quite
some ways from actually making a system as smart as a even a middle school
scholar.
| [
{
"version": "v1",
"created": "Wed, 10 May 2017 15:28:37 GMT"
}
] | 1,494,806,400,000 | [
[
"Bhattacharya",
"Arindam",
""
]
] |
1705.04569 | Max Ostrowski | Mutsunori Banbara and Benjamin Kaufmann and Max Ostrowski and Torsten
Schaub | Clingcon: The Next Generation | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the third generation of the constraint answer set system clingcon,
combining Answer Set Programming (ASP) with finite domain constraint processing
(CP). While its predecessors rely on a black-box approach to hybrid solving by
integrating the CP solver gecode, the new clingcon system pursues a lazy
approach using dedicated constraint propagators to extend propagation in the
underlying ASP solver clasp. No extension is needed for parsing and grounding
clingcon's hybrid modeling language since both can be accommodated by the new
generic theory handling capabilities of the ASP grounder gringo. As a whole,
clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies
on the grounder gringo and the solver clasp. The new approach of clingcon
offers a seamless integration of CP propagation into ASP solving that benefits
from the whole spectrum of clasp's reasoning modes, including for instance
multi-shot solving and advanced optimization techniques. This is accomplished
by a lazy approach that unfolds the representation of constraints and adds it
to that of the logic program only when needed. Although the unfolding is
usually dictated by the constraint propagators during solving, it can already
be partially (or even totally) done during preprocessing. Moreover, clingcon's
constraint preprocessing and propagation incorporate several well established
CP techniques that greatly improve its performance. We demonstrate this via an
extensive empirical evaluation contrasting, first, the various techniques in
the context of CSP solving and, second, the new clingcon system with other
hybrid ASP systems. Under consideration in Theory and Practice of Logic
Programming (TPLP)
| [
{
"version": "v1",
"created": "Fri, 12 May 2017 13:57:31 GMT"
}
] | 1,494,806,400,000 | [
[
"Banbara",
"Mutsunori",
""
],
[
"Kaufmann",
"Benjamin",
""
],
[
"Ostrowski",
"Max",
""
],
[
"Schaub",
"Torsten",
""
]
] |
1705.04665 | Richard Valenzano | Richard Anthony Valenzano and Danniel Sihui Yang | A Formal Characterization of the Local Search Topology of the Gap
Heuristic | Technical report providing proofs of statements appearing in a "An
Analysis and Enhancement of the Gap Heuristic for the Pancake Puzzle" by
Richard Anthony Valenzano and Danniel Yang. This paper appeared at the 2017
Symposium on Combinatorial Search | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The pancake puzzle is a classic optimization problem that has become a
standard benchmark for heuristic search algorithms. In this paper, we provide
full proofs regarding the local search topology of the gap heuristic for the
pancake puzzle. First, we show that in any non-goal state in which there is no
move that will decrease the number of gaps, there is a move that will keep the
number of gaps constant. We then classify any state in which the number of gaps
cannot be decreased in a single action into two groups: those requiring 2
actions to decrease the number of gaps, and those which require 3 actions to
decrease the number of gaps.
| [
{
"version": "v1",
"created": "Fri, 12 May 2017 17:28:43 GMT"
}
] | 1,494,806,400,000 | [
[
"Valenzano",
"Richard Anthony",
""
],
[
"Yang",
"Danniel Sihui",
""
]
] |
1705.04712 | Denis Ponomaryov | Denis Ponomaryov and Mikhail Soutchanski | Progression of Decomposed Local-Effect Action Theories | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many tasks related to reasoning about consequences of a logical theory, it
is desirable to decompose the theory into a number of weakly-related or
independent components. However, a theory may represent knowledge that is
subject to change, as a result of executing actions that have effects on some
of the initial properties mentioned in the theory. Having once computed a
decomposition of a theory, it is advantageous to know whether a decomposition
has to be computed again in the newly-changed theory (obtained from taking into
account changes resulting from execution of an action). In the paper, we
address this problem in the scope of the situation calculus, where a change of
an initial theory is related to the notion of progression. Progression provides
a form of forward reasoning; it relies on forgetting values of those
properties, which are subject to change, and computing new values for them. We
consider decomposability and inseparability, two component properties known
from the literature, and contribute by 1) studying the conditions when these
properties are preserved and 2) when they are lost wrt progression and the
related operation of forgetting. To show the latter, we demonstrate the
boundaries using a number of negative examples. To show the former, we identify
cases when these properties are preserved under forgetting and progression of
initial theories in local-effect basic action theories of the situation
calculus. Our paper contributes to bridging two different communities in
Knowledge Representation, namely research on modularity and research on
reasoning about actions.
| [
{
"version": "v1",
"created": "Fri, 12 May 2017 18:36:21 GMT"
}
] | 1,494,892,800,000 | [
[
"Ponomaryov",
"Denis",
""
],
[
"Soutchanski",
"Mikhail",
""
]
] |
1705.04719 | Denis Ponomaryov | Yevgeny Kazakov and Denis Ponomaryov | On the Complexity of Semantic Integration of OWL Ontologies | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new mechanism for integration of OWL ontologies using semantic
import relations. In contrast to the standard OWL importing, we do not require
all axioms of the imported ontologies to be taken into account for reasoning
tasks, but only their logical implications over a chosen signature. This
property comes natural in many ontology integration scenarios, especially when
the number of ontologies is large. In this paper, we study the complexity of
reasoning over ontologies with semantic import relations and establish a range
of tight complexity bounds for various fragments of OWL.
| [
{
"version": "v1",
"created": "Fri, 12 May 2017 18:54:16 GMT"
}
] | 1,494,892,800,000 | [
[
"Kazakov",
"Yevgeny",
""
],
[
"Ponomaryov",
"Denis",
""
]
] |
1705.04885 | Jose Fontanari | Jos\'e F. Fontanari | Awareness improves problem-solving performance | null | Cogn Syst Res, 45C (2017) 52-58 | 10.1016/j.cogsys.2017.05.003 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain's self-monitoring of activities, including internal activities -- a
functionality that we refer to as awareness -- has been suggested as a key
element of consciousness. Here we investigate whether the presence of an
inner-eye-like process (monitor) that supervises the activities of a number of
subsystems (operative agents) engaged in the solution of a problem can improve
the problem-solving efficiency of the system. The problem is to find the global
maximum of a NK fitness landscape and the performance is measured by the time
required to find that maximum. The operative agents explore blindly the fitness
landscape and the monitor provides them with feedback on the quality (fitness)
of the proposed solutions. This feedback is then used by the operative agents
to bias their searches towards the fittest regions of the landscape. We find
that a weak feedback between the monitor and the operative agents improves the
performance of the system, regardless of the difficulty of the problem, which
is gauged by the number of local maxima in the landscape. For easy problems
(i.e., landscapes without local maxima), the performance improves monotonically
as the feedback strength increases, but for difficult problems, there is an
optimal value of the feedback strength beyond which the system performance
degrades very rapidly.
| [
{
"version": "v1",
"created": "Sat, 13 May 2017 20:40:24 GMT"
}
] | 1,496,016,000,000 | [
[
"Fontanari",
"José F.",
""
]
] |
1705.05098 | Lahari Poddar | Lahari Poddar, Wynne Hsu, Mong Li Lee | Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach | Accepted for publication in IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
| [
{
"version": "v1",
"created": "Mon, 15 May 2017 07:35:59 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2017 08:47:24 GMT"
}
] | 1,495,670,400,000 | [
[
"Poddar",
"Lahari",
""
],
[
"Hsu",
"Wynne",
""
],
[
"Lee",
"Mong Li",
""
]
] |
1705.05316 | Minas Dasygenis Dr. | Minas Dasygenis and Kostas Stergiou | Exploiting the Pruning Power of Strong Local Consistencies Through
Parallelization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Local consistencies stronger than arc consistency have received a lot of
attention since the early days of CSP research. %because of the strong pruning
they can achieve. However, they have not been widely adopted by CSP solvers.
This is because applying such consistencies can sometimes result in
considerably smaller search tree sizes and therefore in important speed-ups,
but in other cases the search space reduction may be small, causing severe run
time penalties. Taking advantage of recent advances in parallelization, we
propose a novel approach for the application of strong local consistencies
(SLCs) that can improve their performance by largely preserving the speed-ups
they offer in cases where they are successful, and eliminating the run time
penalties in cases where they are unsuccessful. This approach is presented in
the form of two search algorithms. Both algorithms consist of a master search
process, which is a typical CSP solver, and a number of slave processes, with
each one implementing a SLC method. The first algorithm runs the different SLCs
synchronously at each node of the search tree explored in the master process,
while the second one can run them asynchronously at different nodes of the
search tree. Experimental results demonstrate the benefits of the proposed
method.
| [
{
"version": "v1",
"created": "Mon, 15 May 2017 16:28:00 GMT"
}
] | 1,494,892,800,000 | [
[
"Dasygenis",
"Minas",
""
],
[
"Stergiou",
"Kostas",
""
]
] |
1705.05326 | Michael Huth | Paul Beaumont and Michael Huth | Constrained Bayesian Networks: Theory, Optimization, and Applications | 43 pages, 18 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop the theory and practice of an approach to modelling and
probabilistic inference in causal networks that is suitable when
application-specific or analysis-specific constraints should inform such
inference or when little or no data for the learning of causal network
structure or probability values at nodes are available. Constrained Bayesian
Networks generalize a Bayesian Network such that probabilities can be symbolic,
arithmetic expressions and where the meaning of the network is constrained by
finitely many formulas from the theory of the reals. A formal semantics for
constrained Bayesian Networks over first-order logic of the reals is given,
which enables non-linear and non-convex optimisation algorithms that rely on
decision procedures for this logic, and supports the composition of several
constrained Bayesian Networks. A non-trivial case study in arms control, where
few or no data are available to assess the effectiveness of an arms inspection
process, evaluates our approach. An open-access prototype implementation of
these foundations and their algorithms uses the SMT solver Z3 as decision
procedure, leverages an open-source package for Bayesian inference to symbolic
computation, and is evaluated experimentally.
| [
{
"version": "v1",
"created": "Mon, 15 May 2017 16:48:12 GMT"
}
] | 1,494,892,800,000 | [
[
"Beaumont",
"Paul",
""
],
[
"Huth",
"Michael",
""
]
] |
1705.05515 | GyongIl Ryang | Jon JaeGyong, Mun JongHui, Ryang GyongIl | A Method for Determining Weights of Criterias and Alternative of Fuzzy
Group Decision Making Problem | 12 pages, 3 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we constructed a model to determine weights of criterias and
presented a solution for determining the optimal alternative by using the
constructed model and relationship analysis between criterias in fuzzy group
decision-making problem with different forms of preference information of
decision makers on criterias.
| [
{
"version": "v1",
"created": "Tue, 16 May 2017 03:10:56 GMT"
}
] | 1,494,979,200,000 | [
[
"JaeGyong",
"Jon",
""
],
[
"JongHui",
"Mun",
""
],
[
"GyongIl",
"Ryang",
""
]
] |
1705.05551 | Katsunari Shibata | Katsunari Shibata and Yuki Goto | New Reinforcement Learning Using a Chaotic Neural Network for Emergence
of "Thinking" - "Exploration" Grows into "Thinking" through Learning - | The Multi-disciplinary Conference on Reinforcement Learning and
Decision Making (RLDM) 2017, 5 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expectation for the emergence of higher functions is getting larger in the
framework of end-to-end reinforcement learning using a recurrent neural
network. However, the emergence of "thinking" that is a typical higher function
is difficult to realize because "thinking" needs non fixed-point, flow-type
attractors with both convergence and transition dynamics. Furthermore, in order
to introduce "inspiration" or "discovery" in "thinking", not completely random
but unexpected transition should be also required.
By analogy to "chaotic itinerancy", we have hypothesized that "exploration"
grows into "thinking" through learning by forming flow-type attractors on
chaotic random-like dynamics. It is expected that if rational dynamics are
learned in a chaotic neural network (ChNN), coexistence of rational state
transition, inspiration-like state transition and also random-like exploration
for unknown situation can be realized.
Based on the above idea, we have proposed new reinforcement learning using a
ChNN as an actor. The positioning of exploration is completely different from
the conventional one. The chaotic dynamics inside the ChNN produces exploration
factors by itself. Since external random numbers for stochastic action
selection are not used, exploration factors cannot be isolated from the output.
Therefore, the learning method is also completely different from the
conventional one.
At each non-feedback connection, one variable named causality trace takes in
and maintains the input through the connection according to the change in its
output. Using the trace and TD error, the weight is updated.
In this paper, as the result of a recent simple task to see whether the new
learning works or not, it is shown that a robot with two wheels and two visual
sensors reaches a target while avoiding an obstacle after learning though there
are still many rooms for improvement.
| [
{
"version": "v1",
"created": "Tue, 16 May 2017 06:54:04 GMT"
}
] | 1,494,979,200,000 | [
[
"Shibata",
"Katsunari",
""
],
[
"Goto",
"Yuki",
""
]
] |
1705.05637 | Jakub Kowalski | Bartosz Kostka, Jaroslaw Kwiecien, Jakub Kowalski, Pawel Rychlikowski | Text-based Adventures of the Golovin AI Agent | null | null | 10.1109/CIG.2017.8080433 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition.
| [
{
"version": "v1",
"created": "Tue, 16 May 2017 10:55:08 GMT"
}
] | 1,554,163,200,000 | [
[
"Kostka",
"Bartosz",
""
],
[
"Kwiecien",
"Jaroslaw",
""
],
[
"Kowalski",
"Jakub",
""
],
[
"Rychlikowski",
"Pawel",
""
]
] |
1705.05756 | Witold Rudnicki | Krzysztof Mnich and Witold R. Rudnicki | All-relevant feature selection using multidimensional filters with
exhaustive search | 27 pages, 11 figures, 3 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a method for identification of the informative variables
in the information system with discrete decision variables. It is targeted
specifically towards discovery of the variables that are non-informative when
considered alone, but are informative when the synergistic interactions between
multiple variables are considered. To this end, the mutual entropy of all
possible k-tuples of variables with decision variable is computed. Then, for
each variable the maximal information gain due to interactions with other
variables is obtained. For non-informative variables this quantity conforms to
the well known statistical distributions. This allows for discerning truly
informative variables from non-informative ones. For demonstration of the
approach, the method is applied to several synthetic datasets that involve
complex multidimensional interactions between variables. It is capable of
identifying most important informative variables, even in the case when the
dimensionality of the analysis is smaller than the true dimensionality of the
problem. What is more, the high sensitivity of the algorithm allows for
detection of the influence of nuisance variables on the response variable.
| [
{
"version": "v1",
"created": "Tue, 16 May 2017 15:11:10 GMT"
}
] | 1,494,979,200,000 | [
[
"Mnich",
"Krzysztof",
""
],
[
"Rudnicki",
"Witold R.",
""
]
] |
1705.05769 | Varun Ojha | Varun Kumar Ojha, Vaclav Snasel, Ajith Abraham | Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees | null | IEEE Transactions on Fuzzy Systems 2017 | 10.1109/TFUZZ.2017.2698399 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An
HFIT produces an optimum treelike structure, i.e., a natural hierarchical
structure that accommodates simplicity by combining several low-dimensional
fuzzy inference systems (FISs). Such a natural hierarchical structure provides
a high degree of approximation accuracy. The construction of HFIT takes place
in two phases. Firstly, a nondominated sorting based multiobjective genetic
programming (MOGP) is applied to obtain a simple tree structure (a low
complexity model) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree's parameters. In the derived
tree, each node acquires a different input's combination, where the
evolutionary process governs the input's combination. Hence, HFIT nodes are
heterogeneous in nature, which leads to a high diversity among the rules
generated by the HFIT. Additionally, the HFIT provides an automatic feature
selection because it uses MOGP for the tree's structural optimization that
accepts inputs only relevant to the knowledge contained in data. The HFIT was
studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed
multiobjective HFIT was compared both theoretically and empirically with
recently proposed FISs methods from the literature, such as McIT2FIS,
TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly
accurate models compared to the models produced by the most of other methods.
Hence, the proposed HFIT is an efficient and competitive alternative to the
other FISs for function approximation and feature selection.
| [
{
"version": "v1",
"created": "Tue, 16 May 2017 15:34:19 GMT"
}
] | 1,494,979,200,000 | [
[
"Ojha",
"Varun Kumar",
""
],
[
"Snasel",
"Vaclav",
""
],
[
"Abraham",
"Ajith",
""
]
] |
1705.05983 | Chien-Ping Lu | Chien-Ping Lu | AI, Native Supercomputing and The Revival of Moore's Law | 17 pages, 13 figures; to be published in IEEE APSIPA Transaction on
Signal and Information Processing as an invited paper on Industrial
Technology Advances | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on Alan Turing's proposition on AI and computing machinery, which
shaped Computing as we know it today, the new AI computing machinery should
comprise a universal computer and a universal learning machine. The later
should understand linear algebra natively to overcome the slowdown of Moore's
law. In such a universal learnig machine, a computing unit does not need to
keep the legacy of a universal computing core. The data can be distributed to
the computing units, and the results can be collected from them through
Collective Streaming, reminiscent of Collective Communication in
Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy,
nor a TPU-like fine-grain mesh.
| [
{
"version": "v1",
"created": "Wed, 17 May 2017 02:15:27 GMT"
},
{
"version": "v2",
"created": "Tue, 23 May 2017 16:30:39 GMT"
}
] | 1,495,584,000,000 | [
[
"Lu",
"Chien-Ping",
""
]
] |
1705.05986 | Srinivasan Parthasarathy | Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga,
Hui Xiong | REMIX: Automated Exploration for Interactive Outlier Detection | To appear in KDD 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Outlier detection is the identification of points in a dataset that do not
conform to the norm. Outlier detection is highly sensitive to the choice of the
detection algorithm and the feature subspace used by the algorithm. Extracting
domain-relevant insights from outliers needs systematic exploration of these
choices since diverse outlier sets could lead to complementary insights. This
challenge is especially acute in an interactive setting, where the choices must
be explored in a time-constrained manner. In this work, we present REMIX, the
first system to address the problem of outlier detection in an interactive
setting. REMIX uses a novel mixed integer programming (MIP) formulation for
automatically selecting and executing a diverse set of outlier detectors within
a time limit. This formulation incorporates multiple aspects such as (i) an
upper limit on the total execution time of detectors (ii) diversity in the
space of algorithms and features, and (iii) meta-learning for evaluating the
cost and utility of detectors. REMIX provides two distinct ways for the analyst
to consume its results: (i) a partitioning of the detectors explored by REMIX
into perspectives through low-rank non-negative matrix factorization; each
perspective can be easily visualized as an intuitive heatmap of experiments
versus outliers, and (ii) an ensembled set of outliers which combines outlier
scores from all detectors. We demonstrate the benefits of REMIX through
extensive empirical validation on real-world data.
| [
{
"version": "v1",
"created": "Wed, 17 May 2017 02:17:48 GMT"
}
] | 1,495,065,600,000 | [
[
"Fu",
"Yanjie",
""
],
[
"Aggarwal",
"Charu",
""
],
[
"Parthasarathy",
"Srinivasan",
""
],
[
"Turaga",
"Deepak S.",
""
],
[
"Xiong",
"Hui",
""
]
] |
1705.06342 | Thommen Karimpanal George | Thommen George Karimpanal, Erik Wilhelm | Identification and Off-Policy Learning of Multiple Objectives Using
Adaptive Clustering | Accepted in Neurocomputing: Special Issue on Multiobjective
Reinforcement Learning: Theory and Applications, 24 pages, 6 figures | Neurocomputing 263, 39-47, 2017 | 10.1016/j.neucom.2017.04.074 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present a methodology that enables an agent to make
efficient use of its exploratory actions by autonomously identifying possible
objectives in its environment and learning them in parallel. The identification
of objectives is achieved using an online and unsupervised adaptive clustering
algorithm. The identified objectives are learned (at least partially) in
parallel using Q-learning. Using a simulated agent and environment, it is shown
that the converged or partially converged value function weights resulting from
off-policy learning can be used to accumulate knowledge about multiple
objectives without any additional exploration. We claim that the proposed
approach could be useful in scenarios where the objectives are initially
unknown or in real world scenarios where exploration is typically a time and
energy intensive process. The implications and possible extensions of this work
are also briefly discussed.
| [
{
"version": "v1",
"created": "Wed, 17 May 2017 20:55:15 GMT"
}
] | 1,547,164,800,000 | [
[
"Karimpanal",
"Thommen George",
""
],
[
"Wilhelm",
"Erik",
""
]
] |
1705.07095 | Ondrej Kuzelka | Ondrej Kuzelka, Jesse Davis, Steven Schockaert | Induction of Interpretable Possibilistic Logic Theories from Relational
Data | Longer version of a paper appearing in IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The field of Statistical Relational Learning (SRL) is concerned with learning
probabilistic models from relational data. Learned SRL models are typically
represented using some kind of weighted logical formulas, which make them
considerably more interpretable than those obtained by e.g. neural networks. In
practice, however, these models are often still difficult to interpret
correctly, as they can contain many formulas that interact in non-trivial ways
and weights do not always have an intuitive meaning. To address this, we
propose a new SRL method which uses possibilistic logic to encode relational
models. Learned models are then essentially stratified classical theories,
which explicitly encode what can be derived with a given level of certainty.
Compared to Markov Logic Networks (MLNs), our method is faster and produces
considerably more interpretable models.
| [
{
"version": "v1",
"created": "Fri, 19 May 2017 17:12:07 GMT"
}
] | 1,495,411,200,000 | [
[
"Kuzelka",
"Ondrej",
""
],
[
"Davis",
"Jesse",
""
],
[
"Schockaert",
"Steven",
""
]
] |
1705.07105 | Charalampos Nikolaou | Charalampos Nikolaou and Egor V. Kostylev and George Konstantinidis
and Mark Kaminski and Bernardo Cuenca Grau and Ian Horrocks | The Bag Semantics of Ontology-Based Data Access | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags.
| [
{
"version": "v1",
"created": "Fri, 19 May 2017 17:33:28 GMT"
}
] | 1,495,411,200,000 | [
[
"Nikolaou",
"Charalampos",
""
],
[
"Kostylev",
"Egor V.",
""
],
[
"Konstantinidis",
"George",
""
],
[
"Kaminski",
"Mark",
""
],
[
"Grau",
"Bernardo Cuenca",
""
],
[
"Horrocks",
"Ian",
""
]
] |
1705.07177 | Mikael Henaff | Mikael Henaff, William F. Whitney, Yann LeCun | Model-Based Planning with Discrete and Continuous Actions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Action planning using learned and differentiable forward models of the world
is a general approach which has a number of desirable properties, including
improved sample complexity over model-free RL methods, reuse of learned models
across different tasks, and the ability to perform efficient gradient-based
optimization in continuous action spaces. However, this approach does not apply
straightforwardly when the action space is discrete. In this work, we show that
it is in fact possible to effectively perform planning via backprop in discrete
action spaces, using a simple paramaterization of the actions vectors on the
simplex combined with input noise when training the forward model. Our
experiments show that this approach can match or outperform model-free RL and
discrete planning methods on gridworld navigation tasks in terms of performance
and/or planning time while using limited environment interactions, and can
additionally be used to perform model-based control in a challenging new task
where the action space combines discrete and continuous actions. We furthermore
propose a policy distillation approach which yields a fast policy network which
can be used at inference time, removing the need for an iterative planning
procedure.
| [
{
"version": "v1",
"created": "Fri, 19 May 2017 20:38:49 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Apr 2018 06:34:26 GMT"
}
] | 1,522,886,400,000 | [
[
"Henaff",
"Mikael",
""
],
[
"Whitney",
"William F.",
""
],
[
"LeCun",
"Yann",
""
]
] |
1705.07339 | Jin-Kao Hao | Yi Zhou and Jin-Kao Hao | Combining tabu search and graph reduction to solve the maximum balanced
biclique problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Maximum Balanced Biclique Problem is a well-known graph model with
relevant applications in diverse domains. This paper introduces a novel
algorithm, which combines an effective constraint-based tabu search procedure
and two dedicated graph reduction techniques. We verify the effectiveness of
the algorithm on 30 classical random benchmark graphs and 25 very large
real-life sparse graphs from the popular Koblenz Network Collection (KONECT).
The results show that the algorithm improves the best-known results (new lower
bounds) for 10 classical benchmarks and obtains the optimal solutions for 14
KONECT instances.
| [
{
"version": "v1",
"created": "Sat, 20 May 2017 17:47:31 GMT"
}
] | 1,495,497,600,000 | [
[
"Zhou",
"Yi",
""
],
[
"Hao",
"Jin-Kao",
""
]
] |
1705.07381 | Luis Pineda | Luis Pineda and Shlomo Zilberstein | Generalizing the Role of Determinization in Probabilistic Planning | null | null | null | UM-CS-2017-006 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The stochastic shortest path problem (SSP) is a highly expressive model for
probabilistic planning. The computational hardness of SSPs has sparked interest
in determinization-based planners that can quickly solve large problems.
However, existing methods employ a simplistic approach to determinization. In
particular, they ignore the possibility of tailoring the determinization to the
specific characteristics of the target domain. In this work we examine this
question, by showing that learning a good determinization for a planning domain
can be done efficiently and can improve performance. Moreover, we show how to
directly incorporate probabilistic reasoning into the planning problem when a
good determinization is not sufficient by itself. Based on these insights, we
introduce a planner, FF-LAO*, that outperforms state-of-the-art probabilistic
planners on several well-known competition benchmarks.
| [
{
"version": "v1",
"created": "Sun, 21 May 2017 02:39:02 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Jul 2017 14:25:10 GMT"
}
] | 1,501,545,600,000 | [
[
"Pineda",
"Luis",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1705.07429 | Sergey Paramonov | Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt | Sketched Answer Set Programming | 15 pages, 11 figures; to appear in ICTAI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer Set Programming (ASP) is a powerful modeling formalism for
combinatorial problems. However, writing ASP models is not trivial. We propose
a novel method, called Sketched Answer Set Programming (SkASP), aiming at
supporting the user in resolving this issue. The user writes an ASP program
while marking uncertain parts open with question marks. In addition, the user
provides a number of positive and negative examples of the desired program
behaviour. The sketched model is rewritten into another ASP program, which is
solved by traditional methods. As a result, the user obtains a functional and
reusable ASP program modelling her problem. We evaluate our approach on 21 well
known puzzles and combinatorial problems inspired by Karp's 21 NP-complete
problems and demonstrate a use-case for a database application based on ASP.
| [
{
"version": "v1",
"created": "Sun, 21 May 2017 11:03:53 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Aug 2018 09:52:51 GMT"
}
] | 1,534,982,400,000 | [
[
"Paramonov",
"Sergey",
""
],
[
"Bessiere",
"Christian",
""
],
[
"Dries",
"Anton",
""
],
[
"De Raedt",
"Luc",
""
]
] |
1705.07460 | Min Xu | Min Xu | Experience enrichment based task independent reward model | 4 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For most reinforcement learning approaches, the learning is performed by
maximizing an accumulative reward that is expectedly and manually defined for
specific tasks. However, in real world, rewards are emergent phenomena from the
complex interactions between agents and environments. In this paper, we propose
an implicit generic reward model for reinforcement learning. Unlike those
rewards that are manually defined for specific tasks, such implicit reward is
task independent. It only comes from the deviation from the agents' previous
experiences.
| [
{
"version": "v1",
"created": "Sun, 21 May 2017 15:19:20 GMT"
}
] | 1,495,497,600,000 | [
[
"Xu",
"Min",
""
]
] |
1705.07615 | John Aslanides | John Aslanides | AIXIjs: A Software Demo for General Reinforcement Learning | Masters thesis. Australian National University, October 2016. 97 pp | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning is a general and powerful framework with which to
study and implement artificial intelligence. Recent advances in deep learning
have enabled RL algorithms to achieve impressive performance in restricted
domains such as playing Atari video games (Mnih et al., 2015) and, recently,
the board game Go (Silver et al., 2016). However, we are still far from
constructing a generally intelligent agent. Many of the obstacles and open
questions are conceptual: What does it mean to be intelligent? How does one
explore and learn optimally in general, unknown environments? What, in fact,
does it mean to be optimal in the general sense? The universal Bayesian agent
AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a
central role in the sub-field of general reinforcement learning (GRL).
Recently, AIXI has been shown to be flawed in important ways; it doesn't
explore enough to be asymptotically optimal (Orseau, 2010), and it can perform
poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI
have been proposed to attempt to address these shortfalls: among them are
entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al.,
2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike,
2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and
Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL
agents. This implementation is accompanied by a framework for running
experiments against various environments, similar to OpenAI Gym (Brockman et
al., 2016), and a suite of interactive demos that explore different properties
of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to
present numerous experiments illustrating fundamental properties of, and
differences between, these agents.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 08:56:54 GMT"
}
] | 1,495,497,600,000 | [
[
"Aslanides",
"John",
""
]
] |
1705.07961 | Irina Georgescu | Irina Georgescu | Compatible extensions and consistent closures: a fuzzy approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper $\ast$--compatible extensions of fuzzy relations are studied,
generalizing some results obtained by Duggan in case of crisp relations. From
this general result are obtained as particular cases fuzzy versions of some
important extension theorems for crisp relations (Szpilrajn, Hansson,
Suzumura). Two notions of consistent closure of a fuzzy relation are
introduced.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 19:27:19 GMT"
}
] | 1,495,584,000,000 | [
[
"Georgescu",
"Irina",
""
]
] |
1705.07996 | Neil Lawrence | Neil D. Lawrence | Living Together: Mind and Machine Intelligence | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider the nature of the machine intelligences we have
created in the context of our human intelligence. We suggest that the
fundamental difference between human and machine intelligence comes down to
\emph{embodiment factors}. We define embodiment factors as the ratio between an
entity's ability to communicate information vs compute information. We
speculate on the role of embodiment factors in driving our own intelligence and
consciousness. We briefly review dual process models of cognition and cast
machine intelligence within that framework, characterising it as a dominant
System Zero, which can drive behaviour through interfacing with us
subconsciously. Driven by concerns about the consequence of such a system we
suggest prophylactic courses of action that could be considered. Our main
conclusion is that it is \emph{not} sentient intelligence we should fear but
\emph{non-sentient} intelligence.
| [
{
"version": "v1",
"created": "Mon, 22 May 2017 20:49:43 GMT"
}
] | 1,495,584,000,000 | [
[
"Lawrence",
"Neil D.",
""
]
] |
1705.08200 | Chaoyang Song | Fang Wan and Chaoyang Song | Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs | 11 pages, 9 figures, 4 tables | Front. Robot. AI, 30 July 2018 | 10.3389/frobt.2018.00086 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 12:11:30 GMT"
}
] | 1,583,798,400,000 | [
[
"Wan",
"Fang",
""
],
[
"Song",
"Chaoyang",
""
]
] |
1705.08218 | Xiaojian Wu | Xiaojian Wu, Yexiang Xue, Bart Selman, Carla P. Gomes | XOR-Sampling for Network Design with Correlated Stochastic Events | In Proceedings of the Twenty-sixth International Joint Conference on
Artificial Intelligence (IJCAI-17). The first two authors contribute equally | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many network optimization problems can be formulated as stochastic network
design problems in which edges are present or absent stochastically.
Furthermore, protective actions can guarantee that edges will remain present.
We consider the problem of finding the optimal protection strategy under a
budget limit in order to maximize some connectivity measurements of the
network. Previous approaches rely on the assumption that edges are independent.
In this paper, we consider a more realistic setting where multiple edges are
not independent due to natural disasters or regional events that make the
states of multiple edges stochastically correlated. We use Markov Random Fields
to model the correlation and define a new stochastic network design framework.
We provide a novel algorithm based on Sample Average Approximation (SAA)
coupled with a Gibbs or XOR sampler. The experimental results on real road
network data show that the policies produced by SAA with the XOR sampler have
higher quality and lower variance compared to SAA with Gibbs sampler.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 12:50:36 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2017 01:38:57 GMT"
}
] | 1,495,670,400,000 | [
[
"Wu",
"Xiaojian",
""
],
[
"Xue",
"Yexiang",
""
],
[
"Selman",
"Bart",
""
],
[
"Gomes",
"Carla P.",
""
]
] |
1705.08245 | Vincent Huang | Vincent Huang, Tobias Ley, Martha Vlachou-Konchylaki, Wenfeng Hu | Enhanced Experience Replay Generation for Efficient Reinforcement
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Applying deep reinforcement learning (RL) on real systems suffers from slow
data sampling. We propose an enhanced generative adversarial network (EGAN) to
initialize an RL agent in order to achieve faster learning. The EGAN utilizes
the relation between states and actions to enhance the quality of data samples
generated by a GAN. Pre-training the agent with the EGAN shows a steeper
learning curve with a 20% improvement of training time in the beginning of
learning, compared to no pre-training, and an improvement compared to training
with GAN by about 5% with smaller variations. For real time systems with sparse
and slow data sampling the EGAN could be used to speed up the early phases of
the training process.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 13:36:00 GMT"
},
{
"version": "v2",
"created": "Mon, 29 May 2017 14:24:08 GMT"
}
] | 1,496,102,400,000 | [
[
"Huang",
"Vincent",
""
],
[
"Ley",
"Tobias",
""
],
[
"Vlachou-Konchylaki",
"Martha",
""
],
[
"Hu",
"Wenfeng",
""
]
] |
1705.08320 | Svetlin Penkov | Svetlin Penkov and Subramanian Ramamoorthy | Explaining Transition Systems through Program Induction | submitted to Neural Information Processing Systems 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to three problems: system
identification of dynamical systems, explaining the behaviour of a DQN agent
and learning by demonstration in a human-robot interaction scenario. Our
experimental results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 14:38:28 GMT"
}
] | 1,501,113,600,000 | [
[
"Penkov",
"Svetlin",
""
],
[
"Ramamoorthy",
"Subramanian",
""
]
] |
1705.08439 | Thomas Anthony | Thomas Anthony, Zheng Tian, David Barber | Thinking Fast and Slow with Deep Learning and Tree Search | v1 to v2: - Add a value function in MCTS - Some MCTS hyper-parameters
changed - Repetition of experiments: improved accuracy and errors shown.
(note the reduction in effect size for the tpt/cat experiment) - Results from
a longer training run, including changes in expert strength in training -
Comparison to MoHex. v3: clarify independence of ExIt and AG0. v4: see
appendix E | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential decision making problems, such as structured prediction, robotic
control, and game playing, require a combination of planning policies and
generalisation of those plans. In this paper, we present Expert Iteration
(ExIt), a novel reinforcement learning algorithm which decomposes the problem
into separate planning and generalisation tasks. Planning new policies is
performed by tree search, while a deep neural network generalises those plans.
Subsequently, tree search is improved by using the neural network policy to
guide search, increasing the strength of new plans. In contrast, standard deep
Reinforcement Learning algorithms rely on a neural network not only to
generalise plans, but to discover them too. We show that ExIt outperforms
REINFORCE for training a neural network to play the board game Hex, and our
final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most
recent Olympiad Champion player to be publicly released.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 17:48:51 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Nov 2017 17:37:18 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Nov 2017 10:01:16 GMT"
},
{
"version": "v4",
"created": "Sun, 3 Dec 2017 10:56:00 GMT"
}
] | 1,512,432,000,000 | [
[
"Anthony",
"Thomas",
""
],
[
"Tian",
"Zheng",
""
],
[
"Barber",
"David",
""
]
] |
1705.08440 | Mieczys{\l}aw K{\l}opotek | M.Michalewicz, S.T.Wierzcho\'n, M.A. K{\l}opotek | Knowledge Acquisition, Representation \& Manipulation in Decision
Support Systems | Intelligent Information Systems Proceedings of a Workshop held in
August\'ow, Poland, 7-11 June, 1993, pages 210- 238 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a methodology and discuss some implementation issues
for a project on statistical/expert approach to data analysis and knowledge
acquisition. We discuss some general assumptions underlying the project.
Further, the requirements for a user-friendly computer assistant are specified
along with the nature of tools aiding the researcher. Next we show some aspects
of belief network approach and Dempster-Shafer (DST) methodology introduced in
practice to system SEAD. Specifically we present the application of DS
methodology to belief revision problem. Further a concept of an interface to
probabilistic and DS belief networks enabling a user to understand the
communication with a belief network based reasoning system is presented
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 17:51:58 GMT"
}
] | 1,495,584,000,000 | [
[
"Michalewicz",
"M.",
""
],
[
"Wierzchoń",
"S. T.",
""
],
[
"Kłopotek",
"M. A.",
""
]
] |
1705.08492 | Yan Zhao | Yan Zhao, Xiao Fang, and David Simchi-Levi | Uplift Modeling with Multiple Treatments and General Response Types | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 19:20:18 GMT"
}
] | 1,495,670,400,000 | [
[
"Zhao",
"Yan",
""
],
[
"Fang",
"Xiao",
""
],
[
"Simchi-Levi",
"David",
""
]
] |
1705.08509 | Pouria Amirian Dr. | Pouria Amirian, Anahid Basiri, Jeremy Morley | Predictive Analytics for Enhancing Travel Time Estimation in Navigation
Apps of Apple, Google, and Microsoft | null | null | 10.1145/3003965.3003976 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The explosive growth of the location-enabled devices coupled with the
increasing use of Internet services has led to an increasing awareness of the
importance and usage of geospatial information in many applications. The
navigation apps (often called Maps), use a variety of available data sources to
calculate and predict the travel time as well as several options for routing in
public transportation, car or pedestrian modes. This paper evaluates the
pedestrian mode of Maps apps in three major smartphone operating systems
(Android, iOS and Windows Phone). In the paper, we will show that the Maps apps
on iOS, Android and Windows Phone in pedestrian mode, predict travel time
without learning from the individual's movement profile. In addition, we will
exemplify that those apps suffer from a specific data quality issue which
relates to the absence of information about location and type of pedestrian
crossings. Finally, we will illustrate learning from movement profile of
individuals using various predictive analytics models to improve the accuracy
of travel time estimation.
| [
{
"version": "v1",
"created": "Tue, 23 May 2017 19:54:19 GMT"
}
] | 1,495,670,400,000 | [
[
"Amirian",
"Pouria",
""
],
[
"Basiri",
"Anahid",
""
],
[
"Morley",
"Jeremy",
""
]
] |
1705.08961 | Roni Stern | Roni Stern and Brendan Juba | Efficient, Safe, and Probably Approximately Complete Learning of Action
Models | null | International Joint Conference on Artificial Intelligence (IJCAI)
2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we explore the theoretical boundaries of planning in a setting
where no model of the agent's actions is given. Instead of an action model, a
set of successfully executed plans are given and the task is to generate a plan
that is safe, i.e., guaranteed to achieve the goal without failing. To this
end, we show how to learn a conservative model of the world in which actions
are guaranteed to be applicable. This conservative model is then given to an
off-the-shelf classical planner, resulting in a plan that is guaranteed to
achieve the goal. However, this reduction from a model-free planning to a
model-based planning is not complete: in some cases a plan will not be found
even when such exists. We analyze the relation between the number of observed
plans and the likelihood that our conservative approach will indeed fail to
solve a solvable problem. Our analysis show that the number of trajectories
needed scales gracefully.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 20:38:52 GMT"
}
] | 1,495,756,800,000 | [
[
"Stern",
"Roni",
""
],
[
"Juba",
"Brendan",
""
]
] |
1705.08968 | Artur Garcez | Ivan Donadello, Luciano Serafini, Artur d'Avila Garcez | Logic Tensor Networks for Semantic Image Interpretation | 14 pages, 2 figures, IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic Image Interpretation (SII) is the task of extracting structured
semantic descriptions from images. It is widely agreed that the combined use of
visual data and background knowledge is of great importance for SII. Recently,
Statistical Relational Learning (SRL) approaches have been developed for
reasoning under uncertainty and learning in the presence of data and rich
knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates
neural networks with first-order fuzzy logic to allow (i) efficient learning
from noisy data in the presence of logical constraints, and (ii) reasoning with
logical formulas describing general properties of the data. In this paper, we
develop and apply LTNs to two of the main tasks of SII, namely, the
classification of an image's bounding boxes and the detection of the relevant
part-of relations between objects. To the best of our knowledge, this is the
first successful application of SRL to such SII tasks. The proposed approach is
evaluated on a standard image processing benchmark. Experiments show that the
use of background knowledge in the form of logical constraints can improve the
performance of purely data-driven approaches, including the state-of-the-art
Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show
that the use of logical background knowledge adds robustness to the learning
system when errors are present in the labels of the training data.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 21:34:14 GMT"
}
] | 1,495,756,800,000 | [
[
"Donadello",
"Ivan",
""
],
[
"Serafini",
"Luciano",
""
],
[
"Garcez",
"Artur d'Avila",
""
]
] |
1705.09045 | Ashley Edwards | Ashley D. Edwards, Srijan Sood, and Charles L. Isbell Jr | Cross-Domain Perceptual Reward Functions | A shorter version of this paper was accepted to RLDM
(http://rldm.org/rldm2017/) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In reinforcement learning, we often define goals by specifying rewards within
desirable states. One problem with this approach is that we typically need to
redefine the rewards each time the goal changes, which often requires some
understanding of the solution in the agents environment. When humans are
learning to complete tasks, we regularly utilize alternative sources that guide
our understanding of the problem. Such task representations allow one to
specify goals on their own terms, thus providing specifications that can be
appropriately interpreted across various environments. This motivates our own
work, in which we represent goals in environments that are different from the
agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned
rewards that represent the visual similarity between an agents state and a
cross-domain goal image. We report results for learning the CDPRs with a deep
neural network and using them to solve two tasks with deep reinforcement
learning.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 04:54:36 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Jun 2017 15:44:37 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Jul 2017 15:40:28 GMT"
}
] | 1,501,027,200,000 | [
[
"Edwards",
"Ashley D.",
""
],
[
"Sood",
"Srijan",
""
],
[
"Isbell",
"Charles L.",
"Jr"
]
] |
1705.09058 | Yihui He | Yihui He, Ming Xiang | An Empirical Analysis of Approximation Algorithms for the Euclidean
Traveling Salesman Problem | 4 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With applications to many disciplines, the traveling salesman problem (TSP)
is a classical computer science optimization problem with applications to
industrial engineering, theoretical computer science, bioinformatics, and
several other disciplines. In recent years, there have been a plethora of novel
approaches for approximate solutions ranging from simplistic greedy to
cooperative distributed algorithms derived from artificial intelligence. In
this paper, we perform an evaluation and analysis of cornerstone algorithms for
the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
several datasets as input for the algorithms including a small dataset, a
mediumsized dataset representing cities in the United States, and a synthetic
dataset consisting of 200 cities to test algorithm scalability. We discover
that the greedy and 2-opt algorithms efficiently calculate solutions for
smaller datasets. Genetic algorithm has the best performance for optimality for
medium to large datasets, but generally have longer runtime. Our
implementations is public available.
| [
{
"version": "v1",
"created": "Thu, 25 May 2017 06:21:39 GMT"
}
] | 1,495,756,800,000 | [
[
"He",
"Yihui",
""
],
[
"Xiang",
"Ming",
""
]
] |
1705.09218 | Mohamed Siala Dr | Begum Genc, Mohamed Siala, Barry O'Sullivan, Gilles Simonin | Finding Robust Solutions to Stable Marriage | IJCAI 2017 proceedings | null | 10.24963/ijcai.2017/88 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the notion of robustness in stable matching problems. We first
define robustness by introducing (a,b)-supermatches. An $(a,b)$-supermatch is a
stable matching in which if $a$ pairs break up it is possible to find another
stable matching by changing the partners of those $a$ pairs and at most $b$
other pairs. In this context, we define the most robust stable matching as a
$(1,b)$-supermatch where b is minimum. We show that checking whether a given
stable matching is a $(1,b)$-supermatch can be done in polynomial time. Next,
we use this procedure to design a constraint programming model, a local search
approach, and a genetic algorithm to find the most robust stable matching. Our
empirical evaluation on large instances show that local search outperforms the
other approaches.
| [
{
"version": "v1",
"created": "Wed, 24 May 2017 07:49:52 GMT"
},
{
"version": "v2",
"created": "Sun, 20 Aug 2017 12:25:42 GMT"
},
{
"version": "v3",
"created": "Fri, 27 Oct 2017 13:53:56 GMT"
}
] | 1,509,321,600,000 | [
[
"Genc",
"Begum",
""
],
[
"Siala",
"Mohamed",
""
],
[
"O'Sullivan",
"Barry",
""
],
[
"Simonin",
"Gilles",
""
]
] |
1705.09545 | Mark Lewis | Fred Glover, Mark Lewis, Gary Kochenberger | Logical and Inequality Implications for Reducing the Size and Complexity
of Quadratic Unconstrained Binary Optimization Problems | 30 pages + 6 pages of Appendices | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The quadratic unconstrained binary optimization (QUBO) problem arises in
diverse optimization applications ranging from Ising spin problems to classical
problems in graph theory and binary discrete optimization. The use of
preprocessing to transform the graph representing the QUBO problem into a
smaller equivalent graph is important for improving solution quality and time
for both exact and metaheuristic algorithms and is a step towards mapping large
scale QUBO to hardware graphs used in quantum annealing computers. In an
earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
achieved significant QUBO reductions as verified through computational testing.
Here this work is extended with additional rules that provide further
reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
An algorithm and associated data structures to efficiently implement the entire
set of rules is detailed and computational experiments are reported that
demonstrate their efficacy.
| [
{
"version": "v1",
"created": "Fri, 26 May 2017 11:59:49 GMT"
}
] | 1,496,016,000,000 | [
[
"Glover",
"Fred",
""
],
[
"Lewis",
"Mark",
""
],
[
"Kochenberger",
"Gary",
""
]
] |
1705.09811 | Torsten Schaub | Martin Gebser and Roland Kaminski and Benjamin Kaufmann and Torsten
Schaub | Multi-shot ASP solving with clingo | Under consideration for publication in Theory and Practice of Logic
Programming (TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new flexible paradigm of grounding and solving in Answer Set
Programming (ASP), which we refer to as multi-shot ASP solving, and present its
implementation in the ASP system clingo.
Multi-shot ASP solving features grounding and solving processes that deal
with continuously changing logic programs. In doing so, they remain operative
and accommodate changes in a seamless way. For instance, such processes allow
for advanced forms of search, as in optimization or theory solving, or
interaction with an environment, as in robotics or query-answering. Common to
them is that the problem specification evolves during the reasoning process,
either because data or constraints are added, deleted, or replaced. This
evolutionary aspect adds another dimension to ASP since it brings about state
changing operations. We address this issue by providing an operational
semantics that characterizes grounding and solving processes in multi-shot ASP
solving. This characterization provides a semantic account of grounder and
solver states along with the operations manipulating them.
The operative nature of multi-shot solving avoids redundancies in relaunching
grounder and solver programs and benefits from the solver's learning
capacities. clingo accomplishes this by complementing ASP's declarative input
language with control capacities. On the declarative side, a new directive
allows for structuring logic programs into named and parameterizable
subprograms. The grounding and integration of these subprograms into the
solving process is completely modular and fully controllable from the
procedural side. To this end, clingo offers a new application programming
interface that is conveniently accessible via scripting languages.
| [
{
"version": "v1",
"created": "Sat, 27 May 2017 11:52:40 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Mar 2018 16:43:53 GMT"
}
] | 1,521,590,400,000 | [
[
"Gebser",
"Martin",
""
],
[
"Kaminski",
"Roland",
""
],
[
"Kaufmann",
"Benjamin",
""
],
[
"Schaub",
"Torsten",
""
]
] |
1705.09844 | Mark Lewis | Mark Lewis, Fred Glover | Quadratic Unconstrained Binary Optimization Problem Preprocessing:
Theory and Empirical Analysis | Benchmark problems used are available from the first author | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a
unifying model for representing a wide range of combinatorial optimization
problems, and for linking a variety of disciplines that face these problems. A
new class of quantum annealing computer that maps QUBO onto a physical qubit
network structure with specific size and edge density restrictions is
generating a growing interest in ways to transform the underlying QUBO
structure into an equivalent graph having fewer nodes and edges. In this paper
we present rules for reducing the size of the QUBO matrix by identifying
variables whose value at optimality can be predetermined. We verify that the
reductions improve both solution quality and time to solution and, in the case
of metaheuristic methods where optimal solutions cannot be guaranteed, the
quality of solutions obtained within reasonable time limits.
We discuss the general QUBO structural characteristics that can take
advantage of these reduction techniques and perform careful experimental design
and analysis to identify and quantify the specific characteristics most
affecting reduction. The rules make it possible to dramatically improve
solution times on a new set of problems using both the exact Cplex solver and a
tabu search metaheuristic.
| [
{
"version": "v1",
"created": "Sat, 27 May 2017 17:09:56 GMT"
}
] | 1,496,102,400,000 | [
[
"Lewis",
"Mark",
""
],
[
"Glover",
"Fred",
""
]
] |
1705.09879 | Patrick Rodler | Patrick Rodler and Wolfgang Schmid and Konstantin Schekotihin | Inexpensive Cost-Optimized Measurement Proposal for Sequential
Model-Based Diagnosis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we present strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and guaranteeing query properties existing
methods cannot provide. Evaluation results using real-world problems indicate
that the new method computes (virtually) optimal queries instantly
independently of the size and complexity of the considered diagnosis problems.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 00:47:29 GMT"
}
] | 1,496,102,400,000 | [
[
"Rodler",
"Patrick",
""
],
[
"Schmid",
"Wolfgang",
""
],
[
"Schekotihin",
"Konstantin",
""
]
] |
1705.09970 | Steven Holtzen | Steven Holtzen and Todd Millstein and Guy Van den Broeck | Probabilistic Program Abstractions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Abstraction is a fundamental tool for reasoning about complex systems.
Program abstraction has been utilized to great effect for analyzing
deterministic programs. At the heart of program abstraction is the relationship
between a concrete program, which is difficult to analyze, and an abstract
program, which is more tractable. Program abstractions, however, are typically
not probabilistic. We generalize non-deterministic program abstractions to
probabilistic program abstractions by explicitly quantifying the
non-deterministic choices. Our framework upgrades key definitions and
properties of abstractions to the probabilistic context. We also discuss
preliminary ideas for performing inference on probabilistic abstractions and
general probabilistic programs.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 17:53:01 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jul 2017 15:46:25 GMT"
}
] | 1,500,249,600,000 | [
[
"Holtzen",
"Steven",
""
],
[
"Millstein",
"Todd",
""
],
[
"Broeck",
"Guy Van den",
""
]
] |
1705.09990 | Smitha Milli | Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell | Should Robots be Obedient? | Accepted to IJCAI 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intuitively, obedience -- following the order that a human gives -- seems
like a good property for a robot to have. But, we humans are not perfect and we
may give orders that are not best aligned to our preferences. We show that when
a human is not perfectly rational then a robot that tries to infer and act
according to the human's underlying preferences can always perform better than
a robot that simply follows the human's literal order. Thus, there is a
tradeoff between the obedience of a robot and the value it can attain for its
owner. We investigate how this tradeoff is impacted by the way the robot infers
the human's preferences, showing that some methods err more on the side of
obedience than others. We then analyze how performance degrades when the robot
has a misspecified model of the features that the human cares about or the
level of rationality of the human. Finally, we study how robots can start
detecting such model misspecification. Overall, our work suggests that there
might be a middle ground in which robots intelligently decide when to obey
human orders, but err on the side of obedience.
| [
{
"version": "v1",
"created": "Sun, 28 May 2017 20:51:19 GMT"
}
] | 1,496,102,400,000 | [
[
"Milli",
"Smitha",
""
],
[
"Hadfield-Menell",
"Dylan",
""
],
[
"Dragan",
"Anca",
""
],
[
"Russell",
"Stuart",
""
]
] |
1705.10044 | Ryuta Arisaka | Ryuta Arisaka, Ken Satoh | Abstract Argumentation / Persuasion / Dynamics | Arisaka R., Satoh K. (2018) Abstract Argumentation / Persuasion /
Dynamics. In: Miller T., Oren N., Sakurai Y., Noda I., Savarimuthu B., Cao
Son T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems.
PRIMA 2018. Lecture Notes in Computer Science, vol 11224. Springer, Cham | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The act of persuasion, a key component in rhetoric argumentation, may be
viewed as a dynamics modifier. We extend Dung's frameworks with acts of
persuasion among agents, and consider interactions among attack, persuasion and
defence that have been largely unheeded so far. We characterise basic notions
of admissibilities in this framework, and show a way of enriching them through,
effectively, CTL (computation tree logic) encoding, which also permits
importation of the theoretical results known to the logic into our
argumentation frameworks. Our aim is to complement the growing interest in
coordination of static and dynamic argumentation.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 06:14:56 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Jun 2017 05:37:28 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Nov 2018 08:28:40 GMT"
}
] | 1,541,635,200,000 | [
[
"Arisaka",
"Ryuta",
""
],
[
"Satoh",
"Ken",
""
]
] |
1705.10201 | Leigh Sheneman | Leigh Sheneman and Arend Hintze | Machine Learned Learning Machines | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | There are two common approaches for optimizing the performance of a machine:
genetic algorithms and machine learning. A genetic algorithm is applied over
many generations whereas machine learning works by applying feedback until the
system meets a performance threshold. Though these are methods that typically
operate separately, we combine evolutionary adaptation and machine learning
into one approach. Our focus is on machines that can learn during their
lifetime, but instead of equipping them with a machine learning algorithm we
aim to let them evolve their ability to learn by themselves. We use evolvable
networks of probabilistic and deterministic logic gates, known as Markov
Brains, as our computational model organism. The ability of Markov Brains to
learn is augmented by a novel adaptive component that can change its
computational behavior based on feedback. We show that Markov Brains can indeed
evolve to incorporate these feedback gates to improve their adaptability to
variable environments. By combining these two methods, we now also implemented
a computational model that can be used to study the evolution of learning.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 14:07:33 GMT"
},
{
"version": "v2",
"created": "Thu, 31 Aug 2017 15:53:28 GMT"
}
] | 1,504,224,000,000 | [
[
"Sheneman",
"Leigh",
""
],
[
"Hintze",
"Arend",
""
]
] |
1705.10217 | Javier \'Alvez | Javier \'Alvez and Paqui Lucio and German Rigau | Black-box Testing of First-Order Logic Ontologies Using WordNet | 59 pages,14 figures, 6 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence aims to provide computer programs with commonsense
knowledge to reason about our world. This paper offers a new practical approach
towards automated commonsense reasoning with first-order logic (FOL)
ontologies. We propose a new black-box testing methodology of FOL SUMO-based
ontologies by exploiting WordNet and its mapping into SUMO. Our proposal
includes a method for the (semi-)automatic creation of a very large benchmark
of competency questions and a procedure for its automated evaluation by using
automated theorem provers (ATPs). Applying different quality criteria, our
testing proposal enables a successful evaluation of a) the competency of
several translations of SUMO into FOL and b) the performance of various
automated ATPs. Finally, we also provide a fine-grained and complete analysis
of the commonsense reasoning competency of current FOL SUMO-based ontologies.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 14:41:20 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Mar 2018 13:28:14 GMT"
},
{
"version": "v3",
"created": "Fri, 23 Mar 2018 14:43:13 GMT"
}
] | 1,522,022,400,000 | [
[
"Álvez",
"Javier",
""
],
[
"Lucio",
"Paqui",
""
],
[
"Rigau",
"German",
""
]
] |
1705.10219 | Javier \'Alvez | Javier \'Alvez and Montserrat Hermo and Paqui Lucio and German Rigau | Automatic White-Box Testing of First-Order Logic Ontologies | 38 pages, 5 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Formal ontologies are axiomatizations in a logic-based formalism. The
development of formal ontologies, and their important role in the Semantic Web
area, is generating considerable research on the use of automated reasoning
techniques and tools that help in ontology engineering. One of the main aims is
to refine and to improve axiomatizations for enabling automated reasoning tools
to efficiently infer reliable information. Defects in the axiomatization can
not only cause wrong inferences, but can also hinder the inference of expected
information, either by increasing the computational cost of, or even
preventing, the inference. In this paper, we introduce a novel, fully automatic
white-box testing framework for first-order logic ontologies. Our methodology
is based on the detection of inference-based redundancies in the given
axiomatization. The application of the proposed testing method is fully
automatic since a) the automated generation of tests is guided only by the
syntax of axioms and b) the evaluation of tests is performed by automated
theorem provers. Our proposal enables the detection of defects and serves to
certify the grade of suitability --for reasoning purposes-- of every axiom. We
formally define the set of tests that are generated from any axiom and prove
that every test is logically related to redundancies in the axiom from which
the test has been generated. We have implemented our method and used this
implementation to automatically detect several non-trivial defects that were
hidden in various first-order logic ontologies. Throughout the paper we provide
illustrative examples of these defects, explain how they were found, and how
each proof --given by an automated theorem-prover-- provides useful hints on
the nature of each defect. Additionally, by correcting all the detected
defects, we have obtained an improved version of one of the tested ontologies:
Adimen-SUMO.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 14:42:48 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jun 2018 19:23:02 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Jan 2019 08:14:56 GMT"
}
] | 1,548,892,800,000 | [
[
"Álvez",
"Javier",
""
],
[
"Hermo",
"Montserrat",
""
],
[
"Lucio",
"Paqui",
""
],
[
"Rigau",
"German",
""
]
] |
1705.10308 | Mieczys{\l}aw K{\l}opotek | Mieczys{\l}aw K{\l}opotek | Learning Belief Network Structure From Data under Causal Insufficiency | A short version of this paper appeared in [Klopotek:94m] M.A.
K{\l}opotek: Learning Belief Network Structure From Data under Causal
Insufficiency. [in:] F. Bergadano, L.DeRaed Eds.: Machine Learning ECML-94 ,
Proc. 13th European Conference on Machine Learning, Catania, Italy, 6-8 April
1994, Lecture Notes in Artificial Intelligence 784, Springer-Verlag, 1994,
pp. 379-382 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Though a belief network (a representation of the joint probability
distribution, see [3]) and a causal network (a representation of causal
relationships [14]) are intended to mean different things, they are closely
related. Both assume an underlying dag (directed acyclic graph) structure of
relations among variables and if Markov condition and faithfulness condition
[15] are met, then a causal network is in fact a belief network. The difference
comes to appearance when we recover belief network and causal network structure
from data.
A causal network structure may be impossible to recover completely from data
as not all directions of causal links may be uniquely determined [15].
Fortunately, if we deal with causally sufficient sets of variables (that is
whenever significant influence variables are not omitted from observation),
then there exists the possibility to identify the family of belief networks a
causal network belongs to [16]. Regrettably, to our knowledge, a similar result
is not directly known for causally insufficient sets of variables. Spirtes,
Glymour and Scheines developed a CI algorithm to handle this situation, but it
leaves some important questions open.
The big open question is whether or not the bidirectional edges (that is
indications of a common cause) are the only ones necessary to develop a belief
network out of the product of CI, or must there be some other hidden variables
added (e.g. by guessing). This paper is devoted to settling this question.
| [
{
"version": "v1",
"created": "Mon, 29 May 2017 17:58:13 GMT"
}
] | 1,496,102,400,000 | [
[
"Kłopotek",
"Mieczysław",
""
]
] |
1705.10443 | Victor Silva | Victor do Nascimento Silva and Luiz Chaimowicz | MOBA: a New Arena for Game AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Games have always been popular testbeds for Artificial Intelligence (AI). In
the last decade, we have seen the rise of the Multiple Online Battle Arena
(MOBA) games, which are the most played games nowadays. In spite of this, there
are few works that explore MOBA as a testbed for AI Research. In this paper we
present and discuss the main features and opportunities offered by MOBA games
to Game AI Research. We describe the various challenges faced along the game
and also propose a discrete model that can be used to better understand and
explore the game. With this, we aim to encourage the use of MOBA as a novel
research platform for Game AI.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 03:12:03 GMT"
}
] | 1,496,188,800,000 | [
[
"Silva",
"Victor do Nascimento",
""
],
[
"Chaimowicz",
"Luiz",
""
]
] |
1705.10557 | John Aslanides | John Aslanides, Jan Leike, Marcus Hutter | Universal Reinforcement Learning Algorithms: Survey and Experiments | 8 pages, 6 figures, Twenty-sixth International Joint Conference on
Artificial Intelligence (IJCAI-17) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many state-of-the-art reinforcement learning (RL) algorithms typically assume
that the environment is an ergodic Markov Decision Process (MDP). In contrast,
the field of universal reinforcement learning (URL) is concerned with
algorithms that make as few assumptions as possible about the environment. The
universal Bayesian agent AIXI and a family of related URL algorithms have been
developed in this setting. While numerous theoretical optimality results have
been proven for these agents, there has been no empirical investigation of
their behavior to date. We present a short and accessible survey of these URL
algorithms under a unified notation and framework, along with results of some
experiments that qualitatively illustrate some properties of the resulting
policies, and their relative performance on partially-observable gridworld
environments. We also present an open-source reference implementation of the
algorithms which we hope will facilitate further understanding of, and
experimentation with, these ideas.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 11:41:00 GMT"
}
] | 1,496,188,800,000 | [
[
"Aslanides",
"John",
""
],
[
"Leike",
"Jan",
""
],
[
"Hutter",
"Marcus",
""
]
] |
1705.10720 | Stuart Armstrong | Stuart Armstrong and Benjamin Levinstein | Low Impact Artificial Intelligences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are many goals for an AI that could become dangerous if the AI becomes
superintelligent or otherwise powerful. Much work on the AI control problem has
been focused on constructing AI goals that are safe even for such AIs. This
paper looks at an alternative approach: defining a general concept of `low
impact'. The aim is to ensure that a powerful AI which implements low impact
will not modify the world extensively, even if it is given a simple or
dangerous goal. The paper proposes various ways of defining and grounding low
impact, and discusses methods for ensuring that the AI can still be allowed to
have a (desired) impact despite the restriction. The end of the paper addresses
known issues with this approach and avenues for future research.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 16:15:16 GMT"
}
] | 1,496,188,800,000 | [
[
"Armstrong",
"Stuart",
""
],
[
"Levinstein",
"Benjamin",
""
]
] |
1705.10726 | Naveen Sundar Govindarajulu | Naveen Sundar Govindarajulu, Selmer Bringsjord | Strength Factors: An Uncertainty System for a Quantified Modal Logic | Presented on August 20, 2017 at the Logical Foundations for
Uncertainty and Machine Learning Workshop @ IJCAI 2017 in Melbourne,
Australia | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new system S for handling uncertainty in a quantified modal
logic (first-order modal logic). The system is based on both probability theory
and proof theory. The system is derived from Chisholm's epistemology. We
concretize Chisholm's system by grounding his undefined and primitive (i.e.
foundational) concept of reasonablenes in probability and proof theory. S can
be useful in systems that have to interact with humans and provide
justifications for their uncertainty. As a demonstration of the system, we
apply the system to provide a solution to the lottery paradox. Another
advantage of the system is that it can be used to provide uncertainty values
for counterfactual statements. Counterfactuals are statements that an agent
knows for sure are false. Among other cases, counterfactuals are useful when
systems have to explain their actions to users. Uncertainties for
counterfactuals fall out naturally from our system.
Efficient reasoning in just simple first-order logic is a hard problem.
Resolution-based first-order reasoning systems have made significant progress
over the last several decades in building systems that have solved non-trivial
tasks (even unsolved conjectures in mathematics). We present a sketch of a
novel algorithm for reasoning that extends first-order resolution.
Finally, while there have been many systems of uncertainty for propositional
logics, first-order logics and propositional modal logics, there has been very
little work in building systems of uncertainty for first-order modal logics.
The work described below is in progress; and once finished will address this
lack.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 16:24:18 GMT"
},
{
"version": "v2",
"created": "Mon, 28 May 2018 06:07:18 GMT"
}
] | 1,527,552,000,000 | [
[
"Govindarajulu",
"Naveen Sundar",
""
],
[
"Bringsjord",
"Selmer",
""
]
] |
1705.10834 | Thommen George Karimpanal | Thommen George Karimpanal, Roland Bouffanais | Experience Replay Using Transition Sequences | 23 pages, 6 figures | Frontiers in Neurorobotics 12 (2018) 32 | 10.3389/fnbot.2018.00032 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experience replay is one of the most commonly used approaches to improve the
sample efficiency of reinforcement learning algorithms. In this work, we
propose an approach to select and replay sequences of transitions in order to
accelerate the learning of a reinforcement learning agent in an off-policy
setting. In addition to selecting appropriate sequences, we also artificially
construct transition sequences using information gathered from previous
agent-environment interactions. These sequences, when replayed, allow value
function information to trickle down to larger sections of the
state/state-action space, thereby making the most of the agent's experience. We
demonstrate our approach on modified versions of standard reinforcement
learning tasks such as the mountain car and puddle world problems and
empirically show that it enables better learning of value functions as compared
to other forms of experience replay. Further, we briefly discuss some of the
possible extensions to this work, as well as applications and situations where
this approach could be particularly useful.
| [
{
"version": "v1",
"created": "Tue, 30 May 2017 19:24:09 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Sep 2019 01:13:39 GMT"
}
] | 1,664,409,600,000 | [
[
"Karimpanal",
"Thommen George",
""
],
[
"Bouffanais",
"Roland",
""
]
] |
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