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1803.02018 | Siyuan Qi | Siyuan Qi, Song-Chun Zhu | Intent-aware Multi-agent Reinforcement Learning | ICRA 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes an intent-aware multi-agent planning framework as well as
a learning algorithm. Under this framework, an agent plans in the goal space to
maximize the expected utility. The planning process takes the belief of other
agents' intents into consideration. Instead of formulating the learning problem
as a partially observable Markov decision process (POMDP), we propose a simple
but effective linear function approximation of the utility function. It is
based on the observation that for humans, other people's intents will pose an
influence on our utility for a goal. The proposed framework has several major
advantages: i) it is computationally feasible and guaranteed to converge. ii)
It can easily integrate existing intent prediction and low-level planning
algorithms. iii) It does not suffer from sparse feedbacks in the action space.
We experiment our algorithm in a real-world problem that is non-episodic, and
the number of agents and goals can vary over time. Our algorithm is trained in
a scene in which aerial robots and humans interact, and tested in a novel scene
with a different environment. Experimental results show that our algorithm
achieves the best performance and human-like behaviors emerge during the
dynamic process.
| [
{
"version": "v1",
"created": "Tue, 6 Mar 2018 04:53:50 GMT"
}
] | 1,520,380,800,000 | [
[
"Qi",
"Siyuan",
""
],
[
"Zhu",
"Song-Chun",
""
]
] |
1803.02208 | Yantian Zha | Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati | Discovering Underlying Plans Based on Shallow Models | arXiv admin note: substantial text overlap with arXiv:1511.05662 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally "matching" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming that complete domain models are available. In real world applications,
however, target plans are often not from plan libraries, and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora, we then discover target plans based on the vector representations.
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target
plans based on vector representations of actions. DUP explores the EM-style
framework to capture local contexts of actions and discover target plans by
optimizing the probability of target plans, while RNNPlanner aims to leverage
long-short term contexts of actions based on RNNs (recurrent neural networks)
framework to help recognize target plans. In the experiments, we empirically
show that our approaches are capable of discovering underlying plans that are
not from plan libraries, without requiring domain models provided. We
demonstrate the effectiveness of our approaches by comparing its performance to
traditional plan recognition approaches in three planning domains. We also
compare DUP and RNNPlanner to see their advantages and disadvantages.
| [
{
"version": "v1",
"created": "Sun, 4 Mar 2018 03:18:22 GMT"
}
] | 1,520,380,800,000 | [
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Zha",
"Yantian",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1803.02476 | Sergio Miguel Tom\'e | Sergio Miguel-Tom\'e | Decision-making processes in the Cognitive Theory of True Conditions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Cognitive Theory of True Conditions (CTTC) is a proposal to design the
implementation of cognitive abilities and to describe the model-theoretic
semantics of symbolic cognitive architectures. The CTTC is formulated
mathematically using the multi-optional many-sorted past present future(MMPPF)
structures. This article discussed how decision-making processes are described
in the CTTC.
| [
{
"version": "v1",
"created": "Tue, 6 Mar 2018 23:46:55 GMT"
}
] | 1,520,467,200,000 | [
[
"Miguel-Tomé",
"Sergio",
""
]
] |
1803.02808 | Dilek K\"u\c{c}\"uk | Dilek K\"u\c{c}\"uk and Do\u{g}an K\"u\c{c}\"uk | OntoWind: An Improved and Extended Wind Energy Ontology | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ontologies are critical sources of semantic information for many application
domains. Hence, there are ontologies proposed and utilized for domains such as
medicine, chemical engineering, and electrical energy. In this paper, we
present an improved and extended version of a wind energy ontology previously
proposed. First, the ontology is restructured to increase its understandability
and coverage. Secondly, it is enriched with new concepts, crisp/fuzzy
attributes, and instances to increase its usability in semantic applications
regarding wind energy. The ultimate ontology is utilized within a Web-based
semantic portal application for wind energy, in order to showcase its
contribution in a genuine application. Hence, the current study is a
significant to wind and thereby renewable energy informatics, with the
presented publicly-available wind energy ontology and the implemented
proof-of-concept system.
| [
{
"version": "v1",
"created": "Wed, 7 Mar 2018 18:34:44 GMT"
}
] | 1,520,467,200,000 | [
[
"Küçük",
"Dilek",
""
],
[
"Küçük",
"Doğan",
""
]
] |
1803.02912 | Atrisha Sarkar | Atrisha Sarkar | A Brandom-ian view of Reinforcement Learning towards strong-AI | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analytic philosophy of Robert Brandom, based on the ideas of pragmatism,
paints a picture of sapience, through inferentialism. In this paper, we present
a theory, that utilizes essential elements of Brandom's philosophy, towards the
objective of achieving strong-AI. We do this by connecting the constitutive
elements of reinforcement learning and the Game Of Giving and Asking For
Reasons. Further, following Brandom's prescriptive thoughts, we restructure the
popular reinforcement learning algorithm A3C, and show that RL algorithms can
be tuned towards the objective of strong-AI.
| [
{
"version": "v1",
"created": "Wed, 7 Mar 2018 23:26:49 GMT"
}
] | 1,520,553,600,000 | [
[
"Sarkar",
"Atrisha",
""
]
] |
1803.03021 | Chengwei Zhang | Chengwei Zhang and Xiaohong Li and Jianye Hao and Siqi Chen and Karl
Tuyls and Wanli Xue | SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially
Optimal Outcomes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multiagent environments, the capability of learning is important for an
agent to behave appropriately in face of unknown opponents and dynamic
environment. From the system designer's perspective, it is desirable if the
agents can learn to coordinate towards socially optimal outcomes, while also
avoiding being exploited by selfish opponents. To this end, we propose a novel
gradient ascent based algorithm (SA-IGA) which augments the basic
gradient-ascent algorithm by incorporating social awareness into the policy
update process. We theoretically analyze the learning dynamics of SA-IGA using
dynamical system theory and SA-IGA is shown to have linear dynamics for a wide
range of games including symmetric games. The learning dynamics of two
representative games (the prisoner's dilemma game and the coordination game)
are analyzed in details. Based on the idea of SA-IGA, we further propose a
practical multiagent learning algorithm, called SA-PGA, based on Q-learning
update rule. Simulation results show that SA-PGA agent can achieve higher
social welfare than previous social-optimality oriented Conditional Joint
Action Learner (CJAL) and also is robust against individually rational
opponents by reaching Nash equilibrium solutions.
| [
{
"version": "v1",
"created": "Thu, 8 Mar 2018 10:02:42 GMT"
}
] | 1,520,553,600,000 | [
[
"Zhang",
"Chengwei",
""
],
[
"Li",
"Xiaohong",
""
],
[
"Hao",
"Jianye",
""
],
[
"Chen",
"Siqi",
""
],
[
"Tuyls",
"Karl",
""
],
[
"Xue",
"Wanli",
""
]
] |
1803.03067 | Drew A. Hudson | Drew A. Hudson and Christopher D. Manning | Compositional Attention Networks for Machine Reasoning | Published as a conference paper at ICLR 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the MAC network, a novel fully differentiable neural network
architecture, designed to facilitate explicit and expressive reasoning. MAC
moves away from monolithic black-box neural architectures towards a design that
encourages both transparency and versatility. The model approaches problems by
decomposing them into a series of attention-based reasoning steps, each
performed by a novel recurrent Memory, Attention, and Composition (MAC) cell
that maintains a separation between control and memory. By stringing the cells
together and imposing structural constraints that regulate their interaction,
MAC effectively learns to perform iterative reasoning processes that are
directly inferred from the data in an end-to-end approach. We demonstrate the
model's strength, robustness and interpretability on the challenging CLEVR
dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy,
halving the error rate of the previous best model. More importantly, we show
that the model is computationally-efficient and data-efficient, in particular
requiring 5x less data than existing models to achieve strong results.
| [
{
"version": "v1",
"created": "Thu, 8 Mar 2018 12:37:14 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Apr 2018 10:25:07 GMT"
}
] | 1,524,614,400,000 | [
[
"Hudson",
"Drew A.",
""
],
[
"Manning",
"Christopher D.",
""
]
] |
1803.03114 | Faisal Abu-Khzam | Faisal N. Abu-Khzam, Rana H. Mouawi, Amer Hajj Ahmad and Sergio Thoumi | Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction
Approach | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The enormous amount of data to be represented using large graphs exceeds in
some cases the resources of a conventional computer. Edges in particular can
take up a considerable amount of memory as compared to the number of nodes.
However, rigorous edge storage might not always be essential to be able to draw
the needed conclusions. A similar problem takes records with many variables and
attempts to extract the most discernible features. It is said that the
``dimension'' of this data is reduced. Following an approach with the same
objective in mind, we can map a graph representation to a $k$-dimensional space
and answer queries of neighboring nodes mainly by measuring Euclidean
distances. The accuracy of our answers would decrease but would be compensated
for by fuzzy logic which gives an idea about the likelihood of error. This
method allows for reasonable representation in memory while maintaining a fair
amount of useful information, and allows for concise embedding in
$k$-dimensional Euclidean space as well as solving some problems without having
to decompress the graph. Of particular interest is the case where $k=2$.
Promising highly accurate experimental results are obtained and reported.
| [
{
"version": "v1",
"created": "Thu, 8 Mar 2018 14:44:56 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Dec 2023 16:04:22 GMT"
}
] | 1,702,857,600,000 | [
[
"Abu-Khzam",
"Faisal N.",
""
],
[
"Mouawi",
"Rana H.",
""
],
[
"Ahmad",
"Amer Hajj",
""
],
[
"Thoumi",
"Sergio",
""
]
] |
1803.03407 | Giovanni Sileno | Alexander Boer and Giovanni Sileno | Institutional Metaphors for Designing Large-Scale Distributed AI versus
AI Techniques for Running Institutions | invited chapter, before proofread | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) started out with an ambition to reproduce the
human mind, but, as the sheer scale of that ambition became manifest, it
quickly retreated into either studying specialized intelligent behaviours, or
proposing over-arching architectural concepts for interfacing specialized
intelligent behaviour components, conceived of as agents in a kind of
organization. This agent-based modeling paradigm, in turn, proves to have
interesting applications in understanding, simulating, and predicting the
behaviour of social and legal structures on an aggregate level. For these
reasons, this chapter examines a number of relevant cross-cutting concerns,
conceptualizations, modeling problems and design challenges in large-scale
distributed Artificial Intelligence, as well as in institutional systems, and
identifies potential grounds for novel advances.
| [
{
"version": "v1",
"created": "Fri, 9 Mar 2018 07:59:21 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Jun 2021 19:11:23 GMT"
}
] | 1,623,888,000,000 | [
[
"Boer",
"Alexander",
""
],
[
"Sileno",
"Giovanni",
""
]
] |
1803.03479 | Maarten Bieshaar | Maarten Bieshaar and G\"unther Reitberger and Viktor Kre{\ss} and
Stefan Zernetsch and Konrad Doll and Erich Fuchs and Bernhard Sick | Highly Automated Learning for Improved Active Safety of Vulnerable Road
Users | 4 pages, 1 figure | published in ACM Chapters Computer Science in Cars Symposium
(CSCS-17). Munich, Germany. 2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Highly automated driving requires precise models of traffic participants.
Many state of the art models are currently based on machine learning
techniques. Among others, the required amount of labeled data is one major
challenge. An autonomous learning process addressing this problem is proposed.
The initial models are iteratively refined in three steps: (1) detection and
context identification, (2) novelty detection and active learning and (3)
online model adaption.
| [
{
"version": "v1",
"created": "Fri, 9 Mar 2018 11:57:36 GMT"
}
] | 1,520,812,800,000 | [
[
"Bieshaar",
"Maarten",
""
],
[
"Reitberger",
"Günther",
""
],
[
"Kreß",
"Viktor",
""
],
[
"Zernetsch",
"Stefan",
""
],
[
"Doll",
"Konrad",
""
],
[
"Fuchs",
"Erich",
""
],
[
"Sick",
"Bernhard",
""
]
] |
1803.03834 | Paul Smolensky | Roland Fernandez, Asli Celikyilmaz, Rishabh Singh, Paul Smolensky | Learning and analyzing vector encoding of symbolic representations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a formal language with expressions denoting general symbol
structures and queries which access information in those structures. A
sequence-to-sequence network processing this language learns to encode symbol
structures and query them. The learned representation (approximately) shares a
simple linearity property with theoretical techniques for performing this task.
| [
{
"version": "v1",
"created": "Sat, 10 Mar 2018 16:44:58 GMT"
}
] | 1,520,899,200,000 | [
[
"Fernandez",
"Roland",
""
],
[
"Celikyilmaz",
"Asli",
""
],
[
"Singh",
"Rishabh",
""
],
[
"Smolensky",
"Paul",
""
]
] |
1803.04263 | Gagan Bansal | Daniel S. Weld and Gagan Bansal | The Challenge of Crafting Intelligible Intelligence | arXiv admin note: text overlap with arXiv:1603.08507 by other authors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since Artificial Intelligence (AI) software uses techniques like deep
lookahead search and stochastic optimization of huge neural networks to fit
mammoth datasets, it often results in complex behavior that is difficult for
people to understand. Yet organizations are deploying AI algorithms in many
mission-critical settings. To trust their behavior, we must make AI
intelligible, either by using inherently interpretable models or by developing
new methods for explaining and controlling otherwise overwhelmingly complex
decisions using local approximation, vocabulary alignment, and interactive
explanation. This paper argues that intelligibility is essential, surveys
recent work on building such systems, and highlights key directions for
research.
| [
{
"version": "v1",
"created": "Fri, 9 Mar 2018 06:38:55 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Jul 2018 00:31:25 GMT"
},
{
"version": "v3",
"created": "Mon, 15 Oct 2018 06:10:30 GMT"
}
] | 1,539,648,000,000 | [
[
"Weld",
"Daniel S.",
""
],
[
"Bansal",
"Gagan",
""
]
] |
1803.04994 | Subhash Kak | Subhash Kak | On the Algebra in Boole's Laws of Thought | 11 pages | Current Science, vol.. 114, pp. 2570-2573, 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article explores the ideas that went into George Boole's development of
an algebra for logical inference in his book The Laws of Thought. We explore in
particular his wife Mary Boole's claim that he was deeply influenced by Indian
logic and argue that his work was more than a framework for processing
propositions. By exploring parallels between his work and Indian logic, we are
able to explain several peculiarities of this work.
| [
{
"version": "v1",
"created": "Tue, 13 Mar 2018 18:13:08 GMT"
}
] | 1,597,104,000,000 | [
[
"Kak",
"Subhash",
""
]
] |
1803.05027 | Mohamed El Halaby | Mohamed El Halaby | Solving the Course-timetabling Problem of Cairo University Using Max-SAT | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the good performance of current SAT (satisfiability) and Max-SAT
(maximum ssatisfiability) solvers, many real-life optimization problems such as
scheduling can be solved by encoding them into Max-SAT. In this paper we tackle
the course timetabling problem of the department of mathematics, Cairo
University by encoding it into Max-SAT. Generating timetables for the
department by hand has proven to be cumbersome and the generated timetable
almost always contains conflicts. We show how the constraints can be modelled
as a Max-SAT instance.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2018 23:40:25 GMT"
}
] | 1,521,072,000,000 | [
[
"Halaby",
"Mohamed El",
""
]
] |
1803.05049 | Sergio Hernandez | Sergio Hernandez Cerezo and Guillem Duran Ballester | Fractal AI: A fragile theory of intelligence | 57 pages, python code on https://github.com/FragileTheory/FractalAI,
V4: typo in formula at 2.2.3, V4.1 typo in pseudocode at 4.3 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Fractal AI is a theory for general artificial intelligence. It allows
deriving new mathematical tools that constitute the foundations for a new kind
of stochastic calculus, by modelling information using cellular automaton-like
structures instead of smooth functions. In the repository included we are
presenting a new Agent, derived from the first principles of the theory, which
is capable of solving Atari games several orders of magnitude more efficiently
than other similar techniques, like Monte Carlo Tree Search. The code provided
shows how it is now possible to beat some of the current State of The Art
benchmarks on Atari games, without previous learning and using less than 1000
samples to calculate each one of the actions when standard MCTS uses 3 Million
samples. Among other things, Fractal AI makes it possible to generate a huge
database of top performing examples with a very little amount of computation
required, transforming Reinforcement Learning into a supervised problem. The
algorithm presented is capable of solving the exploration vs exploitation
dilemma on both the discrete and continuous cases, while maintaining control
over any aspect of the behaviour of the Agent. From a general approach, new
techniques presented here have direct applications to other areas such as
Non-equilibrium thermodynamics, chemistry, quantum physics, economics,
information theory, and non-linear control theory.
| [
{
"version": "v1",
"created": "Tue, 13 Mar 2018 21:17:26 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Jun 2018 11:46:15 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Jul 2018 10:54:18 GMT"
},
{
"version": "v4",
"created": "Mon, 9 Dec 2019 15:11:29 GMT"
},
{
"version": "v5",
"created": "Thu, 30 Jul 2020 09:52:44 GMT"
}
] | 1,596,153,600,000 | [
[
"Cerezo",
"Sergio Hernandez",
""
],
[
"Ballester",
"Guillem Duran",
""
]
] |
1803.05156 | Matthew Stephenson | Matthew Stephenson, Jochen Renz, Xiaoyu Ge, Peng Zhang | The 2017 AIBIRDS Competition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an overview of the sixth AIBIRDS competition, held at the
26th International Joint Conference on Artificial Intelligence. This
competition tasked participants with developing an intelligent agent which can
play the physics-based puzzle game Angry Birds. This game uses a sophisticated
physics engine that requires agents to reason and predict the outcome of
actions with only limited environmental information. Agents entered into this
competition were required to solve a wide assortment of previously unseen
levels within a set time limit. The physical reasoning and planning required to
solve these levels are very similar to those of many real-world problems. This
year's competition featured some of the best agents developed so far and even
included several new AI techniques such as deep reinforcement learning. Within
this paper we describe the framework, rules, submitted agents and results for
this competition. We also provide some background information on related work
and other video game AI competitions, as well as discussing some potential
ideas for future AIBIRDS competitions and agent improvements.
| [
{
"version": "v1",
"created": "Wed, 14 Mar 2018 07:53:31 GMT"
}
] | 1,521,072,000,000 | [
[
"Stephenson",
"Matthew",
""
],
[
"Renz",
"Jochen",
""
],
[
"Ge",
"Xiaoyu",
""
],
[
"Zhang",
"Peng",
""
]
] |
1803.05760 | Boliang Lin | Boliang Lin | A Study of Car-to-Train Assignment Problem for Rail Express Cargos on
Scheduled and Unscheduled Train Service Network | 12 pages, 1 figure | null | 10.1371/journal.pone.0204598 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Freight train services in a railway network system are generally divided into
two categories: one is the unscheduled train, whose operating frequency
fluctuates with origin-destination (OD) demands; the other is the scheduled
train, which is running based on regular timetable just like the passenger
trains. The timetable will be released to the public if determined and it would
not be influenced by OD demands. Typically, the total capacity of scheduled
trains can usually satisfy the predicted demands of express cargos in average.
However, the demands are changing in practice. Therefore, how to distribute the
shipments between different stations to unscheduled and scheduled train
services has become an important research field in railway transportation. This
paper focuses on the coordinated optimization of the rail express cargos
distribution in two service networks. On the premise of fully utilizing the
capacity of scheduled service network first, we established a Car-to-Train
(CTT) assignment model to assign rail express cargos to scheduled and
unscheduled trains scientifically. The objective function is to maximize the
net income of transporting the rail express cargos. The constraints include the
capacity restriction on the service arcs, flow balance constraints, logical
relationship constraint between two groups of decision variables and the due
date constraint. The last constraint is to ensure that the total transportation
time of a shipment would not be longer than its predefined due date. Finally,
we discuss the linearization techniques to simplify the model proposed in this
paper, which make it possible for obtaining global optimal solution by using
the commercial software.
| [
{
"version": "v1",
"created": "Wed, 14 Mar 2018 07:32:14 GMT"
}
] | 1,542,758,400,000 | [
[
"Lin",
"Boliang",
""
]
] |
1803.06422 | Marco Valtorta | Othar Hansson and Andrew Mayer and Marco Valtorta | A New Result on the Complexity of Heuristic Estimates for the A*
Algorithm | null | Artificial Intelligence, 55, 1 (May 1992), 129-143 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relaxed models are abstract problem descriptions generated by ignoring
constraints that are present in base-level problems. They play an important
role in planning and search algorithms, as it has been shown that the length of
an optimal solution to a relaxed model yields a monotone heuristic for an A?
search of a base-level problem. Optimal solutions to a relaxed model may be
computed algorithmically or by search in a further relaxed model, leading to a
search that explores a hierarchy of relaxed models. In this paper, we review
the traditional definition of problem relaxation and show that searching in the
abstraction hierarchy created by problem relaxation will not reduce the
computational effort required to find optimal solutions to the base- level
problem, unless the relaxed problem found in the hierarchy can be transformed
by some optimization (e.g., subproblem factoring). Specifically, we prove that
any A* search of the base-level using a heuristic h2 will largely dominate an
A* search of the base-level using a heuristic h1, if h1 must be computed by an
A* search of the relaxed model using h2.
| [
{
"version": "v1",
"created": "Fri, 16 Mar 2018 22:57:32 GMT"
}
] | 1,521,504,000,000 | [
[
"Hansson",
"Othar",
""
],
[
"Mayer",
"Andrew",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1803.07131 | Niels Justesen | Niels Justesen, Sebastian Risi | Automated Curriculum Learning by Rewarding Temporally Rare Events | 8 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reward shaping allows reinforcement learning (RL) agents to accelerate
learning by receiving additional reward signals. However, these signals can be
difficult to design manually, especially for complex RL tasks. We propose a
simple and general approach that determines the reward of pre-defined events by
their rarity alone. Here events become less rewarding as they are experienced
more often, which encourages the agent to continually explore new types of
events as it learns. The adaptiveness of this reward function results in a form
of automated curriculum learning that does not have to be specified by the
experimenter. We demonstrate that this \emph{Rarity of Events} (RoE) approach
enables the agent to succeed in challenging VizDoom scenarios without access to
the extrinsic reward from the environment. Furthermore, the results demonstrate
that RoE learns a more versatile policy that adapts well to critical changes in
the environment. Rewarding events based on their rarity could help in many
unsolved RL environments that are characterized by sparse extrinsic rewards but
a plethora of known event types.
| [
{
"version": "v1",
"created": "Mon, 19 Mar 2018 19:35:44 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Jun 2018 12:11:35 GMT"
}
] | 1,528,675,200,000 | [
[
"Justesen",
"Niels",
""
],
[
"Risi",
"Sebastian",
""
]
] |
1803.08625 | Kuo-Kai Hsieh | Kuo-Kai Hsieh and Li-C. Wang | A Concept Learning Tool Based On Calculating Version Space Cardinality | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper, we proposed VeSC-CoL (Version Space Cardinality based Concept
Learning) to deal with concept learning on extremely imbalanced datasets,
especially when cross-validation is not a viable option. VeSC-CoL uses version
space cardinality as a measure for model quality to replace cross-validation.
Instead of naive enumeration of the version space, Ordered Binary Decision
Diagram and Boolean Satisfiability are used to compute the version space.
Experiments show that VeSC-CoL can accurately learn the target concept when
computational resource is allowed.
| [
{
"version": "v1",
"created": "Fri, 23 Mar 2018 01:11:01 GMT"
}
] | 1,522,022,400,000 | [
[
"Hsieh",
"Kuo-Kai",
""
],
[
"Wang",
"Li-C.",
""
]
] |
1803.08857 | Nicola Pellicano | Nicola Pellican\`o, Sylvie Le H\'egarat-Mascle, Emanuel Aldea | 2CoBel : An Efficient Belief Function Extension for Two-dimensional
Continuous Spaces | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces an innovative approach for handling 2D compound
hypotheses within the Belief Function Theory framework. We propose a
polygon-based generic rep- resentation which relies on polygon clipping
operators. This approach allows us to account in the computational cost for the
precision of the representation independently of the cardinality of the
discernment frame. For the BBA combination and decision making, we propose
efficient algorithms which rely on hashes for fast lookup, and on a topological
ordering of the focal elements within a directed acyclic graph encoding their
interconnections. Additionally, an implementation of the functionalities
proposed in this paper is provided as an open source library. Experimental
results on a pedestrian localization problem are reported. The experiments show
that the solution is accurate and that it fully benefits from the scalability
of the 2D search space granularity provided by our representation.
| [
{
"version": "v1",
"created": "Fri, 23 Mar 2018 16:05:07 GMT"
}
] | 1,522,022,400,000 | [
[
"Pellicanò",
"Nicola",
""
],
[
"Hégarat-Mascle",
"Sylvie Le",
""
],
[
"Aldea",
"Emanuel",
""
]
] |
1803.08885 | Laura Giordano | Laura Giordano, Daniele Theseider Dupr\'e | Defeasible Reasoning in SROEL: from Rational Entailment to Rational
Closure | Accepted for publication on Fundamenta Informaticae | Fundamenta Informaticae, vol. 161, no. 1-2, pp. 135-161, 2018, IOS
Press | 10.3233/FI-2018-1698 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we study a rational extension $SROEL^R T$ of the low complexity
description logic SROEL, which underlies the OWL EL ontology language. The
extension involves a typicality operator T, whose semantics is based on Lehmann
and Magidor's ranked models and allows for the definition of defeasible
inclusions. We consider both rational entailment and minimal entailment. We
show that deciding instance checking under minimal entailment is in general
$\Pi^P_2$-hard, while, under rational entailment, instance checking can be
computed in polynomial time. We develop a Datalog calculus for instance
checking under rational entailment and exploit it, with stratified negation,
for computing the rational closure of simple KBs in polynomial time.
| [
{
"version": "v1",
"created": "Fri, 23 Mar 2018 17:06:02 GMT"
}
] | 1,539,648,000,000 | [
[
"Giordano",
"Laura",
""
],
[
"Dupré",
"Daniele Theseider",
""
]
] |
1803.09789 | Biplav Srivastava | Biplav Srivastava | On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments | 3 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversation interfaces (CIs), or chatbots, are a popular form of intelligent
agents that engage humans in task-oriented or informal conversation. In this
position paper and demonstration, we argue that chatbots working in dynamic
environments, like with sensor data, can not only serve as a promising platform
to research issues at the intersection of learning, reasoning, representation
and execution for goal-directed autonomy; but also handle non-trivial business
applications. We explore the underlying issues in the context of Water Advisor,
a preliminary multi-modal conversation system that can access and explain water
quality data.
| [
{
"version": "v1",
"created": "Mon, 26 Mar 2018 18:51:33 GMT"
}
] | 1,522,195,200,000 | [
[
"Srivastava",
"Biplav",
""
]
] |
1803.10648 | Luis A. Pineda | Luis A. Pineda | A Distributed Extension of the Turing Machine | 37 pages, 15 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Turing Machine has two implicit properties that depend on its underlying
notion of computing: the format is fully determinate and computations are
information preserving. Distributed representations lack these properties and
cannot be fully captured by Turing's standard model. To address this limitation
a distributed extension of the Turing Machine is introduced in this paper. In
the extended machine, functions and abstractions are expressed extensionally
and computations are entropic. The machine is applied to the definition of an
associative memory, with its corresponding memory register, recognition and
retrieval operations. The memory is tested with an experiment for storing and
recognizing hand written digits with satisfactory results. The experiment can
be seen as a proof of concept that information can be stored and processed
effectively in a highly distributed fashion using a symbolic but not fully
determinate format. The new machine augments the symbolic mode of computing
with consequences on the way Church Thesis is understood. The paper is
concluded with a discussion of some implications of the extended machine for
Artificial Intelligence and Cognition.
| [
{
"version": "v1",
"created": "Wed, 28 Mar 2018 14:36:54 GMT"
}
] | 1,522,281,600,000 | [
[
"Pineda",
"Luis A.",
""
]
] |
1803.10813 | Daniele Ravi' | Javier Andreu-Perez, Fani Deligianni, Daniele Ravi and Guang-Zhong
Yang | Artificial Intelligence and Robotics | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent successes of AI have captured the wildest imagination of both the
scientific communities and the general public. Robotics and AI amplify human
potentials, increase productivity and are moving from simple reasoning towards
human-like cognitive abilities. Current AI technologies are used in a set area
of applications, ranging from healthcare, manufacturing, transport, energy, to
financial services, banking, advertising, management consulting and government
agencies. The global AI market is around 260 billion USD in 2016 and it is
estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is
important to draw lessons from it's past successes and failures and this white
paper provides a comprehensive explanation of the evolution of AI, its current
status and future directions.
| [
{
"version": "v1",
"created": "Wed, 28 Mar 2018 19:11:24 GMT"
}
] | 1,596,758,400,000 | [
[
"Andreu-Perez",
"Javier",
""
],
[
"Deligianni",
"Fani",
""
],
[
"Ravi",
"Daniele",
""
],
[
"Yang",
"Guang-Zhong",
""
]
] |
1803.10981 | Peter Nightingale | Ian P. Gent and Ciaran McCreesh and Ian Miguel and Neil C.A. Moore and
Peter Nightingale and Patrick Prosser and Chris Unsworth | A Review of Literature on Parallel Constraint Solving | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As multicore computing is now standard, it seems irresponsible for
constraints researchers to ignore the implications of it. Researchers need to
address a number of issues to exploit parallelism, such as: investigating which
constraint algorithms are amenable to parallelisation; whether to use shared
memory or distributed computation; whether to use static or dynamic
decomposition; and how to best exploit portfolios and cooperating search. We
review the literature, and see that we can sometimes do quite well, some of the
time, on some instances, but we are far from a general solution. Yet there
seems to be little overall guidance that can be given on how best to exploit
multicore computers to speed up constraint solving. We hope at least that this
survey will provide useful pointers to future researchers wishing to correct
this situation.
Under consideration in Theory and Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Thu, 29 Mar 2018 09:34:09 GMT"
}
] | 1,522,368,000,000 | [
[
"Gent",
"Ian P.",
""
],
[
"McCreesh",
"Ciaran",
""
],
[
"Miguel",
"Ian",
""
],
[
"Moore",
"Neil C. A.",
""
],
[
"Nightingale",
"Peter",
""
],
[
"Prosser",
"Patrick",
""
],
[
"Unsworth",
"Chris",
""
]
] |
1803.11437 | Haris Aziz | Haris Aziz | A Rule for Committee Selection with Soft Diversity Constraints | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Committee selection with diversity or distributional constraints is a
ubiquitous problem. However, many of the formal approaches proposed so far have
certain drawbacks including (1) computationally intractability in general, and
(2) inability to suggest a solution for certain instances where the hard
constraints cannot be met. We propose a practical and polynomial-time algorithm
for diverse committee selection that draws on the idea of using soft bounds and
satisfies natural axioms.
| [
{
"version": "v1",
"created": "Fri, 30 Mar 2018 12:36:36 GMT"
}
] | 1,522,627,200,000 | [
[
"Aziz",
"Haris",
""
]
] |
1804.00168 | Piotr Mirowski | Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz
Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu,
Andrew Zisserman, Raia Hadsell | Learning to Navigate in Cities Without a Map | 17 pages, 16 figures, published at NeurIPS 2018 | Neural Information Processing Systems 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Navigating through unstructured environments is a basic capability of
intelligent creatures, and thus is of fundamental interest in the study and
development of artificial intelligence. Long-range navigation is a complex
cognitive task that relies on developing an internal representation of space,
grounded by recognisable landmarks and robust visual processing, that can
simultaneously support continuous self-localisation ("I am here") and a
representation of the goal ("I am going there"). Building upon recent research
that applies deep reinforcement learning to maze navigation problems, we
present an end-to-end deep reinforcement learning approach that can be applied
on a city scale. Recognising that successful navigation relies on integration
of general policies with locale-specific knowledge, we propose a dual pathway
architecture that allows locale-specific features to be encapsulated, while
still enabling transfer to multiple cities. We present an interactive
navigation environment that uses Google StreetView for its photographic content
and worldwide coverage, and demonstrate that our learning method allows agents
to learn to navigate multiple cities and to traverse to target destinations
that may be kilometres away. The project webpage http://streetlearn.cc contains
a video summarising our research and showing the trained agent in diverse city
environments and on the transfer task, the form to request the StreetLearn
dataset and links to further resources. The StreetLearn environment code is
available at https://github.com/deepmind/streetlearn
| [
{
"version": "v1",
"created": "Sat, 31 Mar 2018 12:58:12 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Apr 2018 11:14:06 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Jan 2019 00:37:15 GMT"
}
] | 1,547,164,800,000 | [
[
"Mirowski",
"Piotr",
""
],
[
"Grimes",
"Matthew Koichi",
""
],
[
"Malinowski",
"Mateusz",
""
],
[
"Hermann",
"Karl Moritz",
""
],
[
"Anderson",
"Keith",
""
],
[
"Teplyashin",
"Denis",
""
],
[
"Simonyan",
"Karen",
""
],
[
"Kavukcuoglu",
"Koray",
""
],
[
"Zisserman",
"Andrew",
""
],
[
"Hadsell",
"Raia",
""
]
] |
1804.00198 | {\L}ukasz Kidzi\'nski | {\L}ukasz Kidzi\'nski, Sharada P. Mohanty, Carmichael Ong, Jennifer L.
Hicks, Sean F. Carroll, Sergey Levine, Marcel Salath\'e, Scott L. Delp | Learning to Run challenge: Synthesizing physiologically accurate motion
using deep reinforcement learning | 16 pages, 8 figures, a competition at NIPS 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthesizing physiologically-accurate human movement in a variety of
conditions can help practitioners plan surgeries, design experiments, or
prototype assistive devices in simulated environments, reducing time and costs
and improving treatment outcomes. Because of the large and complex solution
spaces of biomechanical models, current methods are constrained to specific
movements and models, requiring careful design of a controller and hindering
many possible applications. We sought to discover if modern optimization
methods efficiently explore these complex spaces. To do this, we posed the
problem as a competition in which participants were tasked with developing a
controller to enable a physiologically-based human model to navigate a complex
obstacle course as quickly as possible, without using any experimental data.
They were provided with a human musculoskeletal model and a physics-based
simulation environment. In this paper, we discuss the design of the
competition, technical difficulties, results, and analysis of the top
controllers. The challenge proved that deep reinforcement learning techniques,
despite their high computational cost, can be successfully employed as an
optimization method for synthesizing physiologically feasible motion in
high-dimensional biomechanical systems.
| [
{
"version": "v1",
"created": "Sat, 31 Mar 2018 17:56:28 GMT"
}
] | 1,522,713,600,000 | [
[
"Kidziński",
"Łukasz",
""
],
[
"Mohanty",
"Sharada P.",
""
],
[
"Ong",
"Carmichael",
""
],
[
"Hicks",
"Jennifer L.",
""
],
[
"Carroll",
"Sean F.",
""
],
[
"Levine",
"Sergey",
""
],
[
"Salathé",
"Marcel",
""
],
[
"Delp",
"Scott L.",
""
]
] |
1804.00211 | Lakhdar Sais | Abdelhamid Boudane, Said Jabbour, Badran Raddaoui, and Lakhdar Sais | Efficient Encodings of Conditional Cardinality Constraints | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the encoding of many real-world problems to propositional satisfiability,
the cardinality constraint is a recurrent constraint that needs to be managed
effectively. Several efficient encodings have been proposed while missing that
such a constraint can be involved in a more general propositional formulation.
To avoid combinatorial explosion, Tseitin principle usually used to translate
such general propositional formula to Conjunctive Normal Form (CNF), introduces
fresh propositional variables to represent sub-formulas and/or complex
contraints. Thanks to Plaisted and Greenbaum improvement, the polarity of the
sub-formula $\Phi$ is taken into account leading to conditional constraints of
the form $y\rightarrow \Phi$, or $\Phi\rightarrow y$, where $y$ is a fresh
propositional variable. In the case where $\Phi$ represents a cardinality
constraint, such translation leads to conditional cardinality constraints
subject of the present paper. We first show that when all the clauses encoding
the cardinality constraint are augmented with an additional new variable, most
of the well-known encodings cease to maintain the generalized arc consistency
property. Then, we consider some of these encodings and show how they can be
extended to recover such important property. An experimental validation is
conducted on a SAT-based pattern mining application, where such conditional
cardinality constraints is a cornerstone, showing the relevance of our proposed
approach.
| [
{
"version": "v1",
"created": "Sat, 31 Mar 2018 20:29:07 GMT"
}
] | 1,522,713,600,000 | [
[
"Boudane",
"Abdelhamid",
""
],
[
"Jabbour",
"Said",
""
],
[
"Raddaoui",
"Badran",
""
],
[
"Sais",
"Lakhdar",
""
]
] |
1804.00421 | Michael Gr. Voskoglou Prof. Dr. | Michael Gr. Voskoglou | A Study of Student Learning Skills Using Fuzzy Relation Equations | 8 pages, 1 Table | Egyptian Computer Science Journal, 42(1), 80-87, 2018 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fuzzy relation equations (FRE)are associated with the composition of binary
fuzzy relations. In the present work FRE are used as a tool for studying the
process of learning a new subject matter by a student class. A classroom
application and other csuitable examples connected to the student learning of
the derivative are also presented illustrating our results and useful
conclusions are obtained.
| [
{
"version": "v1",
"created": "Mon, 2 Apr 2018 07:31:34 GMT"
}
] | 1,522,713,600,000 | [
[
"Voskoglou",
"Michael Gr.",
""
]
] |
1804.00423 | Michael Gr. Voskoglou Prof. Dr. | Michael Gr. Voskoglou, Yiannis Theodorou | Application of Grey Numbers to Assessment Processes | null | International Journal of Applications of Fuzzy Sets and Artificial
Intelligence, 7, 273-280, 2017 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theory of grey systems plays an important role in science,engineering and
in the everyday life in general for handling approximate data. In the present
paper grey numbers are used as a tool for assessing with linguistic expressions
the mean performance of a group of objects participating in a certain activity.
Two applications to student and football player assessment are also presented
illustrating our results.
| [
{
"version": "v1",
"created": "Mon, 2 Apr 2018 07:45:55 GMT"
}
] | 1,522,713,600,000 | [
[
"Voskoglou",
"Michael Gr.",
""
],
[
"Theodorou",
"Yiannis",
""
]
] |
1804.00595 | Thibault Gauthier | Thibault Gauthier, Cezary Kaliszyk, Josef Urban | Learning to Reason with HOL4 tactics | LPAR-21. 21st International Conference on Logic for Programming,
Artificial Intelligence and Reasoning. EasyChair 2017 | null | 10.29007/ntlb | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Techniques combining machine learning with translation to automated reasoning
have recently become an important component of formal proof assistants. Such
"hammer" tech- niques complement traditional proof assistant automation as
implemented by tactics and decision procedures. In this paper we present a
unified proof assistant automation approach which attempts to automate the
selection of appropriate tactics and tactic-sequences com- bined with an
optimized small-scale hammering approach. We implement the technique as a
tactic-level automation for HOL4: TacticToe. It implements a modified
A*-algorithm directly in HOL4 that explores different tactic-level proof paths,
guiding their selection by learning from a large number of previous
tactic-level proofs. Unlike the existing hammer methods, TacticToe avoids
translation to FOL, working directly on the HOL level. By combining tactic
prediction and premise selection, TacticToe is able to re-prove 39 percent of
7902 HOL4 theorems in 5 seconds whereas the best single HOL(y)Hammer strategy
solves 32 percent in the same amount of time.
| [
{
"version": "v1",
"created": "Mon, 2 Apr 2018 15:41:09 GMT"
}
] | 1,522,713,600,000 | [
[
"Gauthier",
"Thibault",
""
],
[
"Kaliszyk",
"Cezary",
""
],
[
"Urban",
"Josef",
""
]
] |
1804.01128 | Luis Piloto | Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg
Wayne, David Amos, Chia-chun Hung, Matt Botvinick | Probing Physics Knowledge Using Tools from Developmental Psychology | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to build agents with a rich understanding of their environment, one
key objective is to endow them with a grasp of intuitive physics; an ability to
reason about three-dimensional objects, their dynamic interactions, and
responses to forces. While some work on this problem has taken the approach of
building in components such as ready-made physics engines, other research aims
to extract general physical concepts directly from sensory data. In the latter
case, one challenge that arises is evaluating the learning system. Research on
intuitive physics knowledge in children has long employed a violation of
expectations (VOE) method to assess children's mastery of specific physical
concepts. We take the novel step of applying this method to artificial learning
systems. In addition to introducing the VOE technique, we describe a set of
probe datasets inspired by classic test stimuli from developmental psychology.
We test a baseline deep learning system on this battery, as well as on a
physics learning dataset ("IntPhys") recently posed by another research group.
Our results show how the VOE technique may provide a useful tool for tracking
physics knowledge in future research.
| [
{
"version": "v1",
"created": "Tue, 3 Apr 2018 18:47:46 GMT"
}
] | 1,522,886,400,000 | [
[
"Piloto",
"Luis",
""
],
[
"Weinstein",
"Ari",
""
],
[
"TB",
"Dhruva",
""
],
[
"Ahuja",
"Arun",
""
],
[
"Mirza",
"Mehdi",
""
],
[
"Wayne",
"Greg",
""
],
[
"Amos",
"David",
""
],
[
"Hung",
"Chia-chun",
""
],
[
"Botvinick",
"Matt",
""
]
] |
1804.01193 | Bart Jacobs | Bart Jacobs, Fabio Zanasi | The Logical Essentials of Bayesian Reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter offers an accessible introduction to the channel-based approach
to Bayesian probability theory. This framework rests on algebraic and logical
foundations, inspired by the methodologies of programming language semantics.
It offers a uniform, structured and expressive language for describing Bayesian
phenomena in terms of familiar programming concepts, like channel, predicate
transformation and state transformation. The introduction also covers inference
in Bayesian networks, which will be modelled by a suitable calculus of string
diagrams.
| [
{
"version": "v1",
"created": "Tue, 3 Apr 2018 23:55:41 GMT"
},
{
"version": "v2",
"created": "Fri, 27 Apr 2018 16:49:41 GMT"
}
] | 1,525,046,400,000 | [
[
"Jacobs",
"Bart",
""
],
[
"Zanasi",
"Fabio",
""
]
] |
1804.02393 | Lucas Bechberger | Lucas Bechberger and Kai-Uwe K\"uhnberger | Formal Ways for Measuring Relations between Concepts in Conceptual
Spaces | Submitted to a special issue of the Journal "Expert Systems"
(https://onlinelibrary.wiley.com/journal/14680394). arXiv admin note:
substantial text overlap with arXiv:1707.02292, arXiv:1801.03929,
arXiv:1707.05165, arXiv:1708.05263, arXiv:1706.06366 | null | 10.1111/exsy.12348 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
In this article, we extend our recent mathematical formalization of this
framework by providing quantitative mathematical definitions for measuring
relations between concepts: We develop formal ways for computing concept size,
subsethood, implication, similarity, and betweenness. This considerably
increases the representational capabilities of our formalization and makes it
the most thorough and comprehensive formalization of conceptual spaces
developed so far.
| [
{
"version": "v1",
"created": "Fri, 6 Apr 2018 13:06:01 GMT"
}
] | 1,542,240,000,000 | [
[
"Bechberger",
"Lucas",
""
],
[
"Kühnberger",
"Kai-Uwe",
""
]
] |
1804.02422 | Fabrizio Maria Maggi | Chiara Di Francescomarino and Chiara Ghidini and Fabrizio Maria Maggi
and Fredrik Milani | Predictive Process Monitoring Methods: Which One Suits Me Best? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques.
| [
{
"version": "v1",
"created": "Fri, 6 Apr 2018 18:45:54 GMT"
}
] | 1,523,318,400,000 | [
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Milani",
"Fredrik",
""
]
] |
1804.02573 | Sankalp Arora | Sankalp Arora, Sanjiban Choudhury and Sebastian Scherer | Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP
Solvers for Multi-resolution Information Gathering | 6 pages, 1 figure | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Partially Observable Markov Decision Processes (POMDPs) offer an elegant
framework to model sequential decision making in uncertain environments.
Solving POMDPs online is an active area of research and given the size of
real-world problems approximate solvers are used. Recently, a few approaches
have been suggested for solving POMDPs by using MDP solvers in conjunction with
imitation learning. MDP based POMDP solvers work well for some cases, while
catastrophically failing for others. The main failure point of such solvers is
the lack of motivation for MDP solvers to gain information, since under their
assumption the environment is either already known as much as it can be or the
uncertainty will disappear after the next step. However for solving POMDP
problems gaining information can lead to efficient solutions. In this paper we
derive a set of conditions where MDP based POMDP solvers are provably
sub-optimal. We then use the well-known tiger problem to demonstrate such
sub-optimality. We show that multi-resolution, budgeted information gathering
cannot be addressed using MDP based POMDP solvers. The contribution of the
paper helps identify the properties of a POMDP problem for which the use of MDP
based POMDP solvers is inappropriate, enabling better design choices.
| [
{
"version": "v1",
"created": "Sat, 7 Apr 2018 16:27:33 GMT"
}
] | 1,523,318,400,000 | [
[
"Arora",
"Sankalp",
""
],
[
"Choudhury",
"Sanjiban",
""
],
[
"Scherer",
"Sebastian",
""
]
] |
1804.02759 | Subhash Kak | Subhash Kak | Order Effects for Queries in Intelligent Systems | 11 pages; 5 figures | null | null | Plenary Lecture, TSC2018 (East-West Forum), Tucson, April 2, 2018 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines common assumptions regarding the decision-making internal
environment for intelligent agents and investigates issues related to
processing of memory and belief states to help obtain better understanding of
the responses. In specific, we consider order effects and discuss both
classical and non-classical explanations for them. We also consider implicit
cognition and explore if certain inaccessible states may be best modeled as
quantum states. We propose that the hypothesis that quantum states are at the
basis of order effects be tested on large databases such as those related to
medical treatment and drug efficacy. A problem involving a maze network is
considered and comparisons made between classical and quantum decision
scenarios for it.
| [
{
"version": "v1",
"created": "Sun, 8 Apr 2018 21:18:55 GMT"
}
] | 1,523,318,400,000 | [
[
"Kak",
"Subhash",
""
]
] |
1804.03301 | Daniel Buehrer | Daniel J. Buehrer | A Mathematical Framework for Superintelligent Machines | submitted to IEEE Access | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a class calculus that is expressive enough to describe and
improve its own learning process. It can design and debug programs that satisfy
given input/output constraints, based on its ontology of previously learned
programs. It can improve its own model of the world by checking the actual
results of the actions of its robotic activators. For instance, it could check
the black box of a car crash to determine if it was probably caused by electric
failure, a stuck electronic gate, dark ice, or some other condition that it
must add to its ontology in order to meet its sub-goal of preventing such
crashes in the future. Class algebra basically defines the eval/eval-1 Galois
connection between the residuated Boolean algebras of 1. equivalence classes
and super/sub classes of class algebra type expressions, and 2. a residual
Boolean algebra of biclique relationships. It distinguishes which formulas are
equivalent, entailed, or unrelated, based on a simplification algorithm that
may be thought of as producing a unique pair of Karnaugh maps that describe the
rough sets of maximal bicliques of relations. Such maps divide the
n-dimensional space of up to 2n-1 conjunctions of up to n propositions into
clopen (i.e. a closed set of regions and their boundaries) causal sets. This
class algebra is generalized to type-2 fuzzy class algebra by using relative
frequencies as probabilities. It is also generalized to a class calculus
involving assignments that change the states of programs.
INDEX TERMS 4-valued Boolean Logic, Artificial Intelligence, causal sets,
class algebra, consciousness, intelligent design, IS-A hierarchy, mathematical
logic, meta-theory, pointless topological space, residuated lattices, rough
sets, type-2 fuzzy sets
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 01:26:00 GMT"
}
] | 1,523,404,800,000 | [
[
"Buehrer",
"Daniel J.",
""
]
] |
1804.03342 | Naveen Sundar Govindarajulu | John Angel, Naveen Sundar Govindarajulu, and Selmer Bringsjord | Toward Formalizing Teleportation of Pedagogical Artificial Agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our paradigm for the use of artificial agents to teach requires among other
things that they persist through time in their interaction with human students,
in such a way that they "teleport" or "migrate" from an embodiment at one time
t to a different embodiment at later time t'. In this short paper, we report on
initial steps toward the formalization of such teleportation, in order to
enable an overseeing AI system to establish, mechanically, and verifiably, that
the human students in question will likely believe that the very same
artificial agent has persisted across such times despite the different
embodiments.
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 05:27:49 GMT"
}
] | 1,523,404,800,000 | [
[
"Angel",
"John",
""
],
[
"Govindarajulu",
"Naveen Sundar",
""
],
[
"Bringsjord",
"Selmer",
""
]
] |
1804.03437 | Wojciech Skaba | Wojciech Skaba | The AGINAO Self-Programming Engine | Journal of Artificial General Intelligence | Journal of Artificial General Intelligence 3(3) 2012 | 10.2478/v10229-011-0018-0 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The AGINAO is a project to create a human-level artificial general
intelligence system (HL AGI) embodied in the Aldebaran Robotics' NAO humanoid
robot. The dynamical and open-ended cognitive engine of the robot is
represented by an embedded and multi-threaded control program, that is
self-crafted rather than hand-crafted, and is executed on a simulated Universal
Turing Machine (UTM). The actual structure of the cognitive engine emerges as a
result of placing the robot in a natural preschool-like environment and running
a core start-up system that executes self-programming of the cognitive layer on
top of the core layer. The data from the robot's sensory devices supplies the
training samples for the machine learning methods, while the commands sent to
actuators enable testing hypotheses and getting a feedback. The individual
self-created subroutines are supposed to reflect the patterns and concepts of
the real world, while the overall program structure reflects the spatial and
temporal hierarchy of the world dependencies. This paper focuses on the details
of the self-programming approach, limiting the discussion of the applied
cognitive architecture to a necessary minimum.
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 10:29:14 GMT"
}
] | 1,523,404,800,000 | [
[
"Skaba",
"Wojciech",
""
]
] |
1804.03439 | Wojciech Skaba | Wojciech Skaba | Evaluating Actuators in a Purely Information-Theory Based Reward Model | IEEE SSCI 2013, Singapore | IEEE SSCI 2013 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AGINAO builds its cognitive engine by applying self-programming techniques to
create a hierarchy of interconnected codelets - the tiny pieces of code
executed on a virtual machine. These basic processing units are evaluated for
their applicability and fitness with a notion of reward calculated from
self-information gain of binary partitioning of the codelet's input
state-space. This approach, however, is useless for the evaluation of
actuators. Instead, a model is proposed in which actuators are evaluated by
measuring the impact that an activation of an effector, and consequently the
feedback from the robot sensors, has on average reward received by the
processing units.
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 10:34:36 GMT"
}
] | 1,523,404,800,000 | [
[
"Skaba",
"Wojciech",
""
]
] |
1804.03592 | Ali el Hassouni | Ali el Hassouni, Mark Hoogendoorn, Martijn van Otterlo, A. E. Eiben,
Vesa Muhonen, Eduardo Barbaro | A clustering-based reinforcement learning approach for tailored
personalization of e-Health interventions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Personalization is very powerful in improving the effectiveness of health
interventions. Reinforcement learning (RL) algorithms are suitable for learning
these tailored interventions from sequential data collected about individuals.
However, learning can be very fragile. The time to learn intervention policies
is limited as disengagement from the user can occur quickly. Also, in e-Health
intervention timing can be crucial before the optimal window passes. We present
an approach that learns tailored personalization policies for groups of users
by combining RL and clustering. The benefits are two-fold: speeding up the
learning to prevent disengagement while maintaining a high level of
personalization. Our clustering approach utilizes dynamic time warping to
compare user trajectories consisting of states and rewards. We apply online and
batch RL to learn policies over clusters of individuals and introduce our
self-developed and publicly available simulator for e-Health interventions to
evaluate our approach. We compare our methods with an e-Health intervention
benchmark. We demonstrate that batch learning outperforms online learning for
our setting. Furthermore, our proposed clustering approach for RL finds
near-optimal clusterings which lead to significantly better policies in terms
of cumulative reward compared to learning a policy per individual or learning
one non-personalized policy across all individuals. Our findings also indicate
that the learned policies accurately learn to send interventions at the right
moments and that the users workout more and at the right times of the day.
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 15:38:59 GMT"
},
{
"version": "v2",
"created": "Mon, 18 May 2020 21:33:35 GMT"
},
{
"version": "v3",
"created": "Thu, 21 May 2020 05:10:36 GMT"
}
] | 1,590,105,600,000 | [
[
"Hassouni",
"Ali el",
""
],
[
"Hoogendoorn",
"Mark",
""
],
[
"van Otterlo",
"Martijn",
""
],
[
"Eiben",
"A. E.",
""
],
[
"Muhonen",
"Vesa",
""
],
[
"Barbaro",
"Eduardo",
""
]
] |
1804.03611 | Wojciech Skaba | Wojciech Skaba | Binary Space Partitioning as Intrinsic Reward | AGI 2012 | J. Bach, B. Goertzel, and M. Ikle (Eds.): AGI 2012, LNAI 7716, pp.
242-251, 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An autonomous agent embodied in a humanoid robot, in order to learn from the
overwhelming flow of raw and noisy sensory, has to effectively reduce the high
spatial-temporal data dimensionality. In this paper we propose a novel method
of unsupervised feature extraction and selection with binary space
partitioning, followed by a computation of information gain that is interpreted
as intrinsic reward, then applied as immediate-reward signal for the
reinforcement-learning. The space partitioning is executed by tiny codelets
running on a simulated Turing Machine. The features are represented by concept
nodes arranged in a hierarchy, in which those of a lower level become the input
vectors of a higher level.
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2018 16:03:16 GMT"
}
] | 1,523,404,800,000 | [
[
"Skaba",
"Wojciech",
""
]
] |
1804.03967 | Chiara Ghidini | Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi,
Williams Rizzi, Cosimo Damiano Persia | Incremental Predictive Process Monitoring: How to Deal with the
Variability of Real Environments | This paper is replaced by paper arXiv:2109.03501 which containes a
more recent version of this work which was not submitted as an update by
mistake | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting.
| [
{
"version": "v1",
"created": "Wed, 11 Apr 2018 13:08:26 GMT"
},
{
"version": "v2",
"created": "Wed, 25 Oct 2023 13:49:44 GMT"
}
] | 1,698,278,400,000 | [
[
"Di Francescomarino",
"Chiara",
""
],
[
"Ghidini",
"Chiara",
""
],
[
"Maggi",
"Fabrizio Maria",
""
],
[
"Rizzi",
"Williams",
""
],
[
"Persia",
"Cosimo Damiano",
""
]
] |
1804.04268 | Dylan Hadfield-Menell | Dylan Hadfield-Menell, Gillian Hadfield | Incomplete Contracting and AI Alignment | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We suggest that the analysis of incomplete contracting developed by law and
economics researchers can provide a useful framework for understanding the AI
alignment problem and help to generate a systematic approach to finding
solutions. We first provide an overview of the incomplete contracting
literature and explore parallels between this work and the problem of AI
alignment. As we emphasize, misalignment between principal and agent is a core
focus of economic analysis. We highlight some technical results from the
economics literature on incomplete contracts that may provide insights for AI
alignment researchers. Our core contribution, however, is to bring to bear an
insight that economists have been urged to absorb from legal scholars and other
behavioral scientists: the fact that human contracting is supported by
substantial amounts of external structure, such as generally available
institutions (culture, law) that can supply implied terms to fill the gaps in
incomplete contracts. We propose a research agenda for AI alignment work that
focuses on the problem of how to build AI that can replicate the human
cognitive processes that connect individual incomplete contracts with this
supporting external structure.
| [
{
"version": "v1",
"created": "Thu, 12 Apr 2018 01:22:50 GMT"
}
] | 1,523,577,600,000 | [
[
"Hadfield-Menell",
"Dylan",
""
],
[
"Hadfield",
"Gillian",
""
]
] |
1804.05184 | Muhammad Rizwan Saeed | Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K. Prasanna | Not all Embeddings are created Equal: Extracting Entity-specific
Substructures for RDF Graph Embedding | 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Graphs (KGs) are becoming essential to information systems that
require access to structured data. Several approaches have been recently
proposed, for obtaining vector representations of KGs suitable for Machine
Learning tasks, based on identifying and extracting relevant graph
substructures using uniform and biased random walks. However, such approaches
lead to representations comprising mostly "popular", instead of "relevant",
entities in the KG. In KGs, in which different types of entities often exist
(such as in Linked Open Data), a given target entity may have its own distinct
set of most "relevant" nodes and edges. We propose specificity as an accurate
measure of identifying most relevant, entity-specific, nodes and edges. We
develop a scalable method based on bidirectional random walks to compute
specificity. Our experimental evaluation results show that specificity-based
biased random walks extract more "meaningful" (in terms of size and relevance)
RDF substructures compared to the state-of-the-art and, the graph embedding
learned from the extracted substructures, outperform existing techniques in the
task of entity recommendation in DBpedia.
| [
{
"version": "v1",
"created": "Sat, 14 Apr 2018 08:27:41 GMT"
}
] | 1,523,923,200,000 | [
[
"Saeed",
"Muhammad Rizwan",
""
],
[
"Chelmis",
"Charalampos",
""
],
[
"Prasanna",
"Viktor K.",
""
]
] |
1804.05212 | Avi Segal | Avi Segal, Yossi Ben David, Joseph Jay Williams, Kobi Gal, Yaar Shalom | Combining Difficulty Ranking with Multi-Armed Bandits to Sequence
Educational Content | null | null | 10.1016/j.physletb.2019.04.047 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As e-learning systems become more prevalent, there is a growing need for them
to accommodate individual differences between students. This paper addresses
the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational
approach to this problem called MAPLE (Multi-Armed Bandits based
Personalization for Learning Environments) that combines difficulty ranking
with multi-armed bandits. Given a set of target questions MAPLE estimates the
expected learning gains for each question and uses an exploration-exploitation
strategy to choose the next question to pose to the student. It maintains a
personalized ranking over the difficulties of question in the target set which
is used in two ways: First, to obtain initial estimates over the learning gains
for the set of questions. Second, to update the estimates over time based on
the students responses. We show in simulations that MAPLE was able to improve
students' learning gains compared to approaches that sequence questions in
increasing level of difficulty, or rely on content experts. When implemented in
a live e-learning system in the wild, MAPLE showed promising results. This work
demonstrates the efficacy of using stochastic approaches to the sequencing
problem when augmented with information about question difficulty.
| [
{
"version": "v1",
"created": "Sat, 14 Apr 2018 12:36:00 GMT"
}
] | 1,556,064,000,000 | [
[
"Segal",
"Avi",
""
],
[
"David",
"Yossi Ben",
""
],
[
"Williams",
"Joseph Jay",
""
],
[
"Gal",
"Kobi",
""
],
[
"Shalom",
"Yaar",
""
]
] |
1804.05906 | Zhen Peng | Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun | An information-theoretic on-line update principle for perception-action
coupling | 8 pages, 2017 IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inspired by findings of sensorimotor coupling in humans and animals, there
has recently been a growing interest in the interaction between action and
perception in robotic systems [Bogh et al., 2016]. Here we consider perception
and action as two serial information channels with limited
information-processing capacity. We follow [Genewein et al., 2015] and
formulate a constrained optimization problem that maximizes utility under
limited information-processing capacity in the two channels. As a solution we
obtain an optimal perceptual channel and an optimal action channel that are
coupled such that perceptual information is optimized with respect to
downstream processing in the action module. The main novelty of this study is
that we propose an online optimization procedure to find bounded-optimal
perception and action channels in parameterized serial perception-action
systems. In particular, we implement the perceptual channel as a multi-layer
neural network and the action channel as a multinomial distribution. We
illustrate our method in a NAO robot simulator with a simplified cup lifting
task.
| [
{
"version": "v1",
"created": "Mon, 16 Apr 2018 19:33:39 GMT"
}
] | 1,524,009,600,000 | [
[
"Peng",
"Zhen",
""
],
[
"Genewein",
"Tim",
""
],
[
"Leibfried",
"Felix",
""
],
[
"Braun",
"Daniel A.",
""
]
] |
1804.05917 | Ramon Fraga Pereira | Ramon Fraga Pereira and Felipe Meneguzzi | Heuristic Approaches for Goal Recognition in Incomplete Domain Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent approaches to goal recognition have progressively relaxed the
assumptions about the amount and correctness of domain knowledge and available
observations, yielding accurate and efficient algorithms. These approaches,
however, assume completeness and correctness of the domain theory against which
their algorithms match observations: this is too strong for most real-world
domains. In this paper, we develop goal recognition techniques that are capable
of recognizing goals using \textit{incomplete} (and possibly incorrect) domain
theories. We show the efficiency and accuracy of our approaches empirically
against a large dataset of goal and plan recognition problems with incomplete
domains.
| [
{
"version": "v1",
"created": "Mon, 16 Apr 2018 20:00:41 GMT"
}
] | 1,524,009,600,000 | [
[
"Pereira",
"Ramon Fraga",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
1804.05950 | Shuai Ma | Shuai Ma, Jia Yuan Yu | State-Augmentation Transformations for Risk-Sensitive Reinforcement
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the framework of MDP, although the general reward function takes three
arguments-current state, action, and successor state; it is often simplified to
a function of two arguments-current state and action. The former is called a
transition-based reward function, whereas the latter is called a state-based
reward function. When the objective involves the expected cumulative reward
only, this simplification works perfectly. However, when the objective is
risk-sensitive, this simplification leads to an incorrect value. We present
state-augmentation transformations (SATs), which preserve the reward sequences
as well as the reward distributions and the optimal policy in risk-sensitive
reinforcement learning. In risk-sensitive scenarios, firstly we prove that, for
every MDP with a stochastic transition-based reward function, there exists an
MDP with a deterministic state-based reward function, such that for any given
(randomized) policy for the first MDP, there exists a corresponding policy for
the second MDP, such that both Markov reward processes share the same reward
sequence. Secondly we illustrate that two situations require the proposed SATs
in an inventory control problem. One could be using Q-learning (or other
learning methods) on MDPs with transition-based reward functions, and the other
could be using methods, which are for the Markov processes with a deterministic
state-based reward functions, on the Markov processes with general reward
functions. We show the advantage of the SATs by considering Value-at-Risk as an
example, which is a risk measure on the reward distribution instead of the
measures (such as mean and variance) of the distribution. We illustrate the
error in the reward distribution estimation from the direct use of Q-learning,
and show how the SATs enable a variance formula to work on Markov processes
with general reward functions.
| [
{
"version": "v1",
"created": "Mon, 16 Apr 2018 21:38:40 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Nov 2018 22:40:11 GMT"
}
] | 1,543,795,200,000 | [
[
"Ma",
"Shuai",
""
],
[
"Yu",
"Jia Yuan",
""
]
] |
1804.05997 | Vernon Asuncion Va | Vernon Asuncion and Yan Zhang | A New Decidable Class of Tuple Generating Dependencies: The
Triangularly-Guarded Class | Resubmission for Journal | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we introduce a new class of tuple-generating dependencies
(TGDs) called triangularly-guarded TGDs, which are TGDs with certain
restrictions on the atomic derivation track embedded in the underlying rule
set. We show that conjunctive query answering under this new class of TGDs is
decidable. We further show that this new class strictly contains some other
decidable classes such as weak-acyclic, guarded, sticky and shy, which, to the
best of our knowledge, provides a unified representation of all these
aforementioned classes.
| [
{
"version": "v1",
"created": "Tue, 17 Apr 2018 01:05:45 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Apr 2018 11:30:51 GMT"
}
] | 1,524,182,400,000 | [
[
"Asuncion",
"Vernon",
""
],
[
"Zhang",
"Yan",
""
]
] |
1804.06020 | Qiang Ning | Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth | Improving Temporal Relation Extraction with a Globally Acquired
Statistical Resource | 13 pages, 3 figures, accepted by NAACL'18 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available.
| [
{
"version": "v1",
"created": "Tue, 17 Apr 2018 02:52:30 GMT"
}
] | 1,524,009,600,000 | [
[
"Ning",
"Qiang",
""
],
[
"Wu",
"Hao",
""
],
[
"Peng",
"Haoruo",
""
],
[
"Roth",
"Dan",
""
]
] |
1804.06088 | Shengcai Liu | Shengcai Liu, Ke Tang, Xin Yao | Automatic Construction of Parallel Portfolios via Explicit Instance
Grouping | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simultaneously utilizing several complementary solvers is a simple yet
effective strategy for solving computationally hard problems. However, manually
building such solver portfolios typically requires considerable domain
knowledge and plenty of human effort. As an alternative, automatic construction
of parallel portfolios (ACPP) aims at automatically building effective parallel
portfolios based on a given problem instance set and a given rich design space.
One promising way to solve the ACPP problem is to explicitly group the
instances into different subsets and promote a component solver to handle each
of them.This paper investigates solving ACPP from this perspective, and
especially studies how to obtain a good instance grouping.The experimental
results showed that the parallel portfolios constructed by the proposed method
could achieve consistently superior performances to the ones constructed by the
state-of-the-art ACPP methods,and could even rival sophisticated hand-designed
parallel solvers.
| [
{
"version": "v1",
"created": "Tue, 17 Apr 2018 07:56:15 GMT"
}
] | 1,524,009,600,000 | [
[
"Liu",
"Shengcai",
""
],
[
"Tang",
"Ke",
""
],
[
"Yao",
"Xin",
""
]
] |
1804.06264 | Yingjun Ye | Yingjun Ye, Xiaohui Zhang, Jian Sun | Automated vehicle's behavior decision making using deep reinforcement
learning and high-fidelity simulation environment | 22 pages, 13 figures, CICTP2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated vehicles are deemed to be the key element for the intelligent
transportation system in the future. Many studies have been made to improve the
Automated vehicles' ability of environment recognition and vehicle control,
while the attention paid to decision making is not enough though the decision
algorithms so far are very preliminary. Therefore, a framework of the
decision-making training and learning is put forward in this paper. It consists
of two parts: the deep reinforcement learning training program and the
high-fidelity virtual simulation environment. Then the basic microscopic
behavior, car-following, is trained within this framework. In addition,
theoretical analysis and experiments were conducted on setting reward function
for accelerating training using deep reinforcement learning. The results show
that on the premise of driving comfort, the efficiency of the trained Automated
vehicle increases 7.9% compared to the classical traffic model, intelligent
driver model. Later on, on a more complex three-lane section, we trained the
integrated model combines both car-following and lane-changing behavior, the
average speed further grows 2.4%. It indicates that our framework is effective
for Automated vehicle's decision-making learning.
| [
{
"version": "v1",
"created": "Tue, 17 Apr 2018 13:58:04 GMT"
}
] | 1,524,009,600,000 | [
[
"Ye",
"Yingjun",
""
],
[
"Zhang",
"Xiaohui",
""
],
[
"Sun",
"Jian",
""
]
] |
1804.06748 | Stefan L\"udtke | Stefan L\"udtke, Max Schr\"oder, Frank Kr\"uger, Sebastian Bader,
Thomas Kirste | State-Space Abstractions for Probabilistic Inference: A Systematic
Review | null | null | 10.1613/jair.1.11261 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tasks such as social network analysis, human behavior recognition, or
modeling biochemical reactions, can be solved elegantly by using the
probabilistic inference framework. However, standard probabilistic inference
algorithms work at a propositional level, and thus cannot capture the
symmetries and redundancies that are present in these tasks. Algorithms that
exploit those symmetries have been devised in different research fields, for
example by the lifted inference-, multiple object tracking-, and modeling and
simulation-communities. The common idea, that we call state space abstraction,
is to perform inference over compact representations of sets of symmetric
states. Although they are concerned with a similar topic, the relationship
between these approaches has not been investigated systematically. This survey
provides the following contributions. We perform a systematic literature review
to outline the state of the art in probabilistic inference methods exploiting
symmetries. From an initial set of more than 4,000 papers, we identify 116
relevant papers. Furthermore, we provide new high-level categories that
classify the approaches, based on common properties of the approaches. The
research areas underlying each of the categories are introduced concisely.
Researchers from different fields that are confronted with a state space
explosion problem in a probabilistic system can use this classification to
identify possible solutions. Finally, based on this conceptualization, we
identify potentials for future research, as some relevant application domains
are not addressed by current approaches.
| [
{
"version": "v1",
"created": "Wed, 18 Apr 2018 14:10:10 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Apr 2018 07:18:59 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Dec 2018 08:51:35 GMT"
}
] | 1,544,486,400,000 | [
[
"Lüdtke",
"Stefan",
""
],
[
"Schröder",
"Max",
""
],
[
"Krüger",
"Frank",
""
],
[
"Bader",
"Sebastian",
""
],
[
"Kirste",
"Thomas",
""
]
] |
1804.06763 | Sanjay Modgil | Sanjay Modgil and Henry Prakken | A General Account of Argumentation with Preferences | This paper contains correction to errors in the original paper which
appears in the journal Artificial Intelligence | S. Modgil, H. Prakken. A General Account of Argumentation and
Preferences. In: Artificial Intelligence (AIJ) . 195(0), 361 - 397, 2013 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper builds on the recent ASPIC+ formalism, to develop a general
framework for argumentation with preferences. We motivate a revised definition
of conflict free sets of arguments, adapt ASPIC+ to accommodate a broader range
of instantiating logics, and show that under some assumptions, the resulting
framework satisfies key properties and rationality postulates. We then show
that the generalised framework accommodates Tarskian logic instantiations
extended with preferences, and then study instantiations of the framework by
classical logic approaches to argumentation. We conclude by arguing that
ASPIC+'s modelling of defeasible inference rules further testifies to the
generality of the framework, and then examine and counter recent critiques of
Dung's framework and its extensions to accommodate preferences.
| [
{
"version": "v1",
"created": "Wed, 18 Apr 2018 14:33:44 GMT"
}
] | 1,524,096,000,000 | [
[
"Modgil",
"Sanjay",
""
],
[
"Prakken",
"Henry",
""
]
] |
1804.06907 | Carsten Lutz | Peter Hansen and Carsten Lutz | Computing FO-Rewritings in EL in Practice: from Atomic to Conjunctive
Queries | null | null | 10.1007/978-3-319-68288-4_21 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A prominent approach to implementing ontology-mediated queries (OMQs) is to
rewrite into a first-order query, which is then executed using a conventional
SQL database system. We consider the case where the ontology is formulated in
the description logic EL and the actual query is a conjunctive query and show
that rewritings of such OMQs can be efficiently computed in practice, in a
sound and complete way. Our approach combines a reduction with a decomposed
backwards chaining algorithm for OMQs that are based on the simpler atomic
queries, also illuminating the relationship between first-order rewritings of
OMQs based on conjunctive and on atomic queries. Experiments with real-world
ontologies show promising results.
| [
{
"version": "v1",
"created": "Wed, 18 Apr 2018 20:27:45 GMT"
}
] | 1,524,182,400,000 | [
[
"Hansen",
"Peter",
""
],
[
"Lutz",
"Carsten",
""
]
] |
1804.07013 | Yuncong Li | Yuncong Li, Hankz Hankui Zhuo | An Integrated Development Environment for Planning Domain Modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to make the task, description of planning domains and problems, more
comprehensive for non-experts in planning, the visual representation has been
used in planning domain modeling in recent years. However, current knowledge
engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and
VIZ (Vodr\'a\v{z}ka and Chrpa 2010), are less efficient than the traditional
method of hand-coding by a PDDL expert using a text editor, and rarely involved
in finetuning planning domains depending on the plan validation. Aim at this,
we present an integrated development environment KAVI for planning domain
modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge
base to improve the efficiency of planning domain visual modeling. By
integrating planners and a plan validator, KAVI proposes a method to fine-tune
planning domains based on the plan validation.
| [
{
"version": "v1",
"created": "Thu, 19 Apr 2018 06:39:49 GMT"
}
] | 1,524,182,400,000 | [
[
"Li",
"Yuncong",
""
],
[
"Zhuo",
"Hankz Hankui",
""
]
] |
1804.07088 | George Baryannis | George Baryannis, Ilias Tachmazidis, Sotiris Batsakis, Grigoris
Antoniou, Mario Alviano, Timos Sellis, Pei-Wei Tsai | A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set
Programming | Paper presented at the 34th International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018, 20 pages,
LaTeX, 16 figures | Theory and Practice of Logic Programming 18 (2018) 355-371 | 10.1017/S147106841800011X | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial information is often expressed using qualitative terms such as
natural language expressions instead of coordinates; reasoning over such terms
has several practical applications, such as bus routes planning. Representing
and reasoning on trajectories is a specific case of qualitative spatial
reasoning that focuses on moving objects and their paths. In this work, we
propose two versions of a trajectory calculus based on the allowed properties
over trajectories, where trajectories are defined as a sequence of
non-overlapping regions of a partitioned map. More specifically, if a given
trajectory is allowed to start and finish at the same region, 6 base relations
are defined (TC-6). If a given trajectory should have different start and
finish regions but cycles are allowed within, 10 base relations are defined
(TC-10). Both versions of the calculus are implemented as ASP programs; we
propose several different encodings, including a generalised program capable of
encoding any qualitative calculus in ASP. All proposed encodings are
experimentally evaluated using a real-world dataset. Experiment results show
that the best performing implementation can scale up to an input of 250
trajectories for TC-6 and 150 trajectories for TC-10 for the problem of
discovering a consistent configuration, a significant improvement compared to
previous ASP implementations for similar qualitative spatial and temporal
calculi. This manuscript is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Thu, 19 Apr 2018 11:16:22 GMT"
}
] | 1,596,585,600,000 | [
[
"Baryannis",
"George",
""
],
[
"Tachmazidis",
"Ilias",
""
],
[
"Batsakis",
"Sotiris",
""
],
[
"Antoniou",
"Grigoris",
""
],
[
"Alviano",
"Mario",
""
],
[
"Sellis",
"Timos",
""
],
[
"Tsai",
"Pei-Wei",
""
]
] |
1804.07404 | Mayukh Das | Mayukh Das, Phillip Odom, Md. Rakibul Islam, Janardhan Rao (Jana)
Doppa, Dan Roth, Sriraam Natarajan | Preference-Guided Planning: An Active Elicitation Approach | Under Review at Knowledge-Based Systems (Elsevier); "Extended
Abstract" accepted and to appear at AAMAS 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning with preferences has been employed extensively to quickly generate
high-quality plans. However, it may be difficult for the human expert to supply
this information without knowledge of the reasoning employed by the planner and
the distribution of planning problems. We consider the problem of actively
eliciting preferences from a human expert during the planning process.
Specifically, we study this problem in the context of the Hierarchical Task
Network (HTN) planning framework as it allows easy interaction with the human.
Our experimental results on several diverse planning domains show that the
preferences gathered using the proposed approach improve the quality and speed
of the planner, while reducing the burden on the human expert.
| [
{
"version": "v1",
"created": "Thu, 19 Apr 2018 23:30:37 GMT"
}
] | 1,524,441,600,000 | [
[
"Das",
"Mayukh",
"",
"Jana"
],
[
"Odom",
"Phillip",
"",
"Jana"
],
[
"Islam",
"Md. Rakibul",
"",
"Jana"
],
[
"Rao",
"Janardhan",
"",
"Jana"
],
[
"Doppa",
"",
""
],
[
"Roth",
"Dan",
""
],
[
"Natarajan",
"Sriraam",
""
]
] |
1804.07777 | Per Ola Kristensson | Emli-Mari Nel, Per Ola Kristensson, David J.C. MacKay | The Statistical Model for Ticker, an Adaptive Single-Switch Text-Entry
Method for Visually Impaired Users | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the statistical model for Ticker [1], a novel
probabilistic stereophonic single-switch text entry method for
visually-impaired users with motor disabilities who rely on single-switch
scanning systems to communicate. All terminology and notation are defined in
[1].
| [
{
"version": "v1",
"created": "Fri, 20 Apr 2018 18:04:37 GMT"
}
] | 1,524,528,000,000 | [
[
"Nel",
"Emli-Mari",
""
],
[
"Kristensson",
"Per Ola",
""
],
[
"MacKay",
"David J. C.",
""
]
] |
1804.07805 | Carsten Lutz | Elena Botoeva and Boris Konev and Carsten Lutz and Vladislav Ryzhikov
and Frank Wolter and Michael Zakharyaschev | Inseparability and Conservative Extensions of Description Logic
Ontologies: A Survey | null | null | 10.1007/978-3-319-49493-7_2 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The question whether an ontology can safely be replaced by another, possibly
simpler, one is fundamental for many ontology engineering and maintenance
tasks. It underpins, for example, ontology versioning, ontology modularization,
forgetting, and knowledge exchange. What safe replacement means depends on the
intended application of the ontology. If, for example, it is used to query
data, then the answers to any relevant ontology-mediated query should be the
same over any relevant data set; if, in contrast, the ontology is used for
conceptual reasoning, then the entailed subsumptions between concept
expressions should coincide. This gives rise to different notions of ontology
inseparability such as query inseparability and concept inseparability, which
generalize corresponding notions of conservative extensions. We survey results
on various notions of inseparability in the context of description logic
ontologies, discussing their applications, useful model-theoretic
characterizations, algorithms for determining whether two ontologies are
inseparable (and, sometimes, for computing the difference between them if they
are not), and the computational complexity of this problem.
| [
{
"version": "v1",
"created": "Fri, 20 Apr 2018 19:17:46 GMT"
}
] | 1,524,528,000,000 | [
[
"Botoeva",
"Elena",
""
],
[
"Konev",
"Boris",
""
],
[
"Lutz",
"Carsten",
""
],
[
"Ryzhikov",
"Vladislav",
""
],
[
"Wolter",
"Frank",
""
],
[
"Zakharyaschev",
"Michael",
""
]
] |
1804.07819 | Erik Altman | Erik Altman | Understanding AI Data Repositories with Automatic Query Generation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a set of techniques to generate queries automatically based on
one or more ingested, input corpuses. These queries require no a priori domain
knowledge, and hence no human domain experts. Thus, these auto-generated
queries help address the epistemological question of how we know what we know,
or more precisely in this case, how an AI system with ingested data knows what
it knows. These auto-generated queries can also be used to identify and remedy
problem areas in ingested material -- areas for which the knowledge of the AI
system is incomplete or even erroneous. Similarly, the proposed techniques
facilitate tests of AI capability -- both in terms of coverage and accuracy. By
removing humans from the main learning loop, our approach also allows more
effective scaling of AI and cognitive capabilities to provide (1) broader
coverage in a single domain such as health or geology; and (2) more rapid
deployment to new domains. The proposed techniques also allow ingested
knowledge to be extended naturally. Our investigations are early, and this
paper provides a description of the techniques. Assessment of their efficacy is
our next step for future work.
| [
{
"version": "v1",
"created": "Fri, 20 Apr 2018 20:44:09 GMT"
}
] | 1,524,528,000,000 | [
[
"Altman",
"Erik",
""
]
] |
1804.08032 | Bart Jacobs | Bart Jacobs | A Channel-based Exact Inference Algorithm for Bayesian Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new algorithm for exact Bayesian inference that is
based on a recently proposed compositional semantics of Bayesian networks in
terms of channels. The paper concentrates on the ideas behind this algorithm,
involving a linearisation (`stretching') of the Bayesian network, followed by a
combination of forward state transformation and backward predicate
transformation, while evidence is accumulated along the way. The performance of
a prototype implementation of the algorithm in Python is briefly compared to a
standard implementation (pgmpy): first results show competitive performance.
| [
{
"version": "v1",
"created": "Sat, 21 Apr 2018 21:59:24 GMT"
}
] | 1,524,528,000,000 | [
[
"Jacobs",
"Bart",
""
]
] |
1804.08033 | Xavier Amatriain | Murali Ravuri, Anitha Kannan, Geoffrey J. Tso, Xavier Amatriain | Learning from the experts: From expert systems to machine-learned
diagnosis models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expert diagnostic support systems have been extensively studied. The
practical applications of these systems in real-world scenarios have been
somewhat limited due to well-understood shortcomings, such as lack of
extensibility. More recently, machine-learned models for medical diagnosis have
gained momentum, since they can learn and generalize patterns found in very
large datasets like electronic health records. These models also have
shortcomings - in particular, there is no easy way to incorporate prior
knowledge from existing literature or experts. In this paper, we present a
method to merge both approaches by using expert systems as generative models
that create simulated data on which models can be learned. We demonstrate that
such a learned model not only preserves the original properties of the expert
systems but also addresses some of their limitations. Furthermore, we show how
this approach can also be used as the starting point to combine expert
knowledge with knowledge extracted from other data sources, such as electronic
health records.
| [
{
"version": "v1",
"created": "Sat, 21 Apr 2018 22:01:19 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Aug 2018 05:24:54 GMT"
},
{
"version": "v3",
"created": "Tue, 14 Aug 2018 04:45:23 GMT"
}
] | 1,534,291,200,000 | [
[
"Ravuri",
"Murali",
""
],
[
"Kannan",
"Anitha",
""
],
[
"Tso",
"Geoffrey J.",
""
],
[
"Amatriain",
"Xavier",
""
]
] |
1804.08052 | Anahita Hosseini | Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh | HeteroMed: Heterogeneous Information Network for Medical Diagnosis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the recent availability of Electronic Health Records (EHR) and great
opportunities they offer for advancing medical informatics, there has been
growing interest in mining EHR for improving quality of care. Disease diagnosis
due to its sensitive nature, huge costs of error, and complexity has become an
increasingly important focus of research in past years. Existing studies model
EHR by capturing co-occurrence of clinical events to learn their latent
embeddings. However, relations among clinical events carry various semantics
and contribute differently to disease diagnosis which gives precedence to a
more advanced modeling of heterogeneous data types and relations in EHR data
than existing solutions. To address these issues, we represent how
high-dimensional EHR data and its rich relationships can be suitably translated
into HeteroMed, a heterogeneous information network for robust medical
diagnosis. Our modeling approach allows for straightforward handling of missing
values and heterogeneity of data. HeteroMed exploits metapaths to capture
higher level and semantically important relations contributing to disease
diagnosis. Furthermore, it employs a joint embedding framework to tailor
clinical event representations to the disease diagnosis goal. To the best of
our knowledge, this is the first study to use Heterogeneous Information Network
for modeling clinical data and disease diagnosis. Experimental results of our
study show superior performance of HeteroMed compared to prior methods in
prediction of exact diagnosis codes and general disease cohorts. Moreover,
HeteroMed outperforms baseline models in capturing similarities of clinical
events which are examined qualitatively through case studies.
| [
{
"version": "v1",
"created": "Sun, 22 Apr 2018 00:53:20 GMT"
}
] | 1,524,528,000,000 | [
[
"Hosseini",
"Anahita",
""
],
[
"Chen",
"Ting",
""
],
[
"Wu",
"Wenjun",
""
],
[
"Sun",
"Yizhou",
""
],
[
"Sarrafzadeh",
"Majid",
""
]
] |
1804.08187 | Yi Fan | Yi Fan, Nan Li, Chengqian Li, Zongjie Ma, Longin Jan Latecki, Kaile Su | Advancing Tabu and Restart in Local Search for Maximum Weight Cliques | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tabu and restart are two fundamental strategies for local search. In this
paper, we improve the local search algorithms for solving the Maximum Weight
Clique (MWC) problem by introducing new tabu and restart strategies. Both the
tabu and restart strategies proposed are based on the notion of a local search
scenario, which involves not only a candidate solution but also the tabu status
and unlocking relationship. Compared to the strategy of configuration checking,
our tabu mechanism discourages forming a cycle of unlocking operations. Our new
restart strategy is based on the re-occurrence of a local search scenario
instead of that of a candidate solution. Experimental results show that the
resulting MWC solver outperforms several state-of-the-art solvers on the
DIMACS, BHOSLIB, and two benchmarks from practical applications.
| [
{
"version": "v1",
"created": "Sun, 22 Apr 2018 22:36:00 GMT"
}
] | 1,524,528,000,000 | [
[
"Fan",
"Yi",
""
],
[
"Li",
"Nan",
""
],
[
"Li",
"Chengqian",
""
],
[
"Ma",
"Zongjie",
""
],
[
"Latecki",
"Longin Jan",
""
],
[
"Su",
"Kaile",
""
]
] |
1804.08229 | Shiqi Zhang | Yuqian Jiang and Shiqi Zhang and Piyush Khandelwal and Peter Stone | Task Planning in Robotics: an Empirical Comparison of PDDL-based and
ASP-based Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robots need task planning algorithms to sequence actions toward accomplishing
goals that are impossible through individual actions. Off-the-shelf task
planners can be used by intelligent robotics practitioners to solve a variety
of planning problems. However, many different planners exist, each with
different strengths and weaknesses, and there are no general rules for which
planner would be best to apply to a given problem.
In this article, we empirically compare the performance of state-of-the-art
planners that use either the Planning Domain Description Language (PDDL), or
Answer Set Programming (ASP) as the underlying action language. PDDL is
designed for task planning, and PDDL-based planners are widely used for a
variety of planning problems. ASP is designed for knowledge-intensive
reasoning, but can also be used for solving task planning problems. Given
domain encodings that are as similar as possible, we find that PDDL-based
planners perform better on problems with longer solutions, and ASP-based
planners are better on tasks with a large number of objects or in which complex
reasoning is required to reason about action preconditions and effects. The
resulting analysis can inform selection among general purpose planning systems
for particular robot task planning domains.
| [
{
"version": "v1",
"created": "Mon, 23 Apr 2018 02:46:36 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Jan 2019 04:29:32 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Feb 2019 23:28:49 GMT"
}
] | 1,551,225,600,000 | [
[
"Jiang",
"Yuqian",
""
],
[
"Zhang",
"Shiqi",
""
],
[
"Khandelwal",
"Piyush",
""
],
[
"Stone",
"Peter",
""
]
] |
1804.08299 | Antonio Lieto | Antonio Chella, Marcello Frixione, Antonio Lieto | Representational Issues in the Debate on the Standard Model of the Mind | 7 pages | null | null | Paper is published in the 2017 AAAI Fall Symposium Series, FS-17-05,
pp. 302-307 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we discuss some of the issues concerning the Memory and Content
aspects in the recent debate on the identification of a Standard Model of the
Mind (Laird, Lebiere, and Rosenbloom in press). In particular, we focus on the
representational models concerning the Declarative Memories of current
Cognitive Architectures (CAs). In doing so we outline some of the main problems
affecting the current CAs and suggest that the Conceptual Spaces, a
representational framework developed by Gardenfors, is worth-considering to
address such problems. Finally, we briefly analyze the alternative
representational assumptions employed in the three CAs constituting the current
baseline for the Standard Model (i.e. SOAR, ACT-R and Sigma). In doing so, we
point out the respective differences and discuss their implications in the
light of the analyzed problems.
| [
{
"version": "v1",
"created": "Mon, 23 Apr 2018 09:06:08 GMT"
}
] | 1,524,528,000,000 | [
[
"Chella",
"Antonio",
""
],
[
"Frixione",
"Marcello",
""
],
[
"Lieto",
"Antonio",
""
]
] |
1804.08748 | Sobhan Moosavi | Sobhan Moosavi, Arnab Nandi, Rajiv Ramnath | Discovery of Driving Patterns by Trajectory Segmentation | Accepted in the 3rd PhD workshop, ACM SIGSPATIAL 2016 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Telematics data is becoming increasingly available due to the ubiquity of
devices that collect data during drives, for different purposes, such as usage
based insurance (UBI), fleet management, navigation of connected vehicles, etc.
Consequently, a variety of data-analytic applications have become feasible that
extract valuable insights from the data. In this paper, we address the
especially challenging problem of discovering behavior-based driving patterns
from only externally observable phenomena (e.g. vehicle's speed). We present a
trajectory segmentation approach capable of discovering driving patterns as
separate segments, based on the behavior of drivers. This segmentation approach
includes a novel transformation of trajectories along with a dynamic
programming approach for segmentation. We apply the segmentation approach on a
real-word, rich dataset of personal car trajectories provided by a major
insurance company based in Columbus, Ohio. Analysis and preliminary results
show the applicability of approach for finding significant driving patterns.
| [
{
"version": "v1",
"created": "Mon, 23 Apr 2018 21:28:04 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Apr 2020 06:02:17 GMT"
}
] | 1,586,131,200,000 | [
[
"Moosavi",
"Sobhan",
""
],
[
"Nandi",
"Arnab",
""
],
[
"Ramnath",
"Rajiv",
""
]
] |
1804.09153 | Pier Luca Lanzi | Antonio Umberto Aramini, Pier Luca Lanzi, Daniele Loiacono | An Integrated Framework for AI Assisted Level Design in 2D Platformers | Submitted to the IEEE Game Entertainment and Media Conference 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The design of video game levels is a complex and critical task. Levels need
to elicit fun and challenge while avoiding frustration at all costs. In this
paper, we present a framework to assist designers in the creation of levels for
2D platformers. Our framework provides designers with a toolbox (i) to create
2D platformer levels, (ii) to estimate the difficulty and probability of
success of single jump actions (the main mechanics of platformer games), and
(iii) a set of metrics to evaluate the difficulty and probability of completion
of entire levels. At the end, we present the results of a set of experiments we
carried out with human players to validate the metrics included in our
framework.
| [
{
"version": "v1",
"created": "Tue, 24 Apr 2018 17:20:36 GMT"
}
] | 1,524,614,400,000 | [
[
"Aramini",
"Antonio Umberto",
""
],
[
"Lanzi",
"Pier Luca",
""
],
[
"Loiacono",
"Daniele",
""
]
] |
1804.09465 | Shabnam Sadeghi Esfahlani | Shabnam Sadeghi Esfahlani and Tommy Thompson | Intelligent Physiotherapy Through Procedural Content Generation | 4 pages; 3 figures AAAI Publications, Twelfth Artificial Intelligence
and Interactive Digital Entertainment Conference | Papers from the AIIDE Workshop 2016 AAAI Technical Report WS-16-22 | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | This paper describes an avenue for artificial and computational intelligence
techniques applied within games research to be deployed for purposes of
physical therapy. We provide an overview of prototypical research focussed on
the application of motion sensor input devices and virtual reality equipment
for rehabilitation of motor impairment an issue typical of patient's of
traumatic brain injuries. We highlight how advances in procedural content
generation and player modelling can stimulate development in this area by
improving quality of rehabilitation programmes and measuring patient
performance.
| [
{
"version": "v1",
"created": "Wed, 25 Apr 2018 10:24:41 GMT"
}
] | 1,524,700,800,000 | [
[
"Esfahlani",
"Shabnam Sadeghi",
""
],
[
"Thompson",
"Tommy",
""
]
] |
1804.09817 | Ermo Wei | Ermo Wei, Drew Wicke, David Freelan and Sean Luke | Multiagent Soft Q-Learning | Accepted in AAAI 18 Spring Symposium | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Policy gradient methods are often applied to reinforcement learning in
continuous multiagent games. These methods perform local search in the
joint-action space, and as we show, they are susceptable to a game-theoretic
pathology known as relative overgeneralization. To resolve this issue, we
propose Multiagent Soft Q-learning, which can be seen as the analogue of
applying Q-learning to continuous controls. We compare our method to MADDPG, a
state-of-the-art approach, and show that our method achieves better
coordination in multiagent cooperative tasks, converging to better local optima
in the joint action space.
| [
{
"version": "v1",
"created": "Wed, 25 Apr 2018 22:03:27 GMT"
}
] | 1,524,787,200,000 | [
[
"Wei",
"Ermo",
""
],
[
"Wicke",
"Drew",
""
],
[
"Freelan",
"David",
""
],
[
"Luke",
"Sean",
""
]
] |
1804.09855 | Daniela Inclezan | Daniela Inclezan, Qinglin Zhang, Marcello Balduccini and Ankush
Israney | An ASP Methodology for Understanding Narratives about Stereotypical
Activities | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages,
LaTeX, 3 PDF figures (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an application of Answer Set Programming to the understanding of
narratives about stereotypical activities, demonstrated via question answering.
Substantial work in this direction was done by Erik Mueller, who modeled
stereotypical activities as scripts. His systems were able to understand a good
number of narratives, but could not process texts describing exceptional
scenarios. We propose addressing this problem by using a theory of intentions
developed by Blount, Gelfond, and Balduccini. We present a methodology in which
we substitute scripts by activities (i.e., hierarchical plans associated with
goals) and employ the concept of an intentional agent to reason about both
normal and exceptional scenarios. We exemplify the application of this
methodology by answering questions about a number of restaurant stories. This
paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Thu, 26 Apr 2018 02:10:05 GMT"
}
] | 1,524,787,200,000 | [
[
"Inclezan",
"Daniela",
""
],
[
"Zhang",
"Qinglin",
""
],
[
"Balduccini",
"Marcello",
""
],
[
"Israney",
"Ankush",
""
]
] |
1804.09856 | Lakshmi Nair | Lakshmi Nair and Sonia Chernova | Action Categorization for Computationally Improved Task Learning and
Planning | 10 pages, 13 figures, 3 tables. Extended abstract of the paper
accepted to AAMAS 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the problem of task learning and planning, contributing
the Action-Category Representation (ACR) to improve computational performance
of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic,
abstract data representation that maps objects to action categories (groups of
actions), inspired by the psychological concept of action codes. We validate
our approach in StarCraft and Lightworld domains; our results demonstrate
several benefits of ACR relating to improved computational performance of
planning and RL, by reducing the action space for the agent.
| [
{
"version": "v1",
"created": "Thu, 26 Apr 2018 02:10:22 GMT"
}
] | 1,524,787,200,000 | [
[
"Nair",
"Lakshmi",
""
],
[
"Chernova",
"Sonia",
""
]
] |
1804.10227 | Torsten Schaub | Pedro Cabalar, Roland Kaminski, Torsten Schaub, Anna Schuhmann | Temporal Answer Set Programming on Finite Traces | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 15 pages,
LaTeX, 0 PDF figures (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce an alternative approach to Temporal Answer Set
Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for
finite traces. This approach allows us to even out the expressiveness of TEL
over infinite traces with the computational capacity of (incremental) Answer
Set Programming (ASP). Also, we argue that finite traces are more natural when
reasoning about action and change. As a result, our approach is readily
implementable via multi-shot ASP systems and benefits from an extension of
ASP's full-fledged input language with temporal operators. This includes future
as well as past operators whose combination offers a rich temporal modeling
language. For computation, we identify the class of temporal logic programs and
prove that it constitutes a normal form for our approach. Finally, we outline
two implementations, a generic one and an extension of clingo.
| [
{
"version": "v1",
"created": "Thu, 26 Apr 2018 18:22:02 GMT"
}
] | 1,525,046,400,000 | [
[
"Cabalar",
"Pedro",
""
],
[
"Kaminski",
"Roland",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Schuhmann",
"Anna",
""
]
] |
1804.10247 | Torsten Schaub | Martin Gebser, Philipp Obermeier, Thomas Otto, Torsten Schaub, Orkunt
Sabuncu, Van Nguyen, Tran Cao Son | Experimenting with robotic intra-logistics domains | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages,
LaTeX, 8 PDF figures (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the asprilo [1] framework to facilitate experimental studies of
approaches addressing complex dynamic applications. For this purpose, we have
chosen the domain of robotic intra-logistics. This domain is not only highly
relevant in the context of today's fourth industrial revolution but it moreover
combines a multitude of challenging issues within a single uniform framework.
This includes multi-agent planning, reasoning about action, change, resources,
strategies, etc. In return, asprilo allows users to study alternative solutions
as regards effectiveness and scalability. Although asprilo relies on Answer Set
Programming and Python, it is readily usable by any system complying with its
fact-oriented interface format. This makes it attractive for benchmarking and
teaching well beyond logic programming. More precisely, asprilo consists of a
versatile benchmark generator, solution checker and visualizer as well as a
bunch of reference encodings featuring various ASP techniques. Importantly, the
visualizer's animation capabilities are indispensable for complex scenarios
like intra-logistics in order to inspect valid as well as invalid solution
candidates. Also, it allows for graphically editing benchmark layouts that can
be used as a basis for generating benchmark suites.
[1] asprilo stands for Answer Set Programming for robotic intra-logistics
| [
{
"version": "v1",
"created": "Thu, 26 Apr 2018 19:05:30 GMT"
}
] | 1,525,046,400,000 | [
[
"Gebser",
"Martin",
""
],
[
"Obermeier",
"Philipp",
""
],
[
"Otto",
"Thomas",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Sabuncu",
"Orkunt",
""
],
[
"Nguyen",
"Van",
""
],
[
"Son",
"Tran Cao",
""
]
] |
1804.10437 | Martin Gebser | Martin Gebser, Philipp Obermeier, Michel Ratsch-Heitmann, Mario Runge,
Torsten Schaub | Routing Driverless Transport Vehicles in Car Assembly with Answer Set
Programming | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018; 15 pages,
LaTeX, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated storage and retrieval systems are principal components of modern
production and warehouse facilities. In particular, automated guided vehicles
nowadays substitute human-operated pallet trucks in transporting production
materials between storage locations and assembly stations. While low-level
control systems take care of navigating such driverless vehicles along
programmed routes and avoid collisions even under unforeseen circumstances, in
the common case of multiple vehicles sharing the same operation area, the
problem remains how to set up routes such that a collection of transport tasks
is accomplished most effectively. We address this prevalent problem in the
context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale
producer of commercial vehicles, where routes for automated guided vehicles
used in the production process have traditionally been hand-coded by human
engineers. Such ad-hoc methods may suffice as long as a running production
process remains in place, while any change in the factory layout or production
targets necessitates tedious manual reconfiguration, not to mention the missing
portability between different production plants. Unlike this, we propose a
declarative approach based on Answer Set Programming to optimize the routes
taken by automated guided vehicles for accomplishing transport tasks. The
advantages include a transparent and executable problem formalization, provable
optimality of routes relative to objective criteria, as well as elaboration
tolerance towards particular factory layouts and production targets. Moreover,
we demonstrate that our approach is efficient enough to deal with the transport
tasks evolving in realistic production processes at the car factory of
Mercedes-Benz Ludwigsfelde GmbH.
| [
{
"version": "v1",
"created": "Fri, 27 Apr 2018 11:00:54 GMT"
}
] | 1,525,046,400,000 | [
[
"Gebser",
"Martin",
""
],
[
"Obermeier",
"Philipp",
""
],
[
"Ratsch-Heitmann",
"Michel",
""
],
[
"Runge",
"Mario",
""
],
[
"Schaub",
"Torsten",
""
]
] |
1804.10601 | Petr Novotn\'y | Krishnendu Chatterjee, Adri\'an Elgy\"utt, Petr Novotn\'y, Owen
Rouill\'e | Expectation Optimization with Probabilistic Guarantees in POMDPs with
Discounted-sum Objectives | Full version of a paper published at IJCAI/ECAI 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partially-observable Markov decision processes (POMDPs) with discounted-sum
payoff are a standard framework to model a wide range of problems related to
decision making under uncertainty. Traditionally, the goal has been to obtain
policies that optimize the expectation of the discounted-sum payoff. A key
drawback of the expectation measure is that even low probability events with
extreme payoff can significantly affect the expectation, and thus the obtained
policies are not necessarily risk-averse. An alternate approach is to optimize
the probability that the payoff is above a certain threshold, which allows
obtaining risk-averse policies, but ignores optimization of the expectation. We
consider the expectation optimization with probabilistic guarantee (EOPG)
problem, where the goal is to optimize the expectation ensuring that the payoff
is above a given threshold with at least a specified probability. We present
several results on the EOPG problem, including the first algorithm to solve it.
| [
{
"version": "v1",
"created": "Fri, 27 Apr 2018 17:34:05 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Apr 2018 11:52:15 GMT"
}
] | 1,525,132,800,000 | [
[
"Chatterjee",
"Krishnendu",
""
],
[
"Elgyütt",
"Adrián",
""
],
[
"Novotný",
"Petr",
""
],
[
"Rouillé",
"Owen",
""
]
] |
1804.10765 | Rolf Schwitter | Rolf Schwitter | Specifying and Verbalising Answer Set Programs in Controlled Natural
Language | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018, 15 pages,
LaTeX, (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show how a bi-directional grammar can be used to specify and verbalise
answer set programs in controlled natural language. We start from a program
specification in controlled natural language and translate this specification
automatically into an executable answer set program. The resulting answer set
program can be modified following certain naming conventions and the revised
version of the program can then be verbalised in the same subset of natural
language that was used as specification language. The bi-directional grammar is
parametrised for processing and generation, deals with referring expressions,
and exploits symmetries in the data structure of the grammar rules whenever
these grammar rules need to be duplicated. We demonstrate that verbalisation
requires sentence planning in order to aggregate similar structures with the
aim to improve the readability of the generated specification. Without
modifications, the generated specification is always semantically equivalent to
the original one; our bi-directional grammar is the first one that allows for
semantic round-tripping in the context of controlled natural language
processing. This paper is under consideration for acceptance in TPLP.
| [
{
"version": "v1",
"created": "Sat, 28 Apr 2018 09:12:38 GMT"
}
] | 1,525,132,800,000 | [
[
"Schwitter",
"Rolf",
""
]
] |
1804.10960 | Daniel Hein | Daniel Hein, Steffen Udluft, Thomas A. Runkler | Generating Interpretable Fuzzy Controllers using Particle Swarm
Optimization and Genetic Programming | Accepted at Genetic and Evolutionary Computation Conference 2018
(GECCO '18) | null | 10.1145/3205651.3208277 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomously training interpretable control strategies, called policies,
using pre-existing plant trajectory data is of great interest in industrial
applications. Fuzzy controllers have been used in industry for decades as
interpretable and efficient system controllers. In this study, we introduce a
fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning
(FGPRL) that can select the relevant state features, determine the size of the
required fuzzy rule set, and automatically adjust all the controller parameters
simultaneously. Each GP individual's fitness is computed using model-based
batch reinforcement learning (RL), which first trains a model using available
system samples and subsequently performs Monte Carlo rollouts to predict each
policy candidate's performance. We compare FGPRL to an extended version of a
related method called fuzzy particle swarm reinforcement learning (FPSRL),
which uses swarm intelligence to tune the fuzzy policy parameters. Experiments
using an industrial benchmark show that FGPRL is able to autonomously learn
interpretable fuzzy policies with high control performance.
| [
{
"version": "v1",
"created": "Sun, 29 Apr 2018 16:18:12 GMT"
}
] | 1,525,132,800,000 | [
[
"Hein",
"Daniel",
""
],
[
"Udluft",
"Steffen",
""
],
[
"Runkler",
"Thomas A.",
""
]
] |
1804.11022 | Yevgeniy Vorobeychik | Amin Ghafouri and Yevgeniy Vorobeychik and Xenofon Koutsoukos | Adversarial Regression for Detecting Attacks in Cyber-Physical Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attacks in cyber-physical systems (CPS) which manipulate sensor readings can
cause enormous physical damage if undetected. Detection of attacks on sensors
is crucial to mitigate this issue. We study supervised regression as a means to
detect anomalous sensor readings, where each sensor's measurement is predicted
as a function of other sensors. We show that several common learning approaches
in this context are still vulnerable to \emph{stealthy attacks}, which
carefully modify readings of compromised sensors to cause desired damage while
remaining undetected. Next, we model the interaction between the CPS defender
and attacker as a Stackelberg game in which the defender chooses detection
thresholds, while the attacker deploys a stealthy attack in response. We
present a heuristic algorithm for finding an approximately optimal threshold
for the defender in this game, and show that it increases system resilience to
attacks without significantly increasing the false alarm rate.
| [
{
"version": "v1",
"created": "Mon, 30 Apr 2018 02:09:25 GMT"
}
] | 1,525,132,800,000 | [
[
"Ghafouri",
"Amin",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
],
[
"Koutsoukos",
"Xenofon",
""
]
] |
1805.00634 | Joohyung Lee | Joohyung Lee and Yi Wang | A Probabilistic Extension of Action Language BC+ | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages,
LaTeX, 1 PDF figures (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a probabilistic extension of action language BC+. Just like BC+ is
defined as a high-level notation of answer set programs for describing
transition systems, the proposed language, which we call pBC+, is defined as a
high-level notation of LPMLN programs---a probabilistic extension of answer set
programs. We show how probabilistic reasoning about transition systems, such as
prediction, postdiction, and planning problems, as well as probabilistic
diagnosis for dynamic domains, can be modeled in pBC+ and computed using an
implementation of LPMLN.
| [
{
"version": "v1",
"created": "Wed, 2 May 2018 05:37:42 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Aug 2018 04:09:17 GMT"
}
] | 1,533,513,600,000 | [
[
"Lee",
"Joohyung",
""
],
[
"Wang",
"Yi",
""
]
] |
1805.00643 | Joohyung Lee | Joohyung Lee and Zhun Yang | Translating LPOD and CR-Prolog2 into Standard Answer Set Programs | Paper presented at the 34nd International Conference on Logic
Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 18 pages,
LaTeX, 0 PDF figures (arXiv:YYMM.NNNNN) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logic Programs with Ordered Disjunction (LPOD) is an extension of standard
answer set programs to handle preference using the construct of ordered
disjunction, and CR-Prolog2 is an extension of standard answer set programs
with consistency restoring rules and LPOD-like ordered disjunction. We present
reductions of each of these languages into the standard ASP language, which
gives us an alternative way to understand the extensions in terms of the
standard ASP language.
| [
{
"version": "v1",
"created": "Wed, 2 May 2018 06:16:50 GMT"
}
] | 1,525,305,600,000 | [
[
"Lee",
"Joohyung",
""
],
[
"Yang",
"Zhun",
""
]
] |
1805.00851 | Dimiter Dobrev | Dimiter Dobrev | How does the AI understand what's going on | null | International Journal "Information Theories and Applications",
Vol. 24, Number 4, 2017, pp.345-369 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most researchers regard AI as a static function without memory. This is one
of the few articles where AI is seen as a device with memory. When we have
memory, we can ask ourselves: "Where am I?", and "What is going on?" When we
have no memory, we have to assume that we are always in the same place and that
the world is always in the same state.
| [
{
"version": "v1",
"created": "Fri, 27 Apr 2018 11:06:29 GMT"
}
] | 1,525,305,600,000 | [
[
"Dobrev",
"Dimiter",
""
]
] |
1805.01109 | Tom Everitt | Tom Everitt, Gary Lea, Marcus Hutter | AGI Safety Literature Review | Published in International Joint Conference on Artificial
Intelligence (IJCAI), 2018 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of Artificial General Intelligence (AGI) promises to be a
major event. Along with its many potential benefits, it also raises serious
safety concerns (Bostrom, 2014). The intention of this paper is to provide an
easily accessible and up-to-date collection of references for the emerging
field of AGI safety. A significant number of safety problems for AGI have been
identified. We list these, and survey recent research on solving them. We also
cover works on how best to think of AGI from the limited knowledge we have
today, predictions for when AGI will first be created, and what will happen
after its creation. Finally, we review the current public policy on AGI.
| [
{
"version": "v1",
"created": "Thu, 3 May 2018 04:26:48 GMT"
},
{
"version": "v2",
"created": "Mon, 21 May 2018 16:30:20 GMT"
}
] | 1,526,947,200,000 | [
[
"Everitt",
"Tom",
""
],
[
"Lea",
"Gary",
""
],
[
"Hutter",
"Marcus",
""
]
] |
1805.01214 | Marius Lindauer | Marius Lindauer, Jan N. van Rijn and Lars Kotthoff | The Algorithm Selection Competitions 2015 and 2017 | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The algorithm selection problem is to choose the most suitable algorithm for
solving a given problem instance. It leverages the complementarity between
different approaches that is present in many areas of AI. We report on the
state of the art in algorithm selection, as defined by the Algorithm Selection
competitions in 2015 and 2017. The results of these competitions show how the
state of the art improved over the years. We show that although performance in
some cases is very good, there is still room for improvement in other cases.
Finally, we provide insights into why some scenarios are hard, and pose
challenges to the community on how to advance the current state of the art.
| [
{
"version": "v1",
"created": "Thu, 3 May 2018 10:47:31 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Oct 2018 08:58:54 GMT"
}
] | 1,538,697,600,000 | [
[
"Lindauer",
"Marius",
""
],
[
"van Rijn",
"Jan N.",
""
],
[
"Kotthoff",
"Lars",
""
]
] |
1805.01276 | Zied Bouraoui | Zied Bouraoui and Steven Schockaert | Learning Conceptual Space Representations of Interrelated Concepts | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several recently proposed methods aim to learn conceptual space
representations from large text collections. These learned representations
asso- ciate each object from a given domain of interest with a point in a
high-dimensional Euclidean space, but they do not model the concepts from this
do- main, and can thus not directly be used for catego- rization and related
cognitive tasks. A natural solu- tion is to represent concepts as Gaussians,
learned from the representations of their instances, but this can only be
reliably done if sufficiently many in- stances are given, which is often not
the case. In this paper, we introduce a Bayesian model which addresses this
problem by constructing informative priors from background knowledge about how
the concepts of interest are interrelated with each other. We show that this
leads to substantially better pre- dictions in a knowledge base completion
task.
| [
{
"version": "v1",
"created": "Thu, 3 May 2018 13:08:47 GMT"
},
{
"version": "v2",
"created": "Fri, 4 May 2018 07:59:29 GMT"
}
] | 1,525,651,200,000 | [
[
"Bouraoui",
"Zied",
""
],
[
"Schockaert",
"Steven",
""
]
] |
1805.01954 | Faraz Torabi | Faraz Torabi, Garrett Warnell, Peter Stone | Behavioral Cloning from Observation | International Joint Conference on Artificial Intelligence (IJCAI
2018) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans often learn how to perform tasks via imitation: they observe others
perform a task, and then very quickly infer the appropriate actions to take
based on their observations. While extending this paradigm to autonomous agents
is a well-studied problem in general, there are two particular aspects that
have largely been overlooked: (1) that the learning is done from observation
only (i.e., without explicit action information), and (2) that the learning is
typically done very quickly. In this work, we propose a two-phase, autonomous
imitation learning technique called behavioral cloning from observation (BCO),
that aims to provide improved performance with respect to both of these
aspects. First, we allow the agent to acquire experience in a self-supervised
fashion. This experience is used to develop a model which is then utilized to
learn a particular task by observing an expert perform that task without the
knowledge of the specific actions taken. We experimentally compare BCO to
imitation learning methods, including the state-of-the-art, generative
adversarial imitation learning (GAIL) technique, and we show comparable task
performance in several different simulation domains while exhibiting increased
learning speed after expert trajectories become available.
| [
{
"version": "v1",
"created": "Fri, 4 May 2018 22:36:58 GMT"
},
{
"version": "v2",
"created": "Fri, 11 May 2018 21:48:52 GMT"
}
] | 1,526,342,400,000 | [
[
"Torabi",
"Faraz",
""
],
[
"Warnell",
"Garrett",
""
],
[
"Stone",
"Peter",
""
]
] |
1805.02102 | Maria Luisa Damiani | Maria Luisa Damiani, Fatima Hachem, Issa Hamza, Nathan Ranc, Paul
Moorcroft, Francesca Cagnacci | Cluster-based trajectory segmentation with local noise | 41 pages, Data Mining and Knowledge Discovery (2018) | Data Mining and Knowledge Discovery, 2018, Vol 32, Issue 4,
1017-1055 | 10.1007/s10618-018-0561-2 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a framework for the partitioning of a spatial trajectory in a
sequence of segments based on spatial density and temporal criteria. The result
is a set of temporally separated clusters interleaved by sub-sequences of
unclustered points. A major novelty is the proposal of an outlier or noise
model based on the distinction between intra-cluster (local noise) and
inter-cluster noise (transition): the local noise models the temporary absence
from a residence while the transition the definitive departure towards a next
residence. We analyze in detail the properties of the model and present a
comprehensive solution for the extraction of temporally ordered clusters. The
effectiveness of the solution is evaluated first qualitatively and next
quantitatively by contrasting the segmentation with ground truth. The ground
truth consists of a set of trajectories of labeled points simulating animal
movement. Moreover, we show that the approach can streamline the discovery of
additional derived patterns, by presenting a novel technique for the analysis
of periodic movement. From a methodological perspective, a valuable aspect of
this research is that it combines the theoretical investigation with the
application and external validation of the segmentation framework. This paves
the way to an effective deployment of the solution in broad and challenging
fields such as e-science.
| [
{
"version": "v1",
"created": "Sat, 5 May 2018 18:46:46 GMT"
}
] | 1,529,366,400,000 | [
[
"Damiani",
"Maria Luisa",
""
],
[
"Hachem",
"Fatima",
""
],
[
"Hamza",
"Issa",
""
],
[
"Ranc",
"Nathan",
""
],
[
"Moorcroft",
"Paul",
""
],
[
"Cagnacci",
"Francesca",
""
]
] |
1805.02181 | Christian Jilek | Christian Jilek, Markus Schr\"oder, Sven Schwarz, Heiko Maus, Andreas
Dengel | Context Spaces as the Cornerstone of a Near-Transparent &
Self-Reorganizing Semantic Desktop | 5 pages, 2 figures (high-res versions in attachments), 1 demo video
(in attachments) | The Semantic Web: ESWC 2018 Satellite Events, pp. 89-94, Springer | 10.1007/978-3-319-98192-5_17 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing Semantic Desktops are still reproached for being too complicated to
use or not scaling well. Besides, a real "killer app" is still missing. In this
paper, we present a new prototype inspired by NEPOMUK and its successors having
a semantic graph and ontologies as its basis. In addition, we introduce the
idea of context spaces that users can directly interact with and work on. To
make them available in all applications without further ado, the system is
transparently integrated using mostly standard protocols complemented by a
sidebar for advanced features. By exploiting collected context information and
applying Managed Forgetting features (like hiding, condensation or deletion),
the system is able to dynamically reorganize itself, which also includes a kind
of tidy-up-itself functionality. We therefore expect it to be more scalable
while providing new levels of user support. An early prototype has been
implemented and is presented in this demo.
| [
{
"version": "v1",
"created": "Sun, 6 May 2018 10:07:13 GMT"
}
] | 1,533,513,600,000 | [
[
"Jilek",
"Christian",
""
],
[
"Schröder",
"Markus",
""
],
[
"Schwarz",
"Sven",
""
],
[
"Maus",
"Heiko",
""
],
[
"Dengel",
"Andreas",
""
]
] |
1805.02205 | Ruiwei Wang | Ruiwei Wang, Wei Xia and Roland H. C. Yap | Correlation Heuristics for Constraint Programming | Paper presented at the 29th IEEE International Conference on Tools
with Artificial Intelligence, ICTAI 2017, Boston, Massachusetts, USA,
November 6-8, 2017 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective general-purpose search strategies are an important component in
Constraint Programming. We introduce a new idea, namely, using correlations
between variables to guide search. Variable correlations are measured and
maintained by using domain changes during constraint propagation. We propose
two variable heuristics based on the correlation matrix, crbs-sum and crbs-max.
We evaluate our correlation heuristics with well known heuristics, namely,
dom/wdeg, impact-based search and activity-based search. Experiments on a large
set of benchmarks show that our correlation heuristics are competitive with the
other heuristics, and can be the fastest on many series.
| [
{
"version": "v1",
"created": "Sun, 6 May 2018 13:09:17 GMT"
},
{
"version": "v2",
"created": "Thu, 24 May 2018 08:36:58 GMT"
}
] | 1,527,206,400,000 | [
[
"Wang",
"Ruiwei",
""
],
[
"Xia",
"Wei",
""
],
[
"Yap",
"Roland H. C.",
""
]
] |
1805.02290 | Zahra Riahi Samani | Zahra Riahi Samani, Mehrnoush Shamsfard | The State of the Art in Developing Fuzzy Ontologies: A Survey | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conceptual formalism supported by typical ontologies may not be sufficient to
represent uncertainty information which is caused due to the lack of clear cut
boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer
a way to deal with this uncertainty. This paper describes the state of the art
in developing fuzzy ontologies. The survey is produced by studying about 35
works on developing fuzzy ontologies from a batch of 100 articles in the field
of fuzzy ontologies.
| [
{
"version": "v1",
"created": "Sun, 6 May 2018 22:59:22 GMT"
}
] | 1,525,737,600,000 | [
[
"Samani",
"Zahra Riahi",
""
],
[
"Shamsfard",
"Mehrnoush",
""
]
] |
1805.02363 | Martin Mladenov | Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay
Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans | Planning and Learning with Stochastic Action Sets | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many practical uses of reinforcement learning (RL) the set of actions
available at a given state is a random variable, with realizations governed by
an exogenous stochastic process. Somewhat surprisingly, the foundations for
such sequential decision processes have been unaddressed. In this work, we
formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to
provide these foundations. We show that optimal policies and value functions in
this model have a structure that admits a compact representation. From an RL
perspective, we show that Q-learning with sampled action sets is sound. In
model-based settings, we consider two important special cases: when individual
actions are available with independent probabilities; and a sampling-based
model for unknown distributions. We develop poly-time value and policy
iteration methods for both cases; and in the first, we offer a poly-time linear
programming solution.
| [
{
"version": "v1",
"created": "Mon, 7 May 2018 06:48:41 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Feb 2021 19:31:44 GMT"
}
] | 1,613,433,600,000 | [
[
"Boutilier",
"Craig",
""
],
[
"Cohen",
"Alon",
""
],
[
"Daniely",
"Amit",
""
],
[
"Hassidim",
"Avinatan",
""
],
[
"Mansour",
"Yishay",
""
],
[
"Meshi",
"Ofer",
""
],
[
"Mladenov",
"Martin",
""
],
[
"Schuurmans",
"Dale",
""
]
] |
1805.02861 | Anton\'in Ku\v{c}era | Tom\'a\v{s} Br\'azdil, Anton\'in Ku\v{c}era, Vojt\v{e}ch \v{R}eh\'ak | Synthesizing Efficient Solutions for Patrolling Problems in the Internet
Environment | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose an algorithm for constructing efficient patrolling strategies in
the Internet environment, where the protected targets are nodes connected to
the network and the patrollers are software agents capable of
detecting/preventing undesirable activities on the nodes. The algorithm is
based on a novel compositional principle designed for a special class of
strategies, and it can quickly construct (sub)optimal solutions even if the
number of targets reaches hundreds of millions.
| [
{
"version": "v1",
"created": "Tue, 8 May 2018 07:15:53 GMT"
},
{
"version": "v2",
"created": "Thu, 10 May 2018 10:22:58 GMT"
}
] | 1,525,996,800,000 | [
[
"Brázdil",
"Tomáš",
""
],
[
"Kučera",
"Antonín",
""
],
[
"Řehák",
"Vojtěch",
""
]
] |
1805.02895 | Dong Zhou | Dong Zhou, Huimin Ma, Yuhan Dong | Driving maneuvers prediction based on cognition-driven and data-driven
method | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advanced Driver Assistance Systems (ADAS) improve driving safety
significantly. They alert drivers from unsafe traffic conditions when a
dangerous maneuver appears. Traditional methods to predict driving maneuvers
are mostly based on data-driven models alone. However, existing methods to
understand the driver's intention remain an ongoing challenge due to a lack of
intersection of human cognition and data analysis. To overcome this challenge,
we propose a novel method that combines both the cognition-driven model and the
data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN)
which fuses the data inside the vehicle and the data outside the vehicle in a
cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM)
branches regulated by human reaction time. Experiments on the Brain4Cars
benchmark dataset demonstrate that the proposed method outperforms previous
methods and achieves state-of-the-art performance.
| [
{
"version": "v1",
"created": "Tue, 8 May 2018 08:35:52 GMT"
}
] | 1,525,824,000,000 | [
[
"Zhou",
"Dong",
""
],
[
"Ma",
"Huimin",
""
],
[
"Dong",
"Yuhan",
""
]
] |
1805.03138 | Fatemeh Zahedi | Fatemeh Zahedi and Zahra Zahedi | A review of neuro-fuzzy systems based on intelligent control | 4 pages, 7 figures, 1 table, Journal of Electrical and Electronic
Engineering | Journal of Electrical and Electronic Engineering 2015; 3(2-1):
58-61 | 10.11648/j.jeee.s.2015030201.23 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The system's ability to adapt and self-organize are two key factors when it
comes to how well the system can survive the changes to the environment and the
plant they work within. Intelligent control improves these two factors in
controllers. Considering the increasing complexity of dynamic systems along
with their need for feedback controls, using more complicated controls has
become necessary and intelligent control can be a suitable response to this
necessity. This paper briefly describes the structure of intelligent control
and provides a review on fuzzy logic and neural networks which are some of the
base methods for intelligent control. The different aspects of these two
methods are then compared together and an example of a combined method is
presented.
| [
{
"version": "v1",
"created": "Sun, 6 May 2018 12:30:22 GMT"
}
] | 1,525,824,000,000 | [
[
"Zahedi",
"Fatemeh",
""
],
[
"Zahedi",
"Zahra",
""
]
] |
1805.03545 | Martyn Amos | Huw Lloyd and Martyn Amos | Solving Sudoku with Ant Colony Optimisation | Submitted | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a new Ant Colony Optimisation-based algorithm for
Sudoku, which out-performs existing methods on large instances. Our method
includes a novel anti-stagnation operator, which we call Best Value
Evaporation.
| [
{
"version": "v1",
"created": "Wed, 9 May 2018 14:14:08 GMT"
}
] | 1,525,910,400,000 | [
[
"Lloyd",
"Huw",
""
],
[
"Amos",
"Martyn",
""
]
] |
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