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Nicolas Riesterer, Christian Becker-Asano, Julien Hué, Christian Dornhege und Bernhard Nebel.
The Hybrid Agent MARCO.
In
Proceedings of the 16th International Conference on Multimodal Interaction, S. 80-81.
2014.
(Abstract einblenden)
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(PDF)
We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent’s face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emotions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.
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Christian Becker-Asano, Eduardo Meneses, Nicolas Riesterer, Julien Hué, Christian Dornhege und Bernhard Nebel.
The Hybrid Agent MARCO: A Multimodal Autonomous Robotic Chess Opponent.
In
Proceedings of the 2nd International Confernce on Human-Agent Interaction, S. 173-176.
2014.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent’s face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emo- tions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.
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Matthias Westphal und Julien Hué.
A Concise Horn Theory for RCC8.
In
Proceedings of European Conference on Artificial Intelligence (ECAI'14).
2014.
(Abstract einblenden)
(Abstract ausblenden)
(Translation Script; TAR.GZ)
RCC8 is a well-known constraint language for expressing and reasoning
about spatial knowledge.
We state a simple and concise Horn theory for RCC8
analogous to the ORD-Horn theory for temporal reasoning.
This theory allows for expressing RCC8
and retains tractability of
the well-known Horn reduct of RCC8.
Further,
it is much more adequate
for practical purposes
in the area of logic programming
and surpasses previous attempts.
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Julien Hué, Matthias Westphal und Stefan Wölfl.
Towards a new semantic for Possibilistic Answer Sets.
In
Proceedings of Advances in Artificial Intelligence (KI'14).
2014.
(Abstract einblenden)
(Abstract ausblenden)
(Springer Online; DOI)
(DBLP)
Possibilistic Answer Set Programming is an extension of the standard ASP
framework that allows for attaching degrees of certainty to the rules in
ASP programs. In the literature, several semantics for such PASP-programs
have been presented, each of them having particular strengths and
weaknesses.
In this work we present a new semantics that employs so-called
iota-answer sets, a solution concept introduced by Gebser et al.~(2009), in
order to find solutions for standard ASP programs with odd cycles or
auto-blocking rules. This is achieved by considering maximal subsets of a
given ASP program for which answer sets exist. The main idea of our work is
to integrate iota-semantics into the possibilistic framework in such a way
that degrees of certainty are not only assigned to atoms mentioned in
the answer sets, but also to the answer sets themselves.
Our approach gives more satisfactory solutions and avoids
counter-intuitive examples arising in the other approaches.
We compare our approach to existing ones and present a translation into the
standard ASP framework allowing the computation of solutions by existing
tools.
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Matthias Westphal, Julien Hué und Stefan Wölfl.
On the scope of Qualitative Constraint Calculi.
In
Proceedings of Advances in Artificial Intelligence (KI'14).
2014.
(Abstract einblenden)
(Abstract ausblenden)
(Springer Online; DOI)
(DBLP)
Qualitative constraint calculi are a special kind of relation algebras
defined by Ligozat and Renz for reasoning about binary constraints.
Although this approach is known to be limited it has prevailed in the
area of qualitative spatial and temporal reasoning.
In this paper we revisit the definition of these calculi, contrast it
with alternative approaches, and analyze general properties. Our
results indicate that the concept of qualitative constraint calculi is
both too narrow and too general: it disallows different approaches, but
its setup already enables arbitrarily hard problems.
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Matthias Westphal, Julien Hué, Stefan Wölfl und Bernhard Nebel.
Transition Constraints: A Study on the Computational Complexity of Qualitative Change.
In
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI'13), S. 1169-1175.
2013.
(Abstract einblenden)
(Abstract ausblenden)
(Online; PDF)
(DBLP)
Many formalisms discussed in the literature on
qualitative spatial reasoning are designed for expressing static spatial constraints only. However,
dynamic situations arise in virtually all applications
of these formalisms, which makes it necessary to
study variants and extensions involving change.
This paper presents a study on the computational
complexity of qualitative change. More precisely,
we discuss the reasoning task of finding a solution to a temporal sequence of static reasoning
problems where this sequence is subject to additional transition constraints. Our focus is primarily on smoothness and continuity constraints: we
show how such transitions can be defined as relations and expressed within qualitative constraint
formalisms. Our results demonstrate that for point-based constraint formalisms the interesting fragments become NP-complete in the presence of continuity constraints, even if the satisfiability problem
of its static descriptions is tractable.
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Matthias Westphal, Julien Hué und Stefan Wölfl.
On the Propagation Strength of SAT Encodings for Qualitative Temporal Reasoning.
In
Proceedings of International Conference on Tools for Artificial Intelligence (ICTAI'13).
2013.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
(DOI)
(DBLP)
(Translation Script; TAR.GZ)
Several studies in Qualitative Spatial and Temporal Reasoning
discuss translations of the satisfiability problem on qualitative
constraint languages into propositional SAT.
Most of these encodings focus on compactness, while propagation strength
is seldom discussed.
In this work, we focus on temporal reasoning with the Point Algebra and
Allen's
Interval Algebra.
We understand all encodings as a combination of propagation and
search.
We first give a systematic analysis of existing propagation approaches
for these constraint languages.
They are studied and ordered with respect to their propagation strength
and refutation completeness for classes of input instances.
Secondly, we discuss how existing encodings can be derived from
such propagation approaches.
We conclude our work with an empirical evaluation which shows that the
older
ORD-encoding by Nebel and Bürckert performs better than more recently suggested encodings.
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Julien Hué und Matthias Westphal.
Revising Qualitative Constraint Network: Definition and Implementation.
In
Internationial Conference on Tools for Artificial Intelligence (ICTAI), S. 548-555.
2012.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
Qualitative Spatial and Temporal Reasoning is a
central topic in Artificial Intelligence. In particular, it is aimed at
application scenarios dealing with uncertain information and thus
needs to be able to handle dynamic beliefs. This makes merging
and revision of qualitative information important topics. While
merging has been studied extensively, revision which describes
what is happening when one learns new information about a
static world has been overlooked. In this paper, we propose to
fill the gap by providing two revision operations for qualitative
calculi. In order to implement these operations, we give algo-
rithms for revision and analyze the computational complexity of
these problems. Finally, we present an implementation of these
algorithms based on a qualitative constraint solver and provide
an experimental evaluation.
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Julien Hué, Matthias Westphal und Stefan Wölfl.
An automatic decomposition method for qualitative spatial and temporal reasoning.
In
International Conference on Tools for Artificial Intelligence (ICTAI), S. 588-595.
2012.
(Abstract einblenden)
(Abstract ausblenden)
(PDF)
(DBLP)
Qualitative spatial and temporal reasoning is a
research field that studies relational, constraint-based formalisms
for representing, and reasoning about, spatial and temporal
information. The standard approach for checking consistency is
based on an exhaustive representation of possible configurations
between three entities, the so-called composition tables. These
tables, however, encode semantic background knowledge in a
redundant way, which becomes a size and efficiency issue, when
the composition table needs to be grounded as done in SAT
encodings of problem instances. In this paper, we present a
new framework that allows for decomposing composition tables
into logically simpler parts, while preserving logical equivalence,
e.g., the decomposition in start- and end-points for Allen’s
Interval Calculus. We show that finding such decompositions
is an NP-complete problem and present a SAT-based method to
generate decompositions. Finally, we discuss the impact of our
decomposition method on SAT encodings of problem instances,
and present a reasoning system built on decompositions that
compares favorably with state-of-the-art solvers.
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Matthias Westphal und Julien Hué.
Nogoods in Qualitative Constraint-based Reasoning.
In
KI 2012: Advances in Artificial Intelligence (KI 2012), S. 180-192.
Springer-Verlag 2012.
(Authors' preprint. The final publication is available at
www.springerlink.com.).
(Abstract einblenden)
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(PDF)
The prevalent method of increasing reasoning efficiency in the domain
of qualitative constraint-based spatial and temporal reasoning is to
use domain splitting based on so-called tractable subclasses.
In this paper we analyze the application of nogood learning with
restarts in combination with domain splitting.
Previous results on nogood recording in the constraint satisfaction field
feature learnt nogoods as a global constraint that allows for enforcing
generalized arc consistency. We present an extension of such a technique
capable of handling domain splitting, evaluate its benefits for
qualitative constraint-based reasoning, and compare it with alternative
approaches.
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Salem Benferhat, Julien Hué, Sylvain Lagrue und Julien Rossit.
Merging Interval-Based Possibilistic Belief Bases.
In
International Conference on Scalable Uncertainty Management (SUM), S. 447-458.
2012.
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(PDF)
