1,762 research outputs found
Compact preference representation and combinatorial vote
In many real-world social choice problems, the set of alternatives is defined as the Cartesian product of (finite) domain values for each of a given set of variables, and these variables cannot be asusmed to be prefentially independent (to take an example, if X is the main dish of a dinner and Y the wine, preferences over Y depends on the value taken for X). Such combinatorial domains are much too large to allow for representing preference relations or utility functions explicitly (that is, by listing alternatives together with their rank or utility); for this reason, artificial intelligence researchers have been developing languages for specifying preference relations or utility functions as compactly as possible. This paper first gives a brief survey of compact representation languages, and then discusses its role for representing and solving social choice problems, especially from the point of view of computational complexity
Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change
Copyright in each article is held by the authors.
Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action
and Change (NRAC) has an active and loyal community.
Since its inception in 1995, the workshop has been held seven
times in conjunction with IJCAI, and has experienced growing
success. We hope to build on this success again this eighth
year with an interesting and fruitful day of discussion.
The areas of reasoning about action, non-monotonic reasoning
and belief revision are among the most active research
areas in Knowledge Representation, with rich inter-connections
and practical applications including robotics, agentsystems,
commonsense reasoning and the semantic web.
This workshop provides a unique opportunity for researchers
from all three fields to be brought together at a single forum
with the prime objectives of communicating important recent
advances in each field and the exchange of ideas. As these
fundamental areas mature it is vital that researchers maintain
a dialog through which they can cooperatively explore
common links. The goal of this workshop is to work against
the natural tendency of such rapidly advancing fields to drift
apart into isolated islands of specialization.
This year, we have accepted ten papers authored by a diverse
international community. Each paper has been subject
to careful peer review on the basis of innovation, significance
and relevance to NRAC. The high quality selection of work
could not have been achieved without the invaluable help of
the international Program Committee.
A highlight of the workshop will be our invited speaker
Professor Hector Geffner from ICREA and UPF in Barcelona,
Spain, discussing representation and inference in modern
planning. Hector Geffner is a world leader in planning,
reasoning, and knowledge representation; in addition to his
many important publications, he is a Fellow of the AAAI, an
associate editor of the Journal of Artificial Intelligence Research
and won an ACM Distinguished Dissertation Award
in 1990
Belief merging in Dynamic Logic of Propositional Assignments
International audienceWe study syntactical merging operations that are defined semantically by means of the Hamming distance between valuations; more precisely, we investigate the ÎŁ-semantics, Gmax-semantics and max-semantics. We work with a logical language containing merging operators as connectives, as opposed to the metalanguage operations of the literature. We capture these merging operators as programs of Dynamic Logic of Propositional Assignments DL-PA. This provides a syntactical characterisation of the three semantically defined merging operators, and a proof system for DL-PA therefore also provides a proof system for these merging operators. We explain how PSPACE membership of the model checking and satisfiability problem of star-free DL-PA can be extended to the variant of DL-PA where symbolic disjunctions that are parametrised by sets (that are not defined as abbreviations, but are proper connectives) are built into the language. As our merging operators can be polynomially embedded into this variant of DL-PA, we obtain that both the model checking and the satisfiability problem of a formula containing possibly nested merging operators is in PSPACE
Reasoning and querying bounds on differences with layered preferences
Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and
calories. Recently, some approaches have extended the
BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction,
considering probabilities or preferences. While previous
approaches have mainly aimed at providing an
optimal solution to the set of constraints, we propose
an innovative class of approaches in which constraint
propagation algorithms aim at identifying the \u201cspace of
solutions\u201d (i.e., the minimal network) with their preferences,
and query answering mechanisms are provided
to explore the space of solutions as required, for
example, in decision support tasks. Aiming at generality,
we propose a class of approaches parametrized
over user\u2010defined scales of qualitative preferences (e.g.,
Low, Medium, High, and Very High), utilizing the resume
and extension operations to combine preferences,
and considering different formalisms to associate preferences
with BoDs. We consider both \u201cgeneral\u201d preferences
and a form of layered preferences that we call
\u201cpyramid\u201d preferences. The properties of the class of
approaches are also analyzed. In particular, we show
that, when the resume and extension operations are
defined such that they constitute a closed semiring, a
more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation
of the constraint propagation algorithms
Complexity Results for Manipulation, Bribery and Control of the Kemeny Judgment Aggregation Procedure
We study the computational complexity of several scenarios of strategic
behavior for the Kemeny procedure in the setting of judgment aggregation. In
particular, we investigate (1) manipulation, where an individual aims to
achieve a better group outcome by reporting an insincere individual opinion,
(2) bribery, where an external agent aims to achieve an outcome with certain
properties by bribing a number of individuals, and (3) control (by adding or
deleting issues), where an external agent aims to achieve an outcome with
certain properties by influencing the set of issues in the judgment aggregation
situation. We show that determining whether these types of strategic behavior
are possible (and if so, computing a policy for successful strategic behavior)
is complete for the second level of the Polynomial Hierarchy. That is, we show
that these problems are -complete
Weighted logics for artificial intelligence : an introductory discussion
International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas
Logical preference representation and combinatorial vote,
We introduce the notion of combinatorial vote, where a group of agents (or voters) is supposed to express preferences and come to a common decision concerning a set of nonindependent variables to assign. We study two key issues pertaining to combinatorial vote, namely preference representation and the automated choice of an optimal decision. For each of these issues, we briefly review the state of the art, we try to define the main problems to be solved and identify their computational complexity
Language-Based Causal Representation Learning
Consider the finite state graph that results from a simple, discrete,
dynamical system in which an agent moves in a rectangular grid picking up and
dropping packages. Can the state variables of the problem, namely, the agent
location and the package locations, be recovered from the structure of the
state graph alone without having access to information about the objects, the
structure of the states, or any background knowledge? We show that this is
possible provided that the dynamics is learned over a suitable
domain-independent first-order causal language that makes room for objects and
relations that are not assumed to be known. The preference for the most compact
representation in the language that is compatible with the data provides a
strong and meaningful learning bias that makes this possible. The language of
structured causal models (SCMs) is the standard language for representing
(static) causal models but in dynamic worlds populated by objects, first-order
causal languages such as those used in "classical AI planning" are required.
While "classical AI" requires handcrafted representations, similar
representations can be learned from unstructured data over the same languages.
Indeed, it is the languages and the preference for compact representations in
those languages that provide structure to the world, uncovering objects,
relations, and causes
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