1,166 research outputs found
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
We introduce a logic for temporal beliefs and intentions based on Shoham's
database perspective. We separate strong beliefs from weak beliefs. Strong
beliefs are independent from intentions, while weak beliefs are obtained by
adding intentions to strong beliefs and everything that follows from that. We
formalize coherence conditions on strong beliefs and intentions. We provide
AGM-style postulates for the revision of strong beliefs and intentions. We show
in a representation theorem that a revision operator satisfying our postulates
can be represented by a pre-order on interpretations of the beliefs, together
with a selection function for the intentions
Intention as Commitment toward Time
In this paper we address the interplay among intention, time, and belief in
dynamic environments. The first contribution is a logic for reasoning about
intention, time and belief, in which assumptions of intentions are represented
by preconditions of intended actions. Intentions and beliefs are coherent as
long as these assumptions are not violated, i.e. as long as intended actions
can be performed such that their preconditions hold as well. The second
contribution is the formalization of what-if scenarios: what happens with
intentions and beliefs if a new (possibly conflicting) intention is adopted, or
a new fact is learned? An agent is committed to its intended actions as long as
its belief-intention database is coherent. We conceptualize intention as
commitment toward time and we develop AGM-based postulates for the iterated
revision of belief-intention databases, and we prove a Katsuno-Mendelzon-style
representation theorem.Comment: 83 pages, 4 figures, Artificial Intelligence journal pre-prin
Some approaches to Belief Bases Merge
In this work, we de fine some non-prioritized merge operators, that is, operators for the consistent union of
belief bases. We de ne some postulates for several kinds of merge operator and we give different constructions:
trivial merge, partial meet merge and kernel merge. For some constructions we provide representation theorems
linking construction with a set of postulates. Finally, we propose that the formulated operators can be used in
some multi-agent systemsVII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
AGM 25 years: twenty-five years of research in belief change
The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors,
and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet
Contraction and Revision Functions” was the starting-point of a large and
rapidly growing literature that employs formal models in the investigation
of changes in belief states and databases. In this review, the first twenty five years of this development are summarized. The topics covered include
equivalent characterizations of AGM operations, extended representations of
the belief states, change operators not included in the original framework,
iterated change, applications of the model, its connections with other formal
frameworks, computatibility of AGM operations, and criticism of the model.info:eu-repo/semantics/publishedVersio
Dynamic presupposition of want and polarity sensitivity
In this paper, I argue that content of some presuppositions is determined dynamically. In particular, it is shown that the presupposition of want in control constructions depends on the interpretation of an action in the complement clause. Different presuppositional content of sentences with want is argued for using new and known observations about licensing of Polarity Sensitive Items. I propose to capture the dynamic nature of the presupposition of want using the AGM paradigm for belief revision (Alchourrán, Gärdenfors & Makinson 1985). Finally, I show that sensitivity to the interpretation of an action as intentional versus accidental is not specific to polarity system, but can be found across different domains of the grammar in many unrelated languages
Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments
The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents – or components – each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems which operate in environments pervaded by different types of uncertainty. Furthermore, existing approaches for dealing with uncertainty typically do not offer syntactical or tractable ways of reasoning about uncertainty. This complicates their integration with BDI implementations, which heavily rely on fast and reactive decisions. In this paper, we advance the state-of-the-art w.r.t. handling different types of uncertainty in BDI agents. The contributions of this paper are, first, a new way of modelling the beliefs of an agent as a set of epistemic states. Each epistemic state can use a distinct underlying uncertainty theory and revision strategy, and commensurability between epistemic states is achieved through a stratification approach. Second, we present a novel syntactic approach to revising beliefs given unreliable input. We prove that this syntactic approach agrees with the semantic definition, and we identify expressive fragments that are particularly useful for resource-bounded agents. Third, we introduce full operational semantics that extend Can, a popular semantics for BDI, to establish how reasoning about uncertainty can be tightly integrated into the BDI framework. Fourth, we provide comprehensive experimental results to highlight the usefulness and feasibility of our approach, and explain how the generic epistemic state can be instantiated into various representations
Some approaches to Belief Bases Merge
In this work, we de fine some non-prioritized merge operators, that is, operators for the consistent union of
belief bases. We de ne some postulates for several kinds of merge operator and we give different constructions:
trivial merge, partial meet merge and kernel merge. For some constructions we provide representation theorems
linking construction with a set of postulates. Finally, we propose that the formulated operators can be used in
some multi-agent systemsVII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
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
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