K
Kim Bauters
Researcher at Queen's University Belfast
Publications - 35
Citations - 228
Kim Bauters is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Answer set programming & Stable model semantics. The author has an hindex of 8, co-authored 34 publications receiving 207 citations. Previous affiliations of Kim Bauters include University of Bristol & Ghent University.
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Proceedings Article
Possibilistic answer set programming revisited
TL;DR: This work introduces a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions, and naturally generalized to defineanswer sets of PASP programs.
Posted Content
Possibilistic Answer Set Programming Revisited
TL;DR: In this article, the possibilistic answer set programming (PASP) extends ASP by attaching to each rule a degree of certainty, and a characterization of answer sets of classical ASP programs is presented.
Proceedings Article
CAN(PLAN)+: extending the operational semantics of the BDI architecture to deal with uncertain information
TL;DR: The syntax and semantics of a BDI agent are extended accordingly and fragments with computationally efficient semantics are identified and defined to define an appropriate form of lookahead planning.
Book ChapterDOI
Probabilistic Planning in AgentSpeak using the POMDP framework.
Kim Bauters,Kevin McAreavey,Jun Hong,Yingke Chen,Weiru Liu,Lluís Godo,Lluís Godo,Carles Sierra,Carles Sierra +8 more
TL;DR: This work proposes the \(\text {AgentSpeak}^+\) framework, which extends AgentSpeak with a mechanism for probabilistic planning, and uses epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions.
Proceedings ArticleDOI
Possible and Necessary Answer Sets of Possibilistic Answer Set Programs
TL;DR: The main contribution of this paper is the introduction of a new semantics for PASP as well as a study of the resulting complexity.