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On action theory change

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dc.contributor.author Varzinczak, IJ
dc.date.accessioned 2010-11-15T12:41:19Z
dc.date.available 2010-11-15T12:41:19Z
dc.date.issued 2010-02
dc.identifier.citation Varzinczak, IJ. 2010. On action theory change. Journal of Artificial Intelligence Research, Vol. 37, pp 189-246 en
dc.identifier.issn 1076-9757
dc.identifier.uri http://www.jair.org/media/2959/live-2959-4850-jair.pdf
dc.identifier.uri http://hdl.handle.net/10204/4557
dc.description Copyright: 2010 AI Access Foundation en
dc.description.abstract As historically acknowledged in the Reasoning about Actions and Change community, intuitiveness of a logical domain description cannot be fully automated. Moreover, like any other logical theory, action theories may also evolve, and thus knowledge engineers need revision methods to help in accommodating new incoming information about the behaviour of actions in an adequate manner. The present work is about changing action domain descriptions in multimodal logic. Its contribution is threefold: first we revisit the semantics of action theory contraction proposed in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness with respect to our semantics for those action theories that satisfy a principle of modularity investigated in previous work. Since modularity can be ensured for every action theory and, as we show here, needs to be computed at most once during the evolution of a domain description, it does not represent a limitation at all to the method here studied. Finally we state AGM-like postulates for action theory contraction and assess the behavior of our operators with respect to them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction. en
dc.language.iso en en
dc.publisher AI Access Foundation en
dc.relation.ispartofseries Journal Article en
dc.subject Artificial intelligence en
dc.subject Action theories en
dc.subject Semantics en
dc.subject Knowledge representation en
dc.subject Knowledge reasoning en
dc.subject Contraction en
dc.subject Modularity en
dc.title On action theory change en
dc.type Article en
dc.identifier.apacitation Varzinczak, I. (2010). On action theory change. http://hdl.handle.net/10204/4557 en_ZA
dc.identifier.chicagocitation Varzinczak, IJ "On action theory change." (2010) http://hdl.handle.net/10204/4557 en_ZA
dc.identifier.vancouvercitation Varzinczak I. On action theory change. 2010; http://hdl.handle.net/10204/4557. en_ZA
dc.identifier.ris TY - Article AU - Varzinczak, IJ AB - As historically acknowledged in the Reasoning about Actions and Change community, intuitiveness of a logical domain description cannot be fully automated. Moreover, like any other logical theory, action theories may also evolve, and thus knowledge engineers need revision methods to help in accommodating new incoming information about the behaviour of actions in an adequate manner. The present work is about changing action domain descriptions in multimodal logic. Its contribution is threefold: first we revisit the semantics of action theory contraction proposed in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness with respect to our semantics for those action theories that satisfy a principle of modularity investigated in previous work. Since modularity can be ensured for every action theory and, as we show here, needs to be computed at most once during the evolution of a domain description, it does not represent a limitation at all to the method here studied. Finally we state AGM-like postulates for action theory contraction and assess the behavior of our operators with respect to them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction. DA - 2010-02 DB - ResearchSpace DP - CSIR KW - Artificial intelligence KW - Action theories KW - Semantics KW - Knowledge representation KW - Knowledge reasoning KW - Contraction KW - Modularity LK - https://researchspace.csir.co.za PY - 2010 SM - 1076-9757 T1 - On action theory change TI - On action theory change UR - http://hdl.handle.net/10204/4557 ER - en_ZA


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