DSpace
 

Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4557

Title: On action theory change
Authors: Varzinczak, IJ
Keywords: Artificial intelligence
Action theories
Semantics
Knowledge representation
Knowledge reasoning
Contraction
Modularity
Issue Date: Feb-2010
Publisher: AI Access Foundation
Citation: Varzinczak, IJ. 2010. On action theory change. Journal of Artificial Intelligence Research, Vol. 37, pp 189-246
Series/Report no.: Journal Article
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.
Description: Copyright: 2010 AI Access Foundation
URI: http://www.jair.org/media/2959/live-2959-4850-jair.pdf
http://hdl.handle.net/10204/4557
ISSN: 1076-9757
Appears in Collections:Human factors
Advanced mathematical modelling and simulation
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Varzinczak_2010.pdf588.35 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback