Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Sarah Calderwood, Kim Bauters, Weiru Liu, Jun Hong

Research output: Contribution to conferenceConference Paperpeer-review

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Abstract

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.
Original languageEnglish
Pages9-14
Number of pages6
Publication statusPublished - 10 Nov 2014
Event4th International Workshop on Combinations of Intelligent Methods and Applications (CIMA14) - Limassol, Cyprus
Duration: 10 Nov 201411 Nov 2014
Conference number: 4
http://aigroup.ceid.upatras.gr/cima2014/?q=node/6

Conference

Conference4th International Workshop on Combinations of Intelligent Methods and Applications (CIMA14)
Abbreviated titleCIMA
CountryCyprus
CityLimassol
Period10/11/1411/11/14
Internet address

Keywords

  • Dempster-Shafer theory
  • information fusion
  • context-dependent merging
  • BDI plan selection

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