Alternatives to Threshold-Based Desire Selection in Bayesian BDI Agents

Bernardo Luz, Felipe Meneguzzi, Rosa Vicari

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)


Bayesian BDI agents employ bayesian networks to represent uncertain knowledge within an agent’s beliefs. Although such models allow a richer belief representation, current models of bayesian BDI agents employ a rather limited strategy for desire selection, namely one based on threshold values on belief probability. Consequently, such an approach precludes an agent from selecting desires conditioned on beliefs with probabilities below a certain threshold, even if those desires could be achieved if they had been selected. To address this limitation, we develop three alternative approaches to desire selection under uncertainty. We show how these approaches allow an agent to sometimes select desires whose belief conditions have very low probabilities and discuss experimental scenarios
Original languageEnglish
Title of host publication1st International Workshop on Engineering Multi-Agent Systems
Number of pages20
Publication statusPublished - 2013


  • Bayesian Network
  • Belief Base
  • Harrd Evidence
  • Probability Ranking
  • Security Breach


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