Syntactic Possibilistic Goal Generation

International audienceWe propose syntactic deliberation and goal election al-gorithms for possibilistic agents which are able to deal with incom-plete and imprecise information in a dynamic world. We show that the proposed algorithms are equivalent to their semantic counterparts already presented in the literature. We show that they lead to an ef-ficient implementation of a possibilistic BDI model of agency which integrates goal generation

Belief-goal relationships in possibilistic goal generation

The way in which the relationships between beliefs, goals, and intentions are captured by a formalism can have a significant impact on the design of a rational agent. In particular, what Rao and Georgeff underline about the relationships between goals and beliefs is that it is reasonable to require a rational agent not to allow goal-belief inconsistency, while goal-belief incompleteness can be allowed. We study a theoretical framework, grounded in possibility theory, which (i) accounts for the aspects involved in representing and changing beliefs and goals, and (ii) obeys Rao and Georgeff\u2019s requirement. We propose a formalization of a possibilistic extension of Bratman\u2019s asymmetry thesis to hold between goals and beliefs. Finally, we show that our formalism avoids the side-effect and the transference problems