A Framework for Graded Beliefs,Goals and Intentions

Abstract

In natural language we often use graded concepts, reflecting different intensity degrees of certain features. Whenever such concepts appear in a given real-life context, they need to be appropriately expressed in its models. In this paper, we provide a framework which allows for extending the BGI model of agency by grading beliefs, goals and intentions. We concentrate on TEAMLOG [6, 7, 8, 9, 12] and provide a complexity-optimal decision method for its graded version TEAMLOG ^K by translating it into CPDL _{reg} (propositional dynamic logic with converse and "inclusion axioms" characterized by regular languages). We also develop a tableau calculus which leads to the first EXPTIME (optimal) tableau decision procedure for CPDL _{reg} . As CPDL _{reg} is suitable for expressing complex properties of graded operators, the procedure can also be used as a decision tool for other multiagent formalisms.