A Framework for Graded Beliefs, Goals and Intentions

  • Authors:

  • Barbara Dunin-Kȩplicz;Linh Anh Nguyen;Andrzej Szałas

  • Affiliations:

  • Institute of Informatics, Warsaw University, Warsaw, Poland and Polish Academy of Sciences, Warsaw, Poland. E-mail: keplicz@mimuw.edu.pl;Institute of Informatics, Warsaw University, Warsaw, Poland. E-mail: nguyen@mimuw.edu.pl;Institute of Informatics, Warsaw University, Warsaw, Poland and Department of Computer and Information Science Linköping University, Sweden. E-mail: andsz@mimuw.eu.pl

  • Venue:
  • Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
  • Year:
  • 2010

  • Preface

    Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa

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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.