Belief Generalization for Intelligent Agents

One of the important characteristics for intelligent agents is to be able to assess their environments in order to generate correct beliefs to make right decisions. It is always difficult to do so because many factors including uncertain information, knowledge and bounded time will affect intelligent agents to perceive their environments. In this paper, we propose a procedure descriptive framework rather than a logical model to describe how agents automatically form their beliefs. The process of belief updating in this framework remains to be constantly changing, until the point of decision making is reached. Using this framework, a subject agent can smoothly fuse its knowledge and possible information from the current environment in order to obtain correct beliefs. We also present a solution for conflict resolution between agents in this framework.

[1]  Ronen I. Brafman,et al.  Modeling Agents as Qualitative Decision Makers , 1997, Artif. Intell..

[2]  Yiyu Yao,et al.  Interval Structure: A Framework for Representing Uncertain Information , 1992, UAI.

[3]  Tsau Young Lin,et al.  A Review of Rough Set Models , 1997 .

[4]  Anand S. Rao,et al.  An Abstract Architecture for Rational Agents , 1992, KR.

[5]  Rudolf Kruse,et al.  Uncertainty and vagueness in knowledge based systems: numerical methods , 1991, Artificial intelligence.

[6]  John McCarthy,et al.  Ascribing Mental Qualities to Machines , 1979 .

[7]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[8]  Joseph Y. Halpern,et al.  Modeling Belief in Dynamic Systems, Part I: Foundations , 1997, Artif. Intell..

[9]  Ramesh C. Jain,et al.  Uncertainty Management in a Distributed Knowledge Based System , 1985, IJCAI.

[10]  David M. Kreps.  Notes On The Theory Of Choice , 1988 .

[11]  Ronen I. Brafman,et al.  On the Foundations of Qualitative Decision Theory , 1996, AAAI/IAAI, Vol. 2.

[12]  Leslie Pack Kaelbling,et al.  A Situated View of Representation and Control , 1995, Artif. Intell..

[13]  Joseph Y. Halpern,et al.  Knowledge and common knowledge in a distributed environment , 1984, JACM.

[14]  Edmund H. Durfee,et al.  Distributed Problem Solving and Planning , 2001, EASSS.

[15]  Chengqi Zhang.  Cooperation Under Uncertainty in Distributed Expert Systems , 1992, Artif. Intell..

[16]  P G rdenfors,et al.  Knowledge in flux: modeling the dynamics of epistemic states , 1988 .

[17]  Yuefeng Li,et al.  A Method for Combining Interval Structures , 1998 .

[18]  Guillermo Ricardo Simari,et al.  Multiagent systems: a modern approach to distributed artificial intelligence , 2000 .

[19]  Robert Stalnaker.  Probability and Conditionals , 1970, Philosophy of Science.

[20]  Anand S. Rao,et al.  Deliberation and its Role in the Formation of Intentions , 1991, UAI.

[21]  Ronald Fagin,et al.  Reasoning about knowledge , 1995 .

[22]  Joseph Y. Halpern,et al.  Plausibility Measures: A User's Guide , 1995, UAI.

[23]  Ronald Fagin,et al.  Modelling knowledge and action in distributed systems , 2005, Distributed Computing.

[24]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[25]  Joseph Y. Halpern,et al.  A Guide to Completeness and Complexity for Modal Logics of Knowledge and Belief , 1992, Artif. Intell..

[26]  Rudolf Kruse,et al.  Uncertainty and Vagueness in Knowledge Based Systems , 1991, Artificial Intelligence.

[27]  Ronald R. Yager.  Using Granular Objects in Multi-source Data Fusion , 2002, Rough Sets and Current Trends in Computing.

[28]  Richmond H. Thomason.  Towards a Logical Theory of Practical Reasoning , 1993 .

[29]  Yoav Shoham,et al.  Agent-Oriented Programming , 1992, Artif. Intell..