ERPBAM: A Model for Structure and Reasoning of Agent Based on Entity-Relation-Problem Knowledge Representation System

  • Authors:

  • Xue-Long Chen;Li-Ming Li;Yan-Zhang Wang;Ning Wang;Xin Ye

  • Affiliations:

  • -;-;-;-;-

  • Venue:
  • WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
  • Year:
  • 2009

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Abstract

At first, a new knowledge representation system, named entity-relation-problem (E-R-P) knowledge representation system, is proposed. Then a model for structure and reasoning of agent based on the E-R-P knowledge representation system, named ERPBAM, is put forward. ERPBAM is straightforward, flexible and general. So it solves the problem of complexity of structure and reasoning for agent, which is caused by complex symbol representation and deduction. Furthermore, ERPBAM has the ability to handle all kinds of information, especially the fuzzy information, involved in the reasoning process. Because E-R-P knowledge representation system synthetically represents the knowledge of objective system and realistic problems, the structure and reasoning process of agent in ERPBAM become more integrated, and the corresponding implementation code becomes more compact.