ActiveCBR: An Agent System That Integrates Case-Based Reasoning and Active Databases

Abstract. Case-based reasoning (CBR) is an artificial intelligence (AI) technique for problem solving that uses previous similar examples to solve a current problem. Despite its success, most current CBR systems are passive: they require human users to activate them manually and to provide information about the incoming problem explicitly. In this paper, we present an integrated agent system that integrates CBR systems with an active database system. Active databases, with the support of active rules, can perform event detection, condition monitoring, and event handling (action execution) in an automatic manner. The integrated ActiveCBR system consists of two layers. In the lower layer, the active database is rule-driven; in the higher layer, the result of action execution of active rules is transformed into feature–value pairs required by the CBR subsystem. The layered architecture separates CBR from sophisticated rule-based reasoning, and improves the traditional passive CBR system with the active property. The system has both real-time response and is highly exible in knowledge management as well as autonomously in response to events that a passive CBR system cannot handle. We demonstrate the system efficiency and effectiveness through empirical tests.

[1]  Katia P. Sycara,et al.  Multi-Agent Integration of Information Gathering and Decision Support , 1996, ECAI.

[2]  Craig A. Knoblock,et al.  Retrieving and Integrating Data from Multiple Information Sources , 1993, Int. J. Cooperative Inf. Syst..

[3]  A. Roadmapof.  A Roadmap of Agent Research and Development , 1995 .

[4]  I. Kononenko,et al.  INDUCTION OF DECISION TREES USING RELIEFF , 1995 .

[5]  Alexandros Biliris.  The performance of three database storage structures for managing large objects , 1992, SIGMOD '92.

[6]  Oscar Díaz,et al.  EXACT: an extensible approach to active object-oriented databases , 1997, The VLDB Journal.

[7]  David E. Wilkins,et al.  A Multiagent Planning Architecture , 1998, AIPS.

[8]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[9]  David C. Wilson,et al.  Learning to Improve Case Adaption by Introspective Reasoning and CBR , 1995, ICCBR.

[10]  James Nga-kwok Liu,et al.  Autonomous agents and multi-agent systems , 1999 .

[11]  Alexandros Moukas.  Amalthaea Information Discovery and Filtering Using a Multiagent Evolving Ecosystem , 1997, Appl. Artif. Intell..

[12]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[13]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[14]  Yoav Shoham,et al.  An overview of agent-oriented programming , 1997 .

[15]  Hector Garcia-Molina,et al.  Template-based wrappers in the TSIMMIS system , 1997, SIGMOD '97.

[16]  Norman W. Paton,et al.  Supporting dynamic displays using active rules , 1994, SGMD.

[17]  D. Gentner.  Structure‐Mapping: A Theoretical Framework for Analogy* , 1983 .

[18]  Oren Etzioni,et al.  A softbot-based interface to the Internet , 1994, CACM.

[19]  Jennifer Widom,et al.  Active Database Systems: Triggers and Rules For Advanced Database Processing , 1994 .

[20]  Agnar Aamodt,et al.  CASE-BASED REASONING: FOUNDATIONAL ISSUES, METHODOLOGICAL VARIATIONS, AND SYSTEM APPROACHES AICOM - ARTIFICIAL INTELLIGENCE COMMUNICATIONS , 1994 .

[21]  Letizia Tanca,et al.  Active Rule Management in Chimera , 1996, Active Database Systems: Triggers and Rules For Advanced Database Processing.