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Hybrid case-based reasoning

Published online by Cambridge University Press:  07 July 2009

John Hunt  and
Roger Miles
John Hunt
Affiliation:
Bristol Transputer Centre, University of the West of England, Filton Campus, Bristol, BSI6 IQ Y, UK (Email: jjh, rgm@btc.uwe.ac.uk)
Roger Miles
Affiliation:
Bristol Transputer Centre, University of the West of England, Filton Campus, Bristol, BSI6 IQ Y, UK (Email: jjh, rgm@btc.uwe.ac.uk)
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Abstract

This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact an inherently hybrid process. We review a number of systems and identify three classes of architecture which have been used for hybrid systems. We believe that to successfully exploit a co-reasoner within a CBR system it is necessary to analyse where, when, why and how the information provided by the co-reasoner will be used. We propose the KADS methodology as a suitable way of performing such an analysis and illustrate its use by example. We conclude by considering the control issues associated with the construction of hybrid CBR systems. We review the requirements of such systems and consider how well the two existing cooperation architectures match those requirements.

Information

Type
Research Article
Information
The Knowledge Engineering Review , Volume 9 , Issue 4 , December 1994 , pp. 383 - 397
Copyright
Copyright © Cambridge University Press 1994

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References

Bardasz, T and Zeid, I, 1993, “DEJAVU: Case-based reasoning for mechanical design”. Al EDAM 72(2) 111–124.Google Scholar
Branting, LK, 1991, “Building explanations from rules and structured cases” IJMMS 34 797–837.Google Scholar
Branting, LK and Porter, BW, 1991, “Rules and precedents as complementary warrents.” In: Proc. AAAI–91. AAAI Press/MIT Press.Google Scholar
Englemore, R and Morgan, T, 1988, Blackboard Systems. Addison-Wesley.Google Scholar
Faltings, B, 1991, “Case-based representation of architectural design knowledge”. In: Computational Intelligence 3 (Cercone, N., ed.). North Holland, pp 184–232.Google Scholar
Goel, A, 1991, “A model-based approach to case adaptation.” In: Proc. 13th Annual Conf of the Cognitive Science Society. Erlbaum.Google Scholar
Goel, A, 1992, “Representation of design functions in experience-based design.” In: Intelligent Computer Aided Design (Brown, D., Waldron, M. and Yoshikawa, H, eds). North-Holland.Google Scholar
Goel, A and Callentine, T, 1992, “An experience-based approach to navigational path planning.” In: Proc. 1EEE/RSJ Int. Conf. on Robotics and Systems. IEEE Press.Google Scholar
Goel, A, Callentine, T, Shanker, M and Chandrasekaran, B, 1991, “Representation and organization of topographic models of physical spaces for route planning.” In: Proc. IEEE Conference on Artificial Intelligence Applications, IEEE Press.Google Scholar
Hammond, K, 1986, “CHEF: A model of case-based planning”. In: Proc. of AAAI–86. AAAI Press/MIT Press.Google Scholar
Hinrichs, TR, 1992, Problem solving in open worlds: A case study in design. Erlbaum.Google Scholar
Hinrichs, TR and Kolodner, J, 1991, “The roles of adaptation in case-based design.” In: Proc. AAAI-91. AAAI Press/MIT Press.Google Scholar
Hua, K and Faltings, B, 1993, “Exploring case-based building design—CADRE.” AI EDAM 7(2) 135–143.Google Scholar
Kolodner, J, 1993, Case-Based Reasoning. Morgan Kaufmann.CrossRefGoogle Scholar
Koton, P, 1988, “Reasoning about evidence in causal explanation.” In: Proc. AAAI-88.Google Scholar
Magaldi, R, 1994, “CBR for troubleshooting aircraft on the flightline.” lEE Colloquium on Case Based Reasoning: Prospects for applications pp 6/1–6/9.Google Scholar
Maher, ML and Zhang, DM, 1991, “CADSYN: Using case and decomposition knowledge for design synthesis.” In: AI in Design 1991 (Gero, JS ed). Butterworth-Heinemann.Google Scholar
Maher, ML and Zhang, DM, 1993, “CADSYN: A case-based design process model.” AI EDAM 7(2) 97–110.Google Scholar
Navinchandra, D, Sycara, KP and Narasimhan, S, 1991, “A transformational approach to case-based synthesis.” AI EDAM 5(1) 31–45.Google Scholar
Punch, WF, 1989, A Diagnosis System Using a Task Integrated Problem Solving Architectures (TIPS), Including Causal Reasoning PhD Thesis, The Ohio State University.Google Scholar
Punch, WF and Chandrasekaran, B, 1993, “An investigation of the roles of problem-solving methods in diagnosis.” In: Second Generation Expert Systems (David, J-M, Krivine, J-P and Simmons, R eds). Springer- Verlag, pp. 673–698.CrossRefGoogle Scholar
Riesbeck, CK and Schank, RC, 1989, Inside Case-Based Reasoning. Erlbaum.Google Scholar
Rissland, EL and Skalak, DB, 1991, “CABARET: rule interpretation in a hybrid architecture.” IJMMS 34 839–887.Google Scholar
Roderman, S and Tsatsoulis, C, 1993, “PANDA: A case-based system to aid novice designers.” Al EDAM 7(2) 125–134.Google Scholar
Sycara, K and Navinchandra, D, 1989, “Integrating case-based reasoning and qualitative reasoning in engineering design.” In: Al in Engineering Design (Gero, J, ed).Google Scholar
Tansley, DSW and Hayball, CC, 1993, Knowledge-Based Systems Analysis and Design: A KADS Developer's Handbook. Prentice Hall.Google Scholar
Wang, J and Howard, HC, 1991, “A design-dependent approach to integrating structural design.” In: Artificial Intelligence in Design '91 (Gero, JS, ed). Butterworth-Heinemann, pp. 151–170.CrossRefGoogle Scholar