This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design
September/October 2001 (vol. 13 no. 5)
pp. 793-812

Abstract—Case-based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case so most CBR systems dealing with complex problem solving tasks have to use multiple cases. This paper describes and evaluates the technique of hierarchical case-based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Déjà Vu, a CBR system aimed at automating plant-control software design.

[1] J. Bacchus and Q. Yang, “Downward Refinement and the Efficiency of Hierarchical Problem Solving,” Artificial Intelligence, vol. 71, pp. 43-100, 1994.
[2] R. Bergmann and W. Wilke, “Building and Refining Abstract Planning Cases by Change of Representation Language,” J. Artificial Intelligence Research, vol. 3, pp. 53-118, 1995.
[3] S. Bhansali and M.T. Harandi, "Synthesis of UNIX Programs Using Derivational Analogy," Machine Learning, vol. 10, no. 1, pp. 7-56, 1993.
[4] L.K. Branting and D.W. Aha, “Stratified Case-Based Reasoning: Reusing Hierarchical Problem Solving Episodes,” Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 384-390, 1995.
[5] B. Chandrasekaran, “Design Problem Solving: A Task Analysis,” AI Magazine, pp. 59-71, 1990.
[6] J. Christensen, “A Hierarchical Planner that Generates Its Own Hierarchies,” Proc. Eighth Nat'l Conf. Artificial Intelligence, pp. 1004-1009, 1990.
[7] B. Cox, "Planning the Software Industrial Revolution," IEEE Software, Nov. 1990, pp. 25-33.
[8] K. Doi, Y. Uehara, Y. Kamigane, and M. Ito, “Software Generation System for Mill Operation,” Hitachi Rev., vol. 42, no. 4, pp. 175-178, 1993.
[9] A.G. Francis and A. Ram, “Computational Models of the Utility Problem and their Application to a Utility Analysis of Case-Based Reasoning,” Proc. Workshop Knowledge Compilation and Speedup Learning, 1993.
[10] A.G. Francis and A. Ram, “The Utility Problem in Case-Based Reasoning,” Proc. 1993 Workshop, pp. 160-161, 1993.
[11] K. Hanney, M.T. Keane, B. Smyth, and P. Cunningham, “When Do We Need Adaptation? A Review of Current Practice,” Proc. AAAI Fall Symp. Adaptation, pp. 41-46, 1995.
[12] K. Hanney, M.T. Keane, B. Smyth, and P. Cunningham, “Systems, Tasks, and Adaptation Knowledge,” Case-Based Reasoning: Research&Development, M. Veloso and A. Aamodt, eds., pp. 461-470, 1995.
[13] C. Knoblock, “Search Reduction in Hierarchical Problem Solving,” Proc. Ninth Nat'l Conf. Artificial Intelligence, pp. 686-691, 1991.
[14] C.A. Knoblock, “Automatically Generating Abstractions for Planning,” Artificial Intelligence, vol. 68, no. 2, 1994.
[15] N. Leveson, “The Challenge of Building Process Control Software,” IEEE Software, pp. 55-62, Nov. 1990.
[16] M.L. Maher, “HI-RISE: An Expert System for Preliminary Structural Design” Expert Systems for Eng. Design, E.M. Rychener, pp. 37-52, 1988.
[17] M.H. Maher, “Process Models for Design Synthesis,” AI Magazine, winter, pp. 39-58, 1990.
[18] M.L. Maher and D.M. Zhang, “CADSYN: Using Case and Decomposition Knowledge for Design Synthesis,” Proc. Artificial Intelligence in Design, 1991.
[19] M.L. Maher and D.M. Zhang, “CADSYN: A Case-Based Design Process Model,” Artificial Intelligence for Eng. Design, Analysis, and Manufacturing, vol. 7, no. 2, pp. 97-110, 1993.
[20] S. Markovitch and P.D. Scott, “Information Filtering. Selection Mechanisms in Learning Systems,” Machine Learning, vol. 10, pp. 113-151, 1993.
[21] S. Marcus, J. Stout, and J. McDermott, "VT: An Expert Elevator Designer that Uses Knowledge-Based Backtracking," AI Magazine, vol. 9, no. 1, pp.95-114, Spring 1988.
[22] D. McDermott, “Robot Planning,” AI Magazine, summer, pp. 55-79, 1992.
[23] S. Minton, "Quantitative Results Concerning the Utility of Explanation-Based Learning," Artificial Intelligence, vol. 42, pp. 363-391, 1990.
[24] Y. Ono, L. Tanimoto, T. Matsudaira, and Y. Takeuchi, “Artificial Intelligence Based Programmable Controller Software Designing,” Proc. Int'l Workshop Artificial Intelligence for Industrial Applications, pp. 85-90, 1988.
[25] M.A. Redmond, “Distributed Cases for Case-Based Reasoning: Facilitating the Use of Multiple Cases,” Proc. Eighth Nat'l Conf. Artificial Intelligence, pp. 304-309, 1990.
[26] E.D. Sacerdoti, “Planning in a Hierarchy of Abstraction Spaces,” Artificial Intelligence, vol. 5, no. 2, pp. 115-135, 1974.
[27] E.D. Sacerdoti, “The Non-Linear Nature of Plans,” Proc. Fourth Int'l Joint Conf. Artificial Intelligence, 1975.
[28] E.D. Sacerdoti, A Structure for Plans and Behaviour. New York: American Elsevier.
[29] T. Sakuri, T. Shibagaki, T. Shinbori, M. Itoh, “An Automatic Programming System Based on Modular Integrated-Concept Architecture (MICA),” Proc. 16th Ann. Conf. IEEE Industrial Electronic Soc., pp. 1303-1308, 1990.
[30] B. Smyth and P. Cunningham, “A Hierarchical Case-Based Reasoning System for Software Design,” Proc. 10th European Conf. Artificial Intelligence, pp. 587-589, 1992.
[31] B. Smyth and P. Cunningham, “The Utility Problem Analysed: A Case-Based Reasoning Perspective,” Advances in Case-Based Reasoning, I. Smith and B. Faltings, eds., pp. 392-399, 1996.
[32] B. Smyth and M.T. Keane, “Remembering to Forget: A Competence Preserving Case Deletion Policy for CBR Systems,” Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 377-382, 1995.
[33] B. Smyth and M.T. Keane, “Using Adaptation Knowledge to Retrieve and Adapt Design Cases,” J. Knowledge Based Systems, vol. 9, no. 2, pp. 127-135, 1996.
[34] B. Smyth and M.T. Keane, “Design a la DéjàVu: Reducing the Adaptation Overhead,” Case-Based Reasoning: Experiences, Lessons&Future Directions, D. Leake, ed., pp. 151-166, 1996.
[35] B. Smyth and M.T. Keane, “Adaptation-Guided Retrieval: Questioning the Similarity Assumption in Reasoning,” Artificial Intelligence, vol. 102, no. 2, pp. 249-293, 1998.
[36] K. Sycara and D. Navinchandra, “Influences: A Thematic Abstraction for the Creative Reuse of Multiple Cases,” Proc. Case-Based Reasoning Workshop, pp. 133-144, 1991.
[37] M. Stefik, “Planning and Meta-Planning (MOLGEN: Part 2),” Artificial Intelligence, vol. 16, no. 2, pp. 141-170, 1981.
[38] N. Tambe, A. Newell, and P.S. Rosenbloom, “The Problem of Expensive Chunks and Its Solution by Restricting Expressiveness,” Machine Learning, vol. 5, pp. 289-348, 1990.
[39] N. Tambe and P.S. Rosenbloom, “Eliminating Expensive Chunks by Restricting Expressiveness,” Proc. 11th Int'l Joint Conf. Artificial Intelligence, pp. 731-737, 1989.
[40] A. Tate, “Generating Project Networks,” Proc. Fifth Int'l Joint Conf. Artificial Intelligence, pp. 888-893, 1977.
[41] M. Veloso, “Non-Linear Problem Solving Using Intelligent Causal Commitment,” Technical Report CMU-CS-89-210, School of Computer Science, Carnegie Mellon Univ., 1989.
[42] M. Veloso, Planning and Learning by Analogical Reasoning, Springer-Verlag, 1994.
[43] R.S. Williams, “Learning to Program by Modifying Cases,” Proc. Case-Based Reasoning Workshop, pp. 463-474, 1988.

Index Terms:
Case-based reasoning, case-based organization, hierarchical problem solving, automated software design.
Citation:
Barry Smyth, Mark T. Keane, Pádraig Cunningham, "Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design," IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 5, pp. 793-812, Sept.-Oct. 2001, doi:10.1109/69.956101
Usage of this product signifies your acceptance of the Terms of Use.