CBR for SmartHouse Technology

SMART HOUSE technology offers devices that help the elderly and people with disabilities to live independently in their homes. This paper presents our experiences from a pilot project applying case-based reasoning techniques to match the needs of the elderly and those with disabilities to SMARTHOUSE technology. The SMART HOUSE problem is decomposed into sub-tasks, and generalised concepts added for each sub-task. This decomposition and generalisation enables multiple case reuse employing a standard decision tree index based iterative retrieval strategy. Documented real situations were used to create a small case base. A prototype implemented using RE CALL 1 with TCL script is evaluated empirically using leave-one-out testing, and separately with the domain expert on newly created test cases. Results show the generated solutions to be comparable to those of a domain expert. Importantly, the iterative retrieval strategy employing multiple indices generated solutions that were significantly better compared to a best match retrieval without indices.

[1]  Susan Craw,et al.  Case-Based Design for Tablet Formulation , 1998, EWCBR.

[2]  Terry R. Payne,et al.  Implicit Feature Selection with the Value Difference Metric , 1998, ECAI.

[3]  David McSherry Precision and Recall in Interactive Case-Based Reasoning , 2001, ICCBR.

[4]  Padraig Cunningham,et al.  Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design , 2001, IEEE Trans. Knowl. Data Eng..

[5]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[6]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[7]  David W. Aba,et al.  Comparing Simplification Procedures for Decision Trees on an Economics Classification , 1998 .

[8]  Cynthia R. Marling,et al.  Case-Based Reasoning in the Care of Alzheimer's Disease Patients , 2001, ICCBR.

[9]  Janet L. Kolodner,et al.  Improving Human Decision Making through Case-Based Decision Aiding , 1991, AI Mag..

[10]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[11]  Srinath Perera,et al.  A hierarchical case representation using context guided retrieval , 1998, Knowl. Based Syst..

[12]  Raymond J. Mooney,et al.  Theory Refinement Combining Analytical and Empirical Methods , 1994, Artif. Intell..

[13]  Susan Craw,et al.  Genetic Algorithms to Optimise CBR Retrieval , 2000, EWCBR.

[14]  Mark T. Keane,et al.  The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases , 1997, ICCBR.

[15]  Ralph Bergmann,et al.  Applying Recursive CBR for the Custumization of Structured Products in an Electronic Shop , 2000, EWCBR.