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Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 34 - Number 3 |
| Year of Publication: 2011 |
| Authors: Surjeet Dalal, Dr. Vijay Athavale, Keshav Jindal |
10.5120/4078-5872
|
Surjeet Dalal, Dr. Vijay Athavale, Keshav Jindal . Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures. International Journal of Computer Applications. 34, 3 ( November 2011), 12-18. DOI=10.5120/4078-5872
Abstract
Case based reasoning has become the emerging field of Artificial Intelligence area. It is mostly used in designing the real time application having the decision support capability. It reassembles with human reasoning approach. This reasoning approach contains four phases. It stores the solution of past problems faced in form the case in its case base. In this paper we have discussed about the case retrieval phase of case based reasoning approach. All efficiency of the CBR system depends on the case retrieval process. There are various strategies are used in this phase of case based reasoning. Nearest neighbour & Induction retrieval algorithms are discussed. These algorithms are very simple but inefficient in larger case base & incomplete case. In this paper we will discuss Knowledge-Intensive Similarity measure retrieval strategies for the case base reasoning system & model the knowlededge-intensive similarity measure by using myCBR tool. The basic purpose of our work is to over the bottlenecks of other retrieval strategies.
References
- A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approach” AI Communications 7(1), 39–59, 1994.
- Janet L. Kolodner, “An Introduction to Case-Based Reasoning” Artificial Intelligence Review 6, 3-34, 1992.
- Zhi-We Ni, Shan-Lin “Integrated Case-based Reasoning” Proceedings of the Second International Conference on Machine Learning and Cybernetics, XI; 2-5 November 2003.
- Leake, David, "CBR in Context: The Present and Future (http:/ / www. cs.indiana.edu/~leake/papers/p-96-01_dir.html/paper. html)", In Leake, D., editor, Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/MIT Press, 1-30, 1996.
- Simon C.K. Shiu, “Case-Based Reasoning: Concepts, Features and Soft Computing” Applied Intelligence 21, 233–238, 2004.
- Frode Sqrmo, “Explanation in Case-Based Reasoning–Perspectives and Goals” Artificial Intelligence Review (2005) 24: 109–143, 2005.
- David W. Aha, “Advances in conversational case-based reasoning”, The Knowledge Engineering Review, Vol. 20:3, 247–254, 2006.
- Ramon Lopez De Mantaras, “Retrieval, reuse, revision and retention in case-based reasoning”, The Knowledge Engineering Review, Vol. 20:3, 215–240, 2006.
- Hugh R. Osborne, “A Case Base Similarity Framework” Proceedings of the 3rd Eurepean Workshop on Case-based Reasoning, EWCBR’96, Advance in Case-based Reasoning, Lecture Notes in Artificial Intelligence 1168, Springer Verlag, 1996.
- I.Y. Lodhi, “Optimizing Retrieval Process and using Neural Networks for Adaption Process in Case Based Reasoning Systems” Proceeding of IEEE INMIC 2003, 354-360, 2003.
- Zhi-Ying Zhang, “A Model for Retrieval base on ANN and Nearest Neighbor Algorithm” Proceeding of Seventh International Conference on Machine Learning & Cybernetics, 142-147, 2008.
- Armin Stahl, “Optimizing Retrieval in CBR by Introducing Solution Similarity” Proceeding of the International Conference on Artificial Intelligence (IC-AI’02), 13–23, 2004.
- Mingyang Gu, “Comparing Similarity Calculation Methods in Conversational CBR” Proceedings of the International Conference on Information Reuse and Integration IRI -2005 IEEE, 427-4323, 2005.
- Sae-Hyun Ji, “Similarity measurement method of case-based reasoning for conceptual cost estimation” Proceedings of the 13th International Conference on Computing in Civil and Building Engineering (icccbe 2010) University of Nottingham 2010.
- Chanumin Mi, “Study on Case Retrieving in Case-based Reasoning Based on Grey Incidence Theory and Its Application in Bank Regulation”. Proceedings of Fuzzy Systems, 2008. FUZZ-IEEE 2008, 1530-1533, 2008.
- Mohamad Farhan Mohamad Mohsin “The Development of Hashing Indexing Technique in Case Retrieval” Proceedings of International Symposium in Information Technology (ITSim), 1045-1050, 2010.
- Titilola O. Fanoiki, “Case-Based Reasoning Retrieval and Reuse Using Case Resemblance Hypergraphs” Proceedings of IEEE International Conference on Fuzzy System FUZZ 2010, 1-7, 2010.
- Du Hui, “An improving method of CBR retrieval based on self-organizing map” Proceeding of IEEE International Conference on Intelligent Computing and Intelligent Systems ICIS-2009, 616-620, 2009.
- Stahl, A, “Learning of Knowledge-Intensive Similarity Measures in Case-Based Reasoning, Dissertation”, Verlag dissertation.de, Band 986, (http://www.dfki.uni-kl.de/~stahl/papers/diss.pdf), 2004.
- Stahl, A, “Rapid prototyping of CBR applications with the open source tool myCBR” In Bergmann, R., Altho_, K.D., eds.: Advances in Case-Based Reasoning, Springer Verlag 2008.
- D Bahls, “Explanation Support for the Case-Based Reasoning Tool myCBR” Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence 1844-1846, 2007.
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