Hybrid Reasoning Model for Strengthening the problem solving capability of Expert Systems

Kapil Khandelwal, Durga Prasad
2013 International Journal of Advanced Computer Science and Applications  
In this paper, we briefly outlined popular casebased reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rule based reasoning, and model based reasoning. Further we examined the strengths and weaknesses of various reasoning models, case-based reasoning, rule-based reasoning and model-based reasoning, and discuss how they can be combined to form a more robust and better-performing hybrid. In a decision support system to address the variety of tasks a
more » ... ser performs, a single type of knowledge and reasoning method is often not sufficient. It is often necessary to determine which reasoning method would be the most appropriate for each task, and a combination of different methods has often shown the best results. In this study CBR was mixed with other RBR and MBR approaches to promote synergies and benefits beyond those achievable using CBR or other individual reasoning approaches alone. Each approach has advantages and disadvantages, which are proved to be complementary in a large degree. So, it is well-justified to combine these to produce effective hybrid approaches, surpassing the disadvantages of each component method. "KNAPS-CR" model integrates problem solving with learning from experience within an extensive model of different knowledge types. "KNAPS-CR" has a reasoning strategy which first attempts casebased reasoning, then rule-based reasoning, and, finally, modelbased reasoning. It learns from each problem solving session by updating its collection of cases, irrespective of which reasoning method that succeeded in solving the problem.
doi:10.14569/ijacsa.2013.041014 fatcat:g3junnf7pfau3gkotk4xez372a