| title: | Methodology of Case-Based Reasoning (CBR) for development design environment of technological equipment |
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| reg no: | ETF6183 |
| project type: | Estonian Science Foundation research grant |
| status: | accepted |
| institution: | Tallinn Technical University |
| head of project: | Grigori Nekrassov |
| duration: | 01.01.2005 - 31.12.2007 |
| description: | Over the last few years, one of the methods of artificial intelligence, case-based reasoning (CBR) has attracted a general interest. The main idea of CBR is based on the assumption that the similar problems have the similar solutions. The practice shows that often it is more efficient to solve a problem by starting with a solution of a previous, similar problem than to generate the entire solution from scratch.The central notion of CBR is a case. A case is represented as a pair: problem and its solution.Many cases are collected in a set to build a case library (case base). In solving a current problem, a CBR system retrieves a similar, past problem and its solution using a set of rules for measuring similarity between actual problem and those stored in case base. Usually it is unlikely that an exact match will occur, therefore the retrieved solution must be adapted. The adaptation rules, based on the problem domain theory, are applied to adjust for any differences between the current case and the retrieved one. Finally, the CBR stores the approved solution to the current case, and it can then be used in solving future problems.Typically, a CBR system consists of a data base of past cases and their solutions, a set of rules and functions for measuring similarity, and rules and knowledge base for adaptation. A case base contains case attribute values that identify the problem type and that distinguish one problem type from another. The case attributes that identify the problem type are used as indices in retrieval. Learning of the system takes place when new cases are solved and stored in the case base together with the approved solution.There are several advantages of using CBR. Instead of relying on general knowledge of a problem domain, CBR employs the specific problem situations. CBR is beneficial when the problems are not completely understood so that a reliable model cannot be built. Moreover, the problem may not be completely defined before starting to search for possible solutions.The objective of the proposed project is the development the theory together with an experimental implementation of using CBR to develop DE with them following objectives: 1. In the first phase (2005-2006) of the project the process of methods of representation of knowledge about designing technolgical equipment (workholders etc) is investigated. Eliciting of common regularities of geometrical and technological simulation and developing a knowledge base which supplying applying of a method CBR for developing a construction of the technological equipment. 2. The objective of the second phase (2006-2007) is to extend of applying of these methods for designing other aspects of technological equipment. Realization of a partial implementation of the software. |
| project group | ||||
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| no | name | institution | position | |
| 1. | Grigori Nekrassov | Tallinn Technical University | researcher | |
| 2. | Leonid Portjanski | Senior researcher | ||