Abstract
Case Based Reasoning is a problem solving paradigm which is able to retrieve and reuse solutions that have worked for similar situations in the past. Past situations and their solutions are stored in a memory called case base. To find the good experiment in memory is the key of success in the reasoning. To identify adequate experiment in memory constitutes the process of recall. The study presents an associative memory model used for a Case-Based Reasoner. The search algorithm is funded on an evolutionary approach to compute neighbourhood of a new problem then a direct access is performed.
Highlights
“Reasoning is Remembering” is the slogan of most researchers in Case Based Reasoning field
Case Based Reasoning (CBR) is a problem solving methodology founded on reusing old solution that have worked for similar situations in the past to solve new problem
We present in the following, an approach to perform a quick and complete recall, in an associative memory, using evolutionary computing
Summary
“Reasoning is Remembering” is the slogan of most researchers in Case Based Reasoning field. Case Based Reasoning (CBR) is a problem solving methodology founded on reusing old solution that have worked for similar situations in the past to solve new problem. Case based reasoning is an Artificial Intelligence paradigm that can be synergistically combined with other approaches to facilitate a broad array of tasks [1]. Among those possible combinations, we present in the following, an approach to perform a quick and complete recall, in an associative memory, using evolutionary computing. The main idea is to compute the neighbourhood of a new problem by an evolutionary algorithm This draws up the boundaries of the search space in the case base. The conclusion section goes over the main points, it presents related works and future direction
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