Abstract
Case-based reasoning (CBR) uses the same technique in solving tasks that needs reference from variety of situations. It can render decision-making easier by retrieving past solutions from situations that are similar to the one at hand and make necessary adjustments in order to adopt them. In this paper, an ontology-based fuzzy CBR support system for ship's collision avoidance is presented to avoid the cumbersome tasks of creating a new solution each time, when a new situation is encountered. The first level of the ontology-based CBR identifies the dangerous ships and indexes the new case. The second level retrieves cases from the ontology and adapts the solution to solve for the output. The CBR's accuracy depends on the efficient retrieval of possible solutions, and the proposed algorithm improves the effectiveness of solving the similarity to a new case at hand.
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