Ontology-Based Fuzzy-CBR Support System for Ship´s Collision Avoidance

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.