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This paper presents a computational model capable of improving the action policies for a well-defined domain. Each action policy is represented as a driving plan P, which is composed of a number of actions {a1, ∶ an}. These actions can be used to move a train in a stretch of railroad Sti. The plans are elaborated using a CBR approach and reusing previous solutions and learning from plans. The CBR...
In this paper we propose an architecture of intelligent agent for automatic locomotives operating. The system agent generates its action policy using a set of resources, such as type of railway, composition, belief perception and reasoning about the actions. The focus of the operator agent is directed to the choice of acceleration points (gear) and preparation of travel plans in a journey guided by...
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