Belief Change without Compactness


 Dealing with dynamics is a vital problem in Artificial Intelligence (AI). An intelligent system should be able to perceive and interact its environment perform tasks satisfactorily. To do so, it must sense external actions that might interfere tasks, demanding the agent self-adapt dynamics. In AI, field studies how rational change knowledge order respond new piece of information known as Belief Change. It assumes an agent’s specified underlying logic satisfies some properties including compactness: if entailed by set X formulae, then this also finite subset X. Several logics applications however, not respect property. This case many temporal such LTL CTL. Extending Change these would provide ways devise self-adaptive systems could real time. big challenge AI areas planning, reasoning sensing actions. belief beyond classical spectrum has been shown tough challenge, existing approaches usually put constraints upon system, which are either too restrictive or dispense so desired behaviour present. summary thesis “Belief without Compactness” Jandson S Ribeiro. The extends accommodate non-compact logics, keeping rationality criteria imposing extra constraints. We complete semantic perspectives for extending three main pillars: AGM paradigm, KM paradigm Non-monotonic Reasoning.