A Unified Argumentation-Based Framework for Knowledge Qualification

Among the issues faced by an intelligent agent, central is that of reconciling the, often contradictory, pieces of knowledge — be those given, learned, or sensed — at its disposal. This problem, known as knowledge qualification, requires that pieces of knowledge deemed reliable in some context be given preference over the others. These preferences are typically viewed as encodings of reasoning patterns; so, the frame axiom can be encoded as a preference of persistence over spontaneous change. Qualification, then, results by the principled application of these preferences. We illustrate how this can be naturally done through argumentation, by uniformly treating object-level knowledge and reasoning patterns alike as arguments that can be defeated by other stronger ones. We formulate an argumentation framework for Reasoning about Actions and Change that gives a semantics for Action Theories that include a State Default Theory. Due to their explicit encoding as preferences, reasoning patterns can be adapted, when and if needed, by a domain designer to suit a specific application domain. Furthermore, the reasoning patterns can be defeated in lieu of stronger external evidence, allowing, for instance, the frame axiom to be overridden when unexpected sensory information suggests that spontaneous change may have broken persistence in a particular situation.