Knowledge Qualification through Argumentation

Abstract

We propose a framework that brings together two major forms of default reasoning in Artificial Intelligence: default property classification in static domains, and default property persistence in temporal domains. Emphasis in this work is placed on the qualification problem, central when dealing with default reasoning, and in any attempt to integrate different forms of such reasoning.

Our framework can be viewed as offering a semantics to two natural problems: (i) that of employing default static knowledge in a temporal setting, and (ii) the dual one of temporally projecting and dynamically updating default static knowledge.

The proposed integration is introduced through a series of example domains, and is then formalized through argumentation. The semantics follows a pragmatic approach. At each time-point, an agent predicts the next state of affairs. As long as this is consistent with the available observations, the agent continues to reason forward. In case some of the observations cannot be explained without appealing to some exogenous reason, the agent revisits and revises its past assumptions.

We conclude with some formal results, including an algorithm for computing complete admissible argument sets, and a proof of elaboration tolerance, in the sense that additional knowledge can be gracefully accommodated in any domain.