RatioLog - Rational Extensions of Logical Reasoning

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Human reasoning does not strictly follow the rules of classical logic. Explanations for this may be incomplete knowledge, incorrect beliefs, or inconsistent norms. From the very begin- ning of artificial intelligence (AI) research, there has been a strong emphasis on incorporating mechanisms for rationality into AI reasoning systems. Rationality cannot be restricted to cog- nitive tasks solely, but involves complex behavior and interaction with other subjects and the (physical and spatial) environment.This project aims at establishing a joint model for reasoning and behavior. For this, we will combine logical reasoning with the modeling of continuous systems by building on previous works on non-monotonic calculi, qualitative spatial representation, and hybrid automata. We will extend classical logical reasoning by various forms of non-monotonic aspects, e.g. abduc- tion or defeasible argumentation. This will not be done on a theoretical level only, but these ex- tensions will be incorporated into the existing reasoning system E-KRHyper. The open domain question answering (QA) system LogAnswer that uses E-KRHyper and the free encyclopedia Wikipedia for answering natural language questions will be turned into a system for rational question answering, which offers an excellent testbed for evaluating rational reasoning.