Artificial Intelligence often uses logical models of reasoning. Logic investigates the validity of patterns of reasoning. Standard logic confines itself to the study of fully reliable inferences. Although this is adequate for fields like mathematics, for many other applications standard logic is too restricted. In other scientific areas, as well as in commonsense reasoning, people are often faced with incomplete, uncertain or even inconsistent information. To deal with this, they use reasoning patterns where it can be rational to accept a conclusion even if its truth is not guaranteed by the available information.
This course focuses on logics that systematise rationality criteria for such 'defeasible' reasoning patterns. Logics of this kind are often called 'nonmonotonic logics', since new information may invalidate previously drawn conclusions. This course covers some of the best-known nonmonotonic logics, in particular default logic, circumscription and argumentation systems, as well as formal theories of abductive reasoning. Some attention will also be paid to the use of these formalisms in the specification of dynamic systems and in models of multi-agent interaction.
Upon completion of this course, the student will have obtained insight in and mastery of the main logical techniques for formalising defeasible commonsense reasoning patterns, and will be able to apply these techniques to basic examples of such reasoning.