Non-monotonic Reasoning in Deductive Argumentation
This work addresses foundational challenges in AI and logic for researchers in argumentation and non-monotonic reasoning, but it appears incremental as it builds on existing proposals without introducing a new paradigm.
The paper tackles the problem of capturing defeasible reasoning in argumentation by examining issues related to base logic choice and modeling of defeasible knowledge, with the result being an exploration of insights and tools for non-monotonic logics without providing concrete numerical results.
Argumentation is a non-monotonic process. This reflects the fact that argumentation involves uncertain information, and so new information can cause a change in the conclusions drawn. However, the base logic does not need to be non-monotonic. Indeed, most proposals for structured argumentation use a monotonic base logic (e.g. some form of modus ponens with a rule-based language, or classical logic). Nonetheless, there are issues in capturing defeasible reasoning in argumentation including choice of base logic and modelling of defeasible knowledge. And there are insights and tools to be harnessed for research in non-monontonic logics. We consider some of these issues in this paper.