An approach to Decision Making based on Dynamic Argumentation Systems
This work addresses decision-making for agents in AI, but it appears incremental as it builds on existing argumentation frameworks.
The authors tackled the problem of single-agent decision making by introducing a formalism based on Dynamic Argumentation Frameworks, which justifies choices based on current situations and handles preferences and conflicts among alternatives, and they presented an algorithm and formal results linking it to classical decision theory.
In this paper, we introduce a formalism for single-agent decision making that is based on Dynamic Argumentation Frameworks. The formalism can be used to justify a choice, which is based on the current situation the agent is involved. Taking advantage of the inference mechanism of the argumentation formalism, it is possible to consider preference relations and conflicts among the available alternatives for that reasoning. With this formalization, given a particular set of evidence, the justified conclusions supported by warranted arguments will be used by the agent's decision rules to determine which alternatives will be selected. We also present an algorithm that implements a choice function based on our formalization. Finally, we complete our presentation by introducing formal results that relate the proposed framework with approaches of classical decision theory.