An epistemic logic for modeling decisions in the context of incomplete knowledge
This addresses a challenge in decision modeling for contexts where decisions depend on known or unknown information, but it appears incremental as it builds on existing formalisms.
The paper tackles the problem of expressing decision knowledge under incomplete knowledge by presenting a new language that combines stratification, autoepistemic logic, and definitions to model decisions with epistemic conditions.
Substantial efforts have been made in developing various Decision Modeling formalisms, both from industry and academia. A challenging problem is that of expressing decision knowledge in the context of incomplete knowledge. In such contexts, decisions depend on what is known or not known. We argue that none of the existing formalisms for modeling decisions are capable of correctly capturing the epistemic nature of such decisions, inevitably causing issues in situations of uncertainty. This paper presents a new language for modeling decisions with incomplete knowledge. It combines three principles: stratification, autoepistemic logic, and definitions. A knowledge base in this language is a hierarchy of epistemic theories, where each component theory may epistemically reason on the knowledge in lower theories, and decisions are made using definitions with epistemic conditions.