Possible Controllability of Control Argumentation Frameworks -- Extended Version
This work addresses incremental advancements in formal argumentation theory for AI researchers, focusing on controllability under uncertainty.
The paper introduces the concept of possible controllability in Control Argumentation Frameworks (CAFs), which model agent behavior under environmental uncertainty, and analyzes its computational complexity across four classical semantics while providing a logical encoding for reasoning.
The recent Control Argumentation Framework (CAF) is a generalization of Dung's Argumentation Framework which handles argumentation dynamics under uncertainty; especially it can be used to model the behavior of an agent which can anticipate future changes in the environment. Here we provide new insights on this model by defining the notion of possible controllability of a CAF. We study the complexity of this new form of reasoning for the four classical semantics, and we provide a logical encoding for reasoning with this framework.