Recursively modeling other agents for decision making: A research perspective.

Individuals exhibit theory of mind, attributing beliefs, intent, and mental states to others as explanations of observed actions. Dennett's intentional stance offers an analogous abstraction for computational agents seeking to understand, explain, or predict others' behaviors. These recognized theories provide a formal basis to ongoing investigations of recursive modeling. We review and situate various frameworks for recursive modeling that have been studied in game- and decision- theories, and have yielded methods useful to AI researchers. Sustained attention given to these frameworks has produced new analyses and methods with an aim toward making recursive modeling practicable. Indeed, we also review some emerging uses and the insights these yielded, which are indicative of pragmatic progress in this area. The significance of these frameworks is that higher-order reasoning is critical to correctly recognizing others' intent or outthinking opponents. Such reasoning has been utilized in academic, business, military, security, and other contexts both to train and inform decision-making agents in organizational and strategic contexts, and also to more realistically predict and best respond to other agents' intent. [ABSTRACT FROM AUTHOR]