Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions
It addresses the problem of anticipating opponent behavior in adversarial scenarios for defense and security applications, presenting a conceptual framework rather than incremental technical improvements.
This paper defines adversarial reasoning as computational approaches to inferring and anticipating an opponent's perceptions, intents, and actions, arguing it requires integrating game theory with cognitive modeling, control theory, and AI planning. It illustrates challenges through the CADET battle planning system and describes the DARPA RAID program to build practical capabilities for defense and other applications.
This paper defines adversarial reasoning as computational approaches to inferring and anticipating an enemy's perceptions, intents and actions. It argues that adversarial reasoning transcends the boundaries of game theory and must also leverage such disciplines as cognitive modeling, control theory, AI planning and others. To illustrate the challenges of applying adversarial reasoning to real-world problems, the paper explores the lessons learned in the CADET - a battle planning system that focuses on brigade-level ground operations and involves adversarial reasoning. From this example of current capabilities, the paper proceeds to describe RAID - a DARPA program that aims to build capabilities in adversarial reasoning, and how such capabilities would address practical requirements in Defense and other application areas.