Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions

TL;DR

This paper explores computational methods for adversarial reasoning, emphasizing the integration of disciplines like cognitive modeling and AI planning, illustrated through military systems like CADET and DARPA's RAID program.

Contribution

It presents a research agenda for adversarial reasoning, highlighting its interdisciplinary nature and discussing practical applications in defense through systems like CADET and RAID.

Findings

CADET demonstrates current adversarial reasoning capabilities in brigade-level planning.

RAID aims to develop advanced adversarial reasoning capabilities for defense.

The approach integrates game theory, cognitive modeling, and AI planning.

Abstract

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.