Adversarial Reasoning
Computational Approaches to Reading the Opponent's Mind

Featuring approaches that draw from disciplines such as artificial intelligence and cognitive modeling, this title describes technologies and applications that address a range of practical problems, including military planning and command, military and foreign intelligence, antiterrorism and domestic security, and simulation and training systems.

The techniques discussed originate from a variety of disciplines such as stochastic processes, artificial intelligence planning, cognitive modeling, robotics and agent theory, robust control, game theory, and machine learning, among others. The beginning chapters outline the key concepts related to discovering the opponent's intent and plans while the later chapters journey into mathematical methods for counterdeception. The final chapters employ a range of techniques, including reinforcement learning within a stochastic dynamic games context to devise strategies that combat opponents. By answering specific questions on how to create practical applications that require elements of adversarial reasoning while also exploring theoretical developments, "Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind" is beneficial for practitioners as well as researchers.

ADVERSARIAL MODELS IN OPPONENT INTENT INFERENCING Intent Inferencing Representing and Reasoning under Uncertainty Adversary Intent Inferencing Model (AII) Future Work References
HUMAN FACTORS IN OPPONENT INTENT Intent Recognition in Human Opponents A Cognitive Approach to Modeling Opponents Knowledge-Based Abduction Creating a Model of Opponent's Intent Knowledge-Based Intention Projection KIP Architecture Heuristics for Reducing the Number of Opponent Goals Evidence-Based Goal Selection Experimental Results References EXTRAPOLATION OF THE OPPONENT'S PAST BEHAVIORS
Standing on the Shoulders of Giants Ant-Like Agents with Humanistic Behavior Exploring Possible Worlds Playing Together with Other Approaches Experimental Experience Looking Ahead References PLAN RECOGNITION An Example of Plans and Plan Recognition PHATT System Basics Algorithmic Complexity and Scalability Handling Partial Observability Limitations of the PHATT Algorithm
Lessons Learned: Computer Network Security References DETECTING DECEPTION Why Deception Works
Detecting Deception
Implementing ACH-CD Applying Automated ACH-CD to D-Day Application without Automation: The Battle of Midway Future Applications of ACH-CD References DECEPTION AS A SEMANTIC ATTACK Semantic Attacks in Relation to Other Topics in This Book Changing the Behavior of Humans Perception Management Semantic Attacks and Information Warfare Deception Detection Semantic Attacks and Intelligence and Security Informatics
Current Countermeasures for Semantic Attacks New Countermeasures for Semantic Attacks Information Trajectory Modeling Linguistic Countermeasures to Semantic Attacks
News Verification: An Instance of Multiple Source Semantic Attacks
Process Query Systems for Information Trajectory Countermeasures Tracking Hypotheses in the Financial Fraud Domain References APPLICATION AND VALUE OF DECEPTION Information, Computation, and Deception Games of Deception The Rational Side of Deception References ROBUSTNESS AGAINST DECEPTION
In Search of Anti-Deception Modeling the Game Deception-Rejection Machines A Seemingly Simple Game Analysis of the Fully-Observable Case
Analysis of Partially-Observed Case Pruning
Comparison of the Risk-Averse and Deception-Robust Approaches
Implementation
References THE ROLE OF IMPERFECT INFORMATION Classical Game-Tree Search Game-Tree Search in Imperfect Information Games Case Study: Texas Hold'em Case Study: Kriegspiel Chess Summary
References HANDLING PARTIAL AND CORRUPTED INFORMATION The Deterministic Discrete-Time Linear-Quadratic Game Formulation The Discrete-Time LQG Game: Formulation and Previous Work
LQG Game with Partial Information: An Indirect Approach
Properties of the Blocking Filter Effects of Partial Information Properties of the Equilibrium Cost: The Saddle Interval Application of Results to an Adversarial Environment
References STRATEGIES IN LARGE-SCALE PROBLEMS Game-Solving Approaches in Practical Problems Overview of LG
Game Construction Game Solving Accuracy of the Solution Scale of the Problems
Appendix: State Transition Systems
References LEARNING IN STRATEGIZE Our Wargame Reducing the State-Action Space Dimension through Symmetries and Heuristics
Review of Reinforcement Learning Methods Results Future Possibilities for Abstraction and Approximation in Complex Domains
References LEARNING FROM AND ABOUT THE OPPONENT Fictitious Play
Reinforcement Learning
Extensive Game Learning in Extensive Games
Multi-Agent Learning Automaton (MLA) MQ-Learning
MLA Experiment
MQ-Learning Experiment
Toward Practical Applications References Index