Adversarial Reasoning
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Adversarial Reasoning : Computational Approaches to Reading the Opponent's Mind

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The rising tide of threats, from financial cybercrime to asymmetric military conflicts, demands greater sophistication in tools and techniques of law enforcement, commercial and domestic security professionals, and terrorism prevention. Concentrating on computational solutions to determine or anticipate an adversary's intent, Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind discusses the technologies for opponent strategy prediction, plan recognition, deception discovery and planning, and strategy formulation that not only applies to security issues but also to game industry and business transactions. Addressing a broad range of practical problems, including military planning and command, military and foreign intelligence, antiterrorism, network security, as well as simulation and training systems, this reference presents an overview of each problem and then explores various approaches and applications to understand the minds and negate the actions of your opponents. 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.show more

Product details

  • Hardback | 340 pages
  • 162.6 x 236.2 x 25.4mm | 635.04g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 97 black & white illustrations, 26 black & white tables
  • 1584885882
  • 9781584885887
  • 1,851,982

Table of contents

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 Indexshow more

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