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
adversarialreasoning while also exploring theoretical developments,
Adversarial Reasoning: Computational Approaches to Reading the
Opponent's Mind is beneficial for practitioners as well as
researchers.
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