Download Adversarial Reasoning: Computational Approaches to Reading by Alexander Kott, William M. McEneaney PDF

By Alexander Kott, William M. McEneaney

That includes ways that draw from disciplines reminiscent of man made intelligence and cognitive modeling, hostile Reasoning: Computational ways to analyzing the Opponent's brain describes applied sciences and purposes that tackle a large diversity of useful difficulties, together with army making plans and command, army and international intelligence, antiterrorism and family protection, in addition to simulation and coaching structures. The authors current an summary of every challenge after which speak about techniques and functions, combining theoretical rigor with accessibility. This complete quantity covers rationale and plan acceptance, deception discovery, and method formula.

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Extra resources for Adversarial Reasoning: Computational Approaches to Reading the Opponents Mind

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Increased activities were detected including the digging of berms and reinforcement of artillery positions on January 26 and 27 by the Iraqis, and the movement of armored vehicles into position on January 28. S. Marine Corps outposts along the border. The outposts fell back to preplanned positions while coalition forces responded with air strikes. The Iraqis took control of Khafji and it was then the Coalition’s problem to determine Iraqi intentions, contain the offensive forces, and retake Khafji.

4 AII and Wargaming Our AII model has been integrated into military wargaming environments to assist in simulating intelligent forces. fm Page 13 Friday, June 9, 2006 6:12 PM Adversarial Models for Opponent Intent Inferencing 13 rapid construction and execution of the simulations and “what-if” analysis in the after action review, where an assessment is conducted after the simulation to evaluate what has happened, why it has happened, what could happen, and what should be the response. In this section, we describe our experiments and results of prototyping and deploying AII.

Contrast this idea with deduction, which draws logical consequences from causes, and induction, which is the process of inferring general rules from specific data and is the primary task of machine learning [35]. Abduction is a complex process because many possible explanations of an observed behavior can exist. Bylander, et al. [36] have shown that, in general, abduction is NP-hard. For example, abduction is commonly observed in diagnostic medicine. A patient might go to a doctor complaining of a headache.

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