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

By Alexander Kott, William M. McEneaney

ISBN-10: 1584885882

ISBN-13: 9781584885887

That includes methods that draw from disciplines reminiscent of synthetic intelligence and cognitive modeling, antagonistic Reasoning: Computational methods to examining the Opponent's brain describes applied sciences and functions that tackle a large variety of useful difficulties, together with army making plans and command, army and international intelligence, antiterrorism and household safety, in addition to simulation and coaching structures. The authors current an outline of every challenge after which speak about methods and purposes, combining theoretical rigor with accessibility. This finished quantity covers purpose and plan attractiveness, deception discovery, and technique formula.

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

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Once the candidate actions were generated, EAMS generated the specific instance of a mission, which is then used by FSS to execute the mission. 2). What was surprising is that the passive commander caused more serious damage to the Blue force than the aggressive commander. ), which caused targeting problems for Blue force bombers. Finally, when Red did decide to attack, Blue aircraft were already turning around and disengaging and were thus caught in a disadvantageous firing position. The simulations above demonstrate that the AII model can support existing systems and simulation applications; descriptive elements of adversary composition can be properly classified allowing for the rapid assembly and modification of an adversary force (exploiting an ontology); and intent has great influence on the actions of an adversarial force, where soft factors such as the aggressive stance of an adversary force commander can alter adversary response and mission results.

Int. Symp. on Aerospace/ Defense Sensing and Controls: AeroSense 2003, Orlando, FL, 2003, 182–193. 30. , Jr. , Constructing adversarial models for threat intent prediction and inferencing, in Proc. , 5423, Orlando, FL, 2004. 31. , Zhao, Q. , Lecture Notes in Artif. Intelligence 2702: User Modeling 2003, Johnstown, PA: Springer, 2003, 292–296. 32. , Zhao, Q. , User modelling for intent prediction in information analysis, in Proc. 47th Annu. Meet. for Hum. Factors and Ergonomics Soc. (HFES-03), Denver, CO, 2003, 1034–1038.

Two test scenarios were developed and simulated to highlight the notion that given the execution of a set of Blue missions, two adversarial commanders — differing only by their intent — will perform different sets of actions in response to the same set of Blue missions. In the experiment, one Axiom variable defined simply as “Behavior” was added into the rationale network in the AII model. It has three states: Aggressive, neutral, and passive. The three states of the variable “Behavior” simply reflect possible high, medium, and low levels of aggressiveness of Red.

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Adversarial Reasoning: Computational Approaches to Reading the Opponents Mind by Alexander Kott, William M. McEneaney


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