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

By Alexander Kott, William M. McEneaney

That includes ways that draw from disciplines corresponding to man made intelligence and cognitive modeling, antagonistic Reasoning: Computational ways to interpreting the Opponent's brain describes applied sciences and functions that deal with a huge diversity of useful difficulties, together with army making plans and command, army and overseas intelligence, antiterrorism and family defense, in addition to simulation and coaching platforms. The authors current an summary of every challenge after which speak about methods and purposes, combining theoretical rigor with accessibility. This complete quantity covers reason and plan acceptance, deception discovery, and process formula.

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They are either shortterm or long-term and are stored in a weighted, prioritized list. The goals can be further partitioned into two types: Abstract and concrete. Abstract goals are those that cannot be executed directly — for example, the abstract goal of damaging world opinion concerning Blue. Concrete goals could be something like destroying a Blue force checkpoint. Actions (A) can be carried out to achieve adversarial goals. Actions typically can be observed by friendly forces — for example, launching a surface-to-air missile against Blue aircrafts.

Adversary A possessed a competent air force, a smaller ground force, and WMDs, whereas adversary B was lacking any WMDs, had much less air power, but had a powerful ground force. The test was to see the effects on the wargaming simulation and how much these two adversaries would differ in countering Blue force actions. Two different sets of observations of Blue forces were created and fed into the system. In the first set of inputs for Blue, data indicated a strategic bombing campaign comprised of deploying sea forces and launching cruise missiles and air strikes at strategic targets.

1, a BBN can compute the relative probability of explanations of a collection of observations. A BBN is a causal model with an explicit representation of the probability of observing each piece of evidence given a cause. The BBN is constructed by working with subject matter experts to capture their expertise in reasoning from causes to effects. For example, they might list what happens during an opponent’s maneuver and the likelihood of each of these actions. The computer can then use Bayes’ law to reverse the direction of causality and compute, for example, the probability the opponent is executing a particular tactical maneuver given a collection of observed behaviors.

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