This paper formally describes the context-based reasoning (CxBR) paradigm. CxBR can be used to represent tactical human behavior in simulations or in the real world. In problem solving, the context can be said to inherently contain much knowledge about the situation in which the problem is to be solved and/or the environment in which it must be solved. This paper discusses some of the issues involved in a context-driven representation of human behavior and introduces a formal description of CxBR. C 2008 Wiley Periodicals, Inc.

This paper explores the issues faced in creating a system that can learn tactical human behavior merely by observing a human perform the behavior in a simulation. More specifically, this paper describes a technique based on fuzzy ARTMAP (FAM) neural networks to discover the criteria that cause a transition between contexts during a strategic game simulation. The approach depends on existing context templates that can identify the high-level action of the human, given a description of the situation along with his action. The learning task then becomes the identification and representation of the context sequence executed by the human. In this paper, we present the FAM/Template-based Interpretation Learning Engine (FAMTILE). This system seeks to achieve this learning task by constructing rules that govern the context transitions made by the human. To evaluate FAMTILE, six test scenarios were developed to achieve three distinct evaluation goals: 1) to assess the learning capabilities of FAM; 2) to evaluate the ability of FAMTILE to correctly predict human and context selections, given an observation; and 3) more fundamentally, to create a model of the human's behavior that can perform the high-level task at a comparable level of proficiency.

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