Famtile: An Algorithm For Learning High-level Tactical Behavior From Observation

This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuzzy ARTMAP / Template-Based Interpretation Learning Engine. This algorithm attempts to achieve this learning task by constructing rules that govern the low-level context transitions made by the expert. By combining these rules with models for these low-level context behaviors, it is hypothesized that an intelligent model for the expert can be created that can adequately model his behavior. To evaluate FAMTILE, four testing scenarios were developed that attempt to achieve three distinct evaluation goals: assessing the learning capabilities of Fuzzy ARTMAP, evaluating the ability of FAMTILE to correctly predict expert actions and context choices given an observation, and creating a model of the expert\u27s behavior that can perform the high-level task at a comparable level of proficiency

Intelligent Story Architecture for Training (ISAT)

The Interactive Story Architecture for Training (ISAT) is designed to address the limitations of computer games for advanced distributed learning (ADL) and to fully realize the potential of games to become engaging and individualized training environments. The central component of the ISAT architecture is an intelligent director agent responsible for individualizing the training experience. To achieve this, the director tracks the trainee's demonstration of knowledge and skills during the training experience. Using that information, the director plays a role similar to that of a schoolhouse trainer, customizing training scenarios to meet individual trainee needs. The director can react to trainee actions within a scenario, dynamically adapting the environment to the learning needs of trainee as well as the dramatic needs of the scene

Discovery Of High-Level Behavior From Observation Of Human Performance In A Strategic Game

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\u27s behavior that can perform the high-level task at a comparable level of proficiency. © 2008 IEEE

Formalizing Context-Based Reasoning: A Modeling Paradigm For Representing Tactical Human Behavior

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. © 2008 Wiley Periodicals, Inc

Asymmetric Adversary Tactics For Synthetic Training Environments

We describe an approach for dynamically generating asymmetric tactics that can drive adversary behaviors in synthetic training environments. GAMBIT (Genetically Actualized Models of Behavior for Insurgent Tactics) features a genetic algorithm and tactic evaluation engine that - provided a computational specification of a domain and notional representation of the trainee\u27s tactics - will automatically generate a tactic that will be effective given those inputs. That tactic can then be executed using embedded behavior models within a virtual or constructive simulation. GAMBIT-generated tactics can evolve across training exercises by modifying the representation of the trainee\u27s tactics in response to his observed behavior