As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of computer-controlled entities. Prototype explanation systems have been added to simulators, but designers have not heeded the lessons learned from work in explaining expert system behavior. These new explanation systems are not modular and not portable; they are tied to a particular AI system. In this paper, we present a modular and generic architecture for explaining the behavior of simulated entities. We describe its application to the Virtual Humans, a simulation designed to teach soft skills such as negotiation and cultural awareness.
Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in a search space. Given the large amount of research effort devoted to computer chess throughout the past half-century, insufficient attention has been paid to the issue of determining if a proposed change to an evaluation function is beneficial. We argue that the mapping of an evaluation function from chess positions to heuristic values is of ordinal, but not interval, scale. We identify a robust metric suitable for assessing the quality of an evaluation function, and present a novel method for computing this metric efficiently. Finally, we apply an empirical gradient ascent procedure, also of our design, over this metric to optimize feature weights for the evaluation function of a computer chess program. Our experiments demonstrate that evaluation function weights tuned in this manner give equivalent performance to hand-tuned weights.
Abstract-The Immersive Naval Officer Training System (INOTS) is a blended learning environment that merges traditional classroom instruction with a mixed reality training setting. INOTS supports the instruction, practice and assessment of interpersonal communication skills. The goal of INOTS is to provide a consistent training experience to supplement interpersonal skills instruction for Naval officer candidates without sacrificing trainee throughput and instructor control. We developed an instructional design from cognitive task analysis interviews with experts to serve as a framework for system development. We also leveraged commercial student response technology and research technologies including natural language recognition, virtual humans, realistic graphics, intelligent tutoring and automated instructor support tools. In this paper, we describe our methodologies for developing a blended learning environment, and our challenges adding mixed reality and virtual human technologies to a traditional classroom to support interpersonal skills training.
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