Intelligent tutoring systems research in the training systems division: Space applications

Computer-Aided Instruction (CAI) is a mature technology used to teach students in a wide variety of domains. The introduction of Artificial Intelligence (AI) technology of the field of CAI has prompted research and development efforts in an area known as Intelligent Computer-Aided Instruction (ICAI). In some cases, ICAI has been touted as a revolutionary alternative to traditional CAI. With the advent of powerful, inexpensive school computers, ICAI is emerging as a potential rival to CAI. In contrast to this, one may conceive of Computer-Based Training (CBT) systems as lying along a continuum which runs from CAI to ICAI. Although the key difference between the two is intelligence, there is not commonly accepted definition of what constitutes an intelligent instructional system

An intelligent tutoring system for the investigation of high performance skill acquisition

The issue of training high performance skills is of increasing concern. These skills include tasks such as driving a car, playing the piano, and flying an aircraft. Traditionally, the training of high performance skills has been accomplished through the use of expensive, high-fidelity, 3-D simulators, and/or on-the-job training using the actual equipment. Such an approach to training is quite expensive. The design, implementation, and deployment of an intelligent tutoring system developed for the purpose of studying the effectiveness of skill acquisition using lower-cost, lower-physical-fidelity, 2-D simulation. Preliminary experimental results are quite encouraging, indicating that intelligent tutoring systems are a cost-effective means of training high performance skills

Automated Reasoning Across Tactical Stories to Derive Lessons Learned

The Military Analogical Reasoning System (MARS) is a performance support system and decision aid for commanders in Tactical Operations Centers. MARS enhances and supports the innate human ability for using stories to reason about tactical goals, plans, situations, and outcomes. The system operates by comparing many instances of stored tactical stories, determining which have analogous situations and lessons learned, and then returning a description of the lessons learned. The description of the lessons learned is at a level of abstraction that can be generalized to an appropriate range of tactical situations. The machine-understandable story representation is based on a military operations data model and associated tactical situation ontology. Thus each story can be thought of, and reasoned about, as an instance of an unfolding tactical situation. The analogical reasoning algorithm is based on Gentner's Structure Mapping Theory. Consider the following two stories. In the first, a U.S. platoon in Viet Nam diverts around a minefield and subsequently comes under ambush from a large hill overlooking their new position. In the second, a U.S. task force in Iraq diverts around a biochemical hazard and subsequently comes under ambush from the roof of an abandoned building. MARS recognizes these stories as analogical, and derives the following abstraction: When enemy-placed obstacles force us into an unplanned route, beware of ambush from elevation or concealment. In this paper we describe the MARS interface, military operations data model, tactical situation ontology, and analogical reasoning algorithm