Automated Instructor Assistant for Ship Damage Control

The decision making task of ship damage control includes addressing problems such as fire spread, flooding, smoke, equipment failures, and personnel casualties. It is a challenging and highly stressful domain with a limited provision for real-life training. In response to this need, a multimedia interactive damage control simulator system, called DC-Train 2.0 was recently deployed at a Navy officer training school; it provides officers with an immersive environment for damage control training. This paper describes a component of the DC-Train 2.0 system that provides feedback to the user, called the automated instructor assistant. This assistant is based on a blackboardbased expert system called Minerva-DCA, which is capable of solving damage control scenarios at the “expert” level. Its innovative blackboard architecture facilitates various forms of user assistance, including interactive explanation, advising, and critiquing. In a large exercise involving approximately 500 ship crises scenarios, Minerva-DCA showed a 76% improvement over Navy officers by saving 89 more ships. The Domain of Ship Damage Control The tasks of ship damage control are vital to ship survivability, human life, and operational readiness. Most crises on military and civilian ships could be successfully addressed if handled promptly and properly. Typically the crisis management efforts on a ship are coordinated by a single person called the Damage Control Assistant (DCA). This person is in charge of maintaining situational awareness, directing crisis management crews, and managing other resources. Naturally, crisis management tasks are challenging even for seasoned Navy officers due to the inherent complexity of physical damage, limited resources, information overload, uncertainty, infrequent opportunities for realistic practice, and tremendous psychological stress. Studies have shown that the performance could be significantly improved by providing more opportunities for realistic practice (Ericsson 1993, Baumann et al. 1996). As in many other military domains, real-life training in often infeasible or inadequate due to the high cost and a limited number of possible scenarios. Copyright © 1999, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. The Navy has been involved in supporting the creation of various damage control simulators to compliment textbook training (Jones et al. 1998, Bulitko 1998a, Fuller 1993, Johnson 1994). One of these projects resulted in creation of DC-Train 2.0, an immersive multimedia simulator (Bulitko 1998a). The system is capable of involved simulation including physical phenomena (fire, flooding, smoke spread, equipment failures) and personnel modeling (crisis management activities, casualties, standard procedures, and communications). The physical aspects of the scenarios are simulated from first principles starting with a sophisticated scenario specification tool mapping training objectives to primary damage specifications (Grois et al. 1998). A wide range of realistic scenarios are modeled. However, the system, as described above, still needs a human instructor to (1) demonstrate a successful scenario solution, (2) provide the student with instructional advice, (3) observe the student’s problem-solving and provide a comprehensive critique, and (4) score performance on various scenarios for progress evaluation and comparative analysis purposes. While the simulator itself is implemented with numerical and knowledge-based simulation techniques, requirements of an automated instructor include, first, achievement of the level of expertise sufficient to solve arbitrary scenarios in realtime; and, second, an ability to observe the student in realtime, communicate with the student, and present intelligible feedback in a natural language format. Such functions clearly present an interesting challenge for modern AI technology. In this paper we present an automated instructor assistant, called Minerva-DCA, that is capable of doing the aforementioned four instructor functions. Minerva-DCA has been fielded at the Navy’s Surface Warfare Officer School (SWOS) in Newport, Rhode Island and has shown impressive performance. Minerva-DCA: The Automated Instructor