Inference Engine
- Full name: DC-Train 4.0
- URL: http://www-kbs.ai.uiuc.edu/web/kbs/application-main.htm#Ship%20Damage%20Control
- Version: 4.0
- Description in English: The DC-Train system is an immersive simulation for the Navy damage control domain. It allows a student to interact with virtual agents on a DDG51-class Navy destroyer as he or she coordinates damage control activity during a simulated crisis.
- Source(s):
- Name: Training for Crisis Decision Making: Psychological Issues and Computer-Based Solutions - Journal of Management Information Science - Volume 18, Number 4, 147-168
- URL: http://www-kbs.ai.uiuc.edu/web/kbs/publicLibrary/KBSPubs/RefereedPublications/JMIS%20final%20submission%2011-12-2001.pdf
- Author(s):
- Year: 2002
- Abstract: Crises demand swift and effective decision making; yet there are many problems in training personnel on the skills necessary to achieve the goals of crisis management. This paper has three objectives concerning training for crisis management. First we integrate diverse literatures and present a framework for an understanding of the unique challenges in crisis management training, and the role of training systems with capabilities for simulation, immersion, and critiquing. Second, we describe an example of a trainer for ship damage control, called DC-Train, that addresses these challenges. This system consists of a first-principles simulator that generates large numbers of realistic scenarios, an immersive multimedia interface that helps elicit psychological processes involved in actual crisis management, and a critiquing expert system that provides real-time and post-session feedback on human decision-making performance. Finally, we present an empirical method for evaluating the effectiveness of such a
system for crisis management training. Results of evaluation experiments with participants in a
ship damage control training program indicate that the described computer-based trainer has
psychological realism and improves decision making performance.
- Inference Rule(s):
- Name: DC-Train Agent Simulation
- Example in English: DC-Train User: "Repair Locker 2, investigate compartment 02-112-0-Q."
Agent Simulation: "Repair Locker 2 reports investigators away to compartment 02-112-0-Q."
Agent Simulation: "Repair Locker 2 reports fire in compartment 02-112-0-Q."
- Desctiption Template in English: The DC-Train immersive simulation for the Navy damage control domain is able to simulate many of the human agents, or "stations", on a DDG51-class destroyer, including all personnel directly involved in damage control.
- Source(s):
- Name: DC-Train Sensor Simulation
- Example in English: A primary event of a missile hit is simulated.
Simulation: "DCCO reports fire alarm in compartment 02-112-0-Q."
- Desctiption Template in English: The DC-Train immersive simulation for the Navy damage control domain is able to simulate many sensors on a DDG51-class destroyer, including fire, smoke, flooding, and water pressure alarms.
- Source(s):
- Name: Direct assertion
- Description in English: Direct assertion of a sentence, possibly by loading a sentence (or a set of sentences) from an existing knowledge base.
- Rule Specification:
|- p;; (Sent p)
- Specification Language: Proof Protocol for Deductive Reasoning (PPDR)
- Example in English: One might load the statement from the wines ontology that
RedWine is a subclass of Wine.
Inference Web: [
Home |
Spec |
Browser |
IWBase |
Registrar |
Registry
]
Copyright 2009 Inference Web group.
All Rights Reserved.
