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Learning Agents for Improved Efficiency and Effectiveness in Simulation-Based Training
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- Källström, Johan, 1976- (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten
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- Heintz, Fredrik, 1975- (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten
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(creator_code:org_t)
- 2020
- 2020
- English.
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In: Poceedings of the 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS). ; , s. 1-2
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Abstract
Subject headings
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- Team training in complex domains often requires a substantial amount of resources, e.g., instructors, role-players and vehicles. For this reason, it may be difficult to realize efficient and effective training scenarios in a real-world setting. Instead, intelligent agents can be used to construct synthetic, simulationbased training environments. However, building behavior models for such agents is challenging, especially for the end-users of the training systems, who typically do not have expertise in artificial intelligence. In this PhD project, we study how machine learning can be used to simplify the process of constructing agents for simulation-based training. As a case study we use a simulation-based air combat training system. By constructing smarter synthetic agents the dependency on human training providers can be reduced, and the availability as well as the quality of training can be improved.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Modelling for agent based simulation
- Agents competing and collaborating with humans
- Agents for improving human cooperative activities
- Reinforcement learning
- Multi-agent learning
Publication and Content Type
- vet (subject category)
- kon (subject category)
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