Learning Parameters for a Cognitive Model on Situation Awareness

Koopmanschap, Richard; Hoogendoorn, Mark; Roessingh, Jan Joris

doi:10.1007/978-3-642-38577-3_3

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The focus of recent work has been to generate behavior through the use of cognitive models, and optimizing these models with machine learning (ML) techniques such as neural networks and evolutionary learning [3], [6]. In this paper, we diverge from the earlier approach of using cognitive models by applying ML directly to the generation of behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the new technique should be scalable to the domain of air combat with team missions. The scope of the mentioned research with cognitive models [3], [6] was not scalable, since it was limited to a single learning agent. Third, the chosen machine learning technique should converge to practically usable behavior in a timely fashion, to allow rapid development of new training scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Scripting with Team Coordination in Air Combat Simulation

Toubman

Roessingh

Spronck

et al. 2014

Lecture Notes in Computer Science

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Abstract. Traditionally, behavior of Computer Generated Forces (CGFs) is controlled through scripts. Building such scripts requires time and expertise, and becomes harder as the domain becomes richer and more life-like. These downsides can be reduced by automatically generating behavior for CGFs using machine learning techniques. This paper focuses on Dynamic Scripting (DS), a technique tailored to generating agent behavior. DS searches for an optimal combination of rules from a rule base. Under the assumption that intra-team coordination leads to more effective learning, we propose an extension of DS, called DS+C, with explicit coordination. In a comparison with regular DS we find that the addition of team coordination results in earlier convergence to optimal behavior. In addition, we achieved a performance increase of 20% against an unpredictable enemy. With DS+C, behavior for CGFs can be generated that is more effective since the CGFs act on knowledge achieved by coordination and the behavior converges more efficiently than under regular DS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Scripting with Team Coordination in Air Combat Simulation

Toubman

Roessingh

Spronck

et al. 2014

Lecture Notes in Computer Science

Self Cite

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Co-evolutionary Learning for Cognitive Computer Generated Entities

Wilcke

Hoogendoorn

Roessingh

2014

Modern Advances in Applied Intelligence

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Developing an Expert Agent in an Educational Game with Evolutionary Computation

Moriyama

Numao

Ichise

2015

Transactions of the Japanese Society for Artificial Intelligenc

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Gaming is widely spreading in education. In gaming, learners make decisions iteratively in a simulation environment that replicates a real-world problem, called a game, and study what the proper decisions are in the game before making decisions in the problem. In the game, we need an expert agent that always makes proper decisions and from which the learner can learn such decisions by watching its behavior and/or by investigating it. However, it is difficult to develop such an expert agent manually because it is a very heavy task to implement knowledge of human experts on the problem into the agent, and sometimes it is also hard to extract such knowledge from the experts. Instead, we propose to automatically develop an agent that acts rightly as human experts with evolutionary computation. In particular, in this paper, we evolve such an agent for a research career design game where the player first determines his/her goal and has to decide his/her choices many times to achieve the goal afterward, based on a huge number of different situations. The experimental result shows that the evolved agent is better than or, at least, similar to agents created by a human expert.

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Learning Parameters for a Cognitive Model on Situation Awareness

Cited by 3 publications

References 7 publications

Dynamic Scripting with Team Coordination in Air Combat Simulation

Dynamic Scripting with Team Coordination in Air Combat Simulation

Co-evolutionary Learning for Cognitive Computer Generated Entities

Developing an Expert Agent in an Educational Game with Evolutionary Computation

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