Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/246398 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1-13
Verlag: 
Elsevier, Amsterdam
Zusammenfassung: 
The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.
Schlagwörter: 
Finite state machines
Genetic algorithms
Multiagent simulations
Simheuristics
Stochastic combinatorial optimization
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