Artikel

A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

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.

Language
Englisch

Bibliographic citation
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-13 ; Amsterdam: Elsevier

Classification
Wirtschaft
Subject
Finite state machines
Genetic algorithms
Multiagent simulations
Simheuristics
Stochastic combinatorial optimization

Event
Geistige Schöpfung
(who)
Lam, Chiou-Peng
Masek, Martin
Kelly, Luke
Papasimeon, Michael
Benke, Lyndon
Event
Veröffentlichung
(who)
Elsevier
(where)
Amsterdam
(when)
2019

DOI
doi:10.1016/j.orp.2019.100123
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Lam, Chiou-Peng
  • Masek, Martin
  • Kelly, Luke
  • Papasimeon, Michael
  • Benke, Lyndon
  • Elsevier

Time of origin

  • 2019

Other Objects (12)