Abstract
Traditionally optimization of defence operations are based on the findings of human-based war gaming. However, this approach is extremely expensive and does not enable analysts to explore the problem space properly. Recent research shows that both computer simulations of multi-agent systems and evolutionary computation are valuable tools for optimizing defence operations. A potential maneuver strategy is generated by the evolutionary method then gets evaluated by calling the multi–agent simulation module to simulate the system behavior. The optimization problem in this context is known as a black box optimization problem, where the function that is being optimized is hidden and the only information we have access to is through the value(s) returned from the simulation for a given input set. However, to design efficient search algorithms, it is necessary to understand the properties of this search space; thus unfolding some characteristics of the black box. Without doing so, one cannot estimate how good the results are, neither can we design competent algorithms that are able to deal with the problem properly. In this paper, we provide a first attempt at understanding the characteristics and properties of the search space of complex adaptive combat systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ilachinski, A.: Irreducible Semi-Autonomous Adaptive combat (ISAAC): An Artificial Life Approach to Land Combat. Research Memorandum CRM 97-61, Center for Naval Analyses, Alexandria (1997)
Lauren, M.K.: Modelling Combat Using Fractals and the Satistics of Scaling Ssystems. Military Operations Research 5, 47–58 (2000)
Fogel, D.: Evolutionary Computation: towards a new philosophy of machine intelligence. IEEE Press, New York (1995)
Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Jones, D. (ed.) Proceedings of the Sixth International Congress on Genetics, Brooklyn, NY, vol. 1, pp. 356–366 (1932)
Horn, J., Goldberg, D.E.: Genetic algorithm difficulty and the modality of fitness landscapes. In: Whitley, L.D., Vose, M.D. (eds.) Foundations of Genetic Algorithms, vol. 3, pp. 243–269. Morgan Kaufmann, San Francisco (1999)
Kallel, L., Naudts, B., Reeves, C.R.: Properties of fitness functions and search landscapes. In: Kallel, L., Naudts, B., Rogers, A. (eds.) Theoretical Aspects of Evolutionary Computing, pp. 175–206. Springer, Berlin (2001)
Mitchell, M., Forrest, S., Holland, J.H.: The royal road function for genetic algorithms: Fitness landscapes and ga performance. In: Varela, F.J., Bourgine, P. (eds.) Proceedings of the First European Conference on Artificial Life, Cambridge, MA, pp. 245–254. MIT Press, Cambridge (1992)
Teo, J., Abbass, H.A.: An information-theoretic landscape analysis of neuro-controlled embodied organisms. Neural Computing and Applications Journal 13 (2004)
Vassilev, V.K., Miller, J.F.: The advantages of landscape neutrality in digital circuit evolution. In: Miller, J.F., Thompson, A., Thompson, P., Fogarty, T.C. (eds.) ICES 2000. LNCS, vol. 1801, pp. 252–263. Springer, Heidelberg (2000)
Smith, T., Husbands, P., O’Shea, M.: Not measuring evolvability: Initial investigation of an evolutionary robotics search space. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, pp. 9–16. IEEE Press, Los Alamitos (2001)
Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Information characteristics and the structure of landscapes. Evolutionary Computation 8, 31–60 (2000)
Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (1948)
Chaitin, G.J.: Information, randomness and incompleteness: papers on algorithmic information theory. World Scientific, Singapore (1987)
Ilachinski, A.: Enhanced ISAAC neural simulation toolkit (einstein), an artificial-life laboratory for exploring self-organized emergence in land combat. Beta-Test User’s Guide CIM 610.10, Center for Naval Analyses (1999)
Barlow, M., Easton, A.: CROCADILE - an open, extensible agent-based distillation engine. Information & Security 8, 17–51 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, A., Abbass, H.A., Sarker, R. (2004). Landscape Dynamics in Multi–agent Simulation Combat Systems. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-30549-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
eBook Packages: Computer ScienceComputer Science (R0)