The Battle Warship Simulation of Agent-based with Reinforcement and Evolutionary Learning

Due to the development of technology related to a weapon system and the info-communication, the battle system of a warship has to manage many kinds of human intervention tactics according to the complicated battlefield environment. Therefore, many kinds of studies about M&S(Modeling & Simulation) have been carried out recently. The previous M&S system based on an agent, however, has simply used non-flexible(or fixed) tactics. In this paper, we propose an agent modeling methodology which has reinforcement learning function for spontaneous(active) reaction and generation evolution learning Function using Genetic Algorithm for more proper reaction for warship battle. We experiment with virtual 1:1 warship combat simulation on the west sea so as to test validity of our proposed methodology. We consequently show the possibility of both reinforcement and evolution learning in a warship battle.

[1]  Sung-Do Chi,et al.  Endomorphic modeling of intelligent system: intelligent card game player , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[2]  Amandeep S. Sidhu,et al.  Hierarchical Reinforcement Learning Model for Military Simulations , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[3]  Chi-kit. Ngai,et al.  Reinforcement-learning-based autonomous vehicle navigation in a dynamically changing environment , 2007 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  B. Zeigler.  SOME PROPERTIES OF MODIFIED DEMPSTER-SHAFER OPERATORS IN RULE BASED INFERENCE SYSTEMS , 1988 .

[6]  Hussein A. Abbass,et al.  Evolving agents for network centric warfare , 2005, GECCO '05.

[7]  Melanie Mitchell,et al.  Genetic algorithms , 2003 .

[8]  Kim Jea-Soo,et al.  Optimal Acoustic Search Path Planning Based on Genetic Algorithm in Discrete Path System , 2006 .

[9]  Han Yu,et al.  Evolving Sensor Suites for Enemy Radar Detection , 2003, GECCO.

[10]  Michael Barlow,et al.  CROCADILE: An Agent-based Distillation System Incorporating Aspects of Constructive Simulation , 2002 .

[11]  Michael J. Babilot.  Comparison of a Distributed Operations Force to a Traditional Force in Urban Combat , 2005 .

[12]  Stephen W. Soliday.  A Genetic Algorithm Model for Mission Planning and Dynamic Resource Allocation of Airborne Sensors , 1999 .

[13]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[14]  Sung-Do Chi,et al.  Many-to-Many Warship Combat Tactics Generation Methodology Using the Evolutionary Simulation , 2011 .

[15]  Jong Sik Lee,et al.  Modeling and Simulation of Optimal Path Considering Battlefield-situation in the War-game Simulation , 2010 .

[16]  Melanie Mitchell,et al.  Genetic algorithms and artificial life , 1994 .

[17]  Andrew Ilachinski,et al.  Towards a Science of Experimental Complexity : An Artificial-Life Approach to Modeling Warfare , 1994 .