Abstract
This paper firstly studies intelligent learning techniques based on reinforcement learning theory. It proposes an improved multi-agent cooperative learning method that can be shared through continuous learning and the strategies of individual agents to achieve the integration of multi-agent strategy and learning in order to improve the capabilities of intelligent multi-agent systems. Secondly, according to the analysis of data mining and AHP theory, a new concept is proposed to build a data mining model (based on intelligent learning) that has been named ‘ACMC’ (AHP Construct Mining Component); designed ACMC strategy evaluation and assistant decision-making based on multiagent systems, to achieve a strategic assessment of the current situation and reach a final decision. Finally, after research on Intelligent Decision Technology based on game theory, aspects of game theory are employed to deal with the real demand of confrontational environments.
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References
Niazi, M. and Hussain, A., “Agent-based computing from multi-agent systems to agent-based models: A visual survey, Scientometrics, 2011, vol. 89, no. 2, pp. 479–499.
Longbing, C., Gorodetsky, V., and Mitkas, P.A., Agent mining: The synergy of agents and data mining, IEEE Intell. Syst., 2009, vol. 24, no. 3, pp. 64–72.
Gao Bo, Fei Qi, and Chen Xueguang, The basic architecture of deliberative agent, J. Huazhong Univ. Sci. Technol., 2001, vol. 29, no. 2, pp. 72–76.
Ghasemlou, S., Mohades, A., Shangari, T.A., and Tavassoli, M., Homecoming: A multi-robot exploration method for conjunct environments with a systematic return procedure, European Conference on Multi-Agent Systems, 2014, pp. 111–127.
Zhang Yun, Li Weihua, and Chen Yang, Applying ACMC strategy to multidimensional data mining, J. Northwest. Polytech. Univ., 2011, vol. 29, no. 3, pp. 418–423.
Yun Zhang and Weihua Li, AHP construct mining component strategy applied for data mining process, 2012 IEEE International Conference on Information Science and Technology Wuhan, Hubei, 2012, pp. 591–595.
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Zhang, Y. Key Technologies of Confrontational Intelligent Decision Support for Multi-Agent Systems. Aut. Control Comp. Sci. 52, 283–290 (2018). https://doi.org/10.3103/S0146411618040119
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DOI: https://doi.org/10.3103/S0146411618040119