A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Abstract

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.

Keywords:

• multi-agent systems
• multi-agent credit assignment
• negotiation
• argumentation
• genetic algorithm
• genetic framework
• genetic operator

Notes

1. Denotes the first-order derivative of MAS module.

2. Denotes the second-order derivative of MAS module.

3. Agents who act better in previous trials and get more credits than other agents.

4. Agents who get lower credits than the other agents.

5. Low cards = 2, 3, 4, 5, 6.

6. High cards = 10, jack, queen, king, ace.

7. Neutral cards = 7, 8, 9.

8. Low–low cards = 2, 3.

9. High–low cards = 5, 6.

Additional information

Notes on contributors

Kaveh Pashaei

Kaveh Pashaei is currently a PhD candidate at the Software Engineering Department, School of Electrical and Computer Engineering, University of Tehran, Iran. He is a member of multi-agent group in University of Tehran since 2004. His research is focused on multi-agent systems(MASs), evolutionary algorithms, and pattern recognition. He has been working as a software developer, a project manager, and a software developing consultant in several companies in Iran.

Fattaneh Taghiyareh

Fattaneh Taghiyareh is a faculty member at the Department of Software Engineering & Information Technology, School of Electrical and Computer Engineering, University of Tehran, Iran, since 2001. She received her PhD degree from the Tokyo Institute of Technology in 2000. Her research interests are in technology-enhanced learning, multi-agent systems, and human-centred computing systems. Her current research involves the intersection of web services, personalisation, and adaptive Learning Management Systems (LMSs), as well as applying ontology to semantic web. She was past-manager of the Department of Information Technology at the School of Electrical and Computer Engineering, and former director of Technology Incubator, University of Tehran. Recently, she established eLearning laboratory with the mission of providing a standard view for eLearning materials and platforms. Currently, she is supervising IT Foundation laboratory, eLearning laboratory, and multiagent systems laboratory.

Kambiz Badie

Kambiz Badie received all his degrees from Tokyo Institute of Technology, Japan, majoring in pattern recognition. Within the past years, he has been actively involved in cognitive modelling and systemic knowledge processing in general and analogical knowledge processing, and modelling interpretation process in particular, with emphasis on creating new ideas, techniques, and contents. Dr Badie is an active researcher in the areas of interdisciplinary and transdisciplinary studies in Iran, and has a high motivation for applying intelligent/cognitive modelling methodology to the human issues. Currently, he has become highly interested in modelling the process of phenomenological experience as a step to promote pedagogical quality in cyberspace. At present, he is a member of scientific board of IT Research Faculty at Cyberspace Research Institute, an affiliated professor at Faculty of Engineering Science in the University of Tehran, and the editor-in-chief of International Journal of Information & Communication Technology Research.