Fostering artificial societies using social learning and social control in parallel emergency management systems
Wei DUAN, Xiaogang QIU
Fostering artificial societies using social learning and social control in parallel emergency management systems
How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by designers? We propose a framework for fostering artificial societies using social learning mechanisms and social control approaches. We present the application of fostering artificial societies in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, including observational learning, reinforcement learning, imitation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.
artificial societies / social computing / social learning / social control / agent-based simulation
[1] |
Wang F Y. Toward a paradigm shift in social computing: the ACP approach. IEEE Intelligent System, 2007, 22(5): 65-67
CrossRef
Google scholar
|
[2] |
Wang F Y. PeMS: parallel execution-based emergency man agement systems. China Emergence Management, 2007, 1(12): 22-28
|
[3] |
Epstein J M, Axtell R. Growing Artificial Societies: Social Science from the Bottom Up. Cambridge: The MIT Press, 1996
|
[4] |
Li J, Tang S, Wang X, Duan W, Wang F Y. Growing artificial transportation systems: a rule-based iterative design process. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 322-332
CrossRef
Google scholar
|
[5] |
Conte R, Paolucci M. Intelligent social learning. Journal of Artificial Societies and Social Simulation, 2001, 4(1): 61-82
|
[6] |
Mineka S, Cook M. Social learning and the acquisition of snake fear in monkeys. Social Learning: Psychological and Biological Perspectives, 1988: 51-73
|
[7] |
Oh J. Multiagent social learning in large repeated games. <DissertationTip/>. Pittsburgh: Carnegie Mellon University, 2009
|
[8] |
Noble J, Franks D W. Social learning in a multi-agent system. Computing and Informatics, 2004, 22(6): 561-574
|
[9] |
Takadama K, Kawai T, Koyama Y. Micro-and macro-level validation in agent-based simulation: reproduction of human-like behaviors and thinking in a sequential bargaining game. Journal of Artificial Societies and Social Simulation, 2008, 11(2): 9
|
[10] |
Gomes E R, Kowalczyk R. Dynamic analysis of multiagent Q-learning with ϵ-greedy exploration. In: Proceedings of the 26th International Conference on Machine Learning. 2009
|
[11] |
Priesterjahn S. An evolutionary online adaptation method for modern computer games based on imitation. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. 2007
CrossRef
Google scholar
|
[12] |
Lopes M, Melo F S, Kenward B, Santos-Victor J. A computational model of social-learning mechanisms. Adaptive Behavior, 2009, 19(6): 467-483
CrossRef
Google scholar
|
[13] |
Tove S D. State intervention and social control in nineteenth-century Europe. Crime, Law and Social Change, 1977, 1(2): 163-187
|
[14] |
Andrighetto G, Campenni M, Conte R, Paolucci M. On the immergence of norms: a normative agent architecture. In: Proceedings of AIAA Symposium, Social and Organizational Aspects of Intelligence. 2007
|
[15] |
Criado N, Argente E, Noriega P, Botti V. Towards a normative BDI architecture for norm compliance. The Multi-Agent Logics, Languages, and Organisations Federated Workshops, Lyon, 2010: 65-81
|
[16] |
Kollingbaum M J. Norm-governed practical reasoning agents. <DissertationTip/>. Aberdeen: University of Aberdeen, 2005
|
[17] |
Hollander C D, Wu A S. The current state of normative agent-based systems. Journal of Artificial Societies and Social Simulation, 2011, 14(6): 1-47
|
[18] |
Sycara K, Norman T J, Giampapa J A, Kollingbaum, M J, Burnett C, Masato D, McCallum M, Strub M H. Agent support for policy-driven collaborative mission planning. The Computer Journal, 2009, 53(5): 528-540
CrossRef
Google scholar
|
[19] |
Savarimuthu B T R, Purvis M. Mechanisms for norm emergence in multi-agent societies. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, ACM. 2007
|
[20] |
Vercouter L, Muller G. LIAR: achieving social control in open and decentralized multi-agent systems. Applied Artificial Intelligence, 2010, 4(8): 723-768
CrossRef
Google scholar
|
[21] |
Pujol J M, Sanguesa R, Delgado J. Extracting reputation in multi agent systems by means of social network topology. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, ACM. 2002
|
/
〈 | 〉 |