The evolution of rules for conflicts resolution in self-organizing teams

Author:

Li Jing,Zhou Yuejin

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference22 articles.

1. Akchurina, N. (2008). Optimistic-pessimistic Q-learning algorithm for multi-agent systems. In R. Bergmann et al. (Eds.), MATES 2008. Lecture notes in artificial intelligence (Vol. 5244, pp. 13–24).

2. REsCape: an agent-based framework for modeling resources, ethnicity, and conflict;Bhavnani;Journal of Artificial Societies and Social Simulation,2008

3. Real-time conflicts in multi-robots;Cohen;Engineering Applications of Artificial Intelligence,1995

4. A robust conflict measure of inconsistencies in Bayesian hierarchical models;Dahl;Board of the Foundation of the Scandinavian Journal of Statistics,2007

5. Learning rates for Q-learning;Even-dar;Journal of Machine Learning Research,2003

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1. Conflict Resolution for Product Performance Requirements Based on Propagation Analysis in the Extension Theory;Advances in Mechanical Engineering;2014-01-01

2. Reinforcement Learning for Business Modeling;Encyclopedia of Business Analytics and Optimization;2014

3. A rule conflict resolution method based on Vague set;Soft Computing;2013-07-02

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