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The purpose of the paper is to study the emergency and effects of conflict resolution rules in self-organizing teams. Intelligent agents are used to simulate team members of self-organizing teams. In the virtual self-organizing team, agents adapt the Q-learning algorithm to adjust their actions. Three sets of experiments are manipulated to study the evolution of rules. The results of few experiments show a new rule for conflict resolution emerged from the dynamic interactions of agents. For the other experiments, agents cannot resolve conflicts by themselves.

In self-organizing teams, team members adopt knowledge to work for the team task. The team task consists of several dependent sub-tasks. Each sub-task has requirements for the member who want to accomplish it. Since there are no stronger leaders of the teams, each member chooses the sub-task whose requirements are fit with his abilities. The quality of team tasks depends on the worst quality of team members’ works. Since the characteristics of self-organizing teams, the fit rule between tasks and members is “Do the fittest task”. However, if the team members’ ability is not perfect for the tasks, the fit rule will lead to team problems. For example, two tasks requirements are 50 (t1) and 70 (t2). Two members have the ability of 70 (m1) and 90 (m2). Based on the rule of “Do the fittest task”, m1 chooses the task of t2. In order to accomplish the team task, m2 have to choose t1. Since the distance between m2 and t1, the quality of team task is 40 (90 − 50). This scenario is defined as assignment conflict in the paper. The paper studies the rules to resolve this kind of assignment conflicts in self-organizing teams. For the target of high tasks’ quality, the optimal assignment of this example is m1 choose t1 and m2 do the task of t2. For the optimal assignment, the quality of team task is 20 (70 − 50 or 90 − 70). In the paper, this optimal assignment rule is defined as “Do a fitter task”. Team members following fixed behavioral rules can be limited in performance and efficiency. In order to emerge the rule of “Do a fitter task” from the dynamic interactions of team members, the paper uses multi-agent technology to simulate the self-organizing teams. Intelligent agents are used to simulated team members. Adaptability is key components of intelligent behavior which allow agents to improve performance in a given domain using prior experiences. The Q-learning algorithm is applied to improve the self-adaptive ability of agents. Three sets of experiments are manipulated to analyze the evolution of the rule in self-organizing teams. The emergency and effects of conflict resolution rules are analyzed by the experiments’ results. The rest of the paper is organized as follows. The related literature is reviewed in Section 2 and then the multi-agent model is developed in Section 3. The experiments are conducted in Section 4. A detailed result analysis is presented in Section 4. Finally, the conclusions are summarized and future work is suggested in Section 5.

The motivation of this paper was to analyze the emergency and effects of conflict resolution rules in self-organizing teams. Multi-agent technology was used to simulate the self-organizing team and the Q-learning algorithm was proposed to adjust agents’ behavior. Three sets of experiments were performed to study the emergency and effects of the rule (“Do a fitter task”) in a virtual self-organizing team. The results indicate that the emergency of the new rule depends on the scale of the team and the amount of conflicts at each period. The rule regulates the interaction among virtual members in small teams. However, if the team scale or the amount of conflicts at each period was increased, the rule only can regulates part of virtual members. If there is more than one conflict of the largest scale team, the rule was not emerged in the virtual team. This means the conflicts of tasks assignment can be resolved by virtual members’ self-adaptive behaviors in small virtual teams. If there is more than one conflict at each period, this approach is not valid for large virtual teams. This conclusion shows significance on the management of self-organizing teams in real world. Different approaches should be used for different teams to resolve the conflicts. In the virtual self-organizing team, the paper did not consider other aspects of agents such as trust among agents. The trust among agents may influence the interaction and the emergence of the conflict resolution rules. The paper also did not consider the dynamic structure of self-organizing teams. The dynamic structure of self-organizing teams plays an important role in resolving the conflicts of tasks assignment. Future researches involving trust and dynamic structure are desirable for the studying of conflict resolution in self-organizing teams.