Evolutionary learning of virtual team member preferences

Virtual team members do not have a complete understanding of other team member (agent) preferences, which makes team coordination somewhat difficult. Traditional approaches for team coordination require a lot of inter-agent electronic communication and often result in wasted effort. Methods that reduce inter-agent communication and conflicts are likely to increase productivity of virtual teams. In this research, we propose an evolutionary genetic algorithm based intelligent agent that will learn team member preferences from past actions and develop an agent-coordination schedule by minimizing schedule conflicts between different members serving on a virtual team. Since the intelligent agent learns individual team member preferences, the potential for conflict is greatly reduced, which in turn results in lower inter-agent communication cost and increased team productivity.