Multi-Criteria Evaluation in User-Centric Distributed Scheduling Agents

Distributed scheduling systems have used a variety of mechanisms to maximize the quality of the global solution. Techniques include negotiation frameworks based on market economies [Kis et al. 1996], game theoretic algorithms, and global or shared evaluation functions [Modi et al. 2005]. Our position is that these techniques do not adequately address the situation where self-interested cooperative agents maintain schedules on behalf of users operating in unbounded environments. Here, satisfaction is more important than optimality [Palen 1999], and personalized preferences are paramount to users [Berry et al. 2005a; Faulring and Myers 2005]. In our approach, global utility is secondary; the agent aims to maximize the utility of its user, but folds the positive utility of cooperating with others into that utility. Thus, we treat the utility of others as a single component of a personalized and context-dependent multi-criteria evaluation function, which the agent learns through interactions with its user.