A distributed architecture for planning and scheduling that learns through negotiation cases

In this paper, we propose a new integrated architecture for distributed planning and scheduling that exploits constraints for problem decomposition and coordination. One of the goals in our research is to develop an efficient method to solve densely constrained planning/scheduling problems in a distributed manner, and the other goal is to find an effective way to acquire useful context-dependent knowledge for improving problem solving performance dynamically in the course of interactions among problem solving agents