A parallel architecture for AI nonlinear planning

Abstract

The authors present a resource-level conflict detection and conflict resolution scheme which is combined with a state-level backward planning algorithm and provides efficient conflict detection and global conflict resolution for nonlinear planning. The scheme is to keep track of the usage of individual resources during planning and construct a resource-usage flow (RUF) structure (based on which conflict detection and resolution are accomplished). The RUF structure allows the system to perform minimal and nonredundant operations for conflict detection and resolution. Furthermore, resource-level conflict detection and resolution facilitates problem decomposition in terms of resources, thereby providing easy implementation in a parallel and distributed processing environment. Performance analysis indicates that the proposed architecture has a speed-up factor of the average depth of a plan network, D(N/sub a/), compared to the distributed NOAH where N/sub a/ (the total number of action nodes at the completion of planning) and D(N/sub a/) are considerably larger than the number of resources involved in planning as well as the number of initial goal states. >