Case-based reasoning and learning in manufacturing with the TOLTEC planner

The research presented concentrates on bringing together case-based reasoning and expert knowledge-based reasoning in a planner called TOLTEC. The expert´s domain-specific knowledge is modelled as dynamic memory structures and this representation is used to help the planner reason and control its planning process. TOLTEC uses a complex indexing of its cases, so as to allow incremental retrieval. The TOLTEC planner is applied to a highly constrained domain, and it is shown how the final plan is created by adding memory- and situation-selected chunks of subtask expansion to each subtask, until the problem is reduced to primitive (nonexpandable) tasks. It is also shown how the use of dynamic memory structures and dynamic, user-directed backtracking allows the planner to predict and discover failures, recover from them, and modify its knowledge according to them. Finally, it is shown how in a domain of multiple possible solutions for each goal the methodology developed allows the planner to slowly model itself to the preferences of the user. The paper also discusses some of the application domains where TOLTEC has been used, including process planning of cylindrical and prismatic parts, design checking, material selection, and design of communications systems