Chunking, an experience based-learning mechanism, improves Soar's performance a great deal when viewed in terms of the number of subproblems required and the number of steps within a subproblem. This high-level view of the impact of chunking on performance is based on an deal computational model, which says that the time per step is constant. However, if the chunks created by chunking are expensive, then they consume a large amount of processing in the match, i.e, indexing the knowledge-base, distorting Soar*s constant time-per-stcp model. In these situations, the gain in number of steps does not reflect an improvement in performance; in fact there may be degradation in the total run time of the system. Such chunks form a major problem for the system, since absolutely 10 guarantees can be given about its behavior. I "his article presents a solution to the problem of expensive chunks. The solution is based on the notion of restricting the expressiveness of Soar's representational language to guarantee that chunks formed will require only a limited amount of matching effort. We analyze the tradeoffs involved in restricting expressiveness and present some empirical evidence to support our analysis.
The authors examine the suitability of message-passing computers for parallel implementations of production systems. Two mappings for production systems on these computers, one targeted toward fine-grained message-passing machines and the other targeted toward medium-grained machines, are presented. Simulation results for the medium-grained mapping are presented, and it is shown that it is possible to exploit the available parallelism and to obtain reasonable speedups. The authors perform a detailed analysis of the results and suggest solutions for some of the problems