SOAR: an architecture for general intelligence
Artificial Intelligence
Soar/PSM-E: investigating match parallelism in a learning production sytsem
PPEALS '88 Proceedings of the ACM/SIGPLAN conference on Parallel programming: experience with applications, languages and systems
Symbolic architectures for cognition
Foundations of cognitive science
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Parallelism in production systems
Parallelism in production systems
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Reducing problem-solving variance to improve predictability
Communications of the ACM
Inductive learning of search control rules for planning
Artificial Intelligence
A framework for investigating production system formulations with polynomially bounded match
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Explaining temporal differences to create useful concepts for evaluating states
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Integrating execution, planning, and learning in soar for external environments
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Hi-index | 0.02 |
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.