Programming expert systems in OPS5: an introduction to rule-based programming
Programming expert systems in OPS5: an introduction to rule-based programming
The mathematics of inheritance systems
The mathematics of inheritance systems
SOAR: an architecture for general intelligence
Artificial Intelligence
TREAT: a new and efficient match algorithm for AI production systems
TREAT: a new and efficient match algorithm for AI production systems
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
A four-valued semantics for terminological logics
Artificial Intelligence
Parallel implementation of OPS5 on the encore multiprocessor: results and analysis
International Journal of Parallel Programming
Unified theories of cognition
Approximating learned search control knowledge
Proceedings of the sixth international workshop on Machine learning
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Eliminating expensive chunks by restricting expressiveness
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
Real time constraints on AI systems require guaranteeing bounds on these systems' performance. However, in the presence of sources of uncontrolled combinatorics, it is extremely difficult to guarantee such bounds on their performance. In production systems, the primary source of uncontrolled combinatorics is the production match. To eliminate these combinatorics, the unique-attribute formulation was introduced in (Tambe and Rosenbloom, 1989), which achieved a linear bound on the production match. This formulation leads to several questions: is this unique-attributes formulation the best conceivable production system formulation? In fact, are there other alternative production system formulations? If there are other formulations, how should these alternatives be compared with the unique-attribute formulation? This paper attempts to address these questions in the context of Soar. It identifies independent dimensions along which alternative production system formulations can be specified. These dimensions are based on the fixed class of match algorithms currently employed in production systems. These dimensions create a framework for systematically generating alternative formulations. Using this framework we show that the unique-attribute formulation is the best one within the dimensions investigated. However, if a new class of match algorithms is admitted, by relaxing certain constraints, other competitor formulations emerge. The paper indicates which competitor formulations are promising and why. Although some of the concepts, such as unique-attributes, are introduced in the context of Soar, they should also be relevant to other rule-based systems.