SOAR: an architecture for general intelligence
Artificial Intelligence
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Making situation calculus indexical
Proceedings of the first international conference on Principles of knowledge representation and reasoning
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Decision-Theoretic Control of Reasoning: General Theory and an
Decision-Theoretic Control of Reasoning: General Theory and an
Universal subgoaling
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This paper introduces a partition of the possible forms of knowledge according to their relationship to the basic objective of an intelligent agent, namely to act successfully in response to its environment. The resulting classes of knowledge range from fully declarative to fully compiled. From these classes, it is possible to generate 1) a set of execution architectures, each of which combines some of the classes to produce decisions; and 2) a set of compilation methods, that transform knowledge into more efficient but (approximately) behaviourally equivalent, compiled forms. Existing compilation methods can be understood within this framework, and new compilation methods and execution architectures are indicated. It is proposed that systems with the ability to learn, use and transform between all the types of knowledge may be able to achieve simultaneously higher levels of competence, efficiency and flexibility.