Laird, J. E.


An Architectural Approach to Ensuring Consistency in Hierarchical Execution

arXiv.org Artificial Intelligence

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis.


A preliminary analysis of the Soar architecture as a basis for general intelligence

Classics

"In this article we take a step towards providing an analysis of the Soar architecture as a basis for general intelligence. Included are discussions of the basic assumptions underlying the development of Soar, a description of Soar cast in terms of the theoretical idea of multiple levels of description, an example of Soar performing multi-column subtraction, and three analyses of Soar: its natural tasks, the sources of its power, and its scope and limits"Artificial Intelligence, 47, 289-325.



R1-Soar: An experiment in knowledge-intensive programming in a problem-solving architecture.

Classics

"This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1."IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 561-569.


R1-Soar: An experiment in knowledge-intensive programming in a problem-solving architecture

Classics

"This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1."IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 561-569.