SOAR: an architecture for general intelligence
Artificial Intelligence
Generality in artificial intelligence
Communications of the ACM
Mathematical methods for artificial intelligence and autonomous systems
Mathematical methods for artificial intelligence and autonomous systems
Pattern knowledge and search: the SUPREM architecture
Artificial Intelligence
The creation of digital consciousness
ACM SIGART Bulletin
Models of incremental concept formation
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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This paper describes the Monterège Cogitator, an advanced, original and powerful conceptual structure designed to describe the essential properties of learning.The elaboration of this structure introduces a methodological approach that uses abstract concepts in an expanded intuitive context, a theory of knowledge adapted to Control Systems and the definition and use of specialized automata to build complex abstract machines. From these components, the basic principles of model generation and the essential processes of learning are defined and integrated into a coherent learning system.The Monterège Cogitator is a learning engine which will eventually emulate the human intellect in the scope of learning. It is capable of making full use of parallel architectures. This work is part of ongoing research at Monterège design to define the fundamental structures of a digital intellect: the essential first step in building truly intelligent machines. An objective which can be attained within ten years.