SOAR: an architecture for general intelligence
Artificial Intelligence
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Cyc: toward programs with common sense
Communications of the ACM
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
The problem of serial order: a neural network model of sequence learning and recall
Current research in natural language generation
Neural network architectures: an introduction
Neural network architectures: an introduction
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Integrating rules and connectionism for robust commonsense reasoning
Integrating rules and connectionism for robust commonsense reasoning
Neuro-Soar: a neural-network architecture for goal-oriented behavior
The Soar papers (vol. II)
Neural network models of strategy development in children
Neural Networks
Self-organization in the time domain
The handbook of brain theory and neural networks
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought
Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought
Sparse Distributed Memory
Connectionist inference models
Neural Networks
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
An artificial neural network for multi-level interleaved and creative serial order cognitive behavior
Computer, know thyself: exploring consciousness via self-aware machines
Proceedings of the 49th Annual Southeast Regional Conference
A system for evolving neural architectures
Proceedings of the 50th Annual Southeast Regional Conference
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If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. Its capabilities are described based on simulations calling for increasing levels of functionality and are used to show how the model can progress from fundamental sequence learning and recall tasks to sophisticated behaviors such as an ability to solve simple mathematical expressions and a creative capacity for the formation and application of inductive rules.