Planning for conjunctive goals
Artificial Intelligence
The Stanford cart and the CMU rover
Autonomous robot vehicles
Do the thing right: an architecture for action-expression
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
An Emerging Paradigm in Robot Architecture
Intelligent Autonomous Systems 2, An International Conference
Agent Architecture as Object Oriented Design
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Antiboxology: agent design in cultural context
Antiboxology: agent design in cultural context
Teleo-reactive programs for agent control
Journal of Artificial Intelligence Research
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Building brains for rooms: designing distributed software agents
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evolution, Adaption, and Behavioural Holism in Artificial Intelligence
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Towards Novel Neuroscience-Inspired Computing
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
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This volume is intended to help advance the field of artificial neural networks along the lines of complexity present in animal brains. In particular, we are interested in examining the biological phenomena of modularity and specialized learning. These topics are already the subject of research in another area of artificial intelligence. The design of complete autonomous agents (CAA), such as mobile robots or virtual reality characters, has been dominated by modular architectures and context-driven action selection and learning. In this chapter, we help bridge the gap from neuroscience to artificial neural networks (ANN) by incorporating CAA. We do this both directly, by using CAA as a metaphor to consider requirements for ANN, and indirectly, by using CAA research to better understand and model neuroscience. We discuss the strengths and the limitations of these forms of modeling, and propose as future work extensions to CAA inspired by neuroscience.