Adaptive resonance associative map
Neural Networks
Belief-desire-intention agent architectures
Foundations of distributed artificial intelligence
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
The Belief-Desire-Intention Model of Agency
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Intelligence Through Interaction: Towards a Unified Theory for Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A hybrid architecture combining reactive plan execution and reactive learning
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Action selection and task sequence learning for hybrid dynamical cognitive agents
Robotics and Autonomous Systems
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This paper presents iFALCON, a model of BDI (belief-desire-intention) agent that is fully realized as a self-organizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case study using the blocks world domain shows that an iFALCON agent can also do planning to solve problems when the knowledge is incomplete.