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Full-Text Articles in Physical Sciences and Mathematics
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Research Collection School Of Computing and Information Systems
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …
A Hybrid Agent Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yew-Soon Ong, Akejariyawong Tapanuj
A Hybrid Agent Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yew-Soon Ong, Akejariyawong Tapanuj
Research Collection School Of Computing and Information Systems
This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal DifferenceFusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be …
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
Research Collection School Of Computing and Information Systems
This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions …