Knowledge-based artificial neural networks
Artificial Intelligence
Mental Models for Robot Control
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
On-line learning of predictive compositional hierarchies
On-line learning of predictive compositional hierarchies
Situated agents can have goals
Robotics and Autonomous Systems
Using symbolic learning to improve knowledge-based neural networks
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Towards biomimetic neural learning for intelligent robots
Biomimetic Neural Learning for Intelligent Robots
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To have agents autonomously model a complex environment, it is desirable to use distributed representations that lend themselves to neural learning. Yet developing and executing plans acting on the environment calls for abstract, localist representations of events, objects and categories. To combine these requirements, a formalism that can express neural networks, action sequences and symbolic abstractions with the same means may be considered advantageous. We are currently exploring the use of compositional hierarchies that we treat both as Knowledge Based Artificial Neural Networks and as localist representations for plans and control structures. These hierarchies are implemented using MicroPsi node nets and used in the control of agents situated in a complex simulated environment.