Machine learning of inductive bias
Machine learning of inductive bias
A general theory of discrimination learning
Production system models of learning and development
Functional transformations in AI discovery systems
Artificial Intelligence
Learning from the environment based on percepts and actions
Learning from the environment based on percepts and actions
An Integrated Framework for Extended Discovery in Particle Physics
DS '01 Proceedings of the 4th International Conference on Discovery Science
Complementary discrimination learning: a duality between generalization and discrimination
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Integrating task planning and interactive learning for robots to work in human environments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
| Hi-index | 0.00 |
The task of learning from environment is specified. It requires the learner to infer the laws of the environment in terms of its percepts and actions, and use the laws to solve problems. Based on research on problem space creation and discrimination learning, this paper reports an approach in which exploration, rule creation and rule learning are coordinated in a single framework. With this approach, the system LIVE creates STRIPS-Iike rules by noticing the changes in the environment when actions are taken, and later refines the rules by explaining the failures of their predictions. Unlike many other learning systems, since LIVE treats learning and problem solving as interleaved activities, no training instance nor any concept hierarchy is necessary to start learning. Furthermore, the approach is capable of discovering hidden features from the environment when normal discrimination process fails to make any progress.