Learning investment functions for controlling the utility of control knowledge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Case Retention Policy Based on Detrimental Retrieval
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Speedup learning for repair-based search by identifying redundant steps
The Journal of Machine Learning Research
A selective macro-learning algorithm and its application to the N × N sliding-tile puzzle
Journal of Artificial Intelligence Research
The divide-and-conquer subgoal-ordering algorithm for speeding up logic inference
Journal of Artificial Intelligence Research
Cooperation between top-down and bottom-up theorem provers
Journal of Artificial Intelligence Research
Learning of resource allocation strategies for game playing
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
A distributed case-based reasoning application for engineering sales support
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Improving system performance in case-based iterative optimization through knowledge filtering
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning efficient rules by maintaining the explanation structure
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Knowledge has traditionally been considered to have a beneficial effect on the performance of problem solvers but recent studies indicate that knowledge acquisition is not necessarily a monotonically beneficial process, because additional knowledge sometimes leads to a deterioration in system performance. This paper is concerned with the problem of harmful knowledge: that is, knowledge whose removal would improve a system's performance. In the first part of the paper a unifying framework, called the information filtering model, is developed to define the various alternative methods for eliminating such knowledge from a learning system where selection processes, called filters, may be inserted to remove potentially harmful knowledge. These filters are termed selective experience, selective attention, selective acquisition, selective retention, and selective utilization. The framework can be used by developers of learning systems as a guide for selecting an appropriate filter to reduce or eliminate harmful knowledge.In the second part of the paper, the framework is used to identify a suitable filter for solving a problem caused by the acquisition of harmful knowledge in a learning system called LASSY. LASSY is a system that improves the performance of a PROLOG interpreter by utilizing acquired domain specific knowledge in the form of lemmas stating previously proved results. It is shown that the particular kind of problems that arise with this system are best solved using a novel utilization filter that blocks the use of lemmas in attempts to prove subgoals that have a high probability of failing.