Reasoning about knowledge
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Learning in multiagent systems
Multiagent systems
Artificial Intelligence Review
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
PagePrompter: An Intelligent Web Agent Created Using Data Mining Techniques
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Low-Scan Incremental Association Rule Maintenance Method Based on the Apriori Property
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Incremental learning with partial instance memory
Artificial Intelligence
APS: Agent's LearningWith Imperfect Recall
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
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In many applications software agents are supposed to show adaptive behaviour and learning capabilities in information rich environments. On the other hand agents are often expected to be resource-bounded systems, which do not utilize much memory, disk space and CPU time. In this paper we present a novel framework for incremental, statistical learning, attempting to satisfy both requirements. The new method, called APS, runs in a cycle including such phases as: storing observations in a history, rule discovery using data mining algorithms, and knowledge base maintenance. Once processed, the old facts are removed from the history and in every subsequent learning run only the recent portion of observations is analysed in search of new rules. This approach can substantially save disk space and processing time as compared to batch learning methods.