Models of incremental concept formation
Machine learning: paradigms and methods
Concept formation knowledge and experience in unsupervised learning
Concept formation knowledge and experience in unsupervised learning
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning to Share Meaning in a Multi-Agent System
Autonomous Agents and Multi-Agent Systems
Automatic Fuzzy Ontology Generation for Semantic Web
IEEE Transactions on Knowledge and Data Engineering
ANEMONE: an effective minimal ontology negotiation environment
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Ontology-guided learning to improve communication between groups of agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning Non-Unanimous Ontology Concepts to Communicate with Groups of Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Systems Frontiers
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Given a set of observations or new information, agents should be able to update their understandings of the world. As a part of any agents' world ontology, concepts need to evolve in time. In this paper we present a new representation for non-unanimous concepts based on the combination of feature-values and their probabilities. This representation leads us to incrementally evolve the concepts upon facing with new observations or information. As agents' access to the knowledge structure of the peer agents is limited due to the high cost of communication, we enabled our agents to use any queried object and update the previously calculated probability of every feature-value combination based on the probability of that object being an instance of a concept.