Artificial Intelligence
CSM: A Computational Model of Cumulative Learning
Machine Learning - Special issue on genetic algorithms
Hierarchical censored production rules (HCPRs) system
Data & Knowledge Engineering
Data mining
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Adaptive Hierarchical Censored Production Rule-Based System: A Generic Algorithm Approach
SBIA '96 Proceedings of the 13th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Towards Integrating Hierarchical Censored Production Rule(HCPR) Based and Neural Networks
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Intuitive Representation of Decision Trees Using General Rules and Exceptions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Integration of Hierarchical Censored Production Rule (HCPR)-based System and Neural Networks
SBRN '98 Proceedings of the Vth Brazilian Symposium on Neural Networks
On-line learning of predictive compositional hierarchies
On-line learning of predictive compositional hierarchies
Expert Systems with Applications: An International Journal
Acquiring Knowledge in Extended Hierarchical Censored Production Rules (EHCPRS) System
International Journal of Artificial Life Research
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Cumulative learning, a promising route for automated knowledge acquisition and adaptation, involves using the results of prior learning to facilitate further learning. Achieving this objective would largely depend upon an enriched knowledge representation scheme. One of such efficient representations is Production Rules with Fuzzy Hierarchy (PRFHs) system. A PRFH, a standard production rule augmented with generality and specificity information, is of the form: [image omitted] where P is the set of preconditions = (Ppub)k ∪ (Pspl)k ∪ (Ppvt)k and the specificity element Dki(di) means that Dki is a specific class of Dk with degree of subsumption di. In this paper, a set of related PRFHs is called a cluster and is represented by a PRFH-tree. The proposed scheme incrementally incorporates new knowledge into set of clusters obtained from previous episodes and also maintains summary of clusters to be used in the future episodes. Using the Cumulative_growth algorithm, a new rule is added to the system, the Restructure_cluster algorithm restructures a cluster so as to minimize redundancy, and Merging_clusters algorithm enables merging of two related PRFHs clusters. The proposed system would be particularly useful in mining data streams and dynamic restructuring of knowledge bases.