Paper Info
Title
Decisionmaking With Fuzzy-Belief-State-Based Reasoning
Abstract
An outstanding problem is how to make decisions with uncertain and incomplete data from disparate sources without NP-hard algorithms. Here we introduce a new reasoning methodology, fuzzy-belief-state-based reasoning, to solve this problem. In this methodology, we first create a fuzzy-belief-state base for a system from its historical data. For any component n (n = 1,..., N) of the set of empirical state vectors, the values of that component are clustered into LOW, MEDIUM and HIGH fuzzy sets. Then each state vector is fuzzified into a fuzzy-belief-state vector off triples, where the n-th triple contains the fuzzy truths of membership of the variable value in these respective three fuzzy sets. Each such vector of N triples is associated with a decision to form a fuzzy-belief-case and such cases comprise a fuzzy-belief-state base. Then, when given an observed state vector that is incomplete and uncertain, we mine fuzzy association rules from the fuzzy-belief-state base and apply them to infer the missing values and their fuzzy beliefs based on that incomplete observation. The completed observation is used to match fuzzy-belief-state vectors in the fuzzy-belief-state base. Decisions of the best matching cases are retrieved for use as in case-based reasoning.
Year
DOI
Venue
2004
10.1109/IRI.2004.1431519
PROCEEDINGS OF THE 2004 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI-2004)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Lily R. Liang114311.40
Carl G. Looney219821.58