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Title: Ordering based decision making
Author: Chen, Shuwei
ISNI:       0000 0004 5357 4534
Awarding Body: University of Ulster
Current Institution: Ulster University
Date of Award: 2014
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Decision making is the crucial step in many real applications such as financial planning, organization management, products evaluation and recommendation. Qualitative information is widely used for expressing evaluations or preferences of experts among alternatives under different criteria. In many cases, decision making is to order alternatives and select the top one or few in the rank ordering of the alternatives. Orderings provide a very natural and effective way of resolving indeterminate situations in real life decision making problems. This thesis focuses on the representation and reasoning with qualitative ordering information for decision making, ordering based decision making. Such a decision making paradigm reflects the qualitative nature of various decision making scenarios where the available information for decision making can only be preferential ordering comparisons between decision alternatives and numerical approximation is not available or needed. This thesis proposes a lattice-ordered linguistic-valued logic based reasoning framework for multi-criteria decision making problems where the qualitative preferences from experts are in lattice order, a consensus group decision making model with partially ordered preference associated with belief degrees, and an automated reasoning based hierarchical framework for video based human activity recognition. This ordering based reasoning and decision making research aims to provide an alternative qualitative framework for handling uncertain ordering information in decision making problems. This research intends to enhance the quantitative theory of decision science with qualitative, algebraic and logic-oriented approaches for representing, aggregating and reasoning with ordering information.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available