QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTíNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information （by linguistic labels） besides information expressed quantitatively （by numbers）, since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martfnez＇s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory （DSmT） framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation （ q1 ）- The linguistic information which expresses the expert＇s qualitative beliefs is expressed by means of mixed 2 Tuples （equidistant linguistic labels with a numeric biased value）. Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based （q2 DSmT） fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models （i.e., a distribution function model （DFM） and an improved Herrera-Martinez＇s model） jointly with the fusion rules developed in Dezert-Smarandache Theory （DSmT）, in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert＇s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.

Evaluation of Metal Fatigue Problems Using Qualitative Reasoning Approach

Fatigue failures are often encountered in steel structures under heavy cyclic loadings. This paper presents metal fatigue problems in structural engineering using outcomes of recent advancements in numerical qualitative reasoning. Qualitative reasoning provides an effective and sound technique for solving complex and uncertain scenarios, regardless of the uncertainty or linearity of the design parameters and their constraints. This paper introduces the algorithms behind a software platform, built upon numerical qualitative reasoning for engineering applications. The software expresses the results of the analysis in variable ranges and diagrams showing a two-dimensional design space. The capability of representing design parameters and outcomes in solution spaces provides a practical way for engineers to leverage their existing knowledge and experience. Case studies in metal fatigue design are given to reflect on the capability of qualitative reasoning in engineering applications.

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information,which is of significant importance for decisionmaking and query optimization in spatial analysis.Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules,which can result in enhanced reasoning performance.This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set.Combined with knowledge from the Semantic Web,the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules.The Constraint-Satisfactionbased approach,describing constraint set with semantic expressions,is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results.The verification can eliminate certain reasoning results to ensure the reliability of the final results.Thus,the task of qualitative spatial reasoning on topological relations is completed.