A process for generating quantitative belief functions

Abstract We present a structured methodology for transforming qualitative preference relationships among propositions into appropriate numeric representations. This approach will be useful in the difficult process of knowledge acquisition from experts on the degree of belief in various propositions or the probability of the truthfulness of those propositions. The approach implicitly (through the qualitative assignments) and explicitly (through the vague interval pairwise comparisons) provides for different levels of preference relationships. Among its advantages, it permits the expert to: explore the given problem situation, using linguistic quantifiers; avoid the premature use of numeric measures; and identify input data that are inconsistent with the theory of belief functions.

[1]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[2]  Ayodele Mobolurin,et al.  Aggregating preference rankings: An AHP‐based approach , 1993 .

[3]  Daniel L. Schmoldt,et al.  A prototype vision system for analyzing CT imagery of hardwood logs , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[4]  N. Bryson,et al.  Modelling pairwise comparisons on ratio scales , 1995 .

[5]  Alf Zimmer,et al.  Verbal Vs. Numerical Processing of Subjective Probabilities , 1983 .

[6]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[7]  Avinash C. Kak,et al.  Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation , 1989, UAI.

[8]  Lotfi A. Zadeh,et al.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[9]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[10]  S. K. Michael Wong,et al.  Representation of Qualitative User Preference by Quantitative Belief Functions , 1994, IEEE Trans. Knowl. Data Eng..

[11]  Robert M. Hamm,et al.  Selection of verbal probabilities: A solution for some problems of verbal probability expression. , 1991 .

[12]  Amnon Rapoport,et al.  Direct and indirect scaling of membership functions of probability phrases , 1987 .

[13]  D. Timmermans,et al.  The Roles of Experience and Domain of Expertise in Using Numerical and Verbal Probability Terms in Medical Decisions , 1994, Medical decision making : an international journal of the Society for Medical Decision Making.

[14]  Jing-Yu Yang,et al.  Detection for mobile robot navigation based on multisensor fusion , 1995, Other Conferences.

[15]  M. A. Styblinski,et al.  Fuzzy cognitive maps, signal flow graphs, and qualitative circuit analysis , 1988, IEEE 1988 International Conference on Neural Networks.

[16]  Thomas L. Saaty,et al.  Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation , 1990 .

[17]  Kweku-Muata Osei-Bryson,et al.  A Qualitative Discriminant Process for Scoring and Ranking in Group Support Systems , 1994, Inf. Process. Manag..

[18]  D. Budescu,et al.  Consistency in interpretation of probabilistic phrases , 1985 .

[19]  Hugh F. Durrant-Whyte,et al.  An Evidential Approach to Probabilistic Map-Building , 1995, Reasoning with Uncertainty in Robotics.

[20]  L. Zadeh.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[21]  Simon Parsons,et al.  Some qualitative approaches to applying the Dempster-Shafer theory , 1994 .

[22]  Pekka J. Korhonen,et al.  Multiple criteria decision support: The state of research and future directions , 1992, Comput. Oper. Res..

[23]  K. Tanaka,et al.  A Design Method for Incorporating Human Judgement into Monitoring Systems , 1995 .

[24]  S. Moral,et al.  Calculus with linguistic probabilities and beliefs , 1994 .

[25]  Forouzan Golshani,et al.  Dynamic route planning with uncertain information , 1996, Knowl. Based Syst..