Combining ambiguous evidence with respect to ambiguous a priori knowledge. I. Boolean logic

This paper describes conditioned Dempster-Shafer (CDS) theory, a probabilistic calculus for dealing with possibly non-Bayesian evidence when the underlying a priori knowledge base is possibly non-Bayesian. Specifically, we show that the Dempster-Shafer combination operator can be "conditioned" to reflect the influence of any a priori knowledge base which can be modeled by a Dempster-Shafer belief measure. We show that CDS is firmly grounded in probability theory-specifically, in the theory of random sets. We also show that it is a generalization of the Bayesian theory to the case when both evidence and a priori knowledge are ambiguous. We derive the algebraic properties of the theory when a priori knowledge is assumed fixed. Under this assumption, we also derive the form of CDS in the special case when fixed a priori knowledge is Bayesian.

[1]  Philippe Smets About Updating , 1991, UAI.

[2]  Philippe Smets,et al.  The transferable belief model and random sets , 1992, Int. J. Intell. Syst..

[3]  I. R. Goodman Toward a comprehensive theory of linguistic and probabilistic evidence: two new approaches to conditional event algebra , 1994 .

[4]  Paul Krause,et al.  Representing Uncertain Knowledge , 1993, Springer Netherlands.

[5]  John Yen,et al.  A Reasoning Model Based on an Extended Dempster-Shafer Theory , 1986, AAAI.

[6]  Ronald P. S. Mahler,et al.  The modified Dempster-Shafer approach to classification , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[8]  Glenn Shafer,et al.  Implementing Dempster's Rule for Hierarchical Evidence , 1987, Artif. Intell..

[9]  Hung T. Nguyen,et al.  A theory of conditional information for probabilistic inference in intelligent systems: III. Mathematical appendix , 1993, Inf. Sci..

[10]  Hung T. Nguyen,et al.  Conditional inference and logic for intelligent systems - a theory of measure-free conditioning , 1991 .

[11]  Ronald P. S. Mahler,et al.  Combining ambiguous evidence with respect to ambiguous a priori knowledge. Part II: Fuzzy logic , 1995, Fuzzy Sets Syst..

[12]  M. Spies Conditional Events, Conditioning, and Random Sets , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[13]  Philippe Smets,et al.  Constructing the Pignistic Probability Function in a Context of Uncertainty , 1989, UAI.

[14]  Hung T. Nguyen,et al.  A Theory of Conditional Information For Probabilistic Inference in Intelligent Systems: II. Product Space Approach , 1994, Inf. Sci..

[15]  Hung T. Nguyen,et al.  Uncertainty Models for Knowledge-Based Systems; A Unified Approach to the Measurement of Uncertainty , 1985 .

[16]  Rudolf Kruse,et al.  Uncertainty and Vagueness in Knowledge Based Systems , 1991, Artificial Intelligence.

[17]  Ronald Fagin,et al.  A new approach to updating beliefs , 1990, UAI.

[18]  Rudolf Kruse,et al.  Uncertainty and vagueness in knowledge based systems: numerical methods , 1991, Artificial intelligence.

[19]  Philippe Smets,et al.  The Transferable Belief Model , 1994, Artif. Intell..

[20]  Gian-Carlo Rota,et al.  The theory of Möbius functions , 1986 .

[21]  D. Dubois,et al.  Conditional Objects as Nonmonotonic Consequence Relationships , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[22]  Prakash P. Shenoy,et al.  Propagating Belief Functions with Local Computations , 1986, IEEE Expert.

[23]  Nic Wilson,et al.  Decision-Making with Belief Functions and Pignistic Probabilities , 1993, ECSQARU.

[24]  Ronald P. S. Mahler Classification when a priori evidence is ambiguous , 1994, Defense, Security, and Sensing.