On the Definition of Essential and Contingent Properties of Subjective Belief Bases

In this paper, we introduce several features of subjective belief bases from both individualistic and collective perspectives and hence provide suitable essential and contingent properties for such belief bases. Essential properties reflect the attributes of a belief base being considered in vacuum, whereas contingent properties of a belief base reveal its characteristics with regards to the rest of its peer belief bases. Subjective belief bases employ values from Subjective logic, a type of probabilistic logic that explicitly takes uncertainty and belief ownership into account, to represent the priority information of the formula in each belief base. We show that subjective belief bases are a generalization of prioritized belief bases whose formula are annotated with their degree of necessity from possibilistic logic. We also discuss the role of essential and contingent properties in defining suitable belief base ordering functions.

[1]  Marco Cadoli,et al.  A Survey on Knowledge Compilation , 1997, AI Commun..

[2]  George J. Klir,et al.  ON THE NOTION OF DISTANCE REPRESENTING INFORMATION CLOSENESS: Possibility and Probability Distributions , 1983 .

[3]  Guilin Qi,et al.  Measuring conflict and agreement between two prioritized belief bases , 2005, IJCAI.

[4]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[5]  Khaled Mellouli,et al.  Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information , 2007, ECSQARU.

[6]  Dov M. Gabbay,et al.  Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning , 1994 .

[7]  Weiru Liu.  Measuring Conflict Between Possibilistic Uncertain Information Through Belief Function Theory , 2006, KSEM.

[8]  Guilin Qi,et al.  Combining multiple prioritized knowledge bases by negotiation , 2007, Fuzzy Sets Syst..

[9]  Weiru Liu,et al.  Analyzing the degree of conflict among belief functions , 2006, Artif. Intell..

[10]  Jérôme Lang,et al.  Quantifying information and contradiction in propositional logic through test actions , 2003, IJCAI.

[11]  Anthony Hunter.  Making Argumentation More Believable , 2004, AAAI.

[12]  Audun Jøsang,et al.  A Logic for Uncertain Probabilities , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[13]  Sébastien Konieczny,et al.  Belief base merging as a game , 2004, J. Appl. Non Class. Logics.

[14]  Guilin Qi,et al.  Adaptive Merging of Prioritized Knowledge Bases , 2006, Fundam. Informaticae.

[15]  Richard Booth,et al.  Social contraction and belief negotiation , 2002, Inf. Fusion.

[16]  H. Prade,et al.  Possibilistic logic , 1994 .

[17]  Michael Clarke,et al.  Symbolic and Quantitative Approaches to Reasoning and Uncertainty , 1991, Lecture Notes in Computer Science.

[18]  G. Klir,et al.  MEASURES OF UNCERTAINTY AND INFORMATION BASED ON POSSIBILITY DISTRIBUTIONS , 1982 .

[19]  C. List,et al.  Epistemic democracy : generalizing the Condorcet jury theorem , 2001 .

[20]  Mukesh Dalal,et al.  Investigations into a Theory of Knowledge Base Revision , 1988, AAAI.

[21]  Galina L. Rogova,et al.  Reliability In Information Fusion : Literature Survey , 2004 .