Name
Abstract | ||
|---|---|---|
We propose a new way of extending Logic Programming (LP) for reasoning with uncertainty. Probabilistic finite domains (Pfd) capitalise on ideas introduced by Constraint LP, on how to extend the reasoning capabilities of the LP engine. Unlike other approaches to the field, Pfd syntax can be intuitively related to the axioms defining Probability and to the underlying concepts of Probability Theory, (PT) such as sample space, events, and probability function.Probabilistic variables are core computational units and have two parts. Firstly, a finite domain, which at each stage holds the collection of possible values that can be assigned to the variable, and secondly a probabilistic function that can be used to assign probabilities to the elements of the domain. The two constituents are kept in isolation from each other. There are two benefits in such an approach. Firstly, that propagation techniques from finite domains research are retained, since a domain's representation is not altered. Thus, a probabilistic variable continues to behave as a finite domain variable. Secondly, that the probabilistic function captures the probabilistic behaviour of the variable in a manner which is, to a large extent, independent of the particular domain values. The notion of events as used in PT can be captured by LP predicates containing probabilistic variables and the derives operator (驴) as defined in LP. Pfd stores hold conditional constraints which are a computationally useful restriction of conditional probability from PT. Conditional constraints are defined by D1 : 驴1驴. . .驴Dn : 驴n 驴 Q1驴. . .驴Qm where, Di and Qj are predicates and each 驴i is a probability measure (0 驴 驴i 驴 1, 1 驴 i 驴 n, 1 驴 j 驴 m). The conjuction of Qj's qualifies probabilistic knowledge about Di. In particular, the constraint is evidence that the probability of Di in the qualified cases (i.e. when 驴 Q1, ..., Qm) is equal to 驴i. On the other hand a conditional provides no evidence for the cases where 驴/ Q1, ...,Qm. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45619-8_38 | ICLP |
Keywords | Field | DocType |
finite domain,probabilistic function,probabilistic variable,probabilistic finite domains,finite domain variable,lp engine,probabilistic knowledge,conditional constraint,probabilistic finite domain,probabilistic behaviour,constraint lp,brief overview,conditional probability,probability measure,probability theory | Conditional probability,Axiom,Computer science,Probability measure,Algorithm,Operator (computer programming),Probabilistic logic,Logic programming,Sample space,Probability theory | Conference |
Volume | ISSN | ISBN |
2401 | 0302-9743 | 3-540-43930-7 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicos Angelopoulos | 1 | 53 | 11.48 |