Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Probabilistic logic programming
Information and Computation
Qualitative reasoning with imprecise probabilities
Journal of Intelligent Information Systems - Special issue: fuzzy logic and uncertainty management in information systems
Anytime deduction for probabilistic logic
Artificial Intelligence
Probabilistic Datalog: implementing logical information retrieval for advanced applications
Journal of the American Society for Information Science
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
Belief revision and information fusion on optimum entropy: Research Articles
International Journal of Intelligent Systems - Uncertain Reasoning (Part 2)
International Journal of Approximate Reasoning
Using Histograms to Better Answer Queries to Probabilistic Logic Programs
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Weak nonmonotonic probabilistic logics
Artificial Intelligence
Probabilistic logic programming under inheritance with overriding
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Adaptive dialogue strategy selection through imprecise probabilistic query answering
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
| Hi-index | 0.00 |
In conditional probabilistic logic programming, given a query, the two most common forms for answering the query are either a probability interval or a precise probability obtained by using the maximum entropy principle. The former can be noninformative (e.g., interval [0, 1]) and the reliability of the latter is questionable when the priori knowledge is imprecise. To address this problem, in this paper, we propose some methods to quantitatively measure if a probability interval or a single probability is sufficient for answering a query. We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. The measure of ignorance (w.r.t. a query) reflects how reliable a precise probability聽for the query can be and a high value of ignorance suggests that a single probability is not suitable for the query. We then propose a method to measure the probability that the exact probability of a query falls in a given interval, e.g., a second order probability. We call it the degree of satisfaction. If the degree of satisfaction is high enough w.r.t. the query, then the given interval can be accepted as the answer to the query. We also prove our measures satisfy many properties and we use a case study to demonstrate the significance of the measures.