Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Towards a probabilistic modal logic for semantic-based information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Handbook of logic in artificial intelligence and logic programming (vol. 1)
Reasoning about knowledge
An Introduction to Variational Methods for Graphical Models
Machine Learning
The modal logic of probability
TARK '98 Proceedings of the 7th conference on Theoretical aspects of rationality and knowledge
Modal probability, belief, and actions
Fundamenta Informaticae
Reasoning about knowledge and probability
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generating and solving imperfect information games
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A partition-based first-order probabilistic logic to represent interactive beliefs
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
| Hi-index | 0.00 |
Modal logic represents knowledge that agents have about other agents' knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs. Models in those logics are useful for understanding and decision making in conversations, bargaining situations, and competitions. Unfortunately, probabilistic modal structures are impractical for large real-world applications because they represent their state space explicitly. In this paper we scale up probabilistic modal structures by giving them a factored representation. This representation applies conditional independence for factoring the probabilistic aspect of the structure (as in Bayesian Networks (BN)). We also present two exact and one approximate algorithm for reasoning about the truth value of probabilistic modal logic queries over a model encoded in a factored form. The first exact algorithm applies inference in BNs to answer a limited class of queries. Our second exact method applies a variable elimination scheme and is applicable without restrictions. Our approximate algorithm uses sampling and can be used for applications with very large models. Given a query, it computes an answer and its confidence level efficiently.