Fuzzy logic: mathematical tools for approximate reasoning
Fuzzy logic: mathematical tools for approximate reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
Modeling and Inference with Relational Dynamic Bayesian Networks
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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This study presents the application of first-order Bayesian Networks (FOBN) to model and reason in domains with complex relational and rich probabilistic structures. The FOBN framework used in this study is 'multi-entity Bayesian networks (MEBN). MEBN has its roots in Bayesian networks and aims to overcome some key modeling limitations of Bayesian networks by supplementing them with the expressive power of first-order logic. The study has been conducted in the domain of RoboCup Soccer which provides a challenging benchmark platform to evaluate the applicability of any knowledge representation mechanism. The benchmark scenario in this paper comprises of a soccer playing agent who is in possession of the ball and needs to decide the best action to be performed in a specific game situation. Further intricacies of this scenario have been discussed to thoroughly assess the effectiveness of first-order Bayesian network in the domain of RoboCup Soccer and it is found to provide the essential expressive power required to facilitate decision-making in such complex, stochastic domains.