Abstract
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.
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Raza, S., Haider, S., Williams, MA. (2013). Robot Reasoning Using First Order Bayesian Networks. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_1
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DOI: https://doi.org/10.1007/978-3-642-39515-4_1
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