Book Chapter or Conference Paper Title

Robot reasoning using first order Bayesian networks

Faculty / School

Faculty of Computer Sciences (FCS)

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Conference Paper

Publication Date

7-2013

Author Affiliation

  • Saleha Raza is Ph.D. Scholar at the Faculty of Computer Science, Institute of Business Administration, Karachi
  • Sajjad Haider is Associate Professor at Institute of Business Administration, Karachi

Conference Name

International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making

Conference Location

Beijing, China

Conference Dates

12-14 July 2013

ISBN/ISSN

84880072516 (Scopus)

First Page

1

Last Page

12

Publisher

Springer, Berlin, Heidelberg

Abstract / Description

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.

Citation/Publisher Attribution

Raza, S., Haider, S., & Williams, M. A. (2013, July). Robot reasoning using first order Bayesian networks. In International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (pp. 1-12). Springer, Berlin, Heidelberg.

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