Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://hdl.handle.net/20.500.14279/14767
Τίτλος: | Agent-based parsimonious decision support paradigm employing bayesian belief networks | Συγγραφείς: | Louvieris, Panos Gregoriades, Andreas Mashanovich, Natasha White, Gareth O'Keefe, Robert Levine, Jerry Henderson, Stewart |
Major Field of Science: | Social Sciences | Field Category: | Computer and Information Sciences | Λέξεις-κλειδιά: | Decision making;Information technology;Information theory;Probability;Decision support systems;Knowledge based systems | Ημερομηνία Έκδοσης: | 2006 | Πηγή: | International Workshop on Defence Applications of Multi-Agent Systems, 2005, 25 July 2005, Utrecht, Netherlands | Conference: | International Workshop on Defence Applications of Multi-Agent Systems | Περίληψη: | This paper outlines the application of Bayesian technologies to CSF (Critical Success Factor) assessment for parsimonious military decision making using an agent-based decision support system. The research referred to in this paper is part of a funded project concerned with Smart Decision Support Systems (SDSS) within the General Dynamics led Data and Information Fusion Defence Technology Centre Consortium in the UK. An important factor for successful military missions is information superiority (IS). However, IS is not solely about minimising information related needs to avoid information overload and the reduction of bandwidth. It is concerned with creating information related capabilities that are aligned with achieving operational effects and raising operational tempo. Moreover good military decision making, agent based or otherwise, should take into account the uncertainty inherent in operational situations. While efficient information fusion may be achieved through the deployment of CSFs, Bayesian Belief Networks (BBNs) are employed to model uncertainty. This paper illustrates the application of CSF enabled BBN technology through an agent based paradigm for assessing the likelihood of success of military missions. BBNs are composed of two parts the quantitative and the qualitative. The former models the dependencies between the various random events and the latter the prior domain knowledge embedded in the network in the form of conditional probability tables (CPTs). Modelling prior knowledge in a BBN is a complex and time consuming task and sometimes intractable when the number of nodes and states of the network increases. This paper describes a method that enables the automated configuration of conditional probability tables from hard data generated from simulations of military operational scenarios using a computer generated forces (CGF) synthetic environment. | URI: | https://hdl.handle.net/20.500.14279/14767 | ISBN: | 978-3-540-32835-3 | DOI: | 10.1007/11683704_3 | Rights: | © Springer-Verlag Berlin Heidelberg 2006. | Type: | Conference Papers | Affiliation: | University of Surrey Land Warfare Centre |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
50
1
checked on 6 Νοε 2023
Page view(s) 50
352
Last Week
1
1
Last month
27
27
checked on 14 Μαρ 2025
Google ScholarTM
Check
Altmetric
Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα