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Generic Representation of Military Organisation and Military Behaviour: UML and Bayesian Networks.
2004 (English)In: RTO-MP-MSG-022, 2004Conference paper, Published paper (Refereed)
Abstract [en]

To be able to model systems for C2 we have to evaluate and find appropriate methodology for modelling and representation of our knowledge about military organisations and military behaviour. Military organisation and military behaviour are important parts of C2.In this paper we present a study of modelling military organisation and military behaviour in a generic manner, using two different knowledge representation techniques: the Unified Modeling Language (UML) and Bayesian Networks. The class diagram that is provided by UML is well suited for representing military organisations whose structure is well-known, since military units and their interrelations can be represented as classes and interrelations between the classes. On the other hand, it is a much harder task to represent military organisations that are not well-known or military behaviour because of the uncertainty associated with them. Different behaviours are triggered in different environments using different doctrines, and the outcomes of the behaviours are uncertain. Due to complexity, time constraints and war friction, causal relations between different factors, which play an important role in warfare, may be uncertain.Bayesian Networks seems to be a reasonable choice for representing uncertain military behaviour as well as uncertain military organizations, since this method combines uncertainty and a priori knowledge in a homogeneous way. We can compare those models and facilitate the verifying process. As result we get a more reliable BN and the modelling time decreases.

Place, publisher, year, edition, pages
2004.
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-27751ISBN: 92-837-0038-4 (print)OAI: oai:DiVA.org:kth-27751DiVA: diva2:380672
Conference
Conference on “C3I and M&S Interoperability”, held in Antalya, Turkey, 9-10 October 2003
Note
QC 20101222Available from: 2010-12-22 Created: 2010-12-22 Last updated: 2010-12-22Bibliographically approved
In thesis
1. Knowledge representation and stocastic multi-agent plan recognition
Open this publication in new window or tab >>Knowledge representation and stocastic multi-agent plan recognition
2005 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

To incorporate new technical advances into military domain and make those processes more efficient in accuracy, time and cost, a new concept of Network Centric Warfare has been introduced in the US military forces. In Sweden a similar concept has been studied under the name Network Based Defence (NBD). Here we present one of the methodologies, called tactical plan recognition that is aimed to support NBD in future.

Advances in sensor technology and modelling produce large sets of data for decision makers. To achieve decision superiority, decision makers have to act agile with proper, adequate and relevant information (data aggregates) available. Information fusion is a process aimed to support decision makers’ situation awareness. This involves a process of combining data and information from disparate sources with prior information or knowledge to obtain an improved state estimate about an agent or phenomena. Plan recognition is the term given to the process of inferring an agent’s intentions from a set of actions and is intended to support decision making.

The aim of this work has been to introduce a methodology where prior (empirical) knowledge (e.g. behaviour, environment and organization) is represented and combined with sensor data to recognize plans/behaviours of an agent or group of agents. We call this methodology multi-agent plan recognition. It includes knowledge representation as well as imprecise and statistical inference issues.

Successful plan recognition in large scale systems is heavily dependent on the data that is supplied. Therefore we introduce a bridge between the plan recognition and sensor management where results of our plan recognition are reused to the control of, give focus of attention to, the sensors that are supposed to acquire most important/relevant information.

Here we combine different theoretical methods (Bayesian Networks, Unified Modeling Language and Plan Recognition) and apply them for tactical military situations for ground forces. The results achieved from several proof-ofconcept models show that it is possible to model and recognize behaviour of tank units.

Place, publisher, year, edition, pages
Stockholm: KTH, 2005. viii, 45 p.
Series
Trita-NA, ISSN 0348-2952 ; 0514
Keyword
Datorsystem, plan recognition, decision making, knowledge representation, information fusion, predicitve situation awareness, Datorsystem
National Category
Computer Engineering
Identifiers
urn:nbn:se:kth:diva-314 (URN)91-7178-068-8 (ISBN)
Presentation
2005-05-25, Sal E3, Osquars backe 14, Stockholm, 14:00
Supervisors
Note
QC 20101222Available from: 2005-07-18 Created: 2005-07-18 Last updated: 2011-11-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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