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Representation and Recognition of Uncertain Enemy Policies Using Statistical Models.
2003 (English)In: In Proceedings of the NATO RTO Symposium on Military Data and Information Fusion, Prague, Czech Republic, October 2003., 2003Conference paper, Published paper (Other academic)
Abstract [en]

In this work we extend from the single agent to the on-line multi-agent stochastic policy recognition problem using a network structure. By using knowledge of agents’ interrelations we can create a policy structure that is compatible with that of a hostile military organisation. Using this approach we make use of existing knowledge about the military organisation and thereby strongly reduce the size of the hypothesis space. In this way we are able to bring down the problem complexity to a level that is tractable. Also, by using statistical models in policy recognition we are able to deal with uncertainty in a consistent way. This means that we have achieved improved policy recognition robustness.

 

We have developed a proof of concept Bayesian Network model. For the information fusion purpose, we show with our model that it is possible to integrate the pre-processed uncertain dynamical sensor data such as the enemy position and combine this knowledge with terrain data and uncertain a priori knowledge such as the doctrine knowledge to infer multi-agent policy in a robust and statistically sound manner.

Place, publisher, year, edition, pages
2003.
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-27750OAI: oai:DiVA.org:kth-27750DiVA: diva2:380599
Conference
The NATO RTO Symposium on Military Data and Information Fusion, Prague, Czech Republic, October 2003.
Note
QC 20101221Available from: 2010-12-21 Created: 2010-12-21 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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Citation style
  • apa
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