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Realization of a bridge between high-level information need and sensor management using a common DBN
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
2004 (English)In: PROCEEDINGS OF THE 2004 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI-2004) / [ed] Memon, AM, NEW YORK: IEEE , 2004, 606-611 p.Conference paper (Refereed)
Abstract [en]

In a decision support system for military decision makers a plan recognition process provides estimates Of enemy plans. To respond to a changing and uncertain environment the plan recognition process requires timely and relevant information. We address the rarely discussed, yet crucial, issue of connecting the information needs of plan recognition to management of sensors. We have previously presented a framework for this purpose and here we give details of an implementation and provide some results. In our implementation both plan recognition, sensor management and the functions that connect them utilize the a priori knowledge stored in a Dynamic Bayesian Network.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2004. 606-611 p.
Keyword [en]
belief networks, decision making, decision support systems, inference mechanisms, information needs, military computing, sensor fusion
National Category
Computer Engineering
URN: urn:nbn:se:kth:diva-27747DOI: 10.1109/IRI.2004.1431528ISI: 000225713600101ScopusID: 2-s2.0-16244407762OAI: diva2:380583
IEEE International Conference on Information Reuse and Integration Las Vegas, NV, NOV 08-10, 2004
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.
Trita-NA, ISSN 0348-2952 ; 0514
Datorsystem, plan recognition, decision making, knowledge representation, information fusion, predicitve situation awareness, Datorsystem
National Category
Computer Engineering
urn:nbn:se:kth:diva-314 (URN)91-7178-068-8 (ISBN)
2005-05-25, Sal E3, Osquars backe 14, Stockholm, 14:00
QC 20101222Available from: 2005-07-18 Created: 2005-07-18 Last updated: 2011-11-11Bibliographically approved

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