Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bridging the gap between information need and information acquisition
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
2004 (English)In: Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 / [ed] Svensson, P., Schubert, J., Stockholm, 2004, Vol. 2, 1202-1209 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we address the rarely discussed problem of connecting high-level information (e.g., aggregated states and enemy intentions) to information acquisition. Our approach is to partition the transition of information need to sensor management into a set of comprehensible entities (information types and functions), which we present in a framework. The framework is stepwise (sequential) and first translates actual information (from the data and information fusion process) to information need. The information need is mapped to the task space by a task management function which performs prioritization with respect to information need. A further step includes projection of tasks to service space by an allocation scheme, and finally services give orders to resources. In the terminology of the framework, we discuss the extension of a previous study (that involved plan recognition) with a sensor management function.

Place, publisher, year, edition, pages
Stockholm, 2004. Vol. 2, 1202-1209 p.
Keyword [en]
High-level information, Plan recognition, Sensor management, Situation and threat assessment, Data acquisition, Information dissemination, Mathematical models, Military communications, Military operations, Multi agent systems, Sensor data fusion, Surveillance radar, Information fusion, Information analysis
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-27749Scopus ID: 2-s2.0-6344228160ISBN: 917056115X (print)OAI: oai:DiVA.org:kth-27749DiVA: diva2:380591
Conference
Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004; Stockholm; 28 June 2004 through 1 July 2004
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

Open Access in DiVA

No full text

Other links

Scopushttp://www.scopus.com/inward/record.url?eid=2-s2.0-6344228160&partnerID=40&md5=3c35110b3ab7a0a39b135247a993cb2c

Search in DiVA

By author/editor
Johansson, L. Ronnie M.Suzic, Robert
By organisation
Numerical Analysis and Computer Science, NADA
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 42 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf