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Knowledge representation and stocastic multi-agent plan recognition
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
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 [en]
Datorsystem, plan recognition, decision making, knowledge representation, information fusion, predicitve situation awareness
Keyword [sv]
Datorsystem
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-314ISBN: 91-7178-068-8 (print)OAI: oai:DiVA.org:kth-314DiVA: diva2:8904
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: 2018-01-10Bibliographically approved
List of papers
1. Bridging the gap between information need and information acquisition
Open this publication in new window or tab >>Bridging the gap between information need and information acquisition
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
Keyword
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:nbn:se:kth:diva-27749 (URN)2-s2.0-6344228160 (Scopus ID)917056115X (ISBN)
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: 2018-01-12Bibliographically approved
2. Representation and Recognition of Uncertain Enemy Policies Using Statistical Models.
Open this publication in new window or tab >>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.

National Category
Computer Engineering
Identifiers
urn:nbn:se:kth:diva-27750 (URN)
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: 2018-01-12Bibliographically approved
3. Generic Representation of Military Organisation and Military Behaviour: UML and Bayesian Networks.
Open this publication in new window or tab >>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.

National Category
Computer Engineering
Identifiers
urn:nbn:se:kth:diva-27751 (URN)92-837-0038-4 (ISBN)
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: 2018-01-12Bibliographically approved
4. Knowledge Representation, Modelling of Doctrinesand Information Fusion
Open this publication in new window or tab >>Knowledge Representation, Modelling of Doctrinesand Information Fusion
2003 (English)In: In Proceedings of the CIMI conference Enköping, Sweden, May 20-22. 2003, 2003Conference paper, Published paper (Other academic)
National Category
Computer Engineering
Identifiers
urn:nbn:se:kth:diva-27752 (URN)
Note
QC 20101222Available from: 2010-12-22 Created: 2010-12-22 Last updated: 2018-01-12Bibliographically approved
5. Realization of a bridge between high-level information need and sensor management using a common DBN
Open this publication in new window or tab >>Realization of a bridge between high-level information need and sensor management using a common DBN
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, Published 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
Keyword
belief networks, decision making, decision support systems, inference mechanisms, information needs, military computing, sensor fusion
National Category
Computer Engineering
Identifiers
urn:nbn:se:kth:diva-27747 (URN)10.1109/IRI.2004.1431528 (DOI)000225713600101 ()2-s2.0-16244407762 (Scopus ID)
Conference
IEEE International Conference on Information Reuse and Integration Las Vegas, NV, NOV 08-10, 2004
Note
QC 20101221Available from: 2010-12-21 Created: 2010-12-21 Last updated: 2018-01-12Bibliographically approved

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