Situation Recognition and Hypothesis Management Using Petri Nets

Situation recognition --- the task of tracking states and identifying situations --- is a problem that is important to look into for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast to producing more data and information, to aid decision makers in focusing on information that is important for them, i.e. to detect potentially interesting situations. In this paper we explore the applicability of a Petri net based approach for modeling and recognizing situations, as well as for managing the hypothesis space coupled to matching situation templates with the present stream of data.

[1]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[2]  Amy Loutfi,et al.  Towards template-based situation recognition , 2009, Defense + Commercial Sensing.

[3]  Lars Niklasson,et al.  A component-based simulator for supporting research on situation recognition , 2009, Defense + Commercial Sensing.

[4]  Larry S. Davis,et al.  Representation and Recognition of Events in Surveillance Video Using Petri Nets , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Mica R. Endsley,et al.  Theoretical Underpinnings of Situation Awareness, A Critical Review , 2000 .

[6]  Gary Toth.  Higher-Level Information Fusion : Challenges to the Academic Community , 2008 .

[7]  Matej Kristan,et al.  Recognition of Multi-Agent Activities with Petri Nets , 2008 .

[8]  Ehud Rivlin,et al.  Building Petri Nets from Video Event Ontologies , 2007, ISVC.

[9]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[10]  Fabian Mörchen,et al.  Unsupervised pattern mining from symbolic temporal data , 2007, SKDD.

[11]  Wolfram Burgard,et al.  A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems , 2007, GfKl.

[12]  Malik Ghallab,et al.  Situation Recognition: Representation and Algorithms , 1993, IJCAI.

[13]  Stuart C. Shapiro.  Review of Knowledge representation: logical, philosophical, and computational foundations by John F. Sowa. Brooks/Cole 2000. , 2001 .

[14]  James Llinas,et al.  Revisiting the JDL Data Fusion Model II , 2004 .

[15]  Vladik Kreinovich,et al.  Soft computing in measurement and information acquisition , 2003 .

[16]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[17]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[18]  Daniel J. Garland,et al.  Situation Awareness Analysis and Measurement , 2009 .