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Situational Behavior Modeling

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Rept. for 20 Apr 2006-31 Mar 2009

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Behavior modeling for military applications needs to consider systems in which all kind of entities participate - machines, humans, human organizations like platoons or companies as well as such complex entities like countries, industries and societies. The variety and the structure of entities participating in behaviors in the military domain require the use of representations and tools appropriate for this kind of complexity. Ontological modeling seems to be the best match for this domain. However, there are no known results in the literature on modeling and tracking of behaviors using an ontological approach in which automatic inference over the dynamic models of behaviors can be carried out using inference tools. A behavior model can be conceptualized in a number of ways - as an abstract concept that is independent of any physical or conceptual entity, as a feature of a specific entity, or as an abstract concept that is associated with one or more physical or conceptual entities. Various knowledge representation mechanisms including State Machines, Hidden Markov Models, Petri Nets, Game Theoretic Models and Bayesian Networks have been used extensively for behavior modeling. Most of the studies have been focusing on modeling behavior of a specific type of entity. For instance, organizational behavior modeling considers an organization as a system of interrelated entities humans and then develops models for behavior of humans within an organization. In the approach presented in this document, behavior is treated as being associated with a situation, i.e., with a number of objects e.g., an organization being in some relations with each other. While situation objects will normally have some basic behaviors associated by default, they will be able to participate in complex behaviors involving multiple situation objects.

Subject Categories:

  • Information Science
  • Psychology
  • Statistics and Probability

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