Plan recognition has to be performed in a statistically robust manner concerning a possibly infinite number of tactical situations and different types of units. We need a generic model for tactical plan recognition where we combine observations and a priori knowledge in a flexible manner by using suitable methodologies and by having a large hypothesis space taken into account. Threat and therefore observed agent’s plans should be put into a context.
Here, we propose Multi-Entity Bayesian Networks (MEBN), introduced in [2], which enable the composition of Bayesian Networks from the network pieces, as the key methodology when designing flexible plan recognition models. However, Bayesian network pieces (fragments) must be compatible and therefore we propose ontology for generic plan recognition using Bayesian network fragments. Additionally, we claim that by using multi-entity network fragments we expand the hypothesis space and using this approach various multi-agents structures can be expressed. Our final contribution is that we incorporate the use of explicit utilities in our plan recognition model.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.