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Capabilities-based plan recognition

Authors: Robert Suzic; Pontus Svenson;

Capabilities-based plan recognition

Abstract

The new types of opponents and new kinds of situations that the Swedish defence forces are facing today calls for new information fusion methods. In order to provide commanders with the ability to predict the enemy's future actions, tools for automatic plan recognition are needed. In this paper, we take the first step towards constructing such a method based on recognizing plans using information about the capabilities of the enemy. The method combines our previous work on plan recognition using bayesian networks based on comparing enemy movements to their doctrines and methodology for force aggregation using capabilities. We describe how the plans of the enemy are built up so that their intended effects are achieved. The relations between these, their resources and the context in which they are acting are used to construct the plan recognition network. We discuss the need for including termination states in the plan recognition method and describe ontologies that are aimed to support the construction of the bayesian networks needed for capability-based plan recognition. We conclude with a discussion of possible extensions of the method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Top 10%
Average
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