&lt;title&gt;Training autoassociative recurrent neural network with preprocessed training data&lt;/title&gt;

Maskara, Arun; Noetzel, Andrew S.

doi:10.1117/12.152645

Cited by 1 publication

(1 citation statement)

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“…Other research in intention recognition investigated the use of grammar parsing methodology to recognize behavior as matching previously defined sequences of events [Clark, 1994;Wang & Arbib, 1993] while others investigated neural networks to do the same [Maskara & Noetzel, 1993]. Wang & Arbib's model, however, required that the complete pattern be presented before the pattern recognition would occur.…”

Section: Previous Workmentioning

confidence: 99%

Context-driven Near-term Intention Recognition

González

Gerber

DeMara

et al. 2004

Journal of Defense Modeling & Simulation

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Recognizing the intention of others in real-time is a critical aspect of many human tasks. This article describes a technique for interpreting the near-term intention of an agent performing a task in real-time by inferring the behavioral context of the observed agent. Equally significantly, the knowledge used in this approach can be captured semi-automatically through observation of an agent performing tasks on a simulator in the context to be recognized. A hierarchical, template-based reasoning technique is used as the basis for intention recognition, where there is a one-to-one correspondence between templates and behavioral contexts or sub-contexts. In this approach, the total weight associated with each template is critical to the correct selection of a template that identifies the agent's current intention. A template's total weight is based on the contributions of individual weighted attributes describing the agent's state and its surrounding environment. The investigation described develops and implements a novel means of learning these weight assignments by observing actual human performance. It accomplishes this using back-propagation neural networks and fuzzy sets. This permits early discrimination between different pre-categorized behavioral contexts/sub-contexts on the human-controlled agent such as a military or passenger vehicle. We describe an application of this concept and the experimentation to determine the viability of this approach.2

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Section: Previous Workmentioning

confidence: 99%