We present the Associative Self-Organizing Map (A-SOM) and propose that it could be used to predict an agent's intentions by internally simulating the behaviour likely to follow initial movements. The A-SOM is a neural network that develops a representation of its input space without supervision, while simultaneously learning to associate its activity with an arbitrary number of additional (possibly delayed) inputs. We argue that the A-SOM would be suitable for the prediction of the likely continuation of the perceived behaviour of an agent by learning to associate activity patterns over time, and thus a way to read its intentions. © 2013 Springer-Verlag.
CITATION STYLE
Johnsson, M., & Buonamente, M. (2013). Internal simulation of an agent’s intentions. In Advances in Intelligent Systems and Computing (Vol. 196 AISC, pp. 175–176). Springer Verlag. https://doi.org/10.1007/978-3-642-34274-5_32
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