Abstract
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.
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Johnsson, M., Buonamente, M. (2013). Internal Simulation of an Agent’s Intentions. In: Chella, A., Pirrone, R., Sorbello, R., Jóhannsdóttir, K. (eds) Biologically Inspired Cognitive Architectures 2012. Advances in Intelligent Systems and Computing, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34274-5_32
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DOI: https://doi.org/10.1007/978-3-642-34274-5_32
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