Discriminating and Simulating Actions with the Associative Self-Organizing Map

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title = "Discriminating and Simulating Actions with the Associative Self-Organizing Map",
abstract = "Abstract in Undetermined We propose a system able to represent others' actions as well as to internally simulate their likely continuation from a partial observation. The approach presented here is the first step towards a more ambitious goal of endowing an artificial agent with the ability to recognise and predict others' intentions. Our approach is based on the associative self-organising map, a variant of the self-organising map capable of learning to associate its activity with different inputs over time, where inputs are processed observations of others' actions. We have evaluated our system in two different experimental scenarios obtaining promising results: the system demonstrated an ability to learn discriminable representations of actions, to recognise novel input, and to simulate the likely continuation of partially seen actions.",
keywords = "associative self-organising map, action recognition, internal, simulation, intention understanding, neural network",
author = "Miriam Buonamente and Haris Dindo and Magnus Johnsson",
year = "2015",
doi = "10.1080/09540091.2015.1025571",
language = "English",
volume = "27",
pages = "118--136",
journal = "Connection Science",
issn = "0954-0091",
publisher = "Taylor & Francis",
number = "2",