Recognizing actions with the associative self-organizing map

When artificial agents interact and cooperate with other agents, either human or artificial, they need to recognize others´ actions and infer their hidden intentions from the sole observation of their surface level movements. Indeed, action and intention understanding in humans is believed to facilitate a number of social interactions and is supported by a complex neural substrate (i.e. the mirror neuron system). Implementation of such mechanisms in artificial agents would pave the route to the development of a vast range of advanced cognitive abilities, such as social interaction, adaptation, and learning by imitation, just to name a few. We present a first step towards a fully-fledged intention recognition system by enabling an artificial agent to internally represent action patterns, and to subsequently use such representations to recognize - and possibly to predict and anticipate - behaviors performed by others. We investigate a biologically-inspired approach by adopting the formalism of Associative Self-Organizing Maps (A-SOMs), an extension of the well-known Self-Organizing Maps. The A-SOM learns to associate its activities with different inputs over time, where inputs are high-dimensional and noisy observations of others´ actions. The A-SOM maps actions to sequences of activations in a dimensionally reduced topological space, where each centre of activation provides a prototypical and iconic representation of the action fragment. We present preliminary experiments of action recognition task on a publicly available database of thirteen commonly encountered actions with promising results.