Simulating Actions with the Associative Self-Organizing Map

We present a system that can learn to represent actions as well as to internally simulate the likely continuation of their initial parts. The method we propose is based on the Associative Self Organizing Map (A-SOM), a variant of the Self Organizing Map. By emulating the way the human brain is thought to perform pattern recognition tasks, the A- SOM learns to associate its activity with di erent inputs over time, where inputs are observations of other's actions. Once the A-SOM has learnt to recognize actions, it uses this learning to predict the continuation of an observed initial movement of an agent, in this way reading its intentions. We evaluate the system's ability to simulate actions in an experiment with good results, and we provide a discussion about its generalization ability. The presented research is part of a bigger project aiming at en- dowing an agent with the ability to internally represent action patterns and to use these to recognize and simulate others behaviour.