Chained action learning through real-time interactions
Yilu Zhang, Juyang Weng · 2003
The capability of learning new skills is very important for an artificial agent to scale up. In this paper, we propose a developmental cognitive learning architecture which enables an artificial agent to develop complex behaviors (chained actions) after acquisition of simple ones. The mechanism that makes this possible is chained secondary conditioning. The major challenge of this work is that training and testing must be conducted in the same mode through online real-time interactions between the agent and trainers. Experimental results on a real-time system are reported, in which the trainer shapes the behavior of the agent interactively and continuously through verbal commands and other sensory signals.