The Dynamics of Recurrent Behavior Networks

If behavior networks, which use spreading activation to select actions, are analogous to connectionist methods of pattern recognition, then we suggest that recurrent behavior networks, which use energy minimization, are analogous to Hoppeld networks. Hoppeld networks memorize patterns by making them attractors. We argue that, similarly, each behavior of a recurrent behavior network should be an attractor of the network, to inhibit fruitless, repeated switching between diierent behaviors in response to small changes in the environment and in motivations. We demonstrate that the performance in a test domain of the Do the Right Thing recurrent behavior network is improved by redesigning it to create desirable attractors and basins of attraction. We further show that this performance increase is correlated with an increase in persistence and a decrease in undesirable behavior-switching.