Bayesian network-based behavior control for skilligent robots

A Skilligent robot must be able to learn skills autonomously to accomplish a task. ldquoskilligencerdquo is the capacity of the robot to control behaviors reasonably, based on the skills acquired during run-time. Behavior control based on Bayesian networks is used to control reasonable behaviors. To accomplish this, subgoals are first discovered by clustering similar features of state transition tuples, which are composed of current states, actions, and next states. Here, features used in clustering are produced using changes of the states in the state transition tuples. Parameters of Bayesian networks and utility functions are learned separately using state transition tuples belonging to each subgoal. To select the best action while executing a task, the expected utility of each subgoal is calculated by the expected utility function and the robot chooses the action that maximizes expected utility calculated by the maximum expected utility (MEU) function. The MEU function is based on the conditional probabilistic distributions of Bayesian networks and utility functions. We also propose a method for reconstructing learned networks and increasing subgoals by incremental learning. To show the validities of our proposed methods, a task using dribbling-box-into-a-goal (DBIG) and obstacle-avoidance-while-dribbling-box (OAWDB) skills is simulated and experimented.