Motivation-Based Dependable Behavior Selection Using Probabilistic Affordance

In this paper, we generate probabilistic affordances to select a dependable behavior based on motivation values. Dependable behavior, in our context, refers to behavior that is situation-adequate as well as goaloriented. The probabilistic affordance is designed as a multilayer naïve Bayesian classifier with respect to uncertainties and reusability. A multilayer naïve Bayesian classifier is a probabilistic model with multiple layers comprising conditional probability tables or probability distributions based on equivalence classes. The affordances consider situation-adequateness in given situations and suggest possibilities of behaviors based on Bayesian inference. In order to select a dependable behavior to achieve a task, the affordances are arranged based on a sequential structure. This is because accomplishing a task usually requires sequentially performed behaviors. Motivation values are generated using the arranged affordances and a motivation value propagation algorithm. A robot selects a dependable behavior based on these motivation values. To validate our proposed methods, we present experimental results of an entertainment robot called AIBO handling three tasks.