Goal-oriented dependable action selection using probabilistic affordance

We first generate a probabilistic affordance to select an action based on motivation values. The affordance is designed as a multilayer naïve Bayesian classifier with respect to uncertainties and equivalence classes. The multilayer naïve Bayesian classifier is a probabilistic model with multiple layers of conditional probability tables and/or probability distributions to represent the equivalence classes. The affordances are arranged based on goal-orientedness, since achieving a task usually requires actions performed in a sequence. Additionally, motivation values are generated using the arranged affordances and a motivation value propagation algorithm. A robot selects a goal-oriented as well as a situation-adequate action based on the motivation values. To validate our proposed methods, we present experimental results of an entertainment robot called AIBO, handling three tasks.