Motivation-Based Dependable Behavior Selection Using Probabilistic Affordance

Abstract

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 goal-oriented. The probabilistic affordance is designed as a multilayer naive Bayesian classifier with respect to uncertainties and reusability. A multilayer naive 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. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012