## Fractional Binding in Vector Symbolic Representations for Efficient Mutual Information Exploration

#### ICRA Workshop: Towards Curious Robots: Modern Approaches for Intrinsically-Motivated Intelligent Behavior, 2021

P. Michael Furlong, Terrrence C. Stewart, Chris Eliasmith

### Abstract

Mutual information (MI) is a standard objective function for driving exploration. The use of Gaussian processes to compute information gain is limited by time and memory complexity that grows with the number of observations collected. We present an efficient implementation of MI-driven exploration by combining vector symbolic architectures with Bayesian Linear Regression. We demonstrate equivalent regret performance to a GP-based approach with memory and time complexity that is constant in the number of samples collected, as opposed to $t^2$ and $t^3$, respectively, enabling long-term exploration.