Title:  "SteerBench: A framework for evaluating steering behaviors"

Abstract: Steering is a fundamental challenge for nearly all agents in virtual
worlds. There is a large and growing number of approaches for steering, and it
is becoming increasingly important to ask a fundamental question: how can we
objectively compare steering algorithms? To our knowledge, there is no standard
way of evaluating or comparing the quality of steering solutions. We present
SteerBench: a benchmark suite for objectively evaluating steering behaviors for
virtual agents. We propose a diverse set of test cases, metrics of evaluation,
and a scoring method that can be used to compare different steering algorithms.
Our framework can be easily customized by a user to evaluate specific behaviors
and new test cases. We demonsrate our benchmark process on two example steering
algorithms: a robust rule-based framework and an experimental egocentric fields
framework.

Bio:  Shawn Singh is a Ph.D. candidate at the University of California, Los
Angeles.  His research includes steering and planning behaviors, interactive
photorealistic rendering and graphics hardware.