Conference Paper

SteerBug: an interactive framework for specifying and detecting steering behaviors

DOI: 10.1145/1599470.1599497 Conference: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2009, New Orleans, Louisiana, USA, August 1-2, 2009
Source: DBLP


The size of crowds that modern computer games and urban simulations are capable of handling has given rise to the challenging problem of debugging and testing massive simulations of autonomous agents. In this paper, we propose SteerBug: an interactive framework for specifying and detecting steering behaviors. Our framework computes a set of time-varying metrics for agents and their environment, which characterize steering behaviors. We identify behaviors of interest by applying conditions (rules) or user defined sketches on the associated metrics. The behaviors we can specify and detect include unnatural steering, plainly incorrect results, or application-specific behaviors of interest. Our framework is extensible and independent of the specifics of any steering approach. To our knowledge, this is the first work that aims to provide a computational framework for specifying and detecting crowd behaviors in animation.

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    ABSTRACT: There are very few software frameworks for steering behaviors that are publicly available for developing, evaluating, and sharing steering algorithms. Furthermore, there is no widely accepted methodology for how to evaluate results of agent steering simulations. This situation makes it difficult to identify the real underlying challenges in agent simulations and future research directions to advance the state of the art. With the hope of encouraging community participation to address these issues, we have released SteerSuite, a flexible but easy-to-use set of tools, libraries, and test cases for steering behaviors. The software includes enhanced test cases, an improved version of SteerBench, a modular simulation engine, a novel steering algorithm, and more. Care has been taken to make SteerSuite practical and easy-to-use, yet flexible and forward-looking, to challenge researchers and developers to advance the state of the art in steering.
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    ABSTRACT: The statistical analysis of multi-agent simulations requires a definitive set of benchmarks that represent the wide spectrum of challenging scenarios that agents encounter in dynamic environments, and a scoring method to objectively quantify the performance of a steering algorithm for a particular scenario. In this paper, we first recognize several limitations in prior evaluation methods. Next, we define a measure of normalized effort that penalizes deviation from desired speed, optimal paths, and collisions in a single metric. Finally, we propose a new set of benchmark categories that capture the different situations that agents encounter in dynamic environments and identify truly challenging scenarios for each category. We use our method to objectively evaluate and compare three state of the art steering approaches and one baseline reactive approach. Our proposed scoring mechanism can be used (a) to evaluate a single algorithm on a single scenario, (b) to compare the performance of an algorithm over different benchmarks, and (c) to compare different steering algorithms.
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