面向现实世界多智能体系统的运行时搜索

2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST) Pub Date : 2022-05-01 DOI:10.1145/3526072.3527537

Abigail C. Diller, Erik M. Fredericks

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引用次数: 0

摘要

多智能体系统(MAS)可能会遇到意想不到的环境条件、次优系统配置以及自主智能体之间计划外的交互等形式的不确定性。这种不确定性的组合数量可能是无数的，但是运行时测试可以减少影响这种系统的问题。我们假设搜索启发式可以增加运行时测试过程，在现场，为MAS。为了支持我们的立场，我们讨论了我们正在进行的实验测试平台，以实现这一目标，并强调了我们对该领域的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Towards Run-Time Search for Real-World Multi-Agent Systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.

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来源期刊

2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST)

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