Effects of Training Data Variation and Temporal Representation in a QSR-Based Action Prediction System
Effects of Training Data Variation and Temporal Representation in a QSR-Based Action Prediction System
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发表时间：
2014-03
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通讯作者：
Jay Young;Nick Hawes
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作者：
Jay Young;Nick Hawes
对于人工智能系统来说，对行为的理解是一项关键技能，预计这些系统将与外部主体交互--无论是其他人工智能系统，还是涉及合作的场景中的人类，比如能够帮助做家务的家用机器人，或者预计将协作并向他人提供帮助的救灾机器人。对于这样的系统来说，能够在新的情况下快速学习和重复使用模型和技能是有用的。我们的工作围绕着一个行为学习系统，利用定性的空间关系来减少系统所需的训练数据量，并帮助推广。在这篇文章中，我们分析了QSR的使用给我们的系统带来的优势。我们提供了使用定量和定性表示的各种机器学习技术的比较，并展示了不同数量的训练数据和时间表示对系统的影响。我们的工作主题是模拟RoboCup足球客场比赛。我们的结果表明，在训练数据有限的情况下，使用QSR提供了明显的优势，并在分类器中提供了更好的泛化性能。此外，我们还表明，采用时间的定性表示可以为QSR系统提供显著的性能提升。
Understanding of behaviour is a crucial skill for Artificial Intelligence systems expected to interact with external agents — whether other AI systems, or humans, in scenarios involving co-operation, such as domestic robots capable of helping out with household jobs, or disaster relief robots expected to collaborate and lend assistance to others. It is useful for such systems to be able to quickly learn and re-use models and skills in new situations. Our work centres around a behaviour-learning system utilising Qualitative Spatial Relations to lessen the amount of training data required by the system, and to aid generalisation. In this paper, we provide an analysis of the advantages provided to our system by the use of QSRs. We provide a comparison of a variety of machine learning techniques utilising both quantitative and qualitative representations, and show the effects of varying amounts of training data and temporal representations upon the system. The subject of our work is the game of simulated RoboCup Soccer Keepaway. Our results show that employing QSRs provides clear advantages in scenarios where training data is limited, and provides for better generalisation performance in classifiers. In addition, we show that adopting a qualitative representation of time can provide significant performance gains for QSR systems.