Robotics and Biology Laboratory (RBO), Technische Universität Berlin, Berlin, Germany.
Amazon Research, Berlin, Germany.
PLoS One. 2018 Jun 14;13(6):e0197803. doi: 10.1371/journal.pone.0197803. eCollection 2018.
How to run most effectively to catch a projectile, such as a baseball, that is flying in the air for a long period of time? The question about the best solution to the ball catching problem has been subject to intense scientific debate for almost 50 years. It turns out that this scientific debate is not focused on the ball catching problem alone, but revolves around the research question what constitutes the ingredients of intelligent decision making. Over time, two opposing views have emerged: the generalist view regarding intelligence as the ability to solve any task without knowing goal and environment in advance, based on optimal decision making using predictive models; and the specialist view which argues that intelligent decision making does not have to be based on predictive models and not even optimal, advocating simple and efficient rules of thumb (heuristics) as superior to enable accurate decisions. We study two types of approaches to the ball catching problem, one for each view, and investigate their properties using both a theoretical analysis and a broad set of simulation experiments. Our study shows that neither of the two types of approaches can be regarded as superior in solving all relevant variants of the ball catching problem: each approach is optimal under a different realistic environmental condition. Therefore, predictive models neither guarantee nor prevent success a priori, and we further show that the key difference between the generalist and the specialist approach to ball catching is the type of input representation used to control the agent. From this finding, we conclude that the right solution to a decision making or control problem is orthogonal to the generalist and specialist approach, and thus requires a reconciliation of the two views in favor of a representation-centric view.
如何最有效地跑动以接住一个在空中飞行很长时间的抛射体，例如棒球？关于最佳接球问题的解决方案已经在科学界引发了近 50 年的激烈争论。事实证明，这场科学争论并不仅仅关注于接球问题本身，而是围绕着一个研究问题展开，即构成智能决策的要素有哪些。随着时间的推移，已经出现了两种相反的观点：一种是“通才”观点，认为智能是一种无需事先了解目标和环境就能解决任何任务的能力，其基础是使用预测模型进行最优决策；另一种是“专家”观点，认为智能决策不一定基于预测模型，甚至不一定是最优的，它提倡简单有效的经验法则（启发式）作为更优的决策方法。我们研究了两种解决接球问题的方法，每种方法对应一种观点，并通过理论分析和广泛的模拟实验来研究它们的性质。我们的研究表明，这两种方法都不能被视为解决所有相关变体的接球问题的优势方法：每种方法在不同的现实环境条件下都是最优的。因此，预测模型既不能保证也不能预先阻止成功，我们进一步表明，通才和专家在接球方面的方法的关键区别在于用于控制代理的输入表示类型。从这一发现中，我们得出结论，决策或控制问题的正确解决方案与通才和专家方法是正交的，因此需要调和这两种观点，以支持以表示为中心的观点。
