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Blocksworld glitches6/1/2023 We also study the distribution of hard/easy instances, and identify the structure that AI planners must be able to exploit in order to approach Blocks World successfully. Our results include methods for generating random problems for systematic experimentation, the best domain-specific planning algorithms against which AI planners can be compared, and observations establishing the average plan quality of near-optimal methods. This paper presents a sustained investigation of one such toy: the (in)famous Blocks World planning problem, and provides the level of understanding required for its effective use as a benchmark. A major reason why they have fallen into disrepute is that superficial understanding of them has resulted in poor experimental methodology and consequent failure to extract useful information. Less healthy, however, is the fashionable disparagement of “toy” domains: when properly approached, these domains can at the very least support meaningful systematic experiments, and allow features relevant to many kinds of reasoning to be abstracted and studied. Contemporary AI shows a healthy trend away from artificial problems towards real-world applications.
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