BIPLEX: Creative Problem-Solving by Planning for Experimentation
BIPLEX: Creative Problem-Solving by Planning for Experimentation
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发表时间：
2022
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通讯作者：
Vasanth Sarathy;Matthias Scheutz
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作者：
Vasanth Sarathy;Matthias Scheutz
人类的创造性问题解决通常涉及对现实世界的实验以及对结果的观察，这进而导致解决方案的发现或可能的进一步实验。然而，人工智能中关于创造性问题解决的大多数工作仅仅侧重于心理过程，比如寻找解决方案的搜索和推理的变体。在这篇立场文件中，我们提出了一个名为BIPLEX的新颖算法框架，它更接近于人类创造性地解决问题的方式，因为它将假设生成、实验和结果观察作为寻找解决方案的一部分。我们通过烘焙领域的各种示例来介绍BIPLEX，这些示例展示了该框架的重要特征，包括它根据属性对物体的表示，以及当检测到执行僵局时它能够交错进行实验规划和结果评估的能力，这可能会导致新的解决方案路径。我们认为，这些特征对于解决仅靠搜索无法解决的问题以及大多数现有的创造性问题解决方法所不能解决的问题是必不可少的。
Creative problem-solving in humans often involves real-world experimentation and observation of outcomes that then leads to the discovery of solutions or possibly further experiments. Yet, most work on creative problem-solving in AI has focused on solely mental processes like variants of search and reasoning for finding solutions. In this position paper, we propose a novel algorithmic framework called BIPLEX that is closer to how humans solve problems creatively in that it involves hypothesis generation, experimentation, and outcome observation as part of the search for solutions. We introduce BIPLEX through various examples in a baking domain that demonstrate important features of the framework, including its representation of objects in terms of properties, as well its ability to interleave planning for experimentation and outcome evaluation when execution impasses are detected, which can lead to novel solution paths. We argue that these features are essentially required for solving problems that cannot be solved by search alone and thus most existing creative problem-solving approaches.