BIPLEX: Creative Problem-Solving by Planning for Experimentation
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 as 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.