In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning or as benchmarks in planning competitions. In most cases, these problems are created either by hand or by a domain-specific generator, putting a burden on the human designers. In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve. We formulate problem generation as a Markov Decision Process and train two generative policies with Deep Reinforcement Learning to generate problems with the desired properties. We conduct experiments on several classical domains, comparing our method with handcrafted domain-specific generators that generate valid and diverse problems but do not optimize difficulty. The results show NeSIG is able to automatically generate valid problems of greater difficulty than the competitor approaches, while maintaining good diversity.
翻译:在自动化规划领域,往往需要从某一特定领域提出一系列规划问题,例如,将这些问题用作机器学习的培训数据或作为规划竞争的基准,这些问题大多是由手工或特定领域的发电机造成的,给人类设计者带来负担。在本文中,我们建议根据我们的知识,采用第一个独立领域的方法,自动产生有效、多样和难以解决的规划问题。我们将问题生成过程作为Markov决策程序,并用深加学习培训两种基因化政策,以产生想要的特性的问题。我们在几个古典领域进行实验,将我们的方法与手制特定领域发电机进行比较,这些发电机产生有效、多样的问题,但不会产生最佳的难度。结果显示,Nesig能够自动产生比竞争者方法更困难的有效问题,同时保持良好的多样性。