NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems

Understand

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.