Planning without a Domain Model
Automated Planning focuses on plan search.Traditionally,it aimed at domain-independent methods with handcrafted domain models.However,automated domain model acquisition,especially the action model acquisition is difficult.On the other hand,many problem specific search space pruning techniques were proposed.Therefore,we combine the automated domain model acquisition and problem specific search space pruning to generate plans for all instances of a problem.To this end,we use deep learning techniques to learn planning behaviors,which already considered the domain models and the problem constrains.The biggest challenge of this method is encoding planning knowledge as the inputs of a deep neural network.Experiments showed that we can get instance-oriented perfect classifiers.With these classifiers,we can plan without planning models.Leveraging on transfer learning for generalization abilities is the most important future work.
Automated Planning Domain Model Action Model Acquisition Deep Learning Knowledge Encoding
Zhihua Jiang Dongning Rao
Department of Computer Science Jinan University Guangzhou 510632,China School of Computer Guangdong University of Technology Guangzhou 510006,China
国际会议
重庆
英文
386-390
2017-10-03（万方平台首次上网日期，不代表论文的发表时间）