Learning by comparison: improving the task planning capability
Maria D. del Castillo, Daniel M. Kumpel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
The aim of this work is to build a machine learning system. A new learning method is proposed. The complete system contains a Task Planner, a Problem Generator, a Plan Scheduler and an Apprentice. This system solves problems and at the same time learns to improve its behavior from the experience. The task planner is based on a production system. It uses a parallel planning method as a forward search strategy by matching the domain rules for all the participants of the model. When the planner finds a problem state where it is possible to apply one operator to several participants of the world, it decides to execute only one operation. Then, the other parts of the complete system start to work. The Problem Generator and the Plan Scheduler obtain all the solutions plans associated to the current problem. When the learning system knows which is the best plan it searches all the plans associated with the best plan, we mean, all the possible solutions of the problem starting from the same state of the world. The proposed learning method looks for differences between the steps of these plans and makes up the goal concept. Then, it explains this goal concept finding out differences or similarities between the value of the attributes of the participants implied into this world state.