Transfer of Learned Heuristics among Planners∗
نویسندگان
چکیده
This paper presents a study on the transfer of learned control knowledge between two different planning techniques. We automatically learn heuristics (usually, in planning, heuristics are also named control knowledge) from one planner search process and apply them to a different planner. The goal is to improve this second planner efficiency solving new problems, i.e. to reduce computer resources (time and memory) during the search, or to improve quality solutions. The learning component is based on a deductive learning method (EBL) that is able to automatically acquire control knowledge by generating bounded explanations of the problem solving episodes in a graph-plan based planner. Then, we transform the learned knowledge so that it can be used by a bidirectional planner.
منابع مشابه
Transferring Learned Control-Knowledge between Planners
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several techniques for automatically acquiring those heuristics. Usually, a planner solves a problem, and a ML technique generates knowledge from the search episode in terms of complete plans (macro-operators or cases), or heurist...
متن کاملKnowledge Transfer between Automated Planners
transfer problem-solving experience from previous tasks into the new task. Recently, the artificial intelligence community has attempted to model this transfer in an effort to improve learning on new tasks by using knowledge from related tasks. For example, classification and inference algorithms have been extended to support transfer of conceptual knowledge (for a survey see Torrey and Shavlik...
متن کاملThe Footprint Principle for Heuristics for Probabilistic Planners
Probabilistic back-chaining planners, which use probabilities to represent and reason about uncertainty in the planning domain, typically have a larger search space than their classical counterparts. Therefore heuristics that can reduce their search effectively are even more important. The “footprint” principle leads to a family of heuristics for probabilistic planners produced by attempting to...
متن کاملLearning heuristic functions for cost-based planning
In the last International Planning Competition (IPC 2011), the most efficient planners in the satisficing track were planners that used unit-cost heuristics. These heuristics ignore the real cost of the actions and return instead an estimate of the plan length to the goal. The main advantage of these heuristics compared with real-cost heuristics is that they solve a greater number of problems (...
متن کاملAccelerating Search with Transferred Heuristics
A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target task directly. A more difficult goal is to reduce the total training time so that learning the source task and target task is faster than learning only the target task. This paper addresses the seco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2006