Knowledge Transfer between Automated Planners

Knowledge 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...

Visual Knowledge Transfer between Planners and Business Decision Makers A Framework for Knowledge Visualization

The transfer of knowledge between planners and business decision makers can be improved when planners combine traditional visualizations with business knowledge visualizations. Today architects and urban planners use visualization methods such as sketches, diagrams, drawings, renderings, models and animations to illustrate their projects. While spending an enormous amount of time to illustrate ...

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...

Technology Transfer Using Automated Knowledge- Acquisition Tools

Transfer of Learned Heuristics among Planners∗

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 resource...