Please use this identifier to cite or link to this item:
Title: Using case-based planning to assist local-search-based planning
Authors: HU JUN
Keywords: AI planning, learning, hybrid planning approach, case-based planning, local-search-based planning, Crackpot.
Issue Date: 4-May-2010
Citation: HU JUN (2010-05-04). Using case-based planning to assist local-search-based planning. ScholarBank@NUS Repository.
Abstract: To pursue a better efficiency of handling complex and dynamic Artificial Intelligence planning problems in real world scenarios, this research is to develop an innovative hybrid planning approach that integrates learning into a general problem solver. There have been case-based planning approaches as well as local-search-based planning approaches in AI planning area in the past. Local-search-based planning is good at handling dynamics in environments by iteratively optimizing the plan quality, while case-based planning plans by learning from previous experiences of problems and situations. A hybrid system that uses local search for the main planning process and applies case-based planning for single improvement iterations seems like a promising idea, because case-based planning is very likely to improve the ordinary planning efficiency and local search can directly serve for the revision phase for the case-based planning step. We review previous approaches for case-based planning, discuss ways to develop the desired case-based planning functionality and its integration into the existing pure local-search-based planning system Crackpot. The initial experimental results indicate that, comparing to the original system Crackpot, the hybrid system confirms its better planning efficiency in both planning speed and planning results. With in-depth analysis, these improvements are very likely resulted from the efficient and effective reuse of the stored knowledge inside the cases. Besides the achievements, several limitations in the current stage are also discovered and some future improvements are planned accordingly.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
HuJun.pdf4.45 MBAdobe PDF



Page view(s)

checked on Apr 18, 2019


checked on Apr 18, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.