A dynamic case-based planning system for space station application

We are currently investigating the use of a case-based reasoning approach to develop a dynamic planning system. The dynamic planning system (DPS) is designed to perform resource management, i.e., to efficiently schedule tasks both with and without failed components. This approach deviates from related work on scheduling and on planning in AI in several aspects. In particular, an attempt is made to equip the planner with an ability to cope with a changing environment by dynamic replanning, to handle resource constraints and feedback, and to achieve some robustness and autonomy through plan learning by dynamic memory techniques. We briefly describe the proposed architecture of DPS and its four major components: the PLANNER, the plan EXECUTOR, the dynamic REPLANNER, and the plan EVALUATOR. The planner, which is implemented in Smalltalk, is being evaluated for use in connection with the Space Station Mobile Service System (MSS)

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

RATIONALE: Reasoning by Explaining

NRC publication: Ye

Criteria for Good Explanation the Automatic Generation of Underlying Domain Models

NRC publication: Ye

Improving Explanations in Knowledge-Based Systems: RATIONALE

NRC publication: Ye