An Evolutionary Approach to Intelligent Planning

摘要

With the explosion of information on WWW, planning and decision making has become a tedious task. The huge volume of distributed and heterogeneous information resources and the complexity involved in their coordination and scheduling leads to difficulties in the conception of optimal plans. This paper presents an intelligent planner which uses modified Genetic Algorithm assisted Case Based Reasoning (CBR) to solve the cold start problem faced by CBR systems and generates novel plans. This approach minimizes the need of populating preliminary cases in the CBR systems. The system is capable of generating synchronized optimal plans within the specified constraints. The effectiveness of the approach is demonstrated with the help of case study on e-Travel Planning. Rigorous experiments were performed to generate synchronized plans with one hop and two hops between train and flight modes of transport. Results proved that GA assisted CBR outperforms the traditional CBR significantly in providing the number of optimized plans and solving cold start problem.