الفهرس 
The Proposed FrameworkOnly 14 pages are availabe for public view
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Abstract
Automated Planning is one of the important applications in Artificial Intelligence
that become prominent, recently, in several new technologies and paradigms in a
variety of real applications. Planning is a very hard task even in pure search
version and looking for optimal solutions can only be hard. In fact, there are few
optimal planners and there are a relatively small number of other planners which
usually produce good solutions without guaranteeing the optimality. The problem
of optimal planning is a hard-combinatorial optimization problem, for which
there exist only a few standard algorithmic techniques. Therefore, the main
objective of this thesis is to improve the search technique of Hierarchical Task
Network (HTN) planning in PANDA planner by adapting Ant system algorithm
into refinement planning process by means of plan selection strategy.
A few previous methods have been suggested to improve planning solutions to
get the optimal plan. Each method has its planner, domain and problem
description.
The proposed search strategy fuses the Ant system with plan selection strategy to
improve optimal plan challenges. The evaluation of our proposed framework
showed an outstanding performance against many plan selection strategies.