Investigation into the Theoretical Properties of, and the Relationship Between, AI Planning Domain Models.

Over the past decade, there have been substantial advances in knowledge representation (KR) techniques for AI planning. Typically, planners search a space of solution to find a suitable and most accurate sequence of actions to achieve a specific task from a set of initial and goal states. However, the progress in this field still cannot cope with the ever increasing of the complexities of modern systems, which makes knowledge representation an expensive and error prone process. Planning is considered as one of artificial intelligence fields where knowledge representation (KR) is extremely critical. However, a little work has been aimed at “measuring” domain models; the aim of this research is to develop a set of criteria and metrics to assess the accruing and complexity of a particular classical planning problem domain model. To reach that point the system has to have enough knowledge and knowledge has to be well represented for the problem at hand. In this report, we have outlined the prototype the system and design planning domain model metric tools. 1.2 Introductio