Task plan generation for intelligent machines

The issue of structural formulation of task plan generation for intelligent machines is investigated from the perspective of artificial intelligence. The knowledge base of the machines is represented by a relational database, and the task-planning process is modeled by a condition/event net. It is shown that the task plans generated by the formulation proposed are Petri net languages and that any task-planning strategy can be achieved by a supervisory task planner. The objective of such a structural formulation is to establish a formal framework for the task plan generation of intelligent machines in terms of concepts in mathematical logic. This framework can serve as the foundation of analytical task plan generation at different levels of the hierarchical structure of intelligent machines. In other words, the framework may play the role of domain space upon which various analytical design approaches, such as the probabilistic method and neural network computing, can be integrated to specify the task plan generation in both architectural and computational aspects