A Method for Unifying the Representations of Domain Knowledge and Planning Algorithm in Hierarchical Task Network
Kai Cheng, Gang Chen, Rui Zhang, Wu Liu, Zhiteng Wang, Ruizhi Kang · International Journal of Pattern Recognition and Artificial Intelligence · 2017
Domain knowledge of hierarchical task network (HTN) usually involves logical expressions with predicates. One needs to master two different languages which are used to describe domain knowledge and implement planner. This has presented enormous challenges for most programmers who are not familiar with logical expressions. To solve the problem a method of state variable representation from the programmer’s point of view is introduced. This method has powerful expressivity and can unify the representations of domain knowledge and planning algorithm. In Pyhop a HTN planner written in Python, methods and operators are all as ordinary Python functions rather than using a special-purpose language. Pyhop uses a Python object that contains variable bindings and does not include a horn-clause inference engine for evaluating preconditions of operators and methods. By taking a simple travel-planning problem, it shows that the method is easy to understand and integrate planning into ordinary programming.