This project implements an Hierarchical Task Network (HTN) planning based on Shop2 paper using input as HDDL modelling language.
Please download the SLIME and Steel Bank Common Lisp compiler locally and install the program through git command.
Our team uses SLIME for Common Lisp Development on the ground of Steel Bank Common Lisp compiler. As a suggestion, to run the algorithm, please load the shop2.lisp file first on SLIME with the following code snippet.
(load "./your/path/to/file/shop2.lisp")After loading the file, you are ready to run our program with the following command.
(shop2-operator)The program will ask for a domain and problem filepath for a domain and problem written in HDDL. Please ensure that these filepaths end in ".hddl".
Enter domain filepath"domain_file.hddl"
Enter problem filepath"problem_file.hddl"The program will then run the planner printing out its steps to the console. If a plan is found it is returned as console-output. If it does not find a plan the algorithm returns nil but the partial plan up to that point can be read from the print out.
Since the planner uses a random heuristic it is possible that even an existing plan cannot be found at the first try. It is therefore recommended to call (shop2-operator) more than once.
- This project takes input in the form of simplified HDDL, without method-preconditions, partial ordering of subtasks and global ordering or constraints on the problem. Furthermore this implementation supports only the logic operators (AND, NOT) within the definition of effects, preconditions and subtasks. If the user calls input from more complex HDDL-resources this may cause non-optimal solutions.
- This project supports only backtracking on the layer of one tasklist, i.e. if a chosen task cannot be fulfilled at a point in time the planner will choose another one if there is one. It does not support backtracking from a chosen method once a task has been deconstructed into subtasks.
*SBCL
*SLIME
*Datasets of HDDL
Alisa Véronique Münsterberg and Yi-Shan Lee under the guidance of the department of Smart Environments of the Otto-Friedrich-Universität Bamberg.