README.md

Exercise 2: Automated Planning

A partial implementation of a STRIPS-like planner in Python. The the planner solves a problem based on a domain, where both the problem and the domain are defined in PDDL 1.2. The implemenation is based on pyperplan.

Requirements

The project requires Python >= 3.6.

Task 2: Files of interest

• Complete the implementation of task.py
  • task.py: includes classes for creating Operators and STRIPS-like instances ⟨Propositional arguments, Operators , Initial state , Goals⟩
    • Implement the methods applicable() and apply() of the Operator class
    • Implement the methods goal_reached() and get_successor_states() of the Task class
• Complete the implementation of search/a_star.py
  • a_star.py: includes methods for searching based on the A* search algorithm
    • Complete the method astar_search()
  • searchspace.py: includes a SearchNode class for creating instance nodes that are visited during search

Task 3: Files of interest

• Examine the task-3/t3-domain.py and the task-3/t3-problem.py, and the behavior of the planner when solving the problem in the domain, so that you answer the questions provided to you in the exescise sheet.
  • t3-domain.py: the domain
  • t3-problem.py: the problem

Project structure

├── benchmarks # benchmark examples with domain and problem use cases, which you can use to see the planner behavior
├── examples # simple example of a "block" domain and problem, which you can use to see the planner behavior
│   ├── blocks-world-domain.pddl 
│   └── blocks-world-problem.pddl
├── search # includes implementations of different search algorithms (i.e. astar,wastar,gbf,bfs,ehs,ids,sat)
│   ├── a_star.py # (altered in Task 2) template of A* search algorithm 
│   ├── breadth_first_search.py
│   ├── ...
│   └── searchspace.py # (used in Task 2) includes a class for creating instance nodes that are visited during search 
├── task.py # (altered in for Task 2)  includes classes for creating Operators and STRIPS-like instances  ⟨Propositional arguments, Operators , Initial state , Goals⟩  
├── plan.py # (used in Task 2) the main script for solving a planning problem 
├── pddl # a PDDL parser 
├── heuristics # includes implementations of different heuristic methods (i.e. blind,landmark,lmcut,hadd,hff,hmax,hsa)
├── grounding.py # grounding a schematic PDDL task to a STRIPS planning task
├── planner.py # a STRIPS-like planner
├── ...
└── README.md # this README.md

How to run the project

You can run the script plan.py with Python 3 for solving a problem defined in PDDL. You can specify the following arguments:

• positional arguments:

  • domain: the file path of the domain defined in PDDL
  • problem: the file path of the problem defined in PDDL

• optional arguments:

  • -s {astar,wastar,gbf,bfs,ehs,ids,sat}: the search algorithm from A*, weighted A*, greedy best first, breadth first, enforced hillclimbing, iterative deepening, sat solve (default: bfs)

python3 plan.py [-s {astar,wastar,gbf,bfs,ehs,ids,sat}] [domain] problem

For example, to run the planner with the breadth-first algorithm:

python3 plan.py -s bfs examples/blocks-world-domain.pddl examples/blocks-world-problem.pddl

Or to run the planner with the A* algorithm that you implemented in Task 2:

python3 plan.py -s astar t1-your-domain.pddl t1-your-problem.pddl