Implement a Planning Search

Table of Contents

• Chapter 1 - My Setup and Rubrics Checklist
• Chapter 2 - About
• Chapter 3 - Info from Udacity

Chapter 1 - My Setup and Rubrics Checklist

Approach to Solving

• Example "Have Cake"
  • Inspect example_have_cake.py, specifically imported libraries from AIMA code such as aimacode.logic import PropKB
  • Fork and clone AIMA Code https://github.com/ltfschoen/aima-python
  • Open logic.ipynb with Jupyter Notebook that provides detailed examples and explanations for use: jupyter notebook logic.ipynb'
    • Note: In Jupyter Notebook, show source code of a with print source function %psource (i.e. %psource WalkSAT)
    • Note: In Jupyter Notebook, view abbreviated object description by using trailing question mark (i.e. WalkSAT?)
    • OR inspect on GitHub https://github.com/ltfschoen/aima-python/blob/master/logic.ipynb
  • Open logic.py https://github.com/ltfschoen/aima-python/blob/master/logic.py
  • Open Python Command Line python3
  • Experiment

    from utils import *
    from logic import *
    
    logic.expr((P | "==>") | ~Q) == logic.expr('P ==> ~Q')
    # True
    
    partial = PartialExpr('==>', P)
    partial | ~Q
    logic.expr(partial | ~Q) == logic.expr('P ==> ~Q')
    # True
    
    # Show operators and arguments
    expr('~(P & Q) ==>  (~P | ~Q)').op
    # '==>'
    
    expr('~(P & Q) ==>  (~P | ~Q)').args
    # (~(P & Q), (~P | ~Q))
    
    expr('~(P & Q)  ==>  (~P | ~Q)') == symbols('P, Q')
    # False
    
    expr('(P & Q)  ==>  (P | Q)') == symbols('P, Q')
    # False
    
    

Instructions and Setup Environment

• Use Python 3.4 or higher

• IntelliJ: File > Project Structure > Project Settings > Project > Project SDK > Python 3.6.0 (~/miniconda3/bin/python)

• IntelliJ: Preferences > Editor > File Types > Python

  • Add *.py

• Switch to Miniconda env

  • source activate aind (same steps as in https://github.com/ltfschoen/aind/blob/master/README.md)

• Install dependencies python3 -m pip install mypy typing

• Run example planning problem "Have Cake and Eat it too"

  • python3 example_have_cake.py

• Run script to gather metrics for search methods on all problems

  • python3 run_search.py -h (help)
  • python3 run_search.py -m (interactive mode allows selection of one or more Air Cargo Problems, and allows selection of one or more Search Algorithms to solve with respectively)
  • python3 run_search.py -p 1 2 3 -s 1 2 -s 1 2 3 4 5 6 7 8 9 10 11 (solve all available Air Cargo Problems using all specified Search Algorithms)

• Run other scripts

  • python3 my_planning_graph.py
  • python3 my_air_cargo_problems.py

• Optionally Run MyPy Linter with

  mypy run_search.py
  mypy my_planning_graph.py
  mypy my_air_cargo_problems.py

• Run Unit Tests:

  • python -m unittest tests.test_my_air_cargo_problems -v
  • python -m unittest tests.test_my_planning_graph -v

• TODO (optional if necessary)

  • Setup using a main.py file and logging library for debugging

Project Specification Checklist - Implement a Planning Search https://review.udacity.com/#!/rubrics/681/view

CRITERIA / MEETS SPECIFICATIONS

• Planning Problem Representation

  • - The problems and class methods in the my_air_cargo_problems.py module are correctly represented

  • - An optimal sequence of actions is identified for each problem in the written report.

• Automated Heuristics

  • - Automated heuristics for planning searches including “ignore-preconditions” and “level-sum” (planning graph) are correctly implemented

• Performance Comparison

  • - At least three uninformed planning algorithms (including breadth- and depth-first search) are compared on all three problems. Note: Use python3 run_search.py -m OR python3 run_search.py -p 1 2 3 -s 1 2 -s 1 2 3 4 5 6 7 8 9 10 11 to run performance comparison

  • - At least two automatic heuristics are used with A* search for planning and are compared on all three problems including “ignore-preconditions” and “level-sum” from the Planning Graph.

  • - A brief report lists (using a table and any appropriate visualizations) and verbally describes and analyses the performance of the algorithms on the problems compared, including the optimality of the solutions, time elapsed, and the number of node expansions required.

  • - The report and its performance comparison explains the reason for the observed results using at least one appropriate justification from the video lessons or from outside resources (e.g., Norvig and Russell’s textbook).

• Coding and Analysis

  • - Download the template code from: https://github.com/udacity/AIND-Planning

  • - Open the README.md file and follow the instructions there to complete the project.

  • - Run script works successfully for all problems (unless takes too long)

    • python3 run_search.py -m

  • - All Unit Tests pass when running:

    • python -m unittest tests.test_my_air_cargo_problems
    • python -m unittest tests.test_my_planning_graph

• Reading

  • - Read "Artificial Intelligence: A Modern Approach" 3rd edition chapter 10

• Research Review

  • - The report is complete and includes a summary of at least three key developments in the field of AI planning and search.

  Read up on important historical developments in the field of AI planning and search. Write a one-page report on three of these developments, highlighting the relationships between the developments and their impact on the field of AI as a whole.

  Appropriate sources (such as books or magazine or journal articles) should be cited, and you should use citations in-line for sourced facts, quotations, and inferences.

  Submit this as:

    * research_review.pdf
  

[Tip: The book Artificial Intelligence: A Modern Approach by Norvig and Russell is chock full of references in the Bibliographical and Historical notes at the end of Chapter 10.]

• Submission

  • - Submit your work with all unit tests passing by uploading a .zip file containing all your work, which must include the following files:

    • my_air_cargo_problems.py
    • my_planning_graph.py
    • heuristic_analysis.pdf (written responses and analysis)
    • research_review.pdf

Chapter 2 - About

Define a group of problems in classical PDDL (Planning Domain Definition Language) for the air cargo domain discussed in lectures. You will then set up the problem for search, experiment with various automatically generated heuristics, including planning graph heuristics, to solve the problems, and then provide an analysis of the results. Additionally, you will write a short research review paper on the historical development of planning techniques and their use in artificial intelligence.

Chapter 3 - Info from Udacity

Synopsis

This project includes skeletons for the classes and functions needed to solve deterministic logistics planning problems for an Air Cargo transport system using a planning search agent. With progression search algorithms like those in the navigation problem from lecture, optimal plans for each problem will be computed. Unlike the navigation problem, there is no simple distance heuristic to aid the agent. Instead, you will implement domain-independent heuristics.

• Part 1 - Planning problems:

  • READ: applicable portions of the Russel/Norvig AIMA text
  • GIVEN: problems defined in classical PDDL (Planning Domain Definition Language)
  • TODO: Implement the Python methods and functions as marked in my_air_cargo_problems.py
  • TODO: Experiment and document metrics

• Part 2 - Domain-independent heuristics:

  • READ: applicable portions of the Russel/Norvig AIMA text
  • TODO: Implement relaxed problem heuristic in my_air_cargo_problems.py
  • TODO: Implement Planning Graph and automatic heuristic in my_planning_graph.py
  • TODO: Experiment and document metrics

• Part 3 - Written Analysis

Environment requirements

• Python 3.4 or higher
• Starter code includes a copy of companion code for the Stuart Russel/Norvig AIMA text.

Project Details

Part 1 - Planning problems

READ: Stuart Russel and Peter Norvig text:

"Artificial Intelligence: A Modern Approach" 3rd edition chapter 10 or 2nd edition Chapter 11 on Planning, available on the AIMA book site sections:

• The Planning Problem
• Planning with State-space Search

GIVEN: classical PDDL problems

All problems are in the Air Cargo domain. They have the same action schema defined, but different initial states and goals.

• Air Cargo Action Schema:

Action(Load(c, p, a),
	PRECOND: At(c, a) ∧ At(p, a) ∧ Cargo(c) ∧ Plane(p) ∧ Airport(a)
	EFFECT: ¬ At(c, a) ∧ In(c, p))
Action(Unload(c, p, a),
	PRECOND: In(c, p) ∧ At(p, a) ∧ Cargo(c) ∧ Plane(p) ∧ Airport(a)
	EFFECT: At(c, a) ∧ ¬ In(c, p))
Action(Fly(p, from, to),
	PRECOND: At(p, from) ∧ Plane(p) ∧ Airport(from) ∧ Airport(to)
	EFFECT: ¬ At(p, from) ∧ At(p, to))

• Problem 1 initial state and goal:

Init(At(C1, SFO) ∧ At(C2, JFK) 
	∧ At(P1, SFO) ∧ At(P2, JFK) 
	∧ Cargo(C1) ∧ Cargo(C2) 
	∧ Plane(P1) ∧ Plane(P2)
	∧ Airport(JFK) ∧ Airport(SFO))
Goal(At(C1, JFK) ∧ At(C2, SFO))

• Problem 2 initial state and goal:

Init(At(C1, SFO) ∧ At(C2, JFK) ∧ At(C3, ATL) 
	∧ At(P1, SFO) ∧ At(P2, JFK) ∧ At(P3, ATL) 
	∧ Cargo(C1) ∧ Cargo(C2) ∧ Cargo(C3)
	∧ Plane(P1) ∧ Plane(P2) ∧ Plane(P3)
	∧ Airport(JFK) ∧ Airport(SFO) ∧ Airport(ATL))
Goal(At(C1, JFK) ∧ At(C2, SFO) ∧ At(C3, SFO))

• Problem 3 initial state and goal:

Init(At(C1, SFO) ∧ At(C2, JFK) ∧ At(C3, ATL) ∧ At(C4, ORD) 
	∧ At(P1, SFO) ∧ At(P2, JFK) 
	∧ Cargo(C1) ∧ Cargo(C2) ∧ Cargo(C3) ∧ Cargo(C4)
	∧ Plane(P1) ∧ Plane(P2)
	∧ Airport(JFK) ∧ Airport(SFO) ∧ Airport(ATL) ∧ Airport(ORD))
Goal(At(C1, JFK) ∧ At(C3, JFK) ∧ At(C2, SFO) ∧ At(C4, SFO))

TODO: Implement methods and functions in my_air_cargo_problems.py

• AirCargoProblem.get_actions method including load_actions and unload_actions sub-functions
• AirCargoProblem.actions method
• AirCargoProblem.result method
• air_cargo_p2 function
• air_cargo_p3 function

TODO: Experiment and document metrics for non-heuristic planning solution searches

• Run uninformed planning searches for air_cargo_p1, air_cargo_p2, and air_cargo_p3; provide metrics on number of node expansions required, number of goal tests, time elapsed, and optimality of solution for each search algorithm. Include the result of at least three of these searches, including breadth-first and depth-first, in your write-up (breadth_first_search and depth_first_graph_search).
• If depth-first takes longer than 10 minutes for Problem 3 on your system, stop the search and provide this information in your report.
• Use the run_search script for your data collection: from the command line type python run_search.py -h to learn more.

Why are we setting the problems up this way?

Progression planning problems can be solved with graph searches such as breadth-first, depth-first, and A*, where the nodes of the graph are "states" and edges are "actions". A "state" is the logical conjunction of all boolean ground "fluents", or state variables, that are possible for the problem using Propositional Logic. For example, we might have a problem to plan the transport of one cargo, C1, on a single available plane, P1, from one airport to another, SFO to JFK. In this simple example, there are five fluents, or state variables, which means our state space could be as large as . Note the following:

• While the initial state defines every fluent explicitly, in this case mapped to TTFFF, the goal may be a set of states. Any state that is True for the fluent At(C1,JFK) meets the goal.
• Even though PDDL uses variable to describe actions as "action schema", these problems are not solved with First Order Logic. They are solved with Propositional logic and must therefore be defined with concrete (non-variable) actions and literal (non-variable) fluents in state descriptions.
• The fluents here are mapped to a simple string representing the boolean value of each fluent in the system, e.g. TTFFTT...TTF. This will be the state representation in the AirCargoProblem class and is compatible with the Node and Problem classes, and the search methods in the AIMA library.

Part 2 - Domain-independent heuristics

READ: Stuart Russel and Peter Norvig text

"Artificial Intelligence: A Modern Approach" 3rd edition chapter 10 or 2nd edition Chapter 11 on Planning, available on the AIMA book site section:

• Planning Graph

TODO: Implement heuristic method in my_air_cargo_problems.py

• AirCargoProblem.h_ignore_preconditions method

TODO: Implement a Planning Graph with automatic heuristics in my_planning_graph.py

• PlanningGraph.add_action_level method
• PlanningGraph.add_literal_level method
• PlanningGraph.inconsistent_effects_mutex method
• PlanningGraph.interference_mutex method
• PlanningGraph.competing_needs_mutex method
• PlanningGraph.negation_mutex method
• PlanningGraph.inconsistent_support_mutex method
• PlanningGraph.h_levelsum method

TODO: Experiment and document: metrics of A* searches with these heuristics

• Run A* planning searches using the heuristics you have implemented on air_cargo_p1, air_cargo_p2 and air_cargo_p3. Provide metrics on number of node expansions required, number of goal tests, time elapsed, and optimality of solution for each search algorithm and include the results in your report.
• Use the run_search script for this purpose: from the command line type python run_search.py -h to learn more.

Why a Planning Graph?

The planning graph is somewhat complex, but is useful in planning because it is a polynomial-size approximation of the exponential tree that represents all possible paths. The planning graph can be used to provide automated admissible heuristics for any domain. It can also be used as the first step in implementing GRAPHPLAN, a direct planning algorithm that you may wish to learn more about on your own (but we will not address it here).

Planning Graph example from the AIMA book

Part 3: Written Analysis

TODO: Include the following in your written analysis.

• Provide an optimal plan for Problems 1, 2, and 3.
• Compare and contrast non-heuristic search result metrics (optimality, time elapsed, number of node expansions) for Problems 1,2, and 3. Include breadth-first, depth-first, and at least one other uninformed non-heuristic search in your comparison; Your third choice of non-heuristic search may be skipped for Problem 3 if it takes longer than 10 minutes to run, but a note in this case should be included.
• Compare and contrast heuristic search result metrics using A* with the "ignore preconditions" and "level-sum" heuristics for Problems 1, 2, and 3.
• What was the best heuristic used in these problems? Was it better than non-heuristic search planning methods for all problems? Why or why not?
• Provide tables or other visual aids as needed for clarity in your discussion.

Examples and Testing:

• The planning problem for the "Have Cake and Eat it Too" problem in the book has been implemented in the example_have_cake module as an example.
• The tests directory includes unittest test cases to evaluate your implementations. All tests should pass before you submit your project for review. From the AIND-Planning directory command line:
  • python -m unittest tests.test_my_air_cargo_problems
  • python -m unittest tests.test_my_planning_graph
• The run_search script is provided for gathering metrics for various search methods on any or all of the problems and should be used for this purpose.