- Example "Have Cake"
- Inspect
example_have_cake.py
, specifically imported libraries from AIMA code such asaimacode.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
- Note: In Jupyter Notebook, show source code of a with print source function
- 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
- Inspect
-
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
-
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
ORpython3 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
-
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.
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
- Python 3.4 or higher
- Starter code includes a copy of companion code for the Stuart Russel/Norvig AIMA 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
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))
AirCargoProblem.get_actions
method includingload_actions
andunload_actions
sub-functionsAirCargoProblem.actions
methodAirCargoProblem.result
methodair_cargo_p2
functionair_cargo_p3
function
- Run uninformed planning searches for
air_cargo_p1
,air_cargo_p2
, andair_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
anddepth_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 typepython run_search.py -h
to learn more.
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 fluentAt(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 theNode
andProblem
classes, and the search methods in the AIMA library.
"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
AirCargoProblem.h_ignore_preconditions
method
PlanningGraph.add_action_level
methodPlanningGraph.add_literal_level
methodPlanningGraph.inconsistent_effects_mutex
methodPlanningGraph.interference_mutex
methodPlanningGraph.competing_needs_mutex
methodPlanningGraph.negation_mutex
methodPlanningGraph.inconsistent_support_mutex
methodPlanningGraph.h_levelsum
method
- Run A* planning searches using the heuristics you have implemented on
air_cargo_p1
,air_cargo_p2
andair_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 typepython run_search.py -h
to learn more.
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).
- 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.
- 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 includesunittest
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.