Implement a Planning Search

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
• Part 2 - Domain-independent heuristics
• Part 3 - Written Analysis

Environment requirements

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

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))

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
  • You can run all the test cases with additional context by running python -m unittest -v
• 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.