# Heuristic Analysis for an Air Cargo Problem¶

## Problem Scenario¶

The present project consist on an Air Cargo transport system, where several cargo wants to be moved from one city to another city's airport, having several planes to achieve it.

To do so, we implement a planning search agent to solve the problem with different approaches.

In a regular search algorithm, like for the adversarial search in the Isolation game, the problem-solving agent deals with atomic representation of states and thus needs good domain-specific heuristics to perform well. With first-order-logic we can build domain-independent heuristics based on the logical structure of the problem.

To measure the performance, we first run the already studied uninformed non-heuristic search algorithms. The caractheristics that makes this algorithms uninformed, is the fact that they do not have any information about the states beyond that the one provided in the problem definition. Therefore, they can only 'ask' if each new state that the algorithm creates is the goal or not, and act in consequence by stopping if it is the goal, or creating new states if it is not. An automatic domain-independent heristics with A* that searches on top o a planning graph has been created to compare the search efficiency agains the previously explained methods.

#### STATES AND ACTION SCHEMA¶

Planning Domain Definition Language - PDDL - allows to performe a factored representation of the world in which a state is represented by a collection of variables. This way, 4 things needs to be defined in the search problem:

• The initial state
• The actions that are available in the state
• The result of applying the action
• The goal state

The actions are described by a set of Action schemas that implicitly define the ACTIONS(s) and RESULT(s,a) functions needed to do a problem-solving search. Actions schemas are a lifted (from propositional logic to first-order-logic) representation that describes an action based on the preconditions for that action to occur, and the effects that action will produce. The action-schema for our current problem are the action of loading/unloading a cargo into a plane, and the plane to fly:

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))
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))

As we said, we also need the Initial and Goal state for each of the 3 different problems proposed by Udacity, increasing the complexity:

###### Problem 1: 2 cargos, 2 cities, 2 planes¶
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: 3 cargos, 3 cities, 3 planes¶
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: 4 cargos, 4 cities, 4 planes¶
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))

## Search Results¶

In this link there is a complete description of the search algorithm that had been used in this problem. The search algorithms code has been taken from the well known book: "Artificial Intelligence, a modern approach". The search algorithms can be divided into two subgroups:

Uninformed search, also called blind search, is a class of general purpose search algorithms that operate in a brute-force way. These algorithms can be applied to a variety of search problems, but since they don't take into account the target problem.

If information is available about the problem this could guide the search. Information is put in an evaluation function f(n) to be able to give a value to each state. Sometimes a heuristic function h(n) is used to guess the value if the information isn't perfect.

To run our search algorithms on the different problems we have to run the file run_search.py to which we can pass the parameteres included in that file as:

PROBLEMS = [["Air Cargo Problem 1", air_cargo_p1],
["Air Cargo Problem 2", air_cargo_p2],
["Air Cargo Problem 3", air_cargo_p3]]
['depth_first_graph_search', depth_first_graph_search, ""],
['depth_limited_search', depth_limited_search, ""],
['uniform_cost_search', uniform_cost_search, ""],
['recursive_best_first_search', recursive_best_first_search, 'h_1'],
['greedy_best_first_graph_search', greedy_best_first_graph_search, 'h_1'],
['astar_search', astar_search, 'h_1'],
['astar_search', astar_search, 'h_ignore_preconditions'],
['astar_search', astar_search, 'h_pg_levelsum'],
]


# Uninformed Search algorithms
def tree_search(problem, frontier):
frontier.append(Node(problem.initial))
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
return node
frontier.extend(node.expand(problem))
return None

def graph_search(problem, frontier):
frontier.append(Node(problem.initial))
explored = set()
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
return node
frontier.extend(child for child in node.expand(problem)
if child.state not in explored and
child not in frontier)
return None


The algorithms used following this approach are the following:

• Breadth First Search
def breadth_first_tree_search(problem):
return tree_search(problem, FIFOQueue())

• Breadth First Tree Search
def breadth_first_search(problem):
node = Node(problem.initial)
if problem.goal_test(node.state):
return node
frontier = FIFOQueue()
frontier.append(node)
explored = set()
while frontier:
node = frontier.pop()
for child in node.expand(problem):
if child.state not in explored and child not in frontier:
if problem.goal_test(child.state):
return child
frontier.append(child)
return None

• Depth First Graph Search
def depth_first_graph_search(problem):
return graph_search(problem, Stack())

• Depth Limited Search
def depth_limited_search(problem, limit=50):
def recursive_dls(node, problem, limit):
if problem.goal_test(node.state):
return node
elif limit == 0:
return 'cutoff'
else:
cutoff_occurred = False
for child in node.expand(problem):
result = recursive_dls(child, problem, limit - 1)
if result == 'cutoff':
cutoff_occurred = True
elif result is not None:
return result
return 'cutoff' if cutoff_occurred else None
return recursive_dls(Node(problem.initial), problem, limit)

• Uniform Cost Search

def uniform_cost_search(problem):
return best_first_graph_search(problem, lambda node: node.path_cost)

• Recursive Best First Search

def recursive_best_first_search(problem, h=None):
h = memoize(h or problem.h, 'h')

def RBFS(problem, node, flimit):
if problem.goal_test(node.state):
return node, 0   # (The second value is immaterial)
successors = node.expand(problem)
if len(successors) == 0:
return None, infinity
for s in successors:
s.f = max(s.path_cost + h(s), node.f)
while True:
# Order by lowest f value
successors.sort(key=lambda x: x.f)
best = successors
if best.f > flimit:
return None, best.f
if len(successors) > 1:
alternative = successors.f
else:
alternative = infinity
result, best.f = RBFS(problem, best, min(flimit, alternative))
if result is not None:
return result, best.f

node = Node(problem.initial)
node.f = h(node)
result, bestf = RBFS(problem, node, infinity)
return result

• Greedy Best First Graph Search
def best_first_graph_search(problem, f):
f = memoize(f, 'f')
node = Node(problem.initial)
if problem.goal_test(node.state):
return node
frontier = PriorityQueue(min, f)
frontier.append(node)
explored = set()
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
return node
for child in node.expand(problem):
if child.state not in explored and child not in frontier:
frontier.append(child)
elif child in frontier:
incumbent = frontier[child]
if f(child) < f(incumbent):
# del frontier[incumbent]
frontier.append(child)
return None


Based on Udacity advice somo of the algorithms were not run because of a long execution time:

• For poblem 2 Breadth Dirst Tree Search, Depth Limited Search and Recursive Best Search
• For problem 3: Breadth First Tree Search, Depth Limited Search, Uniform Cost Search, and Recursive Best First Search.

Results can be stored running the next commands:

python run_search.py -p 1 -s 1 2 3 4 5 6 7 >> problem1_uninformed.txt
python run_search.py -p 2 -s 1 3 5 7 >> problem2_uninformed.txt
python run_search.py -p 3 -s 1 3 5 7 >> problem3_uninformed.txt
##### Problem 1¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
Breadth First Search Yes 6 0.034 43 56 180
Breadth First Tree Search Yes 6 1.045 1458 1459 5960
Depth First Graph Search No 12 0.009 12 13 48
Depth Limited Search No 50 0.089 101 271 414
Uniform Cost Search Yes 6 0.038 55 57 224
Recursive Best First Search Yes 6 3.084 4229 4230 17029
Greedy Best First Graph Search Yes 6 0.01 7 9 29
##### Problem 2¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
Breadth First Search Yes 9 12.847 3343 4609 30509
Breadth First Tree Search -- -- -- -- -- --
Depth First Graph Search No 575 4.055 582 583 5211
Depth Limited Search -- -- -- -- -- --
Uniform Cost Search Yes 9 18.379 4853 4855 44041
Recursive Best First Search -- -- -- -- -- --
Greedy Best First Graph Search Yes 9 1.47 399 401 3617
##### Problem 3¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
Breadth First Search Yes 12 74.815 14663 18098 129631
Breadth First Tree Search -- -- -- -- -- --
Depth First Graph Search No 596 4.511 627 628 5176
Depth Limited Search -- -- -- -- -- --
Uniform Cost Search Yes 12 92.658 18223 18225 159618
Recursive Best First Search -- -- -- -- -- --
Greedy Best First Graph Search No 22 28.939 5578 5580 49150

#### Analysis¶

If we consider the most important point to reach the optimal solution within the constraint of 10 minutes, only Breadth First Search and Uniform Cost Search algorithms perform that well.

However, Depth First Graph Search seems to be the fastest (despite for the problem 2 Greedy Best First Graph Search performed amazingly fast) and also seems to need the least number of node expansions i.e. less memory use. However, it didn't find the optimal path at any of the problems.

Therefore, we can only keep Depth First Search and Uniform Cost Search as they are the only ones which always find the optimal path, and between this two, Depth First Search performs a little bit better than Uniform Cost Search in the three cases.

Only in the cases where the optimal path is not the criteria to determine which algorithm to use, the Greedy Best First Graph Search will be the best choice. It's execution time is more than aceptable and it only didn’t find the optimal path in the most complex problem (3). It did find 22 instead of 12, which is not that bad if the look at Depth First Graph which is the fastest but found a path of length 596.

### Informed Search with A*¶

As we have mentioned prevously, informed search uses domain-specific knowledge and can find the solutions more efficiently thanks to knowledge.
3 different heuristics will be implemented for the A* algorithm.

def h_1(self, node: Node):
# note that this is not a true heuristic
h_const = 1
return h_const

def h_pg_levelsum(self, node: Node):
'''
This heuristic uses a planning graph representation of the problem
state space to estimate the sum of all actions that must be carried
out from the current state in order to satisfy each individual goal
condition.
'''
# requires implemented PlanningGraph class
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum

def h_ignore_preconditions(self, node: Node):
'''
This heuristic estimates the minimum number of actions that must be
carried out from the current state in order to satisfy all of the goal
conditions by ignoring the preconditions required for an action to be
executed.
'''
# TODO implement (see Russell-Norvig Ed-3 10.2.3  or Russell-Norvig Ed-2 11.2)
# Bring the knowledge base of locial expressions
kb = PropKB()
# Add the possitive sentence of the current state
kb.tell(decode_state(node.state, self.state_map).pos_sentence())

count = 0
# Iterate over all the goals in the problem
for clause in self.goal:
# If the goal is not already among the positive states - which means
# we have no reach the goal yet - then increase the counter
if clause not in kb.clauses:
count += 1

return count

##### Problem 1¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
A* Search with h1 heuristic Yes 6 0.043 55 57 224
A* Search with Ignore Preconditions heuristic Yes 6 0.039 41 43 170
A* Search with Level Sum heuristic Yes 6 5.10 7 9 28
##### Problem 2¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
A* Search with h1 heuristic Yes 9 18.371 4853 4855 44041
A* Search with Ignore Preconditions heuristic Yes 9 6.270 1428 1430 13085
A* Search with Level Sum heuristic No 21 249.784 97 99 906

--- This last one is raising an error ---

##### Problem 3¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions Goal Tests New Nodes
A* Search with h1 heuristic Yes 12 91.983 18223 18225 159618
A* Search with Ignore Preconditions heuristic Yes 12 27.898 5040 5042 44944
A* Search with Level Sum heuristic No 21 333.905 71 73 687

--- This last one is raising an error ---

#### Analysis¶

The first and very important point of these approaches is that all of them led to the optimal path (except level sum in problem 3 - level sum may have a implementation bug).

However, it is clear that Ignore Preconditions heuristic outperform the others if we look at the execution time.

The Level Sum heuristic on the other hand has expanded way less nodes that the other heuristics. Then, if memory usage is the main criteria, this would be the heuristic to use, with the disadvantage of being vey slow. The low speed is a consequence of having to explore the graph and check in which level the goal is.

If we compare the winning strategy of each block:

##### Problem 1¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions
Breadth First Search Yes 6 0.034 43
A* Search with Ignore Preconditions heuristic Yes 6 0.039 41
##### Problem 2¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions
Breadth First Search Yes 9 12.847 3343
A* Search with Ignore Preconditions heuristic Yes 9 6.270 1428
##### Problem 3¶
Search Strategy Optimal Path Length Execution Time (s) Node Expansions
Breadth First Search Yes 12 74.815 14663
A* Search with Ignore Preconditions heuristic Yes 12 27.898 5040

It looks clear how A* outperforms Bread First Search, and clearer when the problem gains complexity. This shows the benefits of informed search over uninformed search where the results are achieved using less memory and in less time. Furthermore, informed search allows to customize a trade-off between speed and memory by customizing the different heurisitics that can not be done with uninformed search strategies.