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Merged
merged 7 commits into from
Aug 2, 2018
Merged

Search notebook update #933

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b72580e
Added tests for online dfs agent
ad71 Jul 1, 2018
fa7dd42
Minor formatting fixes
ad71 Jul 1, 2018
558b062
Completed notebook sections
ad71 Jul 1, 2018
05327a0
Updated README.md
ad71 Jul 1, 2018
263e2f1
Fixed a test
ad71 Jul 1, 2018
52444b0
Added new algorithms to display_visual notebook function
ad71 Jul 9, 2018
eeaa18b
Added RBFS visualization
ad71 Jul 9, 2018
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8 changes: 4 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -78,13 +78,13 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`][search] | Done | Included |
| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`][search] | Done | Included |
| 3.24 | A\*-Search | `astar_search` | [`search.py`][search] | Done | Included |
| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`][search] | Done | |
| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`][search] | Done | Included |
| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`][search] | Done | Included |
| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`][search] | Done | Included |
| 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`][search] | Done | Included |
| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`][search] | Done | |
| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`][search] | | |
| 4.24 | LRTA\*-Agent | `LRTAStarAgent` | [`search.py`][search] | Done | |
| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`][search] | Done | Included |
| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`][search] | Done | Included |
| 4.24 | LRTA\*-Agent | `LRTAStarAgent` | [`search.py`][search] | Done | Included |
| 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`][games] | Done | Included |
| 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`][games] | Done | Included |
| 6 | CSP | `CSP` | [`csp.py`][csp] | Done | Included |
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7 changes: 6 additions & 1 deletion notebook.py
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Expand Up @@ -991,8 +991,13 @@ def visualize_callback(Visualize):
"Depth First Tree Search",
"Breadth First Search",
"Depth First Graph Search",
"Best First Graph Search",
"Uniform Cost Search",
"A-star Search"})
"Depth Limited Search",
"Iterative Deepening Search",
"Greedy Best First Search",
"A-star Search",
"Recursive Best First Search"})

algo_dropdown = widgets.Dropdown(description="Search algorithm: ",
options=sorted(list(algorithm.keys())),
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5,203 changes: 3,200 additions & 2,003 deletions search.ipynb
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10 changes: 5 additions & 5 deletions search.py
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Expand Up @@ -767,8 +767,8 @@ def __init__(self, problem):
self.problem = problem
self.s = None
self.a = None
self.untried = defaultdict(list)
self.unbacktracked = defaultdict(list)
self.untried = dict()
self.unbacktracked = dict()
self.result = {}

def __call__(self, percept):
Expand All @@ -787,13 +787,13 @@ def __call__(self, percept):
self.a = None
else:
# else a <- an action b such that result[s', b] = POP(unbacktracked[s'])
unbacktracked_pop = self.unbacktracked[s1].pop(0)
unbacktracked_pop = self.unbacktracked.pop(s1)
for (s, b) in self.result.keys():
if self.result[(s, b)] == unbacktracked_pop:
self.a = b
break
else:
self.a = self.untried[s1].pop(0)
self.a = self.untried.pop(s1)
self.s = s1
return self.a

Expand Down Expand Up @@ -1120,7 +1120,7 @@ def distance_to_node(n):
7 - CCL Clean Clean Left
8 - CCR Clean Clean Right
"""
vacumm_world = Graph(dict(
vacuum_world = Graph(dict(
State_1=dict(Suck=['State_7', 'State_5'], Right=['State_2']),
State_2=dict(Suck=['State_8', 'State_4'], Left=['State_2']),
State_3=dict(Suck=['State_7'], Right=['State_4']),
Expand Down
65 changes: 50 additions & 15 deletions tests/test_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,13 @@


romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)
vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], vacumm_world)
vacuum_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], vacuum_world)
LRTA_problem = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space)
eight_puzzle = EightPuzzle((1, 2, 3, 4, 5, 7, 8, 6, 0))
eight_puzzle2 = EightPuzzle((1, 0, 6, 8, 7, 5, 4, 2), (0, 1, 2, 3, 4, 5, 6, 7, 8))
nqueens = NQueensProblem(8)


def test_find_min_edge():
assert romania_problem.find_min_edge() == 70

Expand Down Expand Up @@ -151,7 +152,33 @@ def test_conflict():
def test_recursive_best_first_search():
assert recursive_best_first_search(
romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']
assert recursive_best_first_search(
EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0))).solution() == [
'UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN'
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This is probably ok, but are you sure that there isn't another solution of equal length?

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@ad71 ad71 Jul 6, 2018 •
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@norvig , there is in fact another solution of equal length

['LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'RIGHT']

but this algorithm (or any search algorithm for that matter) gives one particular optimal solution every time, even if there exists another one of the same length. This happens due to an implementational detail in EightPuzzle.
In the EightPuzzle class, the actions method returns the possible actions, but the actions are stored in a list and every time node.expand(problem) is called from RBFS, the neighbors are traversed in the order [up, down, left, right], which is why the algorithm converges to the same solution every time. I also ran it for 1000 cycles just to make sure.
This can be fixed (though I don't think it requires fixing) by returning a set of possible actions instead of a list, but a lot of tests will be rendered useless.

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To keep it simple, I'll accept this as is, but it would be a better test if it didn't rely on an arbitrary choice of ordering.

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That is, the test could be something like : achieves_goal(start, s) and len(s) == 8

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Sure, I'll take care of this in the next update

]

def manhattan(node):
state = node.state
index_goal = {0:[2,2], 1:[0,0], 2:[0,1], 3:[0,2], 4:[1,0], 5:[1,1], 6:[1,2], 7:[2,0], 8:[2,1]}
index_state = {}
index = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]]
x, y = 0, 0

for i in range(len(state)):
index_state[state[i]] = index[i]

mhd = 0

for i in range(8):
for j in range(2):
mhd = abs(index_goal[i][j] - index_state[i][j]) + mhd

return mhd

assert recursive_best_first_search(
EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0)), h=manhattan).solution() == [
'LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'UP', 'DOWN', 'RIGHT'
]

def test_hill_climbing():
prob = PeakFindingProblem((0, 0), [[0, 5, 10, 20],
Expand Down Expand Up @@ -200,23 +227,31 @@ def run_plan(state, problem, plan):
return False
predicate = lambda x: run_plan(x, problem, plan[1][x])
return all(predicate(r) for r in problem.result(state, plan[0]))
plan = and_or_graph_search(vacumm_world)
assert run_plan('State_1', vacumm_world, plan)
plan = and_or_graph_search(vacuum_world)
assert run_plan('State_1', vacuum_world, plan)


def test_online_dfs_agent():
odfs_agent = OnlineDFSAgent(LRTA_problem)
keys = [key for key in odfs_agent('State_3')]
assert keys[0] in ['Right', 'Left']
assert keys[1] in ['Right', 'Left']
assert odfs_agent('State_5') is None


def test_LRTAStarAgent():
my_agent = LRTAStarAgent(LRTA_problem)
assert my_agent('State_3') == 'Right'
assert my_agent('State_4') == 'Left'
assert my_agent('State_3') == 'Right'
assert my_agent('State_4') == 'Right'
assert my_agent('State_5') is None

my_agent = LRTAStarAgent(LRTA_problem)
assert my_agent('State_4') == 'Left'

my_agent = LRTAStarAgent(LRTA_problem)
assert my_agent('State_5') is None
lrta_agent = LRTAStarAgent(LRTA_problem)
assert lrta_agent('State_3') == 'Right'
assert lrta_agent('State_4') == 'Left'
assert lrta_agent('State_3') == 'Right'
assert lrta_agent('State_4') == 'Right'
assert lrta_agent('State_5') is None

lrta_agent = LRTAStarAgent(LRTA_problem)
assert lrta_agent('State_4') == 'Left'

lrta_agent = LRTAStarAgent(LRTA_problem)
assert lrta_agent('State_5') is None


def test_genetic_algorithm():
Expand Down