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The module heapq provides priority queues that are implemented as heaps. Technically, these heaps are just lists. In order to use them as priority queues, the entries of these lists will be pairs of the form $(p, o)$, where $p$ is the priority of the object $o$. Usually, the priorities are numbers and, contra-intuitively, high priorities correspond to small numbers, that is $(p_1, o_1)$ has a higher priority than $(p_2, o_2)$ iff $p_1 < p_2$.
We need only two functions from the module heapq
:
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import heapq
The function search
takes three arguments to solve a search problem:
start
is the start state of the search problem,goal
is the goal state, andnext_states
is a function with signature $\texttt{next_states}:Q \rightarrow 2^Q$, where $Q$ is the set of states.
For every state $s \in Q$, $\texttt{next_states}(s)$ is the set of states that can be reached from $s$ in one step.heuristic
is a function that takes two states as arguments. It returns an estimate of the
length of the shortest path between these states.
If successful, search
returns a path from start
to goal
that is a solution of the search problem
$$ \langle Q, \texttt{next_states}, \texttt{start}, \texttt{goal} \rangle. $$The variable PrioQueue
that is used in the implementation contains pairs of the form
$$ \bigl(\texttt{heuristic}(\texttt{state}, \texttt{goal}), \texttt{Path}\bigr), $$
where Path
is a path from start
to state
and $\texttt{heuristic}(\texttt{state}, \texttt{goal})$
is an estimate of the distance from state
to goal
. The idea is to always extend the most promising Path
, i.e. to extend the Path
, whose last state is closest to goal
.
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def search(start, goal, next_states, heuristic):
PrioQueue = [ (heuristic(start, goal), [start]) ]
while len(PrioQueue) > 0:
_, Path = heapq.heappop(PrioQueue)
state = Path[-1]
if state == goal:
return Path
for ns in next_states(state):
if ns not in Path:
d = heuristic(ns, goal)
heapq.heappush(PrioQueue, (d, Path + [ns]))
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%run Sliding-Puzzle.ipynb
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%%time
Path = search(start, goal, next_states, manhattan)
print(len(Path)-1)
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animation(Path)
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%%time
Path = search(start2, goal2, next_states, manhattan)
print(len(Path)-1)
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