def hill_climbing(problem):
    """From the initial node, keep choosing the neighbor with highest value,
    stopping when no neighbor is better. [Fig. 4.2]"""
    current = Node(problem.initial)
    while True:
        neighbors = current.expand(problem)
        if not neighbors:
            break
        neighbor = argmax_random_tie(neighbors,
                                     lambda node: problem.value(node.state))
        if problem.value(neighbor.state) <= problem.value(current.state):
            break
        current = neighbor
    return current.state
def hill_climbing(problem):
    """From the initial node, keep choosing the neighbor with highest value,
    stopping when no neighbor is better. [Fig. 4.2]"""
    current = Node(problem.initial)
    while True:
        neighbors = current.expand(problem)
        if not neighbors:
            break
        neighbor = argmax_random_tie(neighbors,
                                     lambda node: problem.value(node.state))
        if problem.value(neighbor.state) <= problem.value(current.state):
            break
        current = neighbor
    return current.state
def hill_climbing ( problem ) : current = Node ( problem . initial ) while True : neighbors = current . expand ( problem ) if not neighbors : break neighbor = argmax_random_tie ( neighbors , lambda node : problem . value ( node . state ) ) if problem . value ( neighbor . state ) <= problem . value ( current . state ) : break current = neighbor return current . state
From the initial node, keep choosing the neighbor with highest value,
    stopping when no neighbor is better. [Fig. 4.2]
From the initial node keep choosing the neighbor with highest value stopping when no neighbor is better . [ Fig . 4 . 2 ]