Romania Map
- Change Eforie to Constanza
- Constanza to Tulcea = 169
- Hirsova to Tulcea = 98
- Tulcea(570,375)


class Problem
__init__(initial, goal=None)
actions(state) - returns actions that can be executed in given state
result(state,action) - new state
goal_test(state) - are we there yet?
path_cost(c, state1, action, state2) - cost of getting there, including current cost
value(state) - for optimization problems

class Node - a node in a search tree
__init__(state, parent, action, path_cost)
__repr__()
__lt__(node) - comparison - overrides <
expand(problem) - list of nearest reachable nodes
child_node(problem, action) - generates next node
solution() - sequence of actions to get to goal
path() - list of nodes so far
__eq__(node) - overrides == two nodes have the same state
__hash__()


class SimpleProblemSolvingAgentProgram
__init__(initial_state)
__call__(percept) - populate self.seq actions to get to goal
update_state(state, percept)
formulate_goal(state)
search(problem)


### Uninformed Search Algorithms

breadth_first_tree_search(problem)

depth_first_tree_search(problem)

depth_first_graph_search(problem)

breadth_first_graph_search(problem)

best_first_graph_search(problem, f) where f is the function to minimize

uniform_cost_search(problem)

depth_limited_search(problem, limit=50)

iterative_deepening_search(problem)

### Bidirectional Search

bidirectional_search(problem)
- extend()
- find_min()
- find_key()

### Informed (Heuristic) Search

greedy_best_first_graph_search = best_first_graph_search

astar_search(problem, h=None)

### A* heuristics

class EightPuzzle(Problem)
__init__()
find_blank_square()
actions()
result()
goal_test()
check_solvability()
h()

class PlanRoute(Problem)
__init__()
actions()
result()
goal_test()
h()


### Other Search algorithms

recursive_best_first_search()

hill_climbing()

exp_schedule()

simulated_annealing()

simulated_annealing_full()

and_or_graph()

class PeakFindingProblem()
__initII()
actions()
result()
value()

class OnlineDFSAgent
__init__()
__call__()
update_state()

class OnlineSearchProblem(Problem)
__init__()
actions()
output()
h()
c()
update_state()
goal_test()

class LRTAStarAgent
__init__()
__call__()
LRTA_cost()


### Genetic Algorithm

genetic_search()

genetic_algorithm()

fitness_threshold()
init_population()
select()
recombine()
mutate()

#############################################

class Graph
__init__()
make_undirected()
connect()
connect1()
get()
notes

UndirectedGraph()

RandomGraph()

romania_map = UndirectedGraph(dict(...))

vacuum_world = Graph(dict(...))

one_dim_state_space = Graph(dict(...))

one_dim_state_space.least_costs = dict(...)

australia_map = UndirectedGraph(dict(...))
austalia_map.locations

class GraphProblem(Problem)
__init__()
actions()
result()
path_cost()
find_min_edge()
h()

class GraphProblemStochastic(GraphProblem)
result()
path_cost()


class NQueensProblem(Problem)
__init__()
actions()
result()
conflicted()
conflict()
goal_test()
h()

## Inverse Boggle

random_boggle()
print_boggle()
boggle_neighbors()
exact_sqrt()

class Wordlist
__init__()
lookup()
__contains__()
__len__()


class BoggleFinder
__init__()
set_board()
find()
words()
score()
__len__()

boggle_hill_climbing()

mutate_boggle()

## compare searchers

class InstrumentedProblem(Problem)
__init__()
actions()
result()
goal_test()
path_cost()


compare_searchers()

compare_graph_searchers()