def run_baseline(board, fringe, explored_count, unexplored_cells,
undiscovered_mines, dim, score, knowledge_base):
print "Running baseline"
while (fringe and len(fringe) > 0):
print "Baseline Fringe: ", fringe
((x_cord, y_cord)) = fringe.pop(0)
if explored_count == dim * dim:
return explored_count, unexplored_cells, undiscovered_mines, score, knowledge_base
# base line update
print "Running baseline on: ", x_cord, y_cord
mine_cells, safe_cells = run_clue_check(x_cord, y_cord, board)
print "mine_cells", mine_cells
print "safe_cells", safe_cells
neighbors = []
for cords in mine_cells:
assert board[cords[0]][cords[1]].is_mine == True
neighbors.extend(gb.get_neighbors(board, cords[0], cords[1]))
undiscovered_mines = \
mark_cell_as_mine(cords[0], cords[1], board, undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
print "after marking mine cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
score = score + 1
fringe.extend(mine_cells)
if undiscovered_mines == 0:
for unexplored_cell in unexplored_cells:
if unexplored_cell not in safe_cells:
safe_cells.append(unexplored_cell)
for cords in safe_cells:
assert board[cords[0]][cords[1]].is_mine == False
neighbors.extend(gb.get_neighbors(board, cords[0], cords[1]))
undiscovered_mines = query_cell(cords[0], cords[1], board,
undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
print "after querying safe cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
fringe.extend(safe_cells)
knowledge_base = update_knowledge_base(knowledge_base, board,
mine_cells, safe_cells)
# remove the duplicates from the fringe and keep the latest entry
fringe = list(OrderedDict.fromkeys(fringe))
return explored_count, unexplored_cells, undiscovered_mines, score, knowledge_base
def run_baseline(board, fringe, explored_count, unexplored_cells,
undiscovered_mines, dim, score):
while (fringe and len(fringe) > 0):
((x_cord, y_cord)) = fringe.pop(0)
if explored_count == dim * dim:
return explored_count, unexplored_cells, undiscovered_mines, score
# get the list of safe and mine cells
mine_cells, safe_cells = run_clue_check(x_cord, y_cord, board)
# mark all the cells identified as mines
for cords in mine_cells:
undiscovered_mines = mark_cell_as_mine(cords[0], cords[1], board,
undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
gb.visualize_agent_board(game_board)
score = score + 1
fringe.extend(mine_cells)
if undiscovered_mines == 0:
for unexplored_cell in unexplored_cells:
if unexplored_cell not in safe_cells:
safe_cells.append(unexplored_cell)
# open all the cells identified as safe cells
for cords in safe_cells:
board, undiscovered_mines = query_cell(cords[0], cords[1], board,
undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
gb.visualize_agent_board(game_board)
fringe.extend(safe_cells)
# remove duplicates from the fringe and keep the latest entry
fringe = list(OrderedDict.fromkeys(fringe))
return explored_count, unexplored_cells, undiscovered_mines, score
def start_baseline(board, total_mines, knowledge_base):
explored_count = 0
score = 0
dim = len(board)
unexplored_cells = get_unexplored_cells(dim, board)
undiscovered_mines = total_mines
knowledge_base[copy.deepcopy(frozenset(unexplored_cells))] = total_mines
# For picking the first cell randomly
# (row_index, col_index) = get_min_max_based_random_cell(knowledge_base, unexplored_cells)
(row_index,
col_index) = get_min_avg_based_random_cell(knowledge_base,
unexplored_cells)
print "Initial cell ", row_index, col_index
fringe = [(row_index, col_index)]
inference_fringe = []
undiscovered_mines = \
query_cell(row_index, col_index, board, undiscovered_mines)
explored_count += 1
unexplored_cells.remove((row_index, col_index))
if board[row_index][col_index].value == 0:
knowledge_base = update_knowledge_base(knowledge_base, board, [],
[(row_index, col_index)])
else:
knowledge_base = update_knowledge_base(knowledge_base, board,
[(row_index, col_index)], [])
print "after querying random cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
# run baseline algorithm after opening one cell randomly
explored_count, unexplored_cells, undiscovered_mines, score, knowledge_base = \
run_baseline(board, fringe, explored_count, unexplored_cells, undiscovered_mines, \
dim, score, knowledge_base)
if explored_count == dim * dim:
return score
while True:
while True:
print "Inferring..."
mine_cells_2, safe_cells_2, knowledge_base = \
infer_from_knowledge_base(knowledge_base, board)
print "mine_cells_2 ", mine_cells_2
print "safe_cells_2 ", safe_cells_2
# no inference from basic inference
if len(mine_cells_2) == 0 and len(safe_cells_2) == 0:
print "Advanced Inference..."
mine_cells_3, knowledge_base = \
advanced_inference(knowledge_base, board)
print "mine_cells_3 ", mine_cells_3
if len(mine_cells_3) == 0:
break
print "!!.......EUREKA......!!"
for cords in mine_cells_3:
assert board[cords[0]][cords[1]].is_mine == True
undiscovered_mines = \
mark_cell_as_mine(cords[0], cords[1], board, undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
print "after marking mine cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
score = score + 1
fringe.extend(mine_cells_3)
# some inference from basic inference
else:
for cords in mine_cells_2:
assert board[cords[0]][cords[1]].is_mine == True
undiscovered_mines = \
mark_cell_as_mine(cords[0], cords[1], board, undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
print "after marking mine cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
score = score + 1
fringe.extend(mine_cells_2)
for cords in safe_cells_2:
assert board[cords[0]][cords[1]].is_mine == False
undiscovered_mines = query_cell(cords[0], cords[1], board,
undiscovered_mines)
explored_count += 1
unexplored_cells.remove((cords[0], cords[1]))
print "after querying safe cell knowledge:"
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
fringe.extend(safe_cells_2)
knowledge_base = update_knowledge_base(knowledge_base, board,
mine_cells_2, safe_cells_2)
explored_count, unexplored_cells, undiscovered_mines, score, knowledge_base = \
run_baseline(board, fringe, explored_count, unexplored_cells, undiscovered_mines, \
dim, score, knowledge_base)
if explored_count == dim * dim:
return score
# (row_index, col_index) = get_min_max_based_random_cell(knowledge_base, unexplored_cells)
(row_index,
col_index) = get_min_avg_based_random_cell(knowledge_base,
unexplored_cells)
# no_of_random_cell_calls += 1
print "Random cell ", row_index, col_index
fringe = [(row_index, col_index)]
undiscovered_mines = query_cell(row_index, col_index, board,
undiscovered_mines)
explored_count += 1
unexplored_cells.remove((row_index, col_index))
if board[row_index][col_index].value == 0:
knowledge_base = update_knowledge_base(knowledge_base, board, [],
[(row_index, col_index)])
else:
knowledge_base = update_knowledge_base(knowledge_base, board,
[(row_index, col_index)],
[])
gb.display_knowledge_base(knowledge_base)
gb.visualize_agent_board(game_board)
explored_count, unexplored_cells, undiscovered_mines, score, knowledge_base = \
run_baseline(board, fringe, explored_count, unexplored_cells, undiscovered_mines, \
dim, score, knowledge_base)
if explored_count == dim * dim:
return score
return score