def ba3(agent, r, c):
scale = 1.0 - agent.map[r][c].probability + \
agent.map[r][c].probability * agent.map[r][c].falseNegativeProbability
agent.map[r][c].probability = agent.map[r][c].falseNegativeProbability * \
agent.map[r][c].probability
minScore = -1
new_r = r
new_c = c
i = 0
while i < agent.dim:
j = 0
while j < agent.dim:
if i == r and j == c:
j += 1
continue
agent.map[i][j].probability = agent.map[i][j].probability / scale
dist = manhattanDistance((r, c), (i, j))
agent.map[i][j].score = float(dist) / (
agent.map[i][j].probability *
(1 - agent.map[i][j].falseNegativeProbability))
if ((minScore > agent.map[i][j].score) or minScore == -1):
minScore = agent.map[i][j].score
new_r = i
new_c = j
j = j + 1
i = i + 1
return (new_r, new_c)
def improvedMAMTWithClue(agent, target):
highest = 0
r = c = 0
for row in agent.map:
for cell in row:
cell.score = cell.probability * \
(1 - cell.falseNegativeProbability)
if cell.score > highest:
highest = cell.score
r, c = cell.row, cell.col
while agent.hasFoundTarget == False:
minScore = agent.dim**4
prevr = r
prevc = c
numChecks = int(agent.map[r][c].falseNegativeProbability * 10.0)
for check in range(numChecks):
searchResult = agent.searchCell(r, c, target)
#print('searched cell: ', (prevr,prevc))
#print('Target Position: ', target.position)
if searchResult == False:
target.move()
withinFive = targetInRange(agent, target, prevr, prevc)
#print('target in range? ', withinFive)
scale = 1 - agent.map[r][c].probability + \
agent.map[r][c].probability * \
agent.map[r][c].falseNegativeProbability
agent.map[r][c].probability = agent.map[r][c].probability * \
agent.map[r][c].falseNegativeProbability
i = 0
while i < agent.dim:
j = 0
while j < agent.dim:
if prevr == i and prevc == j:
j += 1
continue
# probabilities/scores are updated here
agent.map[i][j].probability = agent.map[i][
j].probability / scale
agent.map[i][j].score = agent.map[i][j].probability * \
(1 - agent.map[i][j].falseNegativeProbability)
dist = manhattanDistance((prevr, prevc), (i, j))
agent.map[i][j].score = (1 + float(dist)) / (
agent.map[i][j].probability *
(1 - agent.map[i][j].falseNegativeProbability))
j = j + 1
i = i + 1
if check == numChecks - 1:
if withinFive:
minScore, r, c = minInRange(agent, prevr, prevc)
else:
minScore, r, c = minOutRange(agent, prevr, prevc)
numActions((prevr, prevc), (r, c), agent)
else:
break
return agent.numMoves
def improvedMAMTWithoutClue(agent, target):
highest = 0
r = c = 0
for row in agent.map:
for cell in row:
cell.score = cell.probability * (1 - cell.falseNegativeProbability)
# dist = float(manhattanDistance((r,c), (i,j)))
# agent.map[i][j].score = dist / agent.map[i][j].score
if cell.score > highest:
highest = cell.score
r, c = cell.row, cell.col
while (agent.hasFoundTarget == False):
minScore = agent.dim**4
prevr = r
prevc = c
numChecks = int(agent.map[r][c].falseNegativeProbability * 10.0)
for check in range(0, numChecks):
searchResult = agent.searchCell(r, c, target)
if searchResult == False:
target.move()
scale = 1 - agent.map[r][c].probability + \
agent.map[r][c].probability * \
agent.map[r][c].falseNegativeProbability
agent.map[r][c].probability = agent.map[r][c].falseNegativeProbability * \
agent.map[r][c].probability
i = 0
while i < agent.dim:
j = 0
while j < agent.dim:
if i == prevr and j == prevc:
j += 1
continue
agent.map[i][j].probability = agent.map[i][
j].probability / scale
dist = manhattanDistance((prevr, prevc), (i, j))
agent.map[i][j].score = (1 + float(dist)) / (
agent.map[i][j].probability *
(1 - agent.map[i][j].falseNegativeProbability))
if check == numChecks - 1 and minScore > agent.map[i][
j].score:
minScore = agent.map[i][j].score
r = i
c = j
j = j + 1
i = i + 1
if check == numChecks - 1:
numActions((prevr, prevc), (r, c), agent)
else:
break
return agent.numMoves
def rule3MAST(agent, target):
highest = 0
r = c = 0
# the initializing of the first probabilities (no score here)
for row in agent.map:
for cell in row:
cell.score = cell.probability * (1 - cell.falseNegativeProbability)
if cell.score > highest:
highest = cell.score
r, c = cell.row, cell.col
# while the target has not been found, keep searching cells
while (agent.hasFoundTarget == False):
minScore = agent.dim**4
prevr = r
prevc = c
searchResult = agent.searchCell(r, c, target)
if searchResult == False:
# update the probabilties of the cell we just searched
scale = 1 - agent.map[r][c].probability + \
agent.map[r][c].probability * \
agent.map[r][c].falseNegativeProbability
agent.map[r][c].probability = agent.map[r][c].falseNegativeProbability * \
agent.map[r][c].probability
i = 0
while i < agent.dim:
j = 0
while j < agent.dim:
# if we reach the same cell that we already updated, skip, as the distance will be zero and this will always be the lowest scored cell
if i == prevr and j == prevc:
j += 1
continue
# update the probabilties of each of the other cells in the board
agent.map[i][
j].probability = agent.map[i][j].probability / scale
# update the score of each of the other cells in the board.
dist = manhattanDistance((prevr, prevc), (i, j))
agent.map[i][j].score = (1 + float(dist)) / (
agent.map[i][j].probability *
(1 - agent.map[i][j].falseNegativeProbability))
if minScore > agent.map[i][j].score:
minScore = agent.map[i][j].score
r = i
c = j
j = j + 1
i = i + 1
# imcrement number of actions between the previous cell and the one we have just explored
numActions((prevr, prevc), (r, c), agent)
return agent.numMoves