def preExplore(self, nodeList, states):
# play crt move
DarkLogic.apply(self._threadId, self._actionId)
# check if it is a node which leads to loss
if DarkLogic.isAlreadyPlayed(self._threadId):
self._value = Node.VAL_MAX
elif not DarkLogic.canBeDemonstrated(self._threadId):
self._value = Node.VAL_MAX
if not DarkLogic.isEvaluated(self._threadId):
self._isLoss = True
# check if it is a node which leads to win
elif DarkLogic.isDemonstrated(self._threadId):
self._value = 0
# stop reflexion because AI found a demonstration
Node._ai.stopThread(self._threadId)
else:
actions = DarkLogic.getActions(self._threadId)
for action in actions:
self._sons[action] = None
nodeList.append(self)
if self._ai.canEvaluate(DarkLogic.getState(self._threadId)):
states.append(Node._ai.getTrueState(self._threadId))
else:
self._aiValue = Node.VAL_INIT
self._isEvaluated = True
# unplay crt move
DarkLogic.unapply(self._threadId)
def exploreDepthDeep(self, nodeList, states):
DarkLogic.apply(self._threadId, self._actionId)
actions = DarkLogic.getActions(self._threadId)
for action in actions:
if action not in self._sons or not self._sons[action]:
self._sons[action] = Node(actionId=action,
threadId=self._threadId,
depth=self._depth + 1)
node = self._sons[action]
if not node.isEvaluated():
node.preExplore(nodeList, states)
if node.value() < Node.VAL_INIT:
self._value = node.value() + 1
if Node._ai.mustStop(self._threadId):
break
if len(states) == Node._ai.MaxNbNode:
break
if not Node._ai.mustStop(
self._threadId) and len(states) != Node._ai.MaxNbNode:
# We need to go deeper!
nodes = self.getDeepExploreNodes(self._sons.keys())
rand.shuffle(nodes)
for node in nodes:
node.exploreDepthDeep(nodeList, states)
if node.value() < Node.VAL_INIT:
self._value = node.value() + 1
if Node._ai.mustStop(self._threadId):
break
if len(states) == Node._ai.MaxNbNode:
break
DarkLogic.unapply(self._threadId)
def exploreDepthEval(self):
# play crt move
DarkLogic.apply(self._threadId, self._actionId)
# get actions
actions = DarkLogic.getActions(self._threadId)
# no need to go deeper
if len(self._sons) != len(actions):
minSubValue = Node.INIT_SUBVALUE
updateValue = True
for action in actions:
# node = None
if action not in self._sons:
node = Node(actionId=action,
threadId=self._threadId,
depth=self._depth + 1)
self._sons[action] = node
node.eval(self._threadId)
if node.value() < Node.VAL_MAX:
idx = self._dbNode.push(action)
self._dbNode.updateValue(idx, node.realValue())
else:
node = self._sons[action]
subValue = node.subValue()
if subValue < minSubValue:
minSubValue = subValue
# if must stop exploration, stop it
if Node._ai.mustStop(self._threadId):
updateValue = False
break
if updateValue:
if self._dbNode.size() == 0:
# all actions lead to loss
self._value = Node.VAL_MAX
else:
self._subValue = minSubValue + 1
else:
action = self._dbNode.getBestAction()
node = self._sons[action]
# self._dbNode.updateValue(idx, self.realValue(), True) # delete this line
value = node.exploreDepthEval()
if node.value() < Node.VAL_MAX:
self._dbNode.updateValue(action, value)
action = self._dbNode.getBestAction()
node = self._sons[action]
self._subValue = node.subValue() + 1
else:
self._dbNode.removeIdx(action)
DarkLogic.unapply(self._threadId)
return self.realValue()
def getDepthTrainNodes(self, x, y, depth):
DarkLogic.getActions()
DarkLogic.apply(self._actionId)
# print("explore " + str(self.actionId()) + " at depth = " + str(depth) + " with value = " + str(self.value()))
for key in self._sons:
node = self._sons[key]
if node:
if node.isEvaluated():
if node.value() != Node.VAL_INIT and not (
node.value() == Node.VAL_MAX
and not node.isLoss()):
"""print("train with node id = " + str(node.actionId()) + " at depth = " + str(
depth + 1) + " with value = " + str(node.value()))"""
Node._ai.getTrainingStates(node.value(), x, y)
node.getDepthTrainNodes(x, y, depth + 1)
DarkLogic.unapply()
def demonstration(self, name, content, nbThreads):
print("Test AI on " + name + " theorem with " + str(nbThreads) +
" cores")
DarkLogic.init(nbThreads)
ai = AI(nbThreads, 60)
assert DarkLogic.makeTheorem(
name, content), "cannot make " + name + " theorem"
DarkLogic.printTheorem()
start = time.perf_counter()
while not DarkLogic.isOver():
action = ai.play()
DarkLogic.getActions()
print(ai.name() + " plays action with id " + str(action.id()))
DarkLogic.apply(action.id())
DarkLogic.printTheorem()
print(
"____________________________________________________________________________"
)
end = time.perf_counter()
if DarkLogic.hasAlreadyPlayed():
if DarkLogic.isDemonstrated():
print(ai.name() + " won! " + ai.name() +
" finished the demonstration!")
elif DarkLogic.isAlreadyPlayed():
print(ai.name() + " lost! Repetition of theorem!")
elif DarkLogic.isEvaluated():
print(
ai.name() +
" lost! Cannot (\"back-\")demonstrate that a theorem is false with implications"
)
elif not DarkLogic.canBeDemonstrated():
print(
ai.name() +
" lost! This theorem cannot be demonstrated! " +
"It can be true or false according to the values of its variables"
)
else:
if DarkLogic.isDemonstrated():
print("Game Over! the demonstration is already finished!")
self._elapsed_seconds = end - start
elif not DarkLogic.canBeDemonstrated():
print(
"Game Over! This theorem cannot be demonstrated! " +
"It can be true or false according to the values of its variables"
)
self.pushEvent(Event.EventEnum.STOP)
def _pushActionStr(self, action):
id = int(action)
actions = DarkLogic.getHumanActions()
foundAction = False
ruleName = ""
for action in actions:
if id == action.id():
foundAction = True
ruleName = action.ruleName()
break
if foundAction:
DarkLogic.apply(id)
print(self._player.name() + " uses " + ruleName)
print("Current theorem is")
DarkLogic.printTheorem()
else:
print(action + " is not a valid action")
return foundAction
def getDeepDbStates(self, dbStates, depth):
DarkLogic.getActions()
# print("apply action :"+str(self._actionId)+" at depth = "+str(depth))
DarkLogic.apply(self._actionId)
name = DarkLogic.theoremName()
# print("name:" + str(name))
# print("content: "+DarkLogic.toStrTheorem())
content = DarkLogic.toNormStrTheorem()
# print("NormContent = " + str(content))
if self.value() != Node.VAL_INIT and not (self.value() == Node.VAL_MAX
and not self.isLoss()):
dbStates.append(
State(name=name, content=content, value=self._value))
else:
dbStates.append(State(name=name, content=content))
for key in self._sons:
node = self._sons[key]
if node:
node.getDeepDbStates(dbStates, depth + 1)
DarkLogic.unapply()
def eval(self, threadIdx):
# play crt move
DarkLogic.apply(threadIdx, self._actionId)
# check if it is a node which leads to loss
if DarkLogic.isAlreadyPlayed(threadIdx):
self._value = Node.VAL_MAX
elif not DarkLogic.canBeDemonstrated(threadIdx):
self._value = Node.VAL_MAX
if not DarkLogic.isEvaluated(threadIdx):
self._isLoss = True
# check if it is a node which leads to win
elif DarkLogic.isDemonstrated(threadIdx):
self._value = 0
# stop reflexion because AI found a demonstration
Node._ai.stopThread(threadIdx)
else:
self._subValue = Node._ai.eval([DarkLogic.getState(threadIdx)],
threadIdx)
self._isEvaluated = True
# unplay crt move
DarkLogic.unapply(threadIdx)
def _pushAction(self, action):
DarkLogic.getActions()
print(self._player.name() + " played action with id " +
str(action.id()))
DarkLogic.apply(action.id())
DarkLogic.printTheorem()
def exploreDepthStatic(self, maxDepth):
# play crt move
DarkLogic.apply(self._threadId, self._actionId)
"""print("crt action id: " + str(self._actionId) + ", at depth="
+ str(self._depth) + ", crt theorem is: " + DarkLogic.toStrTheorem(self._threadId))"""
# no need to go deeper
if self._depth == maxDepth:
# check if it is a node which leads to loss
if DarkLogic.isAlreadyPlayed(
self._threadId) or not DarkLogic.canBeDemonstrated(
self._threadId):
self._value = Node.VAL_MAX
# check if it is a node which leads to win
elif DarkLogic.isDemonstrated(self._threadId):
self._value = 0
# stop reflexion because AI found a demonstration
Node._ai.stopThread(self._threadId)
"""else:
self._subValue = self._ai.eval([DarkLogic.getState(self._threadId)], self._threadId)"""
elif not Node._ai.mustStop(self._threadId):
# get actions
actions = DarkLogic.getActions(self._threadId)
# add all subnodes if they have not been created yet
if self._depth == maxDepth - 1:
for action in actions:
self._sons[action] = Node(actionId=action,
threadId=self._threadId,
depth=self._depth + 1)
# explore subNodes
hasOnlyLosses = True
self._subValue = Node.INIT_SUBVALUE
for action in actions:
# explore node associated with action
node = self._sons[action]
retValue = Node.VAL_MAX
if node.value() < Node.VAL_MAX:
retValue = node.exploreDepthStatic(maxDepth)
# update m_value
if retValue == Node.VAL_MAX:
if hasOnlyLosses:
self._value = Node.VAL_MAX
else:
hasOnlyLosses = False
if retValue == Node.VAL_INIT and self._value == Node.VAL_MAX:
self._value = Node.VAL_INIT
elif self._value > retValue + 1:
self._value = retValue + 1
# update subValue
if self._subValue > node.subValue():
self._subValue = node.subValue()
# if must stop exploration, stop it
if Node._ai.mustStop(self._threadId):
break
# unplay crt move
DarkLogic.unapply(self._threadId)
return self._value