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Merge pull request #715 from geraintpalmer/3newstrategies
3 New Strategies From the PRISON (http://www.lifl.fr/IPD/ipd.frame.html)
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‎axelrod/strategies/_strategies.py

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GoByMajority5,
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HardGoByMajority, HardGoByMajority10, HardGoByMajority20, HardGoByMajority40,
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HardGoByMajority5)
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from .gradualkiller import GradualKiller
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from .grudger import (Grudger, ForgetfulGrudger, OppositeGrudger, Aggravater,
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SoftGrudger)
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SoftGrudger, GrudgerAlternator, EasyGo)
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from .grumpy import Grumpy
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from .handshake import Handshake
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from .hunter import (
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DefectorHunter,
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Desperate,
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DoubleCrosser,
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EasyGo,
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Eatherley,
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EventualCycleHunter,
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Feld,
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GoByMajority5,
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Golden,
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Gradual,
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GradualKiller,
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Grofman,
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Grudger,
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GrudgerAlternator,
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Grumpy,
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Handshake,
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HardGoByMajority,

‎axelrod/strategies/gradualkiller.py

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from axelrod import Actions, Player, init_args
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from axelrod.strategy_transformers import InitialTransformer
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C, D = Actions.C, Actions.D
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@InitialTransformer((D, D, D, D, D, C, C), name_prefix=None)
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class GradualKiller(Player):
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"""
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It begins by defecting in the first five moves, then cooperates two times.
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It then defects all the time if the opponent has defected in move 6 and 7,
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else cooperates all the time.
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Initially designed to stop Gradual from defeating TitForTat in a 3 Player
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tournament.
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Names
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- Gradual Killer: [PRISON1998]_
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"""
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# These are various properties for the strategy
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name = 'Gradual Killer'
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classifier = {
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'memory_depth': float('Inf'),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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def strategy(self, opponent):
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if opponent.history[5:7] == [D, D]:
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return D
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return C

‎axelrod/strategies/grudger.py

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@@ -171,3 +171,63 @@ def reset(self):
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Player.reset(self)
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self.grudged = False
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self.grudge_memory = 0
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class GrudgerAlternator(Player):
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"""
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A player starts by cooperating until the first opponents defection,
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then alternates D-C.
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Names:
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- c_then_per_dc: [PRISON1998]_
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- Grudger Alternator: Original name by Geraint Palmer
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"""
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name = 'GrudgerAlternator'
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classifier = {
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'memory_depth': float('inf'), # Long memory
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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def strategy(self, opponent):
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"""Begins by playing C, then plays Alternator for the remaining rounds if the opponent ever plays D."""
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if opponent.defections:
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if self.history[-1] == Actions.C:
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return Actions.D
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return Actions.C
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class EasyGo(Player):
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"""
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A player starts by defecting however will cooperate if at any point the opponent has defected.
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Names:
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- Easy Go [PRISON1998]_
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"""
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name = 'EasyGo'
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classifier = {
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'memory_depth': float('inf'), # Long memory
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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@staticmethod
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def strategy(opponent):
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"""Begins by playing D, then plays C for the remaining rounds if the opponent ever plays D."""
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if opponent.defections:
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return C
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return D

‎axelrod/tests/unit/test_gradualkiller.py

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"""Test for the gradual killer strategy."""
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import axelrod
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from .test_player import TestHeadsUp, TestPlayer
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from hypothesis import given
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from hypothesis.strategies import integers
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from axelrod.tests.property import strategy_lists
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import random
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C, D = axelrod.Actions.C, axelrod.Actions.D
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class TestGradualKiller(TestPlayer):
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name = "Gradual Killer"
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player = axelrod.GradualKiller
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expected_classifier = {
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'memory_depth': float('Inf'),
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'stochastic': False,
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'makes_use_of': set(),
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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def test_strategy(self):
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"""Starts by Defecting."""
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self.first_play_test(D)
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def test_effect_of_strategy(self):
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"""Fist seven moves."""
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self.markov_test([D, D, D, D])
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self.responses_test([], [], [D, D, D, D, D, C, C])
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def test_effect_of_strategy_with_history_CC(self):
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"""Continues with C if opponent played CC on 6 and 7."""
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P1 = axelrod.GradualKiller()
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P2 = axelrod.Player()
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P1.history = [D, D, D, D, D, C, C]
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P2.history = [C, C, C, C, C, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C]
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P2.history = [C, C, C, C, C, C, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C]
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P2.history = [C, C, C, C, C, C, C, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C, C]
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P2.history = [C, C, C, C, C, C, C, C, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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def test_effect_of_strategy_with_history_CD(self):
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"""Continues with C if opponent played CD on 6 and 7."""
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P1 = axelrod.GradualKiller()
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P2 = axelrod.Player()
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P1.history = [D, D, D, D, D, C, C]
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P2.history = [C, C, C, C, C, C, D]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C]
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P2.history = [C, C, C, C, C, C, D, D]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C]
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P2.history = [C, C, C, C, C, C, D, D, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C, C]
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P2.history = [C, C, C, C, C, C, D, D, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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def test_effect_of_strategy_with_history_DC(self):
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"""Continues with C if opponent played DC on 6 and 7."""
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P1 = axelrod.GradualKiller()
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P2 = axelrod.Player()
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P1.history = [D, D, D, D, D, C, C]
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P2.history = [C, C, C, C, C, D, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C]
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P2.history = [C, C, C, C, C, D, C, C]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C]
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P2.history = [C, C, C, C, C, D, C, C, D]
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self.assertEqual(P1.strategy(P2), 'C')
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P1.history = [D, D, D, D, D, C, C, C, C, C]
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P2.history = [C, C, C, C, C, D, C, C, D, C]
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self.assertEqual(P1.strategy(P2), 'C')
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def test_effect_of_strategy_with_history_CC(self):
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"""Continues with D if opponent played DD on 6 and 7."""
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P1 = axelrod.GradualKiller()
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P2 = axelrod.Player()
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P1.history = [D, D, D, D, D, C, C]
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P2.history = [C, C, C, C, C, D, D]
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self.assertEqual(P1.strategy(P2), 'D')
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P1.history = [D, D, D, D, D, C, C, D]
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P2.history = [C, C, C, C, C, D, D, C]
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self.assertEqual(P1.strategy(P2), 'D')
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P1.history = [D, D, D, D, D, C, C, D, D]
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P2.history = [C, C, C, C, C, D, D, C, C]
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self.assertEqual(P1.strategy(P2), 'D')
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P1.history = [D, D, D, D, D, C, C, D, D, D]
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P2.history = [C, C, C, C, C, D, D, C, C, D]
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self.assertEqual(P1.strategy(P2), 'D')
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‎axelrod/tests/unit/test_grudger.py

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"""Tests for grudger strategies."""
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import axelrod
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from random import randint
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from .test_player import TestPlayer
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p.reset()
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self.assertFalse(p.grudged)
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self.assertEqual(p.grudge_memory, 0)
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class TestGrudgerAlternator(TestPlayer):
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name = "GrudgerAlternator"
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player = axelrod.GrudgerAlternator
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expected_classifier = {
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'memory_depth': float('inf'), # Long memory
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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def test_initial_strategy(self):
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"""
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Starts by cooperating
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"""
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self.first_play_test(C)
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def test_strategy(self):
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"""
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If opponent defects at any point then the player will alternate D-C.
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"""
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self.responses_test([C, C, C, C, C], [C, C, C, C, C], [C])
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self.responses_test([C, C, C, C, C, C], [C, C, C, C, C, D], [D])
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self.responses_test([C, C, C, C, C, C, D], [C, C, C, C, C, D, D], [C])
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self.responses_test([C, C, C, C, C, C, D, C], [C, C, C, C, C, D, D, C], [D])
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self.responses_test([C, C, C, C, C, C, D, C, D], [C, C, C, C, C, D, D, C, C], [C])
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def test_starategy_random_number_rounds(self):
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"""
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Runs test_strategy for a random number of rounds
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"""
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# Hasn't defected yet
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for _ in range(20):
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i = randint(1, 30)
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j = randint(1, 30)
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opp_hist = [C] * i
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my_hist = [C] * i
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self.responses_test(my_hist, opp_hist, [C]*j)
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# Defected at least once
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for _ in range(20):
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i = randint(1, 30)
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j = randint(1, 30)
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opp_hist = [C for r in range(i)] + [D]
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my_hist = [C] * (i + 1)
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expected_response = [D if r % 2 == 0 else C for r in range(j)]
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self.responses_test(my_hist, opp_hist, expected_response)
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class TestEasyGo(TestPlayer):
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name = "EasyGo"
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player = axelrod.EasyGo
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expected_classifier = {
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'memory_depth': float('inf'), # Long memory
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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'inspects_source': False,
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'manipulates_source': False,
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'manipulates_state': False
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}
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def test_initial_strategy(self):
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"""
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Starts by cooperating
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"""
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self.first_play_test(D)
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def test_strategy(self):
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"""
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If opponent defects at any point then the player will cooperate forever
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"""
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self.responses_test([C, D, D, D], [C, C, C, C], [D])
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self.responses_test([C, C, D, D, D], [C, D, C, C, C], [C])

‎axelrod/tests/unit/test_resultset.py

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@given(tournament=tournaments(max_size=5,
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max_turns=5,
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max_noise=0,
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max_repetitions=3))
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@settings(max_examples=50, timeout=0)
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def test_equality_with_round_robin(self, tournament):

‎docs/reference/all_strategies.rst

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.. automodule:: axelrod.strategies.titfortat
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:members:
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:undoc-members:
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.. automodule:: axelrod.strategies.gradualkiller
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:members:
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:undoc-members:

‎docs/reference/bibliography.rst

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.. [Stewart2012] Stewart, a. J., & Plotkin, J. B. (2012). Extortion and cooperation in the Prisoner’s Dilemma. Proceedings of the National Academy of Sciences, 109(26), 10134–10135. http://doi.org/10.1073/pnas.1208087109
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.. [Szabó1992] Szabó, G., & Fáth, G. (2007). Evolutionary games on graphs. Physics Reports, 446(4-6), 97–216. http://doi.org/10.1016/j.physrep.2007.04.004
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.. [Tzafestas2000] Tzafestas, E. (2000). Toward adaptive cooperative behavior. From Animals to Animals: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior {(SAB-2000)}, 2, 334–340.
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.. [PRISON1998] LIFL (1998) PRISON. Available at: http://www.lifl.fr/IPD/ipd.frame.html (Accessed: 19 September 2016).

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