Added AdaptiveTitForTat (#697)

* Added Strategy AdaptiveTitForTat and tests.

Author: l-liciniuslucullus

axelrod/strategies/_strategies.py

@@ -57,13 +57,14 @@
 from .titfortat import (
     TitForTat, TitFor2Tats, TwoTitsForTat, Bully, SneakyTitForTat,
     SuspiciousTitForTat, AntiTitForTat, HardTitForTat, HardTitFor2Tats,
-    OmegaTFT, Gradual, ContriteTitForTat, SlowTitForTwoTats)
+    OmegaTFT, Gradual, ContriteTitForTat, SlowTitForTwoTats, AdaptiveTitForTat)
 
 
 # Note: Meta* strategies are handled in .__init__.py
 
 all_strategies = [
     Adaptive,
+    AdaptiveTitForTat,
     Aggravater,
     ALLCorALLD,
     Alternator,
@@ -197,4 +198,5 @@
     ZDGen2,
     ZDSet2,
     e,
+    
 ]

axelrod/strategies/titfortat.py

@@ -429,3 +429,79 @@ def strategy(self, opponent):
 
         # Otherwise cooperate
         return C
+        
+class AdaptiveTitForTat(Player):
+    """ATFT - Adaptive Tit for Tat (Basic Model)
+    
+    Algorithm
+    
+    if (opponent played C in the last cycle) then
+    world = world + r*(1-world)
+    else
+    world = world + r*(0-world)
+    If (world >= 0.5) play C, else play D
+    
+    Attributes
+    
+    world : float [0.0, 1.0], set to 0.5
+        continuous variable representing the world's image
+        1.0 - total cooperation
+        0.0 - total defection
+        other values - something in between of the above
+        updated every round, starting value shouldn't matter as long as
+        it's >= 0.5
+        
+    Parameters
+    
+    rate : float [0.0, 1.0], default=0.5
+        adaptation rate - r in Algorithm above
+        smaller value means more gradual and robust
+        to perturbations behaviour
+        
+    Names
+    
+    - Adaptive Tit For Tat: [Tzafestas2000]_
+    """
+    
+    name = 'Adaptive Tit For Tat'
+    classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': False,
+        'makes_use_of': set(),
+        'long_run_time': False,
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+    world = 0.5
+
+    @init_args
+    def __init__(self, rate=0.5):
+    
+        Player.__init__(self)
+        self.rate, self.starting_rate = rate, rate
+        
+    def strategy(self, opponent):
+
+        if len(opponent.history) == 0:
+            return C
+        
+        if opponent.history[-1] == C:
+            self.world += self.rate * (1. - self.world)
+        else:
+            self.world -= self.rate * self.world
+        
+        if self.world >= 0.5:
+            return C
+        
+        return D
+        
+    def reset(self):
+        
+        Player.reset(self)
+        self.world = 0.5
+        self.rate = self.starting_rate
+        
+    def __repr__(self):
+         
+        return "%s: %s" % (self.name, round(self.rate, 2))

axelrod/tests/unit/test_titfortat.py

@@ -462,5 +462,39 @@ def test_effect_of_strategy(self):
         self.responses_test([C]*3, [C, D, C], [C])
         self.responses_test([C]*3, [C, D, D], [D])
 
+class TestAdaptiveTitForTat(TestPlayer):
 
+    name = "Adaptive Tit For Tat: 0.5"
+    player = axelrod.AdaptiveTitForTat
+    expected_classifier = {
+        'memory_depth': float('inf'),
+        'stochastic': False,
+        'makes_use_of': set(),
+        'inspects_source': False,
+        'manipulates_source': False,
+        'manipulates_state': False
+    }
+    
+    def test_strategy(self):
+        """Start by cooperating."""
+        self.first_play_test(C)
+        
+    def test_effect_of_strategy(self):
+        
+        self.markov_test(['C', 'D', 'C', 'D'])
+        
+        p1, p2 = self.player(), self.player()
+        p1.play(p2)
+        p1.play(p2)
+        self.assertEqual(p2.world, 0.75)
+        
+    def test_world_rate_reset(self):
+        p1, p2 = self.player(), self.player()
+        p1.play(p2)
+        p1.play(p2)
+        p2.reset()
+        self.assertEqual(p2.world, 0.5)
+        self.assertEqual(p2.rate, 0.5)
+        
+        
 

docs/reference/bibliography.rst

@@ -17,3 +17,4 @@ documentation.
 .. [Singer-Clark2014] Singer-Clark, T. (2014). Morality Metrics On Iterated Prisoner’s Dilemma Players.
 .. [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
 .. [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
+.. [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.