Add Detective strategy from Nicky Case game

axelrod/strategies/_strategies.py

@@ -163,6 +163,7 @@
 from .oncebitten import FoolMeForever, FoolMeOnce, ForgetfulFoolMeOnce, OnceBitten
 from .prober import (
     CollectiveStrategy,
+    Detective,
     HardProber,
     NaiveProber,
     Prober,
@@ -285,6 +286,7 @@
     DefectorHunter,
     Desperate,
     DelayedAON1,
+    Detective,
     DoubleCrosser,
     Doubler,
     DoubleResurrection,

axelrod/strategies/prober.py

@@ -48,6 +48,45 @@ def strategy(self, opponent: Player) -> Action:
         return D
 
 
+class Detective(Player):
+    """
+    Starts with C, D, C, C, or with the given sequence of actions.
+    If the opponent defects at least once in the first fixed rounds,
+    play as TFT forever, else defect forever.
+
+    Names:
+
+    - Detective: [NC2019]_
+    """
+
+    name = "Detective"
+    classifier = {
+        "memory_depth": float("inf"),
+        "stochastic": False,
+        "makes_use_of": set(),
+        "long_run_time": False,
+        "inspects_source": False,
+        "manipulates_source": False,
+        "manipulates_state": False,
+    }
+
+    def __init__(self, initial_actions: List[Action] = None) -> None:
+        super().__init__()
+        if initial_actions is None:
+            self.initial_actions = [C, D, C, C]
+        else:
+            self.initial_actions = initial_actions
+
+    def strategy(self, opponent: Player) -> Action:
+        hist_size = len(self.history)
+        init_size = len(self.initial_actions)
+        if hist_size < init_size:
+            return self.initial_actions[hist_size]
+        if D not in opponent.history[:init_size]:
+            return D
+        return opponent.history[-1] # TFT
+
+
 class Prober(Player):
     """
     Plays D, C, C initially. Defects forever if opponent cooperated in moves 2

axelrod/tests/strategies/test_prober.py

@@ -36,6 +36,51 @@ def test_strategy(self):
         self.versus_test(opponent=axelrod.Defector(), expected_actions=actions)
 
 
+class TestDetective(TestPlayer):
+
+    name = "Detective"
+    player = axelrod.Detective
+    expected_classifier = {
+        "memory_depth": float("inf"),
+        "stochastic": False,
+        "makes_use_of": set(),
+        "long_run_time": False,
+        "inspects_source": False,
+        "manipulates_source": False,
+        "manipulates_state": False,
+    }
+
+    def test_strategy(self):
+        self.versus_test(
+            opponent=axelrod.TitForTat(),
+            expected_actions=[(C, C), (D, C), (C, D)] + [(C, C)] * 15,
+        )
+
+        self.versus_test(
+            opponent=axelrod.Cooperator(),
+            expected_actions=[(C, C), (D, C), (C, C), (C, C)] + [(D, C)] * 15,
+        )
+
+        self.versus_test(
+            opponent=axelrod.Defector(),
+            expected_actions=[(C, D), (D, D), (C, D), (C, D)] + [(D, D)] * 15,
+        )
+
+    def test_other_initial_actions(self):
+        self.versus_test(
+            opponent=axelrod.TitForTat(),
+            expected_actions=[(C, C), (C, C), (D, C)] + [(D, D)] * 15,
+            init_kwargs={"initial_actions": [C, C]},
+        )
+
+        # Extreme case: no memory at all, it's simply a defector
+        self.versus_test(
+            opponent=axelrod.TitForTat(),
+            expected_actions=[(D, C)] + [(D, D)] * 15,
+            init_kwargs={"initial_actions": []},
+        )
+
+
 class TestProber(TestPlayer):
 
     name = "Prober"

docs/reference/bibliography.rst

@@ -45,6 +45,7 @@ documentation.
   International Conference on Autonomous Agents and Multiagent Systems.
 .. [Mittal2009] Mittal, S., & Deb, K. (2009). Optimal strategies of the iterated prisoner’s dilemma problem for multiple conflicting objectives. IEEE Transactions on Evolutionary Computation, 13(3), 554–565. https://doi.org/10.1109/TEVC.2008.2009459
 .. [Nachbar1992] Nachbar J., Evolution in the finitely repeated prisoner’s dilemma, Journal of Economic Behavior & Organization, 19(3): 307-326, 1992.
+.. [NC2019] https://github.com/ncase/trust (Accessed: 30 October 2019)
 .. [Nowak1989] Nowak, Martin, and Karl Sigmund. "Game-dynamical aspects of the prisoner's dilemma." Applied Mathematics and Computation 30.3 (1989): 191-213.
 .. [Nowak1990] Nowak, M., & Sigmund, K. (1990). The evolution of stochastic strategies in the Prisoner's Dilemma. Acta Applicandae Mathematica. https://link.springer.com/article/10.1007/BF00049570
 .. [Nowak1992] Nowak, M.., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature. http://doi.org/10.1038/359826a0