Strategy Archetypes

Finite State Machines

A finite state machine (FSM) is a general computation model. In the context of Axelrod, it’s a set of states and “transitions.” A transition for a given state/previous-opponent-action combination says how the strategy will respond, both in what action it will take and in what state it will transitions to. That is a transition will specify that in state \(a\), the strategy will respond to action \(X\) by taking action \(Y\) and moving to state \(b\) (which will tell us which transitions to use in later moves). We may write this transition \((a, X, b, Y)\). For Axelrod, a FSM must have a full set of transitions, which specifies a unique response for each state/previous-opponent-action combination.

See [Harper2017] for a more-detailed explanation.

Representing a strategy as a finite state machine has been useful in some research (see [Harper2017] or [Ashlock2006b]). Though it’s theoretically possible to represent all strategies as FSMs, this is impractical for most strategies. However, some strategies lend themselves naturally to a FSM representation. For example, for the Iterated Prisoner’s Dilemma, we could consider a strategy that cooperates (C) until the opponent defects (D) twice in a row, then defect forever thereafter. (We’ll call this strategy grudger_2 for the example.) We could call state 1, the state where the opponent hasn’t started a defect streak; state 2, the state where the opponent is on a 1-defect streak; and state 3, the state where the opponent has defected twice in a row at some point. Then the transitions would be:

>>> from axelrod import Action
>>> C, D = Action.C, Action.D
>>> grudger_2_transitions = (
...    (1, C, 1, C),
...    (1, D, 2, C),
...    (2, C, 1, C),
...    (2, D, 3, D),
...    (3, C, 3, D),
...    (3, D, 3, D)
... )

The Axelrod library includes a FSM meta-strategy player, which will you let you specify a player’s strategy by this transition matrix, along with an initial state and initial action. The syntax for this is:

>>> from axelrod.strategies.finite_state_machines import FSMPlayer
>>> grudger_2 = FSMPlayer(transitions=grudger_2_transitions,
...                       initial_state=1, initial_action=C)

The library also includes the functionality to compute the memory from the set of transitions. In the grudger_2 example, the memory would be 2. Because either the strategy’s own previous move was a defect (in which case, continue to defect) or we just need to check if the last two opponent moves were defects or not. Though this function takes the transitions in a slightly different format:

>>> transition_dict = {
...    (t[0], t[1]): (t[2], t[3]) for t in grudger_2_transitions
... }
>>> from axelrod.compute_finite_state_machine_memory import *
>>> get_memory_from_transitions(transitions=transition_dict,
...                             initial_state=1)