Information Technology Reference
In-Depth Information
Action Agreement Ratio:
The first metric used to evaluate agent to hu-
man likeness is the action agreement ratio (AAR). AAR considers each step of a
human playtrace a distinct decision. To produce the AAR between an agent and
a human player, all distinct game states of the human playtraces are recon-
structed. For each game state, the agent being tested is inserted into the game
state and queried for the next preferred action, essentially asking: “What would
you do?”. If the action is the same as the actual next human action, the agent is
awarded one point. Finally the AAR is computed by dividing the points with the
number of decisions in the human playtrace.
Ta c t i c a l Agreement Ratio:
The second metric used for evaluating the
likeness between agents and humans is the tactical agreement ratio (TAR). TAR
only considers reaching each distinct affordance in the level a significant decision,
ignoring the individual actions in between. For MiniDungeons the affordances
considered relevant are: fighting a monster, drinking a potion, collecting a trea-
sure, or exiting a level. For each affordance reached in the human play trace, the
resulting game state is reconstructed and the agent being tested is inserted into
the game state. The agent is then allowed as many actions as necessary to reach the
next affordance, again asking the question “What would you do?”, but at the
tactical affordance level. If the next encountered affordance matches the actual
next human one, the agent is awarded a point. Finally the TAR is computed by
dividing the points with the number of affordances reached in the human
playtrace.
Evolved Agent Controllers:
The controllers of the game agents are rep-
resented as seven linear perceptrons. Each perceptron takes 8 inputs describing
safe and risky path distances to the nearest affordances in the map. Further de-
tails of the controller representation is given in [5]. Controllers are evolved using a
(µ + α) evolutionary strategy without self-adaptation. For each generation the
top 2% performing elite individuals remain unchanged, the lowest performing half
of the remaining population is removed, and single-parent offspring from the
remaining individuals are produced to maintain the population size. Finally all
individuals not existent in the elite are mutated. Mutation is accomplished by
changing each connection weight in the network with a random number drawn from
a Gaussian distribution centered around zero with a standard variation of 0.3, a
value recommended in [13] and confirmed as useful for this game by in- formal
experimentation. All experiments are done using a population size of 100
individuals, trained for 100 generations. Controllers are initialized with random
connection weights for all connections in the linear perceptrons.
Personas:
For the purpose of the experiments 5 individual personas with
different utility configurations were defined, based on designer interpretations of
likely gameplay in MiniDungeons. The personas were intended to represent five
hypothetical extreme decision making styles in interacting with the game: an
Exit (E) persona who simply tries to escape the level, a Runner (R) persona who
tries to escape the level in as few steps as possible, a Survivalist (S) persona who
