Evolving models of player decision making: Personas versus clones ?
The current paper investigates multiple approaches to modeling human decision making styles for procedural play-testing. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Three kinds of agents are evolved from the same representation: procedural personas, evolved from game designer expert knowledge, clones, evolved from observations of human play and aimed at general behavioral replication, and specialized agents, also evolved from observation, but aimed at determining the maximal behavioral replication ability of the representation. These three methods are then compared on their ability to represent individual human decision makers. Comparisons are conducted using three different proposed metrics that address the problem of matching decisions at the action, tactical, and strategic levels. Results indicate that a small gallery of personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play-traces for the testbed game MiniDungeons.
decision making procedural content generation evolutionary computation player modeling