Rating Players in Games with Real-Valued Outcomes

Game-theoretic models typically associate outcomes with real valued utilities, and rational agents are expected to maximize their expected utility. Currently fielded agent rating systems, which aim to order a population of agents by strength, focus exclusively on games with discrete outcomes, e.g., win-loss in two-agent settings or an ordering in the multi-agent setting. These rating systems are not well-suited for domains where the absolute magnitude of utility rather than just the relative value is important. We introduce the problem of rating agents in games with real-valued outcomes and survey applicable existing techniques for rating agents in this setting. We then propose a novel rating system and an extension for all of these rating systems to games with more than two agents, showing experimentally the advantages of our proposed system.