Game Theoretic and Decision Theoretic Agents
Papers from the AAAI Spring Symposium
Piotr Gmytrasiewicz and Simon Parsons, Cochairs
Recently, game and decision theories have proved to be powerful tools with which to design autonomous agents, and to understand interactions in systems composed of many such agents.
Decision theory provides a general paradigm for designing agents that can operate in complex uncertain environments, and can act rationally to maximize their preferences. Decision-theoretic models use precise mathematical formalism to define the properties of the agent’s environment, the agent’s sensory capabilities, the ways the agent’s actions change the state of the environment, and the agent’s goals and preferences. The agent’s rationality is defined as behavior that maximizes the expectation of the degree to which the preferences are achieved over time, and the planning problem is identified as a search for the rational, or optimal, plan.
Game theory adds to the decision-theoretic framework the idea of multiple agents interacting within a common environment. It provides ways to specify how agents, separately or jointly, can change the environment and how the resulting changes impact their individual preferences.
Building on the assumption that agents are rational and self-interested, game theory uses notions such as Nash equilibrium to design mechanisms and protocols for various forms of interaction and communication that result in the overall system behaving in a stable, efficient, and fair manner.
Applications of intelligent agent technologies are numerous. While prototypical agents are physical, like robots, widely useful are also agents that operate in virtual and electronic environments, like the Internet. They can fetch and filter information, trade, negotiate and participate in auctions on behalf of their human users, and propose solutions to transportation, manufacturing and financial allocation problems.
There is much to be gained from bringing together researchers interested in game theory and decision theory to present recent work on the application of these techniques to agent-based computing.