Abstract

Traditional game-theoretic analysis for decision-making takes a normative approach, in which agents derive rational decisions from the game description. This approach cannot naturally and compactly capture agents that are uncertain about the structure of the game, the strategies of other agents or whether agents may deviate from their optimal strategy. This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents ’ beliefs and decision-making processes. NIDs are graphical structures in which agents ’ mental models are represented as nodes in a network; a mental model for an agent may itself use descriptions of the mental models of other agents. NIDs are demonstrated by examples, showing how they can be used to describe conflicting and cyclic belief structures, and certain forms of bounded rationality. In an opponent modeling domain, NIDs were able to outperform other computational agents whose strategies were not known in advance. A novel equilibrium concept is defined that makes a distinction between agents ’ optimal strategies, and how they actually behave in reality. It is also shown that NIDs are more compact and structured than Bayesian games, the traditional formalism used to model uncertainty in multi-agent decision-making problems.

Citations