Higher levels of the JDL model call for prediction of future development and awareness of the development of a situation. Since the situations handled by Command and Control systems develop by actions performed by opposing agents, pure probabilistic or evidential techniques are not quite sufficient tools for prediction. Game theoretic tools can give an improved appreciation of the real uncertainty in this prediction task, and also be a tool in the planning process. We review recent developments in game theory and apply them in a decision support tool for Command and Control situation awareness enhancements.

We introduce a Game Logic with Preferences (GLP), which makes it possible to reason about how information or assumptions about the preferences of other players can be used by agents in order to realize their own preferences. GLP can be applied to the analysis of social protocols such as voting or fair division problems; we illustrate this use of GLP with a number of worked examples. We then prove that the model checking problem for GLP is tractable, and describe an implemented model checker for the logic -- by using the model checker, it is possible to automate the analysis and verification of social protocols.

Traditional game-theoretic formalisms, commonly used in multi-agent systems, invoke the assumption of common knowledge of rationality to justify a Nash equilibrium solution. It is assumed that all agents know a correct model of the game and are completely rational, and that this is common knowledge. However, real-life agents are partially irrational, they may use models other than the real world to make decisions, and they may be uncertain about their opponents' decision making processes. For modeling boundedly-rational agents, a descriptive approach to game theory is needed, in which agents model their opponents and attempt to predict their behavior using their model. We present Networks of Influence Diagrams (NID), a language for descriptive decision and game theory that is based on graphical models. This paper describes NIDs and their syntax, and provides algorithms for solving NIDs and learning NID parameters. Through an example, we also show that NIDs provide an elegant framework for opponent modeling that is more expressive than current approaches, leads to a better outcome than the Nash equilibrium strategy and is able to capture non-stationary distributions of opponents.

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.

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. 1

Higher levels of the data fusion process call for prediction and awareness of the development of a situation. Since the situations handled by command and control systems develop by actions performed by opposing agents, pure probabilistic or evidential techniques are not fully sufficient tools for prediction. Game-theoretic tools can give an improved appreciation of the real uncertainty in this prediction task, and also be a tool in the planning process. Based on a combination of graphical inference models and game theory, we propose a decision support tool architecture for command and control situation awareness enhancements. This paper outlines a framework for command and control decision-making in multi-agent settings. Decision-makers represent beliefs over models incorporating other decision-makers and the state of the environment. When combined, the decision-makers’ equilibrium strategies of the game can be inserted into a representation of the state of the environment to achieve a joint probability distribution for the whole situation in the form of a Bayesian network representation.

In order to succeed, agents playing games must reason about the mechanics of the game, the strategies of other agents, other agents' reasoning about their strategies, and the rationality of agents. This paper presents a compact, natural and highly expressive language for reasoning about the beliefs and rationality of agents' decision-making processes in games. It extends a previous version of the language in a number of important ways. Agents can reason directly about the rationality of other agents; agents' beliefs are allowed to conflict with one another, including situations in which these beliefs form a cyclic structure; agents' play can deviate from the normative game theoretic solution. The paper formalizes the equilibria that holds with respect to agents' models and behavior, and provides algorithms for computing it. It also shows that the language is strictly more expressive than that of Bayesian games.

My work investigates decision making in heterogeneous groups comprising both people and computational agents. A significant amount of human activity involves people working together in groups, whether competitively, cooperatively or collaboratively. Increasingly, these groups also involve computer systems acting autonomously or as proxies for individual people or organizations. How to design effective computer agents for interacting with people in these settings is a multi-faceted question, drawing on economics (decision and gametheoretic paradigms), psychology (social and cognitive factors) and artificial intelligence (knowledge representation and learning). Solving this problem involves the synthesis of a variety of techniques: Formal, expressive representations are needed for describing the decision-making processes of people and computer agents; efficient algorithms must be developed for learning these representations from observed data; empirical frameworks are necessary for comparing between the performance of different computational strategies and their effect on human behavior. My doctoral work provides the foundation for the program outlined above. It was the first work to demonstrate that computers can learn the social factors affecting people’s