Learn More
This paper considers the design of agent strategies for deciding whether to help other members of a group with whom an agent is engaged in a collaborative activity. Three characteristics of collaborative planning must be addressed by these decision-making strategies: agents may have only partial information about their partners' plans for sub-tasks of the(More)
This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about the social factors that affect people's play. The model predicts how a human player is likely to react to different actions of another player, and these predictions are used to determine the best possible(More)
Computer agents are increasingly deployed in settings in which they make decisions with people, such as electronic commerce, collaborative interfaces, and cognitive assistants. However, the scientific evaluation of computational strategies for human-computer decision-making is a costly process, involving time, effort and personnel. This paper investigates(More)
Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups vary in their capabilities, goals, and strategies; they may cooperate, collaborate, or compete. The presence of people in mixed networks(More)
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(More)
Multi-agent systems that use game-theoretic analysis for decision making traditionally take a normative approach, in which agents' decisions are derived rationally from the game description. This approach is insufficient to capture the decision making processes of real-life agents. Such agents may be partially irrational, they may use models other than the(More)
To establish cooperative relationships, agents must be willing to engage in helpful behavior and to keep their commitments to other agents. However, in uncertain and dynamic environments, it is difficult to identify the degree of helpfulness of other agents. This paper describes a model in which agents' helpfulness is characterized in terms of cooperation(More)