Agent decision-making in open mixed networks

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 raises challenges for the design and the evaluation of decision-making strategies for computer agents. This paper describes several new decision-making models that represent, learn and adapt to various social attributes that influence people's decision-making and presents a novel approach to evaluating such models. It identifies a range of social attributes in an open-network setting that influence people's decision-making and thus affect the performance of computer-agent strategies, and establishes the importance of learning and adaptation to the success of such strategies. The settings vary in the capabilities, goals, an...

• Type: Journal
• Year: 2010
• Where: AI
• Authors: Ya'akov Gal, Barbara J. Grosz, Sarit Kraus, Avi Pfeffer, Stuart M. Shieber
• Keywords: AI 2010, Artificial Intelligence, Decision-making, Extensive Domain Modeling, Strategies