Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past twenty-five years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development and describing some key algorithms for MAL.

Robots (and roboticists) increasingly appear at the Autonomous Agents and Multi-Agent Systems (AAMAS) conferences because the community uses robots both to inspire AAMAS research as well as to conduct it. In this article, I submit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: it is scientifically useful to roboticists and agent researchers to think of robots as agents.

Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past twenty-five years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development and describing some key algorithms for MAL. Moreover, main challenges that the field is facing today are indentified.

In 2011 the Autonomous Agents and Multiagent Systems (AAMAS) conference series celebrated its 10th anniversary, having begun as the successful merger of three related events that had run for some years previously.

He has been chairman/president the MIT Artificial Intelligence Lab. Board of Trustees, as well as treasurer 100 Americans most likely to shape Manuela Veloso, incoming AAAI President, of SSAISB and ECCAI. He is presently the next century; TIME Digital selected and Eric Horvitz, AAAI Past editor-in-chief of the AAAI Press, Spatial her as a member of the Cyber-Elite; President and Awards Committee Cognition and Computation, and the World Economic Forum honored Chair, presented the AAAI Awards in the Artificial Intelligence Journal. He was her with the title Global Leader for Tomorrow; August at AAAI-12 in Toronto. She holds bachelor's and or 1-650-328-3123.)

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, was pleased to present the 2012 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2012 at Stanford University, Stanford, California USA. The six symposia held were AI, The Fundamental Social Aggregation Challenge (cochaired by W. F. Lawless, Don Sofge, Mark Klein, and Laurent Chaudron); Designing Intelligent Robots (cochaired by George Konidaris, Byron Boots, Stephen Hart, Todd Hester, Sarah Osentoski, and David Wingate); Game Theory for Security, Sustainability, and Health (cochaired by Bo An and Manish Jain); Intelligent Web Services Meet Social Computing (cochaired by Tomas Vitvar, Harith Alani, and David Martin); Self-Tracking and Collective Intelligence for Personal Wellness (cochaired by Takashi Kido and Keiki Takadama); and Wisdom of the Crowd (cochaired by Caroline Pantofaru, Sonia Chernova, and Alex Sorokin). The papers of the six symposia were published in the AAAI technical report series.

We present a brief survey of logics for reasoning about multiagent systems. We focus on two paradigms: logics for cognitive models of agency, and logics used to model the strategic structure of a multiagent system.

I Have a Robot, and I'm Not Afraid to Use It! The AAMAS community is investing efforts to encourage robotics research within itself. An annual robotics special track, an associated robotics workshop (Autonomous Robots and Multirobot Systems), and a series of exciting AAMAS-sponsored plenary speakers and awards over a number of years are drawing roboticists in. The number of robotics papers is increasing. There are fruitful interactions with the other communities within AAMAS, such as virtual agents, game theory, and machine learning. Robots are being used both to inspire AAMAS research as well as to conduct it. I posit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: robots are agents.

Distributed problem solving is a subfield within multiagent systems, where agents are assumed to be part of a team and collaborate with each other to reach a common goal. In this article, we illustrate the motivations for distributed problem solving and provide an overview of two distributed problem solving models, namely distributed constraint satisfaction problems (DCSPs) and distributed constraint optimization problems (DCOPs), and some of their algorithms.

Negotiation is a complex emotional decision-making process aiming to reach an agreement to exchange goods or services. From an agent technological perspective creating negotiating agents that can support humans with their negotiations is an interesting challenge. Already more than a decade, negotiating agents can outperform human beings (in terms of deal optimality) if the negotiation space is well-understood. However, the inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligence alone, and a human-machine collaborative system is required. This article presents research goals, challenges, and an approach to create the next generation of negotiation support agents.