The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Further, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.

If you're placing any Golden Globes bets, then maybe you might want to consider what the latest A.I. predictions are saying. In both Golden Globes Best Picture categories -- comedy and drama -- Unanimous A.I. and applied their unique systems to forecast possible winners. And it looks like this year, you can place a lot of faith in a certain sea monster cleaning up. The Shape of Water is looking good in the categories of Best Actress, but also Best Picture, too. On Friday, in order to forecast possible outcomes in a variety of categories at the Golden Globes, Unanimous A.I. used what's known as "swarm intelligence."

The Sixth International Conference on Enterprise Information Systems (ICEIS) was held in Porto, Portugal; previous venues were in Spain, France, and the United Kingdom. Since its inception in 1999, ICEIS has grown steadily, and is now one of the largest international conferences in the area of information systems. In 2004, more than 600 papers were submitted to the conference and its ten satellite workshops. One of the interesting features of this conference is the high number of invited speakers. In 2004, eighteen keynote speakers were featured at ICEIS and its workshops.

AI'2004 was held at the University of Western Ontario in London, Ontario from May 17 to May 19, 2004. The conference was held jointly with the Computer and Robot Vision and Graphics Interface conferences. The three conferences attracted more than 200 attendees. Three workshops and a graduate symposium were held in conjunction with the technical sessions. Three preconference workshops were affiliated with AI'2004: The Third Business Agents and the Semantic Web workshop, organized by Harold Boley, Scott Buffett, Bruce Spencer (National Research Council), Ali Ghorbani (University of New Brunswick), and Said Tabet (Macgregor Inc.); the First Agent Meets Robot workshop, organized by Hamada Ghenniwa, (University of Western Ontario), Weiming Shen (National Research Council) and Mohamed Kamel (University of Waterloo); and the First Causality and Causal Discovery workshop, organized by Kamran Karimi (University of Regina).

Developing agents that could perceive the world, reason about what they perceive in relation to their own goals and acts, has been the Holy Grail of AI. Early attempts at such holistic intelligence (for example, SRI International's AI researchers turned their attention to component technologies for structuring a single agent, such as planning, knowledge representation, diagnosis, and learning. Although most of AI research was focused on single-agent issues, a small number of AI researchers gathered at the Massachusetts Institute of Technology Endicott House in 1980 for the First Workshop on Distributed AI. The main scientific goal of distributed AI (DAI) is to understand the principles underlying the behavior of multiple entities in the world, called agents and their interactions. The discipline is concerned with how agent interactions produce overall multiagent system (MAS) behavior.

The Fourth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-03) took place at the Stanford University Center for the Study of Language and Information in Stanford, California, on 23 to 25 June 2003. Like the previous conferences, CONTEXT-03 fulfilled its aim of bringing together representatives of many different research areas, spanning the whole range of the cognitive and information sciences, and with interests ranging from the use of context in specific, commercial applications to highly general philosophical, psychological, and logical theories. The conference chair was Fausto Giunchiglia, University of Trento. The program chairs were Patrick Blackburn, INRIA Lorraine; Chiara Ghidini, the Centre for Scientific and Technological Research in Trento; and Roy Turner, University of Maine. There were 77 submissions, from which 31 papers and 14 posters were selected. One of the aims of the CONTEXT conferences is to bring together representatives of ...

This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific. It would be quite a daunting task to manually monitor prices and make bidding decisions at all web sites currently offering the camera--especially if accessories such as a flash and a tripod are sometimes bundled with the camera and sometimes auctioned separately. However, for the next generation of trading agents, autonomous bidding in simultaneous auctions will be a routine task.

Much of the economic value of electronic commerce comes from the automation of interactions between businesses and individuals. Game theory is a useful set of tools that can be used by designers of electronic-commerce applications in analyzing and engineering of automated agents and communication protocols. The central theoretical concept used in game theory is the Nash equilibrium. In this article, I show how the outcomes supported by a Nash equilibrium can positively be enlarged using automated negotiations. In addition, despite the sharp falls in the share prices of many "dotcoms" since early 2000, electronic commerce is still likely to have a major and lasting effect on most forms of economic activities.

As a field, knowledge representation has often been accused of being off in a theoretical noman's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR '92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI '93). This article is an edited version of a talk surveying that conference, which I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI '93). Although nominally a conference overview, the article attempts to summarize the state of the conference and the field with respect to the intertwined goals of science and engineering.