Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Introduction <s> Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multi-agent scheduling of people and/or machines; distributed diagnosis of errors in local-area or telephone networks; concurrent engineering; `software agents'' for information gathering. The design of coordination mechanisms for groups of computational agents depends in many ways on the agent''s task environment. Two such dependencies are on the structure of the tasks and on the uncertainty in the task structures. The task structure includes the scope of the problems facing the agents, the complexity of the choices facing the agents, and the the particular kinds and patterns of interrelationships that occur between tasks. A few examples of environmental uncertainty include uncertainty in the {\em a priori\/} structure of any particular problem-solving episode, in the actions of other agents, and in the outcomes of an agent''s own actions. These dependencies hold regardless of whether the system comprises just people, computational agents, or a mixture of the two. Designing coordination mechanisms also depends on properties of the agents themselves. Our thesis is that the design of coordination mechanisms cannot rely on the principled construction of agents alone, but must also rely on the structure and other characteristics of the agents'' task environment. For example, the presence of both uncertainty and high variance in a task structure can lead to better performance in coordination algorithms that adapt to each problem-solving episode. Furthermore, the structure and characteristics of an environment can and should be used as the central guide to the design of coordination mechanisms, and thus must be a part of our eventual goal, a comprehensive theory of coordination, partially developed here. Our approach is to first develop a framework, \tems, to directly represent the salient features of a computational task environment. The unique features of \tems\ include that it quantitatively represents complex task interrelationships, and that it divides a task environment model into generative, objective, and subjective levels. We then extend a standard methodology to use the framework and apply it to the first published analysis, explanation, and prediction of agent performance in a distributed sensor network problem. We predict the effect of adding more agents, changing the relative cost of communication and computation, and changing how the agents are organized. Finally, we show how coordination mechanisms can be designed to respond to particular features of the task environment structure by developing the Generalized Partial Global Planning ( <s> BIB001 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Introduction <s> From the Publisher: ::: What are multi-agent systems? How do they work? What do they do? If you are looking for the answers to these questions, read on; for Jacques Ferber's authoritative book is the first to provide a single, coherent overview of multi-agent systems. Introduces and defines key concepts throughout the text; provides numerous examples to illustrate core principles; draws on contributions from different disciplines to present a holistic, comprehensive picture of state-of-the art agent technology; and describes all the latest developments in the field and encourages the reader to reflect on possibilities for the future. <s> BIB002
The environment has been recognized early as an explicit element of Multi-agent systems (MAS) BIB001  BIB002   , but current applications often consider it as an implicit part of the system. Recent work has however demonstrated that the environment is an exploitable design element. In particular, the environment is defined as a first-class abstraction with the role of providing the surrounding conditions for agents to exist, the mediation of agent interactions, and access to resources  . In other words, the environment in MAS is thought of as a design element responsible for the functions of underlying agent activities, i.e. agent interactions and access to resources. These responsibilities of the environment can be addressed by an ensemble of mechanisms to design an application. In our context, a mechanism is a technical approach to solve a particular problem in the design and development of the environment responsibilities for the target MAS. The goal of this article is to discuss mechanisms used in the MAS research community to address environment responsibilities. The contribution of this work is to survey mechanisms for designing environments, and to determine potential research directions and application opportunities. The survey is not intended to be exhaustive. Its purpose is rather to set forth representative achievements of the community on the issue of environments for MAS. The article proposes a view on mechanisms based on an interpretation of ongoing research and it is consequently subjective. The article is organized as follows. In Sect. 2, we survey a number of mechanisms of the environment and present several examples. In Sect. 3, we describe representative application scenarios of the surveyed mechanisms. In Sect. 4, we propose research directions to the MAS community on the issue of environment in terms of mechanisms, and we present potential opportunities that lend themselves to the usage of such mechanisms. Finally, Sect. 5 summarizes and concludes the article.