Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> This paper presents the notion of multi-agent simulation that is based on the definition of computational agents that represent individual organisms (or groups of organisms) in a one to one correspondence. We discuss the properties of multi-agent simulation. We then present a multiagent simulation system based on the definition of reactive agents whose behavior is governed by the selection of simple competing tasks due to stimulus's perception. An example of a simulation of an ant colony follows as an illustration of the multiple domains in which multi-agent simulation may be used. <s> BIB001 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> The increasing use of information technology within and between companies yields changes in the predominant coordination mechanisms. On one hand it is argued that we witness an overall shift towards market-like coordination of business transactions, whereas others argue that we face more hierarchy-like coordination. In this paper a multi-agent system is described that globally coordinates a multiple step value chain either way by using local optimization rules of self-interested autonomous agents. In further research it will be analyzed how changes in setup parameters (e.g. transaction costs, cooperational behavior) will effect the predominant coordination mechanism among the artificial life agents. <s> BIB002 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multi-agent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents. Copyright Kluwer Academic Publishers 1997 <s> BIB003 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> This paper focuses on the problem of orchestrating the movements of bot agents in the videogame Quake 3 Arena. Since the specific patterns of movement that one may wish to enforce may be various, and serve different purposes (have bots meet somewhere, move in formation, or surrounding human players), a general and flexible approach is required. In this paper we discuss how the Co-Fields coordination model can be effectively exploited to this purpose. The key idea in Co-Fields is to model the agents' environment by means of application-specific computational force fields, leading agents' activities to a globally coordinated and adaptive motion behavior. The Co-Fields model is described both in general terms and in the specific Quake 3 Arena implementation, and several application examples are presented to clarify it. Also, the paper outlines the general applicability of the approach besides the Quake scenario and in areas such as mobile computing and mobile robots. <s> BIB004 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> In this paper, we present, through simulations of the coffee market of the state of Veracruz, how emergence of specialized roles in participatory simulations could be used to design and improve multi-agent systems.The design process of the participatory simulation followed the regular design process of multi-agent simulations: starting from a domain model of coalitions among coffee producers, we built a participatory simulation where humans take the control of agents. Special care was brought to make the simulations playable by humans and to favor the apparition of coalitions. Being controlled by humans, the agents of the initial model became completely pro-active and were able to exchange coffee and money and to form coalitions to fulfill the buyer's offers. Coalitions appeared as expected.Besides, during these experiments, we observed the emergence of specialized roles which were not included in the initial model. We implemented a regular multi-agent system based on the initial model to test the distributed system solving improvements brought by the roles that emerged. <s> BIB005 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> In this paper, we report on experiments with a software multi-agent electronic market where intelligent behaviors are due to both agents internal capabilities and advanced mechanisms in the environment. Our aim is to demonstrate how the environment can be a degree of freedom in the design of intelligent systems. <s> BIB006 </s> Mechanisms for environments in multi-agent systems: Survey and opportunities <s> Simulation <s> Participatory simulations are conducted to improve our knowledge of human behaviors, to help in solving conflicts, to shape interaction protocols between humans and to teach some aspects of collective management.Agent-based participatory simulations differ from other kinds of participatory simulations including role playing games and experimental economics simulations. The control architecture of the agents, in these simulations, is more or less integrally replaced by a human player and the interactions between players are limited by the communication protocols designed for the agents, usually the exchange of electronic messages logged for further analysis. Such systems can be considered as ideal multi-agent systems featuring cognitive and intelligent agents. Previous work demonstrated that running this kind of simulations helps to design and improve multi-agent simulations.In this paper, we present a series of agent-based participatory experiments studying negotiation in an abstract case of common resource pool management. The roles were designed in such a way that conflicts should emerge during the negotiations. Observing the behavior of human players, we noticed the apparition of power relations between players. We observed that this power in negotiations was unrelated to any a priori dependence between agents or between roles but was instead drawn from strategies and, more surprisingly, this power was built on an emerging ontology. <s> BIB007
Successful experiments with interaction mediation mechanisms were conducted over the past decades to reproduce behaviors found in nature, notably in ethology for ants and termites BIB001 BIB003 , and artificial markets BIB002 BIB005 . Although the environment was not the main focus of these experiments, it appears essential in each setting of the agent activity. In the case of the behavioristic reproduction of ant or termite colonies, agents rely on pheromone infrastructures that evolve in complex patterns maintained by the environment (diffusion and evaporation of the pheromone information). In artificial markets, the environment provides renewable resources such as wood or coffee BIB002 BIB005 . In such simulation, the overlay network mechanism allows the description and maintenance of a topology that provides agents with various interaction means in the simulation. This mechanism is also combined with environment-mediated interaction channels to model complex social behaviors BIB006 , and with the resource and context manager when agents exploit resources in the environment. The main contribution of the environment in the above applications is the coordination of agents in a decentralized way, and the separation of the coordination concern from other design issues in the application. MABS require explicit time management in the agent context, as many simulation systems. Typically, discrete-time simulations need a 'ticker' that defines the pace of the simulation execution. Time management relies primarily on the notification of context events, which provides system-wide time references such as the clock 'ticks'. In addition, the notification is often exploited simultaneously with a synchronization mechanism as their contributions to time management in the system are complementary. Synchronization mechanisms deal with timing by ensuring that agent actions in the system are consistently executed, i.e. they verify proper action interleaving  . Mechanisms can also be identified in agent-based simulations of the behavior of social insects and the coordinated motion of agents in video games BIB001 BIB004 . In both cases, the mechanism provides agents with access to a rich data structure that has its proper dynamics. The overlay data structure provides the necessary functionalities in such case. The access rights depend on the agent context, so that ant agents can, for example, only sense pheromones in their vicinity. Farther pheromones do not influence the behaviors of ants. The dynamics of the resources are simulated by specific components of the system that are independent from agents. For example, SimCafe and SimCommod simulates the evolution of coffee and wood resources as a cellular automaton BIB007 BIB005 . The example of time management illustrates how mechanisms are exploited to design a specific requirement of MABS. This example can be generalized to most requirements that consist in a single cross-cutting concern, such as the Laws of Physics (Laws of Mechanics, Principles of Thermodynamics, etc.) when their dynamics is required. Mechanisms then allow a separation of concerns and provide a design abstraction in the development process of such applications. Mechanisms for pheromone and other resources emphasize the separation of concerns by clearly distinguishing resources from agents.