The role of the environment in simulating multi-agent systems (original) (raw)

Masim: A methodology for the development of agent-based simulations

2004

This paper presents the general aspects that motivated the construction of the MASim methodology, aimed for development of agent-based simulations. MASim employs features common to the development of agent-based software as well as to the development of simulation models. MASim is described in terms of agent-based concepts. It also borrows concepts used in mainstream software engineering process frameworks, defining workflows where users, simulation modelers, software developers, testers and experts of the simulation domain collaborate with the purpose of streamlining the development and reuse of simulations and agent components.

Opening Pandora's box: Some Insight into the Inner Workings of an Agent Based Simulation Environment

Agent-Based Simulation (ABS) environments are somewhat of a black box to many modelers and their inner workings are often only understood by the people who developed them. We intend to shed some light into the inner workings of such systems. For the purposes of understanding such systems more in detail, we have developed our own simple ABS environment in C++ using hierarchical state machines. In this paper, we describe the inner workings of our ABS environment in detail, then test the performance of our ABS environment by comparing it to that of an "off the shelf" commercial package. While some programming knowledge is required to understand the paper in all its depth, we believe that non programming experts will also benefit from this paper as it provides insight into the underlying mechanisms operating within an ABS using graphical representations and explanations that avoid heavy technical jargon.

Models, Abstractions and Phases in Multi-Agent Based Simulation

Workshop From Objects to Agents, 2006

This paper introduces some considerations about simulation practice and a schema of the models that are implic- itly and explicitly involved in a Multi-Agent Based Simulation (MABS). The aim of this work is to set simulation inside scientific framework. In order to do that we give an interpretation of the levels that compound a simulation and that constitute different kinds

DEVS/RAP: AGENT-BASED SIMULATION

This paper discusses representation of behavioral dynamics of agent with simulation models. Conceptualization of such systems is shown in terms of how agents and simulation models may interact with one another (i.e., communicate and cooperate with one another). We employ Reactive Action Packages and DEVS to characterize agent-hood and dynamic behavior, respectively. We illustrate the aforementioned mixed agent/simulation conceptualization using the DEVS/RAP environment. An extra effort is spent to show what kind of properties a simulation-modeling environment has to have and how well DEVS meets them in simulation part as exemplified with a tank-war example.

Environment updating and agent scheduling policies in agent-based simulators

Since Schelling's segregation model, the ability to represent individual behaviours and to execute them to produce emergent collective behaviour has enabled interesting studies in diverse domains, like artificial financial markets, crowd simulation or biological simulations. Nevertheless, the description of such experiments are focused on the agents behaviours, and seldom clarify the exact process used to execute the simulation. In other words, little details are known on the assumptions, the choices and the design that have been done on the simulator on fundamental notions like time, simultaneity, agent scheduling or sequential/parallel execution. Though, these choices are crucial because they impact simulation results. This paper is focused on parameter sensitivity of agent-based simulators implementations, specifically on environment updating and agent scheduling policies. We highlight concepts that simulator designers have to define and presents several possible implementations and their impact.

Multi-Agent Systems and Simulation: a Survey From the Agents Community's Perspective

2009

Setting: Simulation in the Sciences of Complex Systems As social and economic systems are among the most complex systems in our world, the chapter will mainly deal with applications of simulation in general and agent-based simulation in particular in economics and the social sciences. Thus it will start with a discussion of the predecessors and origins of agent-based simulation mainly, but not only, in these sciences from the time when the first simulation models were created that used, or rather should have used, multi-agent systems. If one accepts that multi-agent systems have object-oriented languages as their prerequisites, one has also to accept that multi-agent systems proper could only be implemented after the early 1980s, but much earlier, namely in the 1960s first simulations, for instance in political science, were built that can be described as forerunners of multi-agent systems. At the same time, ingredients were developed that nowadays are a defining part of the agents in multi-agent systems, such as fact and rule bases in which early "agents" stored information that they communicated among each other, although they lacked the defining feature of autonomy. But for a long time, simulation approaches prevailed that did not address the fact that in social and economic systems there are actors who are endowed with a very high degree of autonomy and with the capability to deliberate. Although not for all purposes of the sciences dealing with these systems, autonomy and deliberation are necessary ingredients of theory and models, one would not content oneself with humans being modelled as deterministic or stochastic automata but prefer models that reflect some typically human capability.

On the synergy of simulation and agents: an innovation paradigm perspective

2009

The advances of modeling and simulation as well as agent systems have been phenomenal. The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems are opening new frontiers for advancing the studies of the physical, natural, social, military, and information sciences and engineering. This survey paper provides a comprehensive analysis of the evolution of the synergy of modeling and simulation and agent systems and their applications to various fields. The analysis framework explores the evolution of the application and technology of agent-directed simulation. We present a unified and comprehensive perspective regarding the broad range for the synergy of simulation and agents. In drawing the trace of the evolution of agents and simulation to a close, we offer comments on three aspects. The first involves the breadth and extent of agent simulation applications; the second relates to the role of agent simulation in computational experimentation and exploratory analysis; and the third pertains to the future of the use of agents in simulation, as well as simulation for agents.

On the role of software architecture for simulating multi-agent systems

2009

A control system is a software system connected to an underlying environment. The environment is the part of the external world with which the control system interacts, and in which the effects of the control system will be observed [Jackson, 1997]. The task of a control system is to ensure that particular functionalities are achieved in the environment. A multi-agent control system is a control system of which the software is a multi-agent