Modeling Artificial Life Through Multi-Agent Based Simulation (original) (raw)
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Agent Based Modeling and Simulation
Computational Complexity, 2012
H ow is it possible that a whole ancient civilization disappeared? Was this caused by climate changes? What type of recruitment strategy among social insects is best adapted to their particular environment? How much time does it take to evacuate an airport if people have limited perception caused by smoke as well as restricted mobility? What if many of these people travel in groups or families? How long does it take for commuters to reach their destinations if an important arterial in the Los Angeles area is closed? These are the kind of questions that can be and are answered by agent-based modeling and simulation (ABMS). In this paradigm, simulated human beings or animals are modeled as agents, interacting with some of their peers as well as with their environment. The environment, as in many multiagent systems, plays a key role and must therefore be carefully taken into account. For instance, passengers seeking to leave the airport just mentioned try to find the shortest way to an exit, which may be partially hindered by debris. These are only some examples of scenarios-also characterized as complex adaptive systems-that can be investigated using ABMS. The core idea here is to use simulated agents for producing a phenomenon that shall be analyzed, reproduced, or predicted. This generative, bottom-up nature of modeling and simulation provides great potential for dealing with problems in which conventional modeling and simulation paradigms have difficulties capturing the core features of the original system. In what follows, this particular modeling and simulation paradigm, its concept, properties, and application are introduced Articles
A multi-agent theory for simulation
The Fifth IASTED …, 2005
This paper discusses a multi-agent simulation theory which is serving as a formal specification to guide the development of a multi-agent simulation platform. We have extended an existing simulation language: GLIDER, with abstractions to model systems where autonomous entities (agents) perceive and act upon their environments. Thus far, we have completed the development of the platform that implements the theory and we are now applying it to the study of multi-agent systems. In particular, an Implementation on Biocomplexity is briefly discussed in the paper.
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.
Artificial intelligence and simulation
Simulation & Gaming, 2007
The use of simulation gaming in education, health, training, public policy, social change, and various technical and social disciplines is aiding decision makers and trainees to gain experience by acting on realistic dynamic scenarios. The number of nonentertainment games under development is rapidly increasing. For instance, the recent Serious Games initiative relies on using such simulation gaming environments to explore management and leadership challenges facing the public sector. The appreciation for the ideas, skills, technologies, and techniques used in commercial entertainment games is at an all-time high. Many commercial games are already in use for purposes other than entertainment. Titles such as SIM CITY, CIVILIZATION , HIDDEN AGENDA, and others have been used as learning tools in schools and universities across the globe. The need for increased level of reality and fidelity in such domain-specific games calls for the use of advanced computational technology such as high-resolution graphics, distributed and powerful gaming engines, and methods that bring realism and intelligence to actors and scenarios. Nowadays, agent-directed simulation modeling is very popular for representing complex social phenomena. The premise of the agent paradigm, its related theory and methodologies, together with advances in multilevel modeling of complex systems of interactions, are opening new frontiers for advancing the physical, natural, social, military, and information sciences and engineering. Recent trends have made it clear that simulation model complexity will continue to increase dramatically in the coming decades. The dynamic and distributed nature of simulation gaming applications, the significance of exploratory analysis of complex scientific phenomena, and the need for modeling the micro-level interactions, collaboration, and cooperation among real-world entities is bringing a shift in the way that simulation games are being conceptualized. Using intelligent agents in simulation models is based on the idea that it is possible to represent the behavior of active entities in the world in terms of the interactions of an assembly of agents with their own operational autonomy. The possibility to model complex situations whose overall structures emerge from interactions among individual entities and to cause structures on the macro level to emerge from the models at the micro level is making agent paradigm a critical enabler in modeling and simulation of complex adaptive systems.
Proceedings of the 2010 Winter Simulation Conference, 2010
This tutorial demonstrates the use of agent-based simulation (ABS) in modeling emergent behaviors. We first introduce key concepts of ABS by using two simple examples: the Game of Life and the Boids models. We illustrate agent-based modeling issues and simulation of emergent behaviors by using examples in social networks, auction-type markets, emergency evacuation, crowd behavior under normal situations, biology, material science, chemistry, and archaeology. Finally, we discuss the relationship between ABS and other simulation methodologies and outline some research challenges in ABS.
Tutorial on agent-based modelling and simulation
Journal of Simulation, 2010
Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.
Application of Agent-Based Modelling and Simulation Tools
DAAAM International Scientific Book, 2021
Simulation is experimenting with an imitation, or model, of the observed system, and observing its behaviour over time, with the purpose of better understanding and/or improving the system. If the system is inaccessible, dangerous or unacceptable for inclusion in the research, if it is designed but not yet built, abstract or just does not exist, system simulation is often the best option for researching such systems. Elements of these models can be implemented as software entities that perceive their environment and respond autonomously to stimulation. We call these entities intelligent agents. Software tools that enable the exploration of complex natural, social, and technical phenomena and systems are called Agent-Based Modelling and Simulation tools. This paper provides an overview of the wide range of possible applications of these modern computer tools.
The role of the environment in simulating multi-agent systems
2007
Simulation is important to support the development of multi-agent systems (MAS). Simulation offers a safe and cost-effective way for studying, evaluating and configuring the behavior of a MAS in a simulated environment before the MAS is deployed in the real world. Such simulations are often referred to as software-in-the-loop simulations [2]: the software of the real MAS application is embedded in the simulation. The MAS is not substituted by a model, but the MAS software itself is part of the simulation loop.