هم افزایی شبیه سازی، عوامل، و مهندسی سیستم
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4168||2012||8 صفحه PDF||سفارش دهید||4856 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 1, January 2012, Pages 81–88
Simulation, software agents, and systems engineering are three important disciplines; each of which support many application areas. In this article it is pointed out that their usefulness and efficacy can be significantly improved by first and higher order synergies.
Simulation, software agents, and systems engineering are independent and important disciplines. The synergy of simulation and software agents is the essence of agent-directed simulation (ADS) which has important practical implications (Yilmaz and Ören, 2009a and Yilmaz and Ören, 2009b). ADS as clarified in the sequel, consists of contributions of simulation to agents (i.e., agent simulation) and contributions of agents to simulation (i.e., agent-supported simulation and agent-based simulation). Synergy of simulation and systems engineering has two important aspects: contributions of simulation to enhance systems engineering and contributions of systems engineering to simulation. In the article these synergies and the synergy of agent-directed simulation and systems engineering as well as synergy of agents and systems engineering are outlined in a systematic way. Furthermore, the synergies have cumulative effects; while synergy of simulation and systems engineering is very important, synergy of agent-directed simulation and systems engineering opens new vistas. Recent trends in technology as well as the use of simulation in exploring complex artificial and natural information processes (Denning, 2007 and Luck et al., 2003) have made it clear that simulation model fidelity and complexity will continue to increase dramatically in the coming decades. The dynamic and distributed nature of simulation applications, the significance of exploratory analysis of complex phenomena (Miller & Page, 2007), and the need for modeling the micro-level interactions, collaboration, and cooperation among real-world entities is bringing a shift in the way systems are being conceptualized. The emergent need to model complex situations whose overall structures emerge from interactions between individual entities and 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 systems. This paper aims to provide a basic overview of potential synergies of systems engineering with uses of agents for simulation, as well as the use of simulation technologies to study simulation for agents. Agent systems are defined as systems that are composed of a collection of goal-directed and autonomous physical, human, and logical software agents situated in an organizational context to cooperate via flexible and adaptive interaction and cognitive mechanisms to achieve objectives that can not be achieved by an individual agent. Agent-Directed Simulation (ADS) is promoted as a unified and comprehensive framework that extends the narrow view of using agents simply as system or model specification metaphors (Yilmaz & Ören, 2009a). Fig. 1 represents synergies of simulation, software agents, and systems engineering. The rest of the paper is structures as follows: First, the salient features of simulation, agents, and systems engineering are given; then the four synergies are elaborated on.
نتیجه گیری انگلیسی
In this article, the highlights of the characteristics of three disciplines, namely, simulation, agents, and systems engineering are clarified, with some key references. Then three types of first order synergies between them are outlined. These are: (1) Synergy of simulation and agents, i.e., agent-directed simulation (ADS) which consists of agent simulation, agent-supported simulation, and agent-based simulation. (2) Synergy of simulation and systems engineering which consists of contributions of systems engineering to simulation (i.e., simulation systems engineering) and contributions of simulation to systems engineering (i.e., simulation-based systems engineering). (3) Synergy of agents and systems engineering consists of agent-based systems engineering and systems engineering agents. As an example to second order synergy (MetaDesign, 2011) synergy of agent-directed simulation and systems engineering is elaborated on. The possibilities lie in systems engineering for ADS systems (ADSS) and ADS for systems engineering (i.e., ADS-based systems engineering). The first and higher order synergies of simulation, agent, and systems engineering are gaining momentum to tackle more and more complex problems. On the other hand, advances in hardware speed have been phenomenal. For example 1.75 petaflops (1015 = million * billion floating point operations per second) was realized in 2009 (Cray, 2009) and exaflops (=1018 flops) is expected to be realized by the end of the 2010s and zettaflops (1021 flops) might be built around 2030. You may want to compare these speeds with kilo instructions per second (KIPS) in pre-1960 to 1970. Advanced knowledge processing abilities based on the first and high order synergies of simulation, agents, and systems engineering coupled with the advances in hardware speed opens completely new vistas to tackle complex problems. We anticipate that the use of agents will increase as the technological context extends with emerging trends and critical drivers such as augmented cognition, semantic web, web services and service-oriented computing, grid computing, ambient intelligence, and autonomic computing become more pervasive. Simulation-based design of such systems will require seamless introduction of agents and/or agent technologies. We expect that the use of simulation for the design of agents will become a new avenue for research. Analytic and heuristic methods in specifying the deliberative architecture of agents are common. Simulation can play a significant new role in the design of agents in such a way that software agents use simulation to make decisions before acting on the environment.