Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Abstract

Etmen-tabanlı sistemler, yapay zekanın önemli bir uygulama alanıdır ve karar destek sistemlerinde kullanılmaktadır. Etmen-tabanlı sistem, bir problem çözme aracı olmaktan çok, çeşitli senaryolara göre çözüm alternatiflerini geliştirmek ve test etmek için kullanılan bir araçtır. Bu bağlamda etmen-tabanlı modelleme, acil durumlarda karar vericilerin farklı risk senaryolarını değerlendirip daha sonra hızlı ve etkili kararlar almalarına destek olmak için oldukça etkili bir yöntemdir. Ayrıca etmen-tabanlı modelleme, yüksek karmaşıklık ve belirsizlik durumlarında karar vericileri desteklemek için çok yararlı bir yöntemdir. Bu çalışmanın amacı, en güncel araştırmaları incelemek ve araştırmacılara karar destek sistemleri geliştirirken etmen-tabanlı modellemenin nasıl kullanılacağına dair fikir vermektir. Bu makale, NetLogo, AnyLogic, MATSim ve Repast gibi çeşitli etmen-tabanlı modelleme araçları ve yazılım ortamları ile gerçekleştirilen güncel çalışmaları tanıtmaktadır. Bu makalede, etmen-tabanlı bir sistemin kısa bir tanımı yapıldıktan ve belirme ve karmaşıklık gibi kavramların etmen-tabanlı modelleme alanındaki önemi açıklandıktan sonra, etmen-tabanlı sistemleri kimlerin kullandığı, etmen-tabanlı modellemenin hangi amaçla, ne zaman, nerede, neden ve nasıl kullanıldığı, farklı alanlarda gerçekleştirilmiş en güncel çalışmalardan seçilmiş örnekler üzerinden açıklanmıştır. Ayrıca, mevcut çalışmaların bize ne öğrettiği ve gelecekteki çalışmaların etmen-tabanlı modellerden nasıl yararlanabileceği kısaca tartışılmıştır.

Keywords

• Etmen-tabanlı modelleme
• belirme
• karmaşıklık
• belirsizlik
• benzetim

Emergence and complexity in agent-based modeling: Review of state-of-the-art research

Abstract

Agent-based systems are an important application area of artificial intelligence and are used in decision support systems. Rather than being a problem-solving tool, agent-based system is a tool for developing and testing alternative solutions according to various scenarios. In this context, agent-based modeling is a very effective method to support decision makers in emergency situations to evaluate different risk scenarios and then make decisions quickly and effectively. Moreover, agent-based modeling is a very useful method to support decision makers in situations of high complexity and uncertainty. The aim of this study is to review state-of-the-art research and give researchers insights into how to use agent-based modeling while developing decision support systems. This paper introduces current studies performed with several agent-based modeling toolkits and software environments such as NetLogo, AnyLogic, MATSim and Repast. In this paper, after giving a brief definition of an agent-based system and explaining the importance of concepts such as emergence and complexity in the field of agent-based modeling, it is explained who uses the agent-based models for what purpose, when, where, why and how to use agent-based modeling through selected examples from state-of-the-art studies carried out in different research fields. Furthermore, what current studies teach us and how future studies can benefit from agent-based models are briefly discussed.

Keywords

• Agent-based modeling
• emergence
• complexity
• uncertainty
• simulation

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