Adaptive Simulation of Automated Guided Vehicle Systems Using Multi Agent Based Approach for Supplying Materials

2014 ◽  
Vol 474 ◽  
pp. 79-84 ◽  
Author(s):  
Angéla Rinkács ◽  
András Gyimesi ◽  
Gábor Bohács
Keyword(s):  
Artificial Intelligence ◽  
Physical System ◽  
Material Flow ◽  
Automated Guided Vehicle ◽  
Agent Based ◽  
Adaptive Simulation ◽  
Modeling Problem ◽  
Vehicle Systems ◽  
Flow Systems ◽  
Multi Agent

Application of agent based approaches is frequent in systems with artificial intelligence. This method has been used in simulations as well, however application in this field is rather novel. The paper surveys formation possibilities of agent based structures in the simulation of an example material flow systems. Further, considerations are also presented, how these models can be made adaptive, which makes them capable of continuously modify their features to the modeled physical system. Finally a framework for a proposed modeling problem is introduced.

Model-based development of a multi-agent system for controlling material flow systems

2018 ◽  
Vol 66 (5) ◽  
pp. 438-448 ◽  
Author(s):  
Juliane Fischer ◽  
Marga Marcos ◽  
Birgit Vogel-Heuser
Keyword(s):  
Decentralized Control ◽  
Flexible Manufacturing ◽  
Material Flow ◽  
Manufacturing Systems ◽  
Multi Agent System ◽  
Control Task ◽  
Agent Based ◽  
Control Approach ◽  
Flow Systems ◽  
Multi Agent

Abstract The rising number of product variants requires flexible manufacturing systems, including their internal material flow systems (MFSs). An approach to design MFSs reconfigurably is the use of a decentralized control based on software agents. For implementing an agent-based control approach for MFSs this paper presents a meta model describing the knowledge base of individual agents and the overall control task to be fulfilled by the MFS.

scholarly journals Can models of agents be transferred between different areas?

2000 ◽  
Vol 15 (2) ◽  
pp. 197-203 ◽  
Author(s):  
RUTH AYLETT ◽  
KERSTIN DAUTENHAHN ◽  
JIM DORAN ◽  
MICHAEL LUCK ◽  
SCOTT MOSS ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Multi Agent Systems ◽  
Ongoing Research ◽  
Agent Based ◽  
Agent Systems ◽  
Sustained Activity ◽  
Diverse Environment ◽  
The World ◽  
Multi Agent

One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated.

scholarly journals AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

2019 ◽  
Author(s):  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Electricity Markets ◽  
Evolutionary Computing ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
Competitive Electricity Markets

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

scholarly journals Towards an Agent-Based Architecture using Deep Reinforcement Learning for Intelligent Internet of Things Applications.pdf

2020 ◽  
Author(s):  
Dhouha Ben Noureddine ◽  
Moez Krichen ◽  
Seifeddine Mechti ◽  
Tarik Nahhal ◽  
Wilfried Yves Hamilton Adoni
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Agent Technology ◽  
Smart Manufacturing ◽  
The Internet ◽  
Agent Based ◽  
Multi Agent ◽  
Iot Devices ◽  
Future Work ◽  
Share Information

Internet of Things (IoT) is composed of many IoT devices connected throughout the Internet, that collect and share information to represent the environment. IoT is currently restructuring the actual manufacturing to smart manufacturing. However, inherent characteristics of IoT lead to a number of titanic challenges such as decentralization, weak interoperability, security, etc. The artificial intelligence provides opportunities to address IoT’s challenges, e.g the agent technology. This paper presents first an overview of ML and discusses some related work. Then, we briefly present the classic IoT architecture. Then we introduce our proposed Intelligent IoT (IIoT) architecture. We next concentrate on introducing the approach using multi-agent DRL in IIoT. Finally, in this promising field, we outline the open directions of future work.

REINFORCEMENT LEARNING IN SUPPLY CHAINS

2009 ◽  
Vol 19 (05) ◽  
pp. 331-344 ◽  
Author(s):  
ANNAPURNA VALLURI ◽  
MICHAEL J. NORTH ◽  
CHARLES M. MACAL
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Reinforcement Learning ◽  
Supply Chains ◽  
Human Beings ◽  
Agent Based ◽  
Multi Stage ◽  
Long Time ◽  
Multi Agent ◽  
First Time

Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

Demo: The multi-agent based evaluation of connected vehicle systems

2014 ◽  
Author(s):  
Yi Zhou ◽  
Jun Wang ◽  
Haishun Du ◽  
Huiping Li ◽  
Po Hu ◽  
...  
Keyword(s):  
Connected Vehicle ◽  
Agent Based ◽  
Vehicle Systems ◽  
Multi Agent

scholarly journals Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records

2020 ◽  
Vol 6 ◽  
pp. e323
Author(s):  
Fahad F. Alruwaili
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Privacy Preservation ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Health Records ◽  
Agent Based ◽  
Distributed Ledger ◽  
Multi Agent ◽  
Electronic Health

Background Application of Artificial Intelligence (AI) and the use of agent-based systems in the healthcare system have attracted various researchers to improve the efficiency and utility in the Electronic Health Records (EHR). Nowadays, one of the most important and creative developments is the integration of AI and Blockchain that is, Distributed Ledger Technology (DLT) to enable better and decentralized governance. Privacy and security is a critical piece in EHR implementation and/or adoption. Health records are updated every time a patient visits a doctor as they contain important information about the health and wellbeing of the patient and describes the history of care received during the past and to date. Therefore, such records are critical to research, hospitals, emergency rooms, healthcare laboratories, and even health insurance providers. Methods In this article, a platform employing the AI and the use of multi-agent based systems along with the DLT technology for privacy preservation is proposed. The emphasis of security and privacy is highlighted during the process of collecting, managing and distributing EHR data. Results This article aims to ensure privacy, integrity and security metrics of the electronic health records are met when such copies are not only immutable but also distributed. The findings of this work will help guide the development of further techniques using the combination of AI and multi-agent based systems backed by DLT technology for secure and effective handling EHR data. This proposed architecture uses various AI-based intelligent based agents and blockchain for providing privacy and security in EHR. Future enhancement in this work can be the addition of the biometric based systems for improved security.

ViSe2 - An Agent-Based Expert Consulting System with Efficient Cooperation

1998 ◽  
Vol 2 (3) ◽  
pp. 104-110
Author(s):  
Cheng-Gang Bian ◽  
◽  
Wen Cao ◽  
Gunnar Hartvigsen
Keyword(s):  
Artificial Intelligence ◽  
Software Agents ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Current Version ◽  
Distributed Knowledge ◽  
Agent Based ◽  
Agent Systems ◽  
Distributed Problem Solving ◽  
Multi Agent

ViSe2 l is an expert consulting system which employs software agents to manage distributed knowledge sources. These individual software agents solve users’ problems either by themselves or via cooperation. The efficiency of cooperation plays a serious role in Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS). We have focused on the development of a twin-base approach for agents to model the capabilities of each other, and thus achieve efficient cooperation. The current version of the ViSe2 implementation is an experimental model of an agent-based expert system. Compared with other cooperation approaches in Distributed Artificial Intelligence (DAI) area, the results received so far indicate that the ViSe2 agents serve their users in an efficient cooperation manner.

scholarly journals Towards an argument interchange format

2006 ◽  
Vol 21 (4) ◽  
pp. 293-316 ◽  
Author(s):  
CARLOS CHESÑEVAR ◽  
MCGINNIS ◽  
SANJAY MODGIL ◽  
IYAD RAHWAN ◽  
CHRIS REED ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Abstract Model ◽  
Multi Agent Systems ◽  
Case Scenario ◽  
Major Barrier ◽  
Current Form ◽  
Agent Based ◽  
Starting Point ◽  
Multi Agent ◽  
Interchange Format

The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.

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