Evolutionary Auction Design for Agent-based Marketplaces

2011 ◽  
pp. 78-95
Author(s):  
Zengchang Qin
Keyword(s):  
Design Research ◽  
Evolutionary Computing ◽  
Computing Methods ◽  
Market Mechanism ◽  
Auction Design ◽  
Agent Based ◽  
Space Model ◽  
Trading Agents ◽  
Auction Mechanisms ◽  
Multi Agent

Market mechanism or auction design research is playing an important role in computational economics for resolving multi-agent allocation problems. In this chapter, we review relevant background of trading agents, and market designs by evolutionary computing methods. In particular, a GA can be used to design auction mechanisms in order to automatically generate a desired market mechanism for electronic markets populated with trading agents. In previous research, an auction space model was studied, in which the probability that buyers and sellers are able to quote on a given time step is optimized by a simple GA in order to maximize the market efficiency in terms of Smith’s coefficient of convergence. In this chapter, we also show some new results based on experiments with homogeneous and heterogeneous agents in a more realistic auction space model. This research provides a way of designing efficient auctions by evolutionary computing approaches.

Negotiation in Agent Based Manufacturing Scheduling Using Auction Models

2005 ◽  
Author(s):  
Chun Wang ◽  
Weiming Shen ◽  
Hamada Ghenniwa
Keyword(s):  
Theoretical Models ◽  
Shop Floor ◽  
Multi Agent Systems ◽  
Solution Quality ◽  
Manufacturing Scheduling ◽  
Agent Based ◽  
Auction Models ◽  
Auction Mechanisms ◽  
Multi Agent

This paper investigates issues in the application of auctions as negotiation mechanisms to agent based manufacturing scheduling. We model the negotiation environments that agents encounter as inter-enterprise environment and intra-enterprise environment. A formulation of intra-enterprise scheduling economy is presented. We proved that at price equilibrium, the solution computed by the agents in the economy is a Pareto optimal. AS our first attempt, we formally formulate automated auction configuration as an optimization problem. By solving the problem adaptive negotiation in multi-agent systems can be achieved. In addition to the theoretical models, we discussed various types of auction mechanisms and their applications to agent based manufacturing scheduling. Heuristics and procedures are proposed for solving the automated auction configuration problem. To validate the analysis and proposed approaches, as a case study, we apply the automated auction configuration heuristics and the procedure to an agent based shop floor scheduling environment. Experimental results show that the auction protocol selected by the proposed heuristics provides correct system functionalities. In addition, we compared the selected mechanism with other candidate mechanisms. We found that the selected one performs better in terms of reducing communication cost and improving solution quality.

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 Fine Tuning of Agent-Based Evolutionary Computing

2019 ◽  
Vol 9 (2) ◽  
pp. 81-97 ◽  
Author(s):  
Michal Mizera ◽  
Pawel Nowotarski ◽  
Aleksander Byrski ◽  
Marek Kisiel-Dorohinicki
Keyword(s):  
Science And Technology ◽  
Evolutionary Computing ◽  
Fine Tuning ◽  
High Dimensional ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Optimization System ◽  
Computing Algorithm ◽  
Multi Agent

Abstract Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

A Multi-agent based Micro Grid Operation Method

2013 ◽  
Vol 133 (9) ◽  
pp. 1652-1657 ◽  
Author(s):  
Takeshi Nagata ◽  
Kosuke Kato ◽  
Masahiro Utatani ◽  
Yuji Ueda ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Agent Based ◽  
Operation Method ◽  
Multi Agent ◽  
Micro Grid ◽  
Grid Operation

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

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