AgentLight : a multilateral negotiation strategy in multiple-issues domains

The increasing importance of business to business electronic trading has driven interest in automated negotiation to soaring heights. To support the automated negotiation research, and provide a unique benchmark for evaluating practical negotiation strategies in multi-issue domains, competitions lik...

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Main Author: Zhang, Liangliang
Other Authors: Bo An
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/67402
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-674022023-03-03T20:23:17Z AgentLight : a multilateral negotiation strategy in multiple-issues domains Zhang, Liangliang Bo An School of Computer Engineering DRNTU::Engineering The increasing importance of business to business electronic trading has driven interest in automated negotiation to soaring heights. To support the automated negotiation research, and provide a unique benchmark for evaluating practical negotiation strategies in multi-issue domains, competitions like the Automated Negotiating Agents Competition (ANAC) have been introduced. This paper presents the negotiation strategy used in AgentLight which is developed in accordance with the regulation of the ANAC 2016. The agent is first designed for bilateral negotiation using a Tit for Tat strategy which decides actions based on the behavior of the opponents. Asides from the Tit for Tat, the agent also deploys a time based concession function to further control the concession rate. And to analyze an opponent’s model, the agent adopts a frequency based strategy to model the opponent’s preference in linear utility space and analyzes the standard deviation of opponent’s offer in non-linear utility space in order to push the offer near the Pareto Optimal Frontier. The Agent has been evaluated through the competition with other 5 agents in a tournament setting in bilateral negotiation and with another 5 agents in a tournament setting in multilateral negotiation under the GENIUS simulation environment. Although the results presented a workable agent for multilateral negotiation, some points still should be noted down in improving the agents in the future. For future enhancement, other negotiation theories proposed for multilateral negotiation and algorithms on opponents modelling can be studied. Further testing is recommended Bachelor of Engineering (Computer Science) 2016-05-16T07:12:59Z 2016-05-16T07:12:59Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67402 en Nanyang Technological University 39 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Zhang, Liangliang
AgentLight : a multilateral negotiation strategy in multiple-issues domains
description The increasing importance of business to business electronic trading has driven interest in automated negotiation to soaring heights. To support the automated negotiation research, and provide a unique benchmark for evaluating practical negotiation strategies in multi-issue domains, competitions like the Automated Negotiating Agents Competition (ANAC) have been introduced. This paper presents the negotiation strategy used in AgentLight which is developed in accordance with the regulation of the ANAC 2016. The agent is first designed for bilateral negotiation using a Tit for Tat strategy which decides actions based on the behavior of the opponents. Asides from the Tit for Tat, the agent also deploys a time based concession function to further control the concession rate. And to analyze an opponent’s model, the agent adopts a frequency based strategy to model the opponent’s preference in linear utility space and analyzes the standard deviation of opponent’s offer in non-linear utility space in order to push the offer near the Pareto Optimal Frontier. The Agent has been evaluated through the competition with other 5 agents in a tournament setting in bilateral negotiation and with another 5 agents in a tournament setting in multilateral negotiation under the GENIUS simulation environment. Although the results presented a workable agent for multilateral negotiation, some points still should be noted down in improving the agents in the future. For future enhancement, other negotiation theories proposed for multilateral negotiation and algorithms on opponents modelling can be studied. Further testing is recommended
author2 Bo An
author_facet Bo An
Zhang, Liangliang
format Final Year Project
author Zhang, Liangliang
author_sort Zhang, Liangliang
title AgentLight : a multilateral negotiation strategy in multiple-issues domains
title_short AgentLight : a multilateral negotiation strategy in multiple-issues domains
title_full AgentLight : a multilateral negotiation strategy in multiple-issues domains
title_fullStr AgentLight : a multilateral negotiation strategy in multiple-issues domains
title_full_unstemmed AgentLight : a multilateral negotiation strategy in multiple-issues domains
title_sort agentlight : a multilateral negotiation strategy in multiple-issues domains
publishDate 2016
url http://hdl.handle.net/10356/67402
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