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RECON: A Robust multi-agent Environment for simulating COncurrent Negotiations

Alrayes, Bedour and Kafalı, Özgur and Stathis, Kostas (2016) RECON: A Robust multi-agent Environment for simulating COncurrent Negotiations. In: Recent advances in agent-based complex automated negotiation. Studies in Computational Intelligence . Springer, pp. 157-174. ISBN 978-3-319-30305-5. (doi:10.1007/978-3-319-30307-9_10) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:65893)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1007/978-3-319-30307-9_10

Abstract

Recon is an experimental simulation platform that supports the development of software agents interacting concurrently with other agents in negotiation domains. Unlike existing simulation toolkits that support only imperative negotiation strategies, Recon also supports declarative strategies, for applications where logic-based agents need to explain their negotiation decisions to a user. Recon is built on top of the GOLEM agent platform, specialized with a set of infrastructure agents that can manage an electronic market and extract statistics from the negotiations that take place. We evaluate the performance of Recon by showing how by increasing the number of agents in a simulation affects the agents’ time to make an offer during negotiation.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-30307-9_10
Uncontrolled keywords: Electronic markets, Automated negotiation, Simulation testbed
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Ozgur Kafali
Date Deposited: 04 Feb 2018 20:09 UTC
Last Modified: 17 Aug 2022 12:22 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65893 (The current URI for this page, for reference purposes)

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