Automated negotiations under user preference uncertainty: A linear programming approach

Dimitrios Tsimpoukis, Tim Baarslag, Michael Kaisers, Nikolaos G. Paterakis

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.

TaalEngels
TitelAgreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers
RedacteurenMarin Lujak
Plaats van productieCham
UitgeverijSpringer
Pagina's115-129
Aantal pagina's15
ISBN van geprinte versie9783030172930
DOI's
StatusGepubliceerd - 4 apr 2019
Evenement6th International Conference on Agreement Technologies, (AT2018) - Bergen, Noorwegen
Duur: 6 dec 20187 dec 2018
https://at2018conference.wixsite.com/at2018

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11327 LNAI
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres6th International Conference on Agreement Technologies, (AT2018)
Verkorte titelAT2018
LandNoorwegen
StadBergen
Periode6/12/187/12/18
Internet adres

Vingerafdruk

Automated Negotiation
User Preferences
Linear programming
Uncertainty
Partial Information
Smart Grid
Autonomous agents
Cloud computing
Linear Optimization
Nominal or categorical data
User Model
Autonomous Agents
Pairwise Comparisons
Cloud Computing
Feedback
Trade-offs
Model
Scenarios
Energy
Estimate

Citeer dit

Tsimpoukis, D., Baarslag, T., Kaisers, M., & Paterakis, N. G. (2019). Automated negotiations under user preference uncertainty: A linear programming approach. In M. Lujak (editor), Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers (blz. 115-129). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11327 LNAI). Cham: Springer. DOI: 10.1007/978-3-030-17294-7_9
Tsimpoukis, Dimitrios ; Baarslag, Tim ; Kaisers, Michael ; Paterakis, Nikolaos G./ Automated negotiations under user preference uncertainty : A linear programming approach. Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers. redacteur / Marin Lujak. Cham : Springer, 2019. blz. 115-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{a4bc448501da428e98a0c2430cd58333,
title = "Automated negotiations under user preference uncertainty: A linear programming approach",
abstract = "Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.",
author = "Dimitrios Tsimpoukis and Tim Baarslag and Michael Kaisers and Paterakis, {Nikolaos G.}",
year = "2019",
month = "4",
day = "4",
doi = "10.1007/978-3-030-17294-7_9",
language = "English",
isbn = "9783030172930",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "115--129",
editor = "Marin Lujak",
booktitle = "Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers",
address = "Germany",

}

Tsimpoukis, D, Baarslag, T, Kaisers, M & Paterakis, NG 2019, Automated negotiations under user preference uncertainty: A linear programming approach. in M Lujak (redactie), Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11327 LNAI, Springer, Cham, blz. 115-129, Bergen, Noorwegen, 6/12/18. DOI: 10.1007/978-3-030-17294-7_9

Automated negotiations under user preference uncertainty : A linear programming approach. / Tsimpoukis, Dimitrios; Baarslag, Tim; Kaisers, Michael; Paterakis, Nikolaos G.

Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers. redactie / Marin Lujak. Cham : Springer, 2019. blz. 115-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11327 LNAI).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

TY - GEN

T1 - Automated negotiations under user preference uncertainty

T2 - A linear programming approach

AU - Tsimpoukis,Dimitrios

AU - Baarslag,Tim

AU - Kaisers,Michael

AU - Paterakis,Nikolaos G.

PY - 2019/4/4

Y1 - 2019/4/4

N2 - Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.

AB - Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user’s preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent’s negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.

UR - http://www.scopus.com/inward/record.url?scp=85064942807&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-17294-7_9

DO - 10.1007/978-3-030-17294-7_9

M3 - Conference contribution

SN - 9783030172930

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 115

EP - 129

BT - Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers

PB - Springer

CY - Cham

ER -

Tsimpoukis D, Baarslag T, Kaisers M, Paterakis NG. Automated negotiations under user preference uncertainty: A linear programming approach. In Lujak M, redacteur, Agreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers. Cham: Springer. 2019. blz. 115-129. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Beschikbaar vanaf, DOI: 10.1007/978-3-030-17294-7_9