A multi-choice offer strategy for bilateral multi-issue negotiations using modified DWM learning

Authors:
Hae Young Noh;Kivanc Ozonat;Sharad Singhal;Yinping Yang
Affiliations:
Stanford University, Stanford, CA;Hewlett-Packard Laboratories, Palo Alto, CA;Hewlett-Packard Laboratories, Palo Alto, CA;IHPC, Agency for Science, Technology, and Research, Singapore
Venue:
Proceedings of the 13th International Conference on Electronic Commerce
Year:
2011

Citing 12
Cited 1

Bayesian learning in negotiation

International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
On-line incremental learning in bilateral multi-issue negotiation

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions

Computational Economics
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs

ICEC '04 Proceedings of the 6th international conference on Electronic commerce
A Negotiation Meta Strategy Combining Trade-off and Concession Moves

Autonomous Agents and Multi-Agent Systems
A machine-learning approach to automated negotiation and prospects for electronic commerce

Journal of Management Information Systems - Special issue: Information technology and its organizational impact
Searching for joint gains in automated negotiations based on multi-criteria decision making theory

Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

The Journal of Machine Learning Research
Opponent modelling in automated multi-issue negotiation using Bayesian learning

Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Searching for fair joint gains in agent-based negotiation

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On-line estimation of hidden Markov model parameters based on theKullback-Leibler information measure

IEEE Transactions on Signal Processing

A review of strategy design and evaluation of software negotiation agents

Proceedings of the 14th Annual International Conference on Electronic Commerce

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Abstract

This paper introduces a "multi-choice" offer strategy for an automated agent conducting bilateral multi-issue negotiations in an agent-to-human negotiation setting. Assuming that a rational human counterpart is more likely to concede on less important issues, we developed a modified dynamic weighted majority (DWM) learning algorithm for the negotiation agent to estimate the issue weights and issue ranks of the human counterpart. The agent then utilizes these estimates to strategically propose counter-offers with multiple choices to the human counterpart. This strategy allows the agent to expedite the negotiation process and increase the chance of agreement by improving the satisfaction level of the counterpart. We validated this offer strategy using two sets of buyer behavior data: one simulated based on time-dependent behavior models used in the literature, and another collected from a human experiment on automated negotiations. Results indicate that, when compared to other offer strategies described in the literature with similar learning speeds, (i) the modified DWM-based learning algorithm estimates the counterpart's issue weight/rank more accurately, and (ii) the multi-choice offer strategy utilizing the learning algorithm makes more attractive offers to the counterpart while maintaining the same utility for the agent.