How can an agent learn to negotiate?

Dajun Zeng, Katia Sycara

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Negotiation has been extensively discussed in game-theoretic, economic, and management science literatures for decades. Recent growing interest in autonomous interacting software agents and their potential application in areas such as electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payoff might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. It provides an adaptive, multi-issue negotiation model capable of exhibiting a rich set of negotiation behaviors. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. We prove that under certain conditions learning is indeed beneficial.

Original languageEnglish (US)
Pages (from-to)233-244
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1193
StatePublished - 2015
Externally publishedYes

Fingerprint

Management science
Software agents
Electronic commerce
Automated Negotiation
Decision making
Software Agents
Electronic Commerce
Economics
Interaction
Theoretical Analysis
Update
Decision Making
Model
Game
Learning
Observation
Evidence
Human
Framework
Beliefs

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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