Can agents acquire human-like behaviors in a sequential bargaining game? - Comparison of roth's and Q-learning agents

Keiki Takadama, Tetsuro Kawai, Yuhsuke Koyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper addresses agent modeling in multiagent-based simulation (MABS) to explore agents who can reproduce human-like behaviors in the sequential bargaining game, which is more difficult to be reproduced than in the ultimate game (i.e., one time bargaining game). For this purpose, we focus on the Roth's learning agents who can reproduce human-like behaviors in several simple examples including the ultimate game, and compare simulation results of Roth's learning agents and Q-learning agents in the sequential bargaining game. Intensive simulations have revealed the following implications: (1) Roth's basic and three parameter reinforcement learning agents with any type of three action selections (i.e., e-greed, roulette, and Boltzmann distribution selections) can neither learn consistent behaviors nor acquire sequential negotiation in sequential bargaining game; and (2) Q-learning agents with any type of three action selections, on the other hand, can learn consistent behaviors and acquire sequential negotiation in the same game. However, Q-learning agents cannot reproduce the decreasing trend found in subject experiments.

Original languageEnglish
Title of host publicationMulti-Agent-Based Simulation VII - International Workshop, MABS 2006, Revised and Invited Papers
PublisherSpringer Verlag
Pages156-171
Number of pages16
ISBN (Print)9783540765363
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event7th International Workshop on Multi-Agent-Based Simulation, MABS 2006 - Hakodate, Japan
Duration: 2007 May 82007 May 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4442 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Multi-Agent-Based Simulation, MABS 2006
CountryJapan
CityHakodate
Period07/5/807/5/8

Keywords

  • Agent modeling
  • Agent-based simulation
  • Human-like behaviors
  • Reinforcement learning
  • Sequential bargaining game

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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