Article

Learning executable agent behaviors from observation

Authors:

Andrew Guillory,

Hai Nguyen,

Tucker Balch,

Charles Lee Isbell, Jr.Authors Info & Claims

AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

Pages 795 - 797

https://doi.org/10.1145/1160633.1160774

Published: 08 May 2006 Publication History

Get Access

Abstract

We present a method for learning a human understandable, executable model of an agent's behavior using observations of its interaction with the environment. By executable we mean that the model is suitable for direct execution by an agent. Traditional models of behavior used for recognition tasks (e.g., Hidden Markov Models) are insufficent for this problem because they cannot respond to input from the environment. We train an Input/Output Hidden Markov Model where the output distributions are mixtures of learned low level actions and the transition distributions are conditional on features detected by the agent's sensors. We show that the system is able to learn both the behavior and human-understandable structure of a simulated model.

References

[1]

R. C. Arkin and T. R. Balch. Aura: Principles and practice in review. Journal of Experimental and Theoretical Artificial Intelligence (JETAI), 9(2--3):175--188, April 1997.

Google Scholar

[2]

Y. Bengio and P. Frasconi. Input-output HMM's for sequence processing. IEEE Transactions on Neural Networks, 7(5):1231--1249, September 1996.

Digital Library

Google Scholar

[3]

D. C. Bentivegna, A. Ude, C. G. Atkeson, and G. Cheng. Humanoid robot learning and game playing using pc-based vision. In 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), volume 3, pages 2449--2454, 2002.

Crossref

Google Scholar

[4]

F. Delmotte and M. Egerstedt. Reconstruction of low-complexity control programs from data. In 43rd IEEE Conference on Decision and Control, volume 2, pages 1460--1465, December 2004.

Crossref

Google Scholar

[5]

D. Goldberg and M. J. Matarić. Detecting regime changes with a mobile robot using multiple models. In 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS01), volume 2, pages 619--624, 2001.

Crossref

Google Scholar

[6]

L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77:257--286, February 1989.

Crossref

Google Scholar

[7]

C. Sammu, S. Hurst, D. Kedzier, and D. Michie. Learning to fly. In Ninth International Conference on Machine Learning, 1992.

Digital Library

Google Scholar

Cited By

View all

Petrov MValkanov V(2023)Analysis of Source Code Based on Changes in its State Over Time - Using User Behavior Models2023 International Conference Automatics and Informatics (ICAI)10.1109/ICAI58806.2023.10339100(367-372)Online publication date: 5-Oct-2023
https://doi.org/10.1109/ICAI58806.2023.10339100
Petrov MValkanov V(2023)System Tempura - A Modern Approach for Describing and Managing Temporal Processes in a Virtual Educational Space2023 International Conference Automatics and Informatics (ICAI)10.1109/ICAI58806.2023.10339086(490-495)Online publication date: 5-Oct-2023
https://doi.org/10.1109/ICAI58806.2023.10339086
Rocher GLavirotte STigli JCotte GDechavanne F(2021)An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based SystemsSensors10.3390/s2102052721:2(527)Online publication date: 13-Jan-2021
https://doi.org/10.3390/s21020527
Show More Cited By

Index Terms

Learning executable agent behaviors from observation
1. Computing methodologies
  1. Machine learning

Recommendations

Learning Sharing Behaviors with Arbitrary Numbers of Agents
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected ...
An Overview of Opponent Modeling for Multi-agent Competition
Machine Learning for Cyber Security
Abstract
Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that ...
Simulating human behaviors in agent societies
AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3

As increasing numbers of processors and agents pervade the human environment, societies comprising both humans and agents will emerge. Presently, it is unknown how a person might fair in such mixed societies. For the societies to operate effectively and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

May 2006

1631 pages

ISBN:1595933034

DOI:10.1145/1160633

General Chairs:
Hideyuki Nakashima
Future University - Hakodate, Japan
,
Michael Wellman
University of Michigan
,
Program Chairs:
Gerhard Weiss
Technical University Munich, Germany
,
Peter Stone
The University of Texas at Austin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

AAMAS06

Sponsor:

IFMAS
SIGAI
ATAL

AAMAS06: AAMAS '06 - 5th International Joint Conference on Autonomous Agents and Multi-agent Systems 2006

May 8 - 12, 2006

Japan, Hakodate

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
192
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Petrov MValkanov V(2023)Analysis of Source Code Based on Changes in its State Over Time - Using User Behavior Models2023 International Conference Automatics and Informatics (ICAI)10.1109/ICAI58806.2023.10339100(367-372)Online publication date: 5-Oct-2023
https://doi.org/10.1109/ICAI58806.2023.10339100
Petrov MValkanov V(2023)System Tempura - A Modern Approach for Describing and Managing Temporal Processes in a Virtual Educational Space2023 International Conference Automatics and Informatics (ICAI)10.1109/ICAI58806.2023.10339086(490-495)Online publication date: 5-Oct-2023
https://doi.org/10.1109/ICAI58806.2023.10339086
Rocher GLavirotte STigli JCotte GDechavanne F(2021)An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based SystemsSensors10.3390/s2102052721:2(527)Online publication date: 13-Jan-2021
https://doi.org/10.3390/s21020527
Rocher GTigli JLavirotte S(2021)A Generic Clustering-Based Algorithm for Approximating IOHMM Topology and ParametersIEEE Access10.1109/ACCESS.2021.30842369(79491-79504)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3084236
Parsons BVidal JHuynh NSnyder R(2015)Automatic Generation of Agent Behavior Models from Raw Observational DataMulti-Agent-Based Simulation XV10.1007/978-3-319-14627-0_9(121-132)Online publication date: 4-Jan-2015
https://doi.org/10.1007/978-3-319-14627-0_9
Berzan CScheutz Mvan der Hoek WPadgham LConitzer VWinikoff M(2012)What am I doing?Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343603(189-196)Online publication date: 4-Jun-2012
https://dl.acm.org/doi/10.5555/2343576.2343603
Mehta TEgerstedt M(2007)A variational approach to constructivist learning for mobile robot navigation2007 46th IEEE Conference on Decision and Control10.1109/CDC.2007.4434336(4179-4184)Online publication date: Dec-2007
https://doi.org/10.1109/CDC.2007.4434336
Sun JMehta TWooden DPowers MRehg JBalch TEgerstedt M(2007)Learning from examples in unstructured, outdoor environmentsJournal of Field Robotics10.1002/rob.2016723:11-12(1019-1036)Online publication date: 23-Jan-2007
https://doi.org/10.1002/rob.20167

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Learning Sharing Behaviors with Arbitrary Numbers of Agents

An Overview of Opponent Modeling for Multi-agent Competition

Simulating human behaviors in agent societies