skip to main content
10.1145/1329125.1329367acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaamasConference Proceedingsconference-collections
research-article

A globally optimal algorithm for TTD-MDPs

Published: 14 May 2007 Publication History

Abstract

In this paper, we discuss the use of Targeted Trajectory Distribution Markov Decision Processes (TTD-MDPs)---a variant of MDPs in which the goal is to realize a specified distribution of trajectories through a state space---as a general agent-coordination framework.
We present several advances to previous work on TTD-MDPs. We improve on the existing algorithm for solving TTD-MDPs by deriving a greedy algorithm that finds a policy that provably minimizes the global KL-divergence from the target distribution. We test the new algorithm by applying TTD-MDPs to drama management, where a system must coordinate the behavior of many agents to ensure that a game follows a coherent storyline, is in keeping with the author's desires, and offers a high degree of replayability.
Although we show that suboptimal greedy strategies will fail in some cases, we validate previous work that suggests that they can work well in practice. We also show that our new algorithm provides guaranteed accuracy even in those cases, with little additional computational cost. Further, we illustrate how this new approach can be applied online, eliminating the memory-intensive offline sampling necessary in the previous approach.

References

[1]
J. Bates. Virtual reality, art, and entertainment. Presence: The Journal of Teleoperators and Virtual Environments, 2(1):133--138, 1992.
[2]
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
[3]
C. L. Isbell, Jr., C. R. Shelton, M. Kearns, S. Singh, and P. Stone. A social reinforcement learning agent. In Proceedings of the Fifth International Conference on Autonomous Agents (Agents-01), pages 377--384, 2001.
[4]
M. Kearns, Y. Mansour, and A. Y. Ng. Approximate planning in large POMDPs via reusable trajectories. Advances in Neural Information Processing Systems, 12, 2000.
[5]
A. Lamstein and M. Mateas. A search-based drama manager. In Proceedings of the AAAI-04 Workshop on Challenges in Game AI, 2004.
[6]
B. Laurel. Toward the Design of a Computer-Based Interactive Fantasy System. PhD thesis, Drama department, Ohio State University, 1986.
[7]
M. L. Littman. Markov games as a framework for multiagent reinforcement learning. In Proceedings of the Eleventh International Conference on Machine Learning (ICML-94), pages 157--163, 1994.
[8]
B. Magerko. Story representation and interactive drama. In Proceedings of the First Annual Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-05), 2005.
[9]
M. Mateas. An Oz-centric review of interactive drama and believable agents. In M. Woodridge and M. Veloso, editors, AI Today: Recent Trends and Developments. Lecture Notes in AI 1600. Springer, Berlin, NY, 1999. First appeared in 1997 as Technical Report CMU-CS-97-156, Computer Science Department, Carnegie Mellon University.
[10]
M. Mateas and A. Stern. Integrating plot, character, and natural language processing in the interactive drama Façade. In Proceedings of the 1st International Conference on Technologies for Interactive Digital Storytelling and Entertainment (TIDSE-03), 2003.
[11]
B. Mott and J. Lester. U-director: A decision-theoretic narrative planning architecture for storytelling environments. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), 2006.
[12]
M. J. Nelson and M. Mateas. Search-based drama management in the interactive fiction Anchorhead. In Proceedings of the First Annual Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-05), 2005.
[13]
M. J. Nelson, D. L. Roberts, C. L. Isbell, Jr., and M. Mateas. Reinforcement learning for declarative optimization-based drama management. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), 2006.
[14]
A. Y. Ng and S. Russell. Algorithms for inverse reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-00), pages 663--670, 2000.
[15]
Z. Rabinovich and J. S. Rosenschein. Multiagent coordination by extended Markov tracking. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-05), pages 431--438, 2005.
[16]
Z. Rabinovich and J. S. Rosenschein. On the response of EMT-based control to interacting targets and models. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), 2006.
[17]
M. O. Riedl, A. Stern, and D. Dini. Mixing story and simulation in interactive narrative. In Proceedings of the Second Annual Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-06), 2006.
[18]
D. L. Roberts, M. J. Nelson, C. L. Isbell, M. Mateas, and M. L. Littman. Targeting specific distributions of trajectories in MDPs. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), Boston, MA, 2006.
[19]
G. Tesauro. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Unpublished. URL: http://web.cps.msu.edu/rlr/pub/Tesauro2.html.
[20]
G. Tesauro. Practical issues in temporal difference learning. Machine Learning, 8:257--277, 1992.
[21]
G. Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3):58--68, 1995.
[22]
P. Weyhrauch. Guiding Interactive Drama. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1997. Technical Report CMU-CS-97-109.
[23]
R. M. Young, M. O. Riedl, M. Branly, A. Jhala, R. J. Martin, and C. J. Saretto. An architecture for integrating plan-based behavior generation with interactive game environments. Journal of Game Development, 1(1), 2004.

Cited By

View all
  • (2014)Story similarity measures for drama management with ttd-mdpsProceedings of the 2014 international conference on Autonomous agents and multi-agent systems10.5555/2615731.2615747(77-84)Online publication date: 5-May-2014
  • (2014)Lessons on Using Computationally Generated Influence for Shaping Narrative ExperiencesIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2013.22871546:2(188-202)Online publication date: Jun-2014
  • (2014)Personalized Interactive Narratives via Sequential Recommendation of Plot PointsIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2013.22827716:2(174-187)Online publication date: Jun-2014
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
May 2007
1585 pages
ISBN:9788190426275
DOI:10.1145/1329125
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]

Sponsors

  • IFAAMAS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Markov decision processes
  2. convex optimization
  3. interactive entertainment

Qualifiers

  • Research-article

Funding Sources

Conference

AAMAS07
Sponsor:

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2014)Story similarity measures for drama management with ttd-mdpsProceedings of the 2014 international conference on Autonomous agents and multi-agent systems10.5555/2615731.2615747(77-84)Online publication date: 5-May-2014
  • (2014)Lessons on Using Computationally Generated Influence for Shaping Narrative ExperiencesIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2013.22871546:2(188-202)Online publication date: Jun-2014
  • (2014)Personalized Interactive Narratives via Sequential Recommendation of Plot PointsIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2013.22827716:2(174-187)Online publication date: Jun-2014
  • (2012)A sequential recommendation approach for interactive personalized story generationProceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343586(71-78)Online publication date: 4-Jun-2012
  • (2010)Investigating director agents' decision making in interactive narrativeProceedings of the Intelligent Narrative Technologies III Workshop10.1145/1822309.1822322(1-8)Online publication date: 18-Jun-2010
  • (2009)Using influence and persuasion to shape player experiencesProceedings of the 2009 ACM SIGGRAPH Symposium on Video Games10.1145/1581073.1581077(23-30)Online publication date: 4-Aug-2009
  • (2008)Computational influence for training and entertainmentProceedings of the 23rd national conference on Artificial intelligence - Volume 310.5555/1620270.1620392(1865-1866)Online publication date: 13-Jul-2008
  • (2008)Another look at search-based drama managementProceedings of the 23rd national conference on Artificial intelligence - Volume 210.5555/1620163.1620195(792-797)Online publication date: 13-Jul-2008
  • (2008)Another look at search-based drama managementProceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 310.5555/1402821.1402854(1293-1298)Online publication date: 12-May-2008

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media