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Intention Progression with Maintenance Goals

Published: 30 May 2023 Publication History

Abstract

One of the key advantages of Belief-Desire-Intention (BDI) agents [7] is their ability to pursue multiple goals in parallel. When multiple goals are pursued at the same time, an agent has to decide which of its intentions should be progressed, and if the next step of the selected intention is a subgoal, the agent also has to decide which plan should be used. These two choices together form the intention progression problem [5]. Previous work on the intention progression problem is limited to scheduling achievement goals [8 13, 16]. In addition to achieving certain states, in many applications agents must also maintain particular states of the environment, e.g., not running out of power, avoiding collisions, etc. Such goals are termed maintenance goals, as they specify a state of the environment an agent should maintain, and maintenance goals are supported by many BDI systems, including Jadex [6] and JAM [4]. Previous ap- proaches to [2] proactively reasoning about maintenance goals are based on summary-information [9]. However, the approach in [2] assumes that preventive measures to maintain a goal do not interact with the agent's other intentions.

References

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Guillaume Chaslot, Sander Bakkes, Istvan Szita, and Pieter Spronck. 2008. Monte-Carlo Tree Search: A New Framework for Game AI. In Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, Christian Darken and Michael Mateas (Eds.). The AAAI Press, Stanford, California, USA.
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Simon Duff, James Harland, and John Thangarajah. 2006. On proactivity and maintenance goals. In 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 8-12, 2006. ACM, 1033--1040. https://doi.org/10.1145/1160633.1160817
[3]
Simon Duff, John Thangarajah, and James Harland. 2014. Maintenance Goals in Intelligent Agents. Comput. Intell., Vol. 30, 1 (2014), 71--114. https://doi.org/10.1111/coin.12000
[4]
Marcus J. Huber. 1999. JAM: A BDI-Theoretic Mobile Agent Architecture. In Proceedings of the Third Annual Conference on Autonomous Agents, AGENTS 1999, Seattle, WA, USA, May 1--5, 1999. ACM, 236--243. https://doi.org/10.1145/301136.301202
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Brian Logan, John Thangarajah, and Neil Yorke-Smith. 2017. Progressing Intention Progresson: A Call for a Goal-Plan Tree Contest. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), S. Das, E. Durfee, K. Larson, and M. Winikoff (Eds.). IFAAMAS, IFAAMAS, Sao Paulo, Brazil, 768--772.
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Alexander Pokahr, Lars Braubach, and Winfried Lamersdorf. 2005. Jadex: A BDI Reasoning Engine. In Multi-Agent Programming, RafaelH. Bordini, Mehdi Dastani, Jürgen Dix, and Amal El Fallah Seghrouchni (Eds.). Multiagent Systems, Artificial Societies, and Simulated Organizations, Vol. 15. Springer US, 149--174. https://doi.org/10.1007/0-387-26350-0_6
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A. S. Rao and M. P. Georgeff. 1992. An abstract architecture for rational agents. In Proceedings of Knowledge Representation and Reasoning (KR&R-92), C. Rich, W. Swartout, and B. Nebel (Eds.). 439--449.
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John Thangarajah and Lin Padgham. 2011. Computationally Effective Reasoning About Goal Interactions. Journal of Automated Reasoning, Vol. 47, 1 (2011), 17--56.
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John Thangarajah, Lin Padgham, and Michael Winikoff. 2003 a. Detecting & Avoiding Interference Between Goals in Intelligent Agents. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), Georg Gottlob and Toby Walsh (Eds.). Morgan Kaufmann, Acapulco, Mexico, 721--726.
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John Thangarajah, Lin Padgham, and Michael Winikoff. 2003 b. Detecting & exploiting positive goal interaction inintelligent agents. In The Second International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2003. ACM, Melbourne, Victoria, Australia, 401--408.
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John Thangarajah, Michael Winikoff, Lin Padgham, and Klaus Fischer. 2002. Avoiding Resource Conflicts in Intelligent Agents. In Proceedings of the 15th Eureopean Conference on Artificial Intelligence. IOS Press, Lyon, France, 18--22.
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Max Waters, Lin Padgham, and Sebastian Sardina. 2014. Evaluating Coverage Based Intention Selection. In Proceedings of the 13th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2014) (Paris, France), Alessio Lomuscio, Paul Scerri, Ana Bazzan, and Michael Huhns (Eds.). IFAAMAS, 957--964. http://dl.acm.org/citation.cfm?id=2617388.2617398
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Max Waters, Lin Padgham, and Sebastian Sardi n a. 2015. Improving domain-independent intention selection in BDI systems. Autonomous Agents and Multi-Agent Systems, Vol. 29, 4 (2015), 683--717. https://doi.org/10.1007/s10458-015--9293--5
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Yuan Yao, Lavindra de Silva, and Brian Logan. 2016a. Reasoning about the Executability of Goal-Plan Trees. In Proceedings of the 4th International Workshop on Engineering Multi-Agent Systems (EMAS 2016), Matteo Baldoni, Jorg P. Muller, Ingrid Nunes, and Rym Zalila-Wenkstern (Eds.). Singapore, 181--196.
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Yuan Yao and Brian Logan. 2016. Action-Level Intention Selection for BDI Agents. In 15th International Conference on Autonomous Agents and Multiagent Systems. IFAAMAS, 1227--1236.
[16]
Yuan Yao, Brian Logan, and John Thangarajah. 2016b. Robust Execution of BDI Agent Programs by Exploiting Synergies Between Intentions. In 30th AAAI Conference on Artificial Intelligence. AAAI Press, 2558--2565.

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Published In

cover image ACM Conferences
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems
May 2023
3131 pages
ISBN:9781450394321
  • General Chairs:
  • Noa Agmon,
  • Bo An,
  • Program Chairs:
  • Alessandro Ricci,
  • William Yeoh

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

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Published: 30 May 2023

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Author Tags

  1. bdi agents
  2. intention progression problem
  3. maintenance goals

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  • National Natural Science Foundation of China
  • Yongjiang Talent Introduction Programme

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

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