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Model-Based Diagnosis of Multi-Agent Systems: A Survey
Open accessAbstract
As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well-known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system is observed. A failure is announced when the real system's output contradicts the model's expected output. The model is then used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. non-temporal. Temporal diagnosis is used to diagnose the MAS's temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field for the AI community.
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“By leveraging multimodal technologies, AI agents can simultaneously analyze various data sources—such as medical imaging, electronic health records, and genetic data—to assist in diagnosis, predict patient outcomes, and recommend personalized treatment plans.46,47,48”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [46]
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Cites
A survey on deep learning based bearing fault diagnosis
Hoang, D.-T., & Kang, H.-J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.06.078 · Cited by 866
Research works printed but not linked: 48
- Abreu, R.; Zoeteweij, P.; and van Gemund, A. J. C. 2011. Simultaneous debugging of software faults. Journal of Sys- tems and Software, 84(4): 573-586.
- Barták, R.; Švancara, J.; and Krasičenko, I. 2020. MAPF Scenario: Software for Evaluating MAPF Plans on Real Robots. In Proceedings of the AAAI Conference on Artifi- cial Intelligence, volume 34, 13602-13603.
- Beer, M.; d'Inverno, M.; Luck, M.; Jennings, N.; Preist, C.; and Schroeder, M. 1999. Negotiation in multi-agent sys- tems. The Knowledge Engineering Review, 14(3): 285-289.
- Brusoni, V.; Console, L.; Terenziani, P.; and Dupré, D. T. 1998. A spectrum of definitions for temporal model-based diagnosis. Artificial Intelligence, 102(1): 39-79.
- Bunte, A.; Stein, B.; and Niggemann, O. 2019. Model-based diagnosis for cyber-physical production systems based on machine learning and residual-based diagnosis models. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 33, 2727-2735.
- Cazes, D.; and Kalech, M. 2020. Model-Based Diagnosis with Uncertain Observations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 2766- 2773.
- Cazes, D.; and Kalech, M. 2021. Model-based diagnosis with uncertain observations. Int. J. Intell. Syst., 36(7): 3259- 3292.
- Daigle, M.; Koutsoukos, X.; and Biswas, G. 2006. Dis- tributed diagnosis of coupled mobile robots. In Proceed- ings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 3787-3794. IEEE.
- De Jonge, F.; Roos, N.; and Witteveen, C. 2009. Primary and secondary diagnosis of multi-agent plan execution. Au- tonomous Agents and Multi-Agent Systems, 18(2): 267-294.
- de Kleer, J.; and Williams, B. C. 1987. Diagnosing Multiple faults. Artificial Intelligence, 32(1): 97-130.
- El Koujok, M.; Ragab, A.; Ghezzaz, H.; and Amazouz, M. 2020. A Multi-Agent-Based Methodology for Known and Novel Faults Diagnosis in Industrial Processes. IEEE Trans- actions on Industrial Informatics.
- Erdem, E.; Kisa, D. G.; Oztok, U.; and Schüller, P. 2013. A general formal framework for pathfinding problems with multiple agents. In Twenty-Seventh AAAI Conference on Ar- tificial Intelligence.
- Geihs, K. 2020. Engineering challenges ahead for robot teamwork in dynamic environments. Applied Sciences, 10(4): 1368.
- Hausman, W. H.; Schwarz, L. B.; and Graves, S. C. 1976. Optimal storage assignment in automatic warehousing sys- tems. Management science, 22(6): 629-638.
- Kalech, M. 2012. Diagnosis of coordination failures: a matrix-based approach. Autonomous Agents and Multi- Agent Systems, 24(1): 69-103.
- Kalech, M.; and Kaminka, G. A. 2005. Towards model- based diagnosis of coordination failures. In AAAI, volume 5, 102-107.
- Kalech, M.; and Kaminka, G. A. 2007. On the design of co- ordination diagnosis algorithms for teams of situated agents. Artificial Intelligence, 171(8-9): 491-513.
- Kalech, M.; and Kaminka, G. A. 2011. Coordination diag- nostic algorithms for teams of situated agents: Scaling up. Computational Intelligence, 27(3): 393-421.
- Kalech, M.; Kaminka, G. A.; Meisels, A.; and Elmaliach, Y. 2006. Diagnosis of multi-robot coordination failures using distributed CSP algorithms. In AAAI, 970-975.
- Katewa, V.; Pasqualetti, F.; and Gupta, V. 2018. On privacy vs. cooperation in multi-agent systems. International Jour- nal of Control, 91(7): 1693-1707.
- Kato, S.; Takeuchi, E.; Ishiguro, Y.; Ninomiya, Y.; Takeda, K.; and Hamada, T. 2015. An open approach to autonomous vehicles. IEEE Micro, 35(6): 60-68.
- Khalastchi, E.; and Kalech, M. 2019. Fault detection and diagnosis in multi-robot systems: a survey. Sensors, 19(18): 4019.
- Koujok, M. E.; Ragab, A.; and Amazouz, M. 2019. A Multi- Agent Approach Based on Machine-Learning for Fault Di- agnosis. IFAC-PapersOnLine, 52(10): 103-108. 13th IFAC Workshop on Intelligent Manufacturing Systems IMS 2019.
- Li, J.; Ruml, W.; and Koenig, S. 2021. EECBS: A Bounded- Suboptimal Search for Multi-Agent Path Finding. In Pro- ceedings of the AAAI Conference on Artificial Intelligence (AAAI).
- Metodi, A.; Stern, R.; Kalech, M.; and Codish, M. 2014. A novel sat-based approach to model based diagnosis. Journal of Artificial Intelligence Research, 51: 377-411.
- Micalizio, R. 2009. A distributed control loop for au- tonomous recovery in a multi-agent plan. In Twenty-First International Joint Conference on Artificial Intelligence.
- Micalizio, R.; and Torasso, P. 2007. Plan diagnosis and agent diagnosis in multi-agent systems. In Congress of the Italian Association for Artificial Intelligence, 434-446. Springer.
- Micalizio, R.; and Torasso, P. 2014. Cooperative monitoring to diagnose multiagent plans. Journal of Artificial Intelli- gence Research, 51: 1-70.
- Micalizio, R.; Torasso, P.; and Torta, G. 2004. On-line mon- itoring and diagnosis of multi-agent systems: a model based approach. In Proceedings of the 16th European Conference on Artificial Intelligence, 848-852. IOS Press.
- Micalizio, R.; and Torta, G. 2012. Diagnosing delays in multi-agent plans execution. In European Conference on Artificial Intelligence, 594-599. IOS Press.
- Natan, A.; and Kalech, M. 2020. Distributed Diagnosis of Multi-Agent Plans. In 31st International Workshop on Prin- ciple of Diagnosis (DX-20).
- Pallottino, L.; Scordio, V. G.; Bicchi, A.; and Frazzoli, E. 2007. Decentralized cooperative policy for conflict res- olution in multivehicle systems. IEEE Transactions on Robotics, 23(6): 1170-1183.
- Passos, L. S.; Abreu, R.; and Rossetti, R. J. 2015. Spectrum- based fault localisation for multi-agent systems. In Twenty- Fourth International Joint Conference on Artificial Intelli- gence.
- Pynadath, D. V.; and Tambe, M. 2003. An automated team- work infrastructure for heterogeneous software agents and humans. Autonomous Agents and Multi-Agent Systems, 7(1): 71-100.
- Qin, L.; He, X.; Zhou, D.; Cui, J.; Li, F.; and Wang, G. 2018. A new local-model-based distributed fault diagnosis scheme for multi-agent systems with actuator faults. IFAC- PapersOnLine, 51(24): 292-299.
- Rehák, M.; Pěchouček, M.; and Tožička, J. 2005. Adversar- ial behavior in multi-agent systems. In International Central and Eastern European Conference on Multi-Agent Systems, 470-479. Springer.
- Reiter, R. 1987. A Theory of Diagnosis from First Princi- ples. Artificial Intelligence, 32(1): 57-96.
- Roos, N.; Ten Teije, A.; and Witteveen, C. 2003. A protocol for multi-agent diagnosis with spatially distributed knowl- edge. In Proceedings of the second international joint con- ference on Autonomous agents and multiagent systems, 655- 661. ACM.
- Roos, N.; and Witteveen, C. 2009. Models and methods for plan diagnosis. Autonomous Agents and Multi-Agent Sys- tems, 19(1): 30-52.
- Su, R.; Wonham, W.; Kurien, J.; and Koutsoukos, X. 2002. Distributed diagnosis for qualitative systems. In Sixth In- ternational Workshop on Discrete Event Systems, 169-174. IEEE.
- Such, J. M.; Espinosa, A.; and García-Fornes, A. 2014. A survey of privacy in multi-agent systems. Knowledge Engi- neering Review, 29(3): 314-344.
- Surynek, P.; Felner, A.; Stern, R.; and Boyarski, E. 2016. Efficient SAT approach to multi-agent path finding under the sum of costs objective. In Proceedings of the Twenty-second European Conference on Artificial Intelligence, 810-818.
- Švancara, J.; Vlk, M.; Stern, R.; Atzmon, D.; and Barták, R. 2019. Online multi-agent pathfinding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, 7732-7739.
- Sycara, K.; and Sukthankar, G. 2006. Literature review of teamwork models. Robotics Institute, Carnegie Mellon Uni- versity, 31: 31.
- Tambe, M. 1997. Towards flexible teamwork. Journal of artificial intelligence research, 7: 83-124.
- Torta, G.; and Micalizio, R. 2018. SMT-Based Diagnosis of Multi-Agent Temporal Plans. In Proceedings of the 17th In- ternational Conference on Autonomous Agents and MultiA- gent Systems, 2097-2099. International Foundation for Au- tonomous Agents and Multiagent Systems.
- Torta, G.; Micalizio, R.; and Sormano, S. 2019a. Explain- ing Failures Propagations in the Execution of Multi-Agent Temporal Plans. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Sys- tems, 2232-2234. International Foundation for Autonomous Agents and Multiagent Systems.
- Torta, G.; Micalizio, R.; and Sormano, S. 2019b. Tem- poral Multiagent Plan Execution: Explaining What Hap- pened. In International Workshop on Explainable, Transpar- ent Autonomous Agents and Multi-Agent Systems, 167-185. Springer.
Cited by
Sorted by citations
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41
Metadata
- Authors
- Kalech, M., & Natan, A.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2022-06-28
- Volume
- 36
- Issue
- 11
- Pages
- 12334-12341
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4283814904
- Cited by
- 16
- References
- 1
- Citations by year
- 2026: 6 · 2024: 5 · 2023: 4 · 2022: 1
- FWCI
- 1.07
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- Israel Science Foundation (1716/17)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- model-based diagnosis, multi-agent systems, fault diagnosis, distributed systems
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