The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Review of Agent-Based Programming for Multi-Agent Systems
Open accessAbstract
Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Multi-step, multi-layered tasks over extended time frames”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [10]
In the citing paper
“The action system of AI agents refers to their ability to execute decisions and perform tasks based on the data they process and the decisions they make.38,39”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [38]
In the citing paper
“However, across these diverse systems, early AI agents shared common limitations: they lacked self-learning, generative reasoning, and adaptability to unstructured or evolving environments.”
From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [6]
Citations
Cites
Intelligent agents: theory and practice
Wooldridge, M., & Jennings, N. R. (1995) · The Knowledge Engineering Review · doi:10.1017/s0269888900008122 · Cited by 6,810
GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation
Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., & Drogoul, A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-44927-7_9 · Cited by 227
Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review
González-Briones, A., De La Prieta, F., Mohamad, M. S., Omatu, S., & Corchado, J. M. (2018) · Energies · doi:10.3390/en11081928 · Cited by 169
BDI agents in social simulations: a survey
Adam, C., & Gaudou, B. (2016) · The Knowledge Engineering Review · doi:10.1017/s0269888916000096 · Cited by 165
Research works printed but not linked: 82
- Wooldridge, M. (2009). An Introduction to MultiAgent Systems, John Wiley and Sons. [2nd ed.].
- Logan, 2018, An agent programming manifesto, Int. J. Agent-Oriented Softw. Eng., 6, 187, 10.1504/IJAOSE.2018.094374 · DOI
- Russell, S.J., and Norvig, P. (2010). Artificial Intelligence: A Modern Approach, Prentice Hall. [3rd ed.].
- Bordini, 2020, Agent programming in the cognitive era, Auton. Agents Multi Agent Syst., 34, 37, 10.1007/s10458-020-09453-y · DOI
- Mao, 2017, A survey of agent-oriented programming from software engineering perspective, Web Intell., 15, 143, 10.3233/WEB-170357 · DOI
- Kravari, K., and Bassiliades, N. (2015). A Survey of Agent Platforms. J. Artif. Soc. Soc. Simul., 18. · DOI · Paper page
- Abar, 2017, Agent Based Modelling and Simulation tools: A review of the state-of-art software, Comput. Sci. Rev., 24, 13, 10.1016/j.cosrev.2017.03.001 · DOI
- Isern, 2016, A systematic literature review of agents applied in healthcare, J. Med Syst., 40, 43, 10.1007/s10916-015-0376-2 · DOI
- Shoham, 1993, Agent-oriented Programming, Artif. Intell., 60, 51, 10.1016/0004-3702(93)90034-9 · DOI
- Georgeff, 1986, Procedural Knowledge, Proc. IEEE (Spec. Issue Knowl. Represent.), 74, 1383
- Rao, A.S., and Georgeff, M. (1995, January 12–14). BDI Agents: From Theory to Practice. Proceedings of the First International Conference on Multiagent Systems (ICMAS), San Francisco, CA, USA.
- Perram, 1996, AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language, Agents Breaking Away, Proceedings of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Eindhoven, The Netherlands, 22–25 January 1996, Volume 1038, 42
- Meltzer, B., and Michie, D. (1969). Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4, Edinburgh University Press.
- Issicaba, D., Rosa, M.A., Prostejovsky, A.M., and Bindner, H.W. (2017, January 26–29). Experimental validation of BDI agents for distributed control of electric power grids. Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Torino, Italy. · DOI
- Sorici, A., Boissier, O., Picard, G., and Santi, A. (2011, January 1–31). Exploiting the JaCaMo Framework for Realising an Adaptive Room Governance Application. Proceedings of the Compilation of the Co-Located Workshops on DSM’11, TMC’11, AGERE! 2011, AOOPES’11, NEAT’11, and VMIL’11, New York, NY, USA. · DOI
- Demazeau, Y., Müller, J.P., Rodríguez, J.M.C., and Pérez, J.B. (2012). A JaCaMo-Based Governance of Machine-to-Machine Systems. Advances on Practical Applications of Agents and Multi-Agent Systems, Springer. · DOI
- Krupa, 2012, Handling Privacy as Contextual Integrity in Decentralized Virtual Communities: The PrivaCIAS Framework, Web Intelli. Agent Syst., 10, 105, 10.3233/WIA-2012-0235 · DOI
- Chen, 2015, Reflecting on Agent Programming with AgentSpeak(L), Proceedings of the PRIMA 2015: Principles and Practice of Multi-Agent Systems—18th International Conference, Bertinoro, Italy, 26–30 October 2015, Volume 9387, 351
- Millar, 2019, Chromar, a language of parameterised agents, Theor. Comput. Sci., 765, 97, 10.1016/j.tcs.2017.07.034 · DOI
- Hindriks, K.V., de Boer, F.S., van der Hoek, W., and Meyer, J.J.C. (2020, January 7–9). Agent Programming with Declarative Goals. Proceedings of the 7th International Workshop on Agent Theories, Architectures, Boston, MA, USA.
- Bordini, R.H., Wooldridge, M., and Hübner, J.F. (2007). Programming Multi-Agent Systems in AgentSpeak Using Jason, John Wiley & Sons. · DOI
- Boissier, 2013, Multi-agent oriented programming with JaCaMo, Sci. Comput. Program., 78, 747, 10.1016/j.scico.2011.10.004 · DOI · Paper page
- Boissier, O., Bordini, R., Hubner, J., and Ricci, A. (2020). Multi-Agent Oriented Programming: Programming Multi-Agent Systems Using JaCaMo, MIT Press. · DOI
- Ricci, A., Piunti, M., Viroli, M., and Omicini, A. (2009). Environment Programming in CArtAgO. Multi-Agent Programming: Languages, Tools and Applications, Springer. Chapter 8. · DOI
- Sichman, 2007, Developing organised multiagent systems using the MOISE+ model: Programming issues at the system and agent levels, Int. J. Agent-Oriented Softw. Eng., 1, 370, 10.1504/IJAOSE.2007.016266 · DOI
- Bellifemine, F.L., Caire, G., and Greenwood, D. (2007). Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology), John Wiley & Sons. · DOI
- Bergenti, 2017, Agent-oriented model-driven development for JADE with the JADEL programming language, Comput. Lang. Syst. Struct., 50, 142
- Bergenti, F., Monica, S., and Petrosino, G. (2018, January 5). A scripting language for practical agent-oriented programming. Proceedings of the 8th ACM SIGPLAN International Workshop on Programming Based on Actors, Agents, and Decentralized Control, AGERE!@SPLASH 2018, Boston, MA, USA. · DOI
- Pokahr, A., Braubach, L., and Lamersdorf, W. (2005). Jadex: A BDI Reasoning Engine. Multi-Agent Programming: Languages, Platforms and Applications, Springer. · DOI
- Aschermann, M., Dennisen, S., Kraus, P., and Müller, J.P. (2018, January 10–15). LightJason, a Highly Scalable and Concurrent Agent Framework: Overview and Application. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden.
- Hashmi, M.A., Seghrouchni, A.E.F., and Akram, M.U. (2015, January 6–9). A Planning Based Agent Programming Language Supporting Environment Modeling. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015, Singapore. · DOI
- Kilaru, J. (2018). PLASA: Programming Language for Synchronous Agents. [Master’s Thesis, California State University].
- Flocchini, 2005, Gathering of asynchronous robots with limited visibility, Theor. Comput. Sci., 337, 147, 10.1016/j.tcs.2005.01.001 · DOI
- Bonci, A., Pirani, M., Bianconi, C., and Longhi, S. (2018, January 2–4). RMAS: Relational Multiagent System for CPS Prototyping and Programming. Proceedings of the 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018, Oulu, Finland. · DOI
- Rodriguez, S., Gaud, N., and Galland, S. (2014, January 11–14). SARL: A General-Purpose Agent-Oriented Programming Language. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland. · DOI
- Molesini, 2013, Simulation in agent-oriented software engineering: The SODA case study, Sci. Comput. Program., 78, 705, 10.1016/j.scico.2011.09.007 · DOI
- Moreno, 2015, PEABS: A Process for developing Efficient Agent-Based Simulators, Eng. Appl. Artif. Intell., 46, 104, 10.1016/j.engappai.2015.09.003 · DOI
- Pavón, J., Gómez-Sanz, J., and Fuentes-Fernández, R. (2005). The INGENIAS methodology and tools. Agent-Oriented Methodol, IGI Global. · DOI
- Caillou, P., Gaudou, B., Grignard, A., Truong, Q.C., and Taillandier, P. (2015, January 14–18). A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation. Proceedings of the European Social Simulation Association 2015, Groningen, The Netherlands.
- Taillandier, P., Bourgais, M., Caillou, P., Adam, C., and Gaudou, B. (2016, January 10). A BDI Agent Architecture for the GAMA Modeling and Simulation Platform. Proceedings of the Multi-Agent Based Simulation XVII—International Workshop, MABS 2016, Singapore. · DOI
- Singh, 2016, Integrating BDI Agents with Agent-Based Simulation Platforms, Auton. Agents Multi Agent Syst., 30, 1050, 10.1007/s10458-016-9332-x · DOI
- Belle, V., and Levesque, H.J. (2014, January 27–31). PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, QC, Canada. · DOI
- Belle, V., and Levesque, H.J. (2015, January 25–31). ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina.
- Levesque, 1997, GOLOG: A Logic Programming Language for Dynamic Domains, J. Log. Program., 31, 59, 10.1016/S0743-1066(96)00121-5 · DOI
- Ferrein, A., Maier, C., Mühlbacher, C., Niemueller, T., Steinbauer, G., and Vassos, S. (2016, January 22–24). Controlling Logistics Robots with the Action-Based Language YAGI. Proceedings of the Intelligent Robotics and Applications—9th International Conference, ICIRA 2016, Tokyo, Japan. · DOI
- Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A. (2009, January 12–13). ROS: An open-source Robot Operating System. Proceedings of the Workshop on Open Source Software at the International Conference on Robotics and Automation, Kobe, Japan.
- Kaptein, F., Broekens, J., Hindriks, K.V., and Neerincx, M.A. (2016, January 20–23). CAAF: A Cognitive Affective Agent Programming Framework. Proceedings of the Intelligent Virtual Agents—16th International Conference, IVA 2016, Los Angeles, CA, USA. · DOI
- Ramos, 2003, MASCEM: A multiagent system that simulates competitive electricity markets, IEEE Intell. Syst., 18, 54, 10.1109/MIS.2003.1249170 · DOI
- Santos, 2016, MASCEM: Optimizing the performance of a multi-agent system, Energy, 111, 513, 10.1016/j.energy.2016.05.127 · DOI
- Navarro, 2017, TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions, Simul. Model. Pract. Theory, 77, 84, 10.1016/j.simpat.2017.05.006 · DOI
- Cich, G., Galland, S., Knapen, L., Yasar, A., Bellemans, T., and Janssens, D. (2017, January 21–23). Addressing the Challenges of Conservative Event Synchronization for the SARL Agent-Programming Language. Proceedings of the Advances in Practical Applications of Cyber-Physical Multi-Agent Systems, PAAMS Collection—15th International Conference, PAAMS 2017, Porto, Portugal. · DOI
- Jain, 2016, Programming an expressive autonomous agent, Expert Syst. Appl., 43, 131, 10.1016/j.eswa.2015.08.037 · DOI
- Jain, 2015, EMIA: Emotion model for intelligent agent, J. Intell. Syst., 24, 449
- Dastani, 2008, 2APL: A practical agent programming language, Auton. Agents Multi-Agent Syst., 16, 214, 10.1007/s10458-008-9036-y · DOI
- Pantoja, C.E., Stabile, M.F., Lazarin, N.M., and Sichman, J.S. (2016, January 9–10). ARGO: An Extended Jason Architecture that Facilitates Embedded Robotic Agents Programming. Proceedings of the Engineering Multi-Agent Systems—4th International Workshop, EMAS 2016, Singapore. · DOI
- Leask, S., and Logan, B. (2015, January 26–30). Programming deliberation strategies in meta-APL. Proceedings of the International Conference on Principles and Practice of Multi-Agent Systems, Bertinoro, Italy. · DOI
- Doan, T.T., Yao, Y., Alechina, N., and Logan, B. (2014, January 5–9). Verifying heterogeneous multi-agent programs. Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, AAMAS ’14, Paris, France.
- Cardoso, R.C., Ferrando, A., Dennis, L.A., and Fisher, M. (2020, January 14–15). An Interface for Programming Verifiable Autonomous Agents in ROS. Proceedings of the European Conference on Multi-Agent Systems (EUMAS), Thessaloniki, Greece. · DOI
- Onyedinma, C., Gavigan, P., and Esfandiari, B. (2020). Toward Campus Mail Delivery Using BDI. J. Sens. Actuator Netw., 9. · DOI
- Bosello, M., and Ricci, A. (2019, January 13–14). From Programming Agents to Educating Agents - A Jason-Based Framework for Integrating Learning in the Development of Cognitive Agents. Proceedings of the Engineering Multi-Agent Systems—7th International Workshop, EMAS 2019, Montreal, QC, Canada.
- Cardoso, R.C., Zatelli, M.R., Hübner, J.F., and Bordini, R.H. (2013, January 27). Towards Benchmarking Actor- and Agent-Based Programming Languages. Proceedings of the Workshop on Programming Based on Actors, Agents, and Decentralized Control, Indianapolis, IN, USA. · DOI
- Challenger, 2016, A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems, Softw. Qual. J., 24, 755, 10.1007/s11219-015-9291-5 · DOI
- Bergenti, F., Iotti, E., Monica, S., and Poggi, A. (2017, January 24–26). A Comparison between Asynchronous Backtracking Pseudocode and its JADEL Implementation. Proceedings of the 9th International Conference on Agents and Artificial Intelligence, ICAART, Porto, Portugal. · DOI
- Rousset, 2016, A survey on parallel and distributed multi-agent systems for high performance computing simulations, Comput. Sci. Rev., 22, 27, 10.1016/j.cosrev.2016.08.001 · DOI
- Sun, 2016, Simple or complicated agent-based models? A complicated issue, Environ. Model. Softw., 86, 56, 10.1016/j.envsoft.2016.09.006 · DOI
- Cardoso, 2018, SMART-JaCaMo: An organization-based team for the multi-agent programming contest, Ann. Math. Artif. Intell., 84, 75, 10.1007/s10472-018-9584-z · DOI
- Ahlbrecht, T., Dix, J., and Fiekas, N. (2019). SMART–JaCaMo: An Organisation-Based Team for the Multi-Agent Programming Contest. The Multi-Agent Programming Contest 2018, Springer International Publishing. · DOI
- Cardoso, R.C., Ferrando, A., and Papacchini, F. (2019). LFC: Combining Autonomous Agents and Automated Planning in the Multi-Agent Programming Contest. Multi-Agent Progamming Contest, Springer.
- Ahlbrecht, T., Dix, J., Fiekas, N., and Krausburg, T. (2020). The Requirement Gatherers’ Approach to the 2019 Multi-Agent Programming Contest Scenario. The Multi-Agent Programming Contest 2019, Springer International Publishing. · DOI
- Ahlbrecht, T., Dix, J., and Fiekas, N. (2019). Multi-Agent Programming Contest 2018—The Jason-DTU Team. The Multi-Agent Programming Contest 2018, Springer International Publishing. · DOI
- Ahlbrecht, T., Dix, J., Fiekas, N., and Krausburg, T. (2020). GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest. The Multi-Agent Programming Contest 2019, Springer International Publishing. · DOI
- Wolfram, C. (2020). An Agent-Based Model of COVID-19. Complex Syst., 29. · DOI
- Prudhomme, C., Cruz, C., and Cherifi, H. (2020, January 14–15). An Agent based model for the transmission and control of the COVID-19 in Dijon (extended abstract). Proceedings of the MARAMI 2020—Modèles & Analyse des Réseaux: Approches Mathématiques & Informatiques—The 11th Conference on Network Modeling and Analysis, Virtual Conference, Montpellier, France.
- Khan, 2017, The research on multi-agent system for microgrid control and optimization, Renew. Sustain. Energy Rev., 80, 1399, 10.1016/j.rser.2017.05.279 · DOI
- Kantamneni, 2015, Survey of multi-agent systems for microgrid control, Eng. Appl. Artif. Intell., 45, 192, 10.1016/j.engappai.2015.07.005 · DOI
- QuanLi, 2015, Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: A case study of the Erhai Lake Basin, China, Nat. Hazards, 75, 95, 10.1007/s11069-014-1303-4 · DOI
- North, 2013, Complex Adaptive Systems Modeling with Repast Simphony, Complex Adapt. Syst. Model., 1, 1, 10.1186/2194-3206-1-3 · DOI
- Mariethoz, 2015, An agent-based platform for simulating complex human-aquifer interactions in managed groundwater systems, Environ. Model. Softw., 73, 305, 10.1016/j.envsoft.2015.08.018 · DOI
- Savaglio, C., Fortino, G., Ganzha, M., Paprzycki, M., Badica, C., and Ivanovic, M. (2017, January 11–13). Agent-Based Computing in the Internet of Things: A Survey. Proceedings of the Intelligent Distributed Computing XI—11th International Symposium on Intelligent Distributed Computing—IDC 2017, Belgrade, Serbia. · DOI
- Krivic, P., Skocir, P., Kusek, M., and Jezic, G. (2017, January 21–23). Microservices as Agents in IoT Systems. Proceedings of the Agent and Multi-Agent Systems: Technology and Applications, 11th KES International Conference, KES-AMSTA 2017, Vilamoura, Algarve, Portugal. · DOI
- Ayala, 2015, A Software Product Line Process to Develop Agents for the IoT, Sensors, 15, 15640, 10.3390/s150715640 · DOI
- Iotti, E., Petrosino, G., Monica, S., and Bergenti, F. (2020, January 4). Exploratory Experiments on Programming Autonomous Robots in Jadescript. Proceedings of the First Workshop on Agents and Robots for reliable Engineered Autonomy, AREA@ECAI 2020, Virtual Event, Santiago de Compostela, Spain. · DOI
Cited by
Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 671
“Multi-agent systems facilitated coordination among distributed entities, exemplified by auction-based resource allocation in supply chain management [16–18].”
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186
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
“38,39 In healthcare, this capability is exemplified through tools that allow the agent to carry out precise actions, from generating diagnostic reports to assisting in clinical procedures.”
Metadata
- Authors
- Cardoso, R. C., & Ferrando, A.
- Affiliations
- University of Manchester (GB)
- Corresponding author
- Cardoso, Rafael C.
- Ferrando, Angelo
- Venue
- Computers
- Publisher
- MDPI AG
- Published
- 2021-01-27
- Volume
- 10
- Issue
- 2
- Pages
- 16
- ISSN
- 2073-431X (ISSN-L)
- OpenAlex
- W3121694942
- Cited by
- 133
- References
- 4
- Citations by year
- 2026: 25 · 2025: 34 · 2024: 19 · 2023: 30 · 2022: 16 · 2021: 9
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers (publishedVersion, cc-by)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- Funders
- UK Industrial Strategy Challenge Fund, UK Research and Innovation, Engineering and Physical Sciences Research Council (EP/R026084/1, EP/R026092/1, EP/R026173/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- agent-oriented programming, multi-agent systems, agent programming languages, cross-linguistic comparison, multi-agent planning, negotiation protocols, agent-based modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Review of Agent-Based Programming for Multi-Agent Systems. https://app.cochat.ai/papers/10.3390/computers10020016
- The paper
- Cardoso, R. C., & Ferrando, A. (2021). A Review of Agent-Based Programming for Multi-Agent Systems. Computers. https://doi.org/10.3390/computers10020016