Formalization of the Project Planning Problem When Working with an Unstructured Description of the Goal
DOI:
https://doi.org/10.14529/cmse260206Keywords:
planning, large language models, formalization, LLM agentAbstract
Large language models demonstrate high efficiency in natural language processing tasks. However, their application to planning as planners under complex dependencies remains limited. One promising direction is to use large language models as a tool for constructing formal planning models. This paper considers the project planning problem in the case where the initial project information is given in an unstructured textual form. A formalization is proposed in which a large language model is used to extract this information and construct a formal model in PDDL. This approach makes it possible to connect textual descriptions of tasks, dependencies, deadlines, and resources with formal tools for automated planning. As a result, the project planning problem is transformed into a form suitable for verification, validation, and subsequent solution by algorithmic methods. In addition, this study extends the mathematical apparatus of the classical resource-constrained project scheduling problem. In addition to the total project completion time, penalties for missed deadlines and the quality of performer assignment are taken into account. Thus, the classical formulation is supplemented with criteria that reflect more complex and practically significant project management requirements. These changes make it possible to move from a simplified single-criterion model to a more realistic formulation. Translation into PDDL makes it possible to use automated tools for validation and planning of the stated problem. The computational experiments demonstrated that the proposed approach increased the proportion of valid plans from 0.1–0.9 to 0.95–1.0 for all four tested models compared with the direct use of an LLM as a planner.
References
Wang L., Ma C., Feng X., et al. A survey on large language model based autonomous agents. Frontiers of Computer Science. 2024. Vol. 18, no. 6. P. 186345. DOI: 10.1007/s11704-024-40231-1.
Valmeekam K., Marquez M., Olmo A., et al. PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change. Advances in Neural Information Processing Systems. Vol. 36. 2023. P. 38975–38987. DOI: 10.52202/075280-1693.
McDermott D., Ghallab M., Howe A., et al. PDDL – The Planning Domain Definition Language: Technical Report / Yale Center for Computational Vision; Control. 1998.. URL: https://ipc08.icaps-conference.org/deterministic/data/mcdermott-et-al-tr-1998.pdf.
Helmert M. The Fast Downward Planning System. Journal of Artificial Intelligence Research. 2006. Vol. 26. P. 191–246. DOI: 10.1613/jair.1705.
Richter S., Westphal M. The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks. Journal of Artificial Intelligence Research. 2010. Vol. 39. P. 127–177. DOI: 10.1613/jair.2972.
Tantakoun M., Muise C., Zhu X. LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models. Findings of the Association for Computational Linguistics: ACL 2025 / ed. by W. Che, J. Nabende, E. Shutova, M.T. Pilehvar. Vienna, Austria: Association for Computational Linguistics, 2025. P. 25167–25188. DOI: 10.18653/v1/2025.findings-acl.1291.
Howey R., Long D., Fox M. VAL: Automatic Plan Validation, Continuous Effects and Mixed Initiative Planning Using PDDL. 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), 15–17 November 2004, Boca Raton, FL, USA. IEEE Computer Society, 2004. P. 294–301. DOI: 10.1109/ICTAI.2004.120.
Atkinson R. Project Management: Cost, Time and Quality, Two Best Guesses and a Phenomenon, Its Time to Accept Other Success Criteria. International Journal of Project Management. 1999. Vol. 17, no. 6. P. 337–342. DOI: 10.1016/S0263-7863(98)00069-6.
Brucker P., Drexl A., Moehring R., et al. Resource-Constrained Project Scheduling: Notation, Classification, Models, and Methods. European Journal of Operational Research. 1999. Vol. 112, no. 1. P. 3–41. DOI: 10.1016/S0377-2217(98)00204-5.
Herroelen W., Leus R. Project Scheduling Under Uncertainty: Survey and Research Potentials. European Journal of Operational Research. 2005. Vol. 165, no. 2. P. 289–306. DOI: 10.1016/j.ejor.2004.04.002.
Bahroun Z., As’ad R., Tanash M., Athamneh R. The Multi-Skilled Resource-Constrained Project Scheduling Problem: A Systematic Review and an Exploration of Future Landscapes. Management Systems in Production Engineering. 2024. Vol. 32, no. 1. P. 108–132. DOI: 10.2478/mspe-2024-0012.
Yehudai A., Eden L., Li A., et al. A Survey on Evaluation of LLM-based Agents. Findings of the Association for Computational Linguistics: ACL 2026 / ed. by M. Liakata, V.P. Moreira, J. Zhang, D. Jurgens. San Diego, California, United States: Association for Computational Linguistics, 2026. P. 26690–26714. DOI: 10.18653/v1/2026.findings-acl.1330.
Xie J., Zhang K., Chen J., et al. TravelPlanner: A Benchmark for Real-World Planning with Language Agents. Proceedings of the 41st International Conference on Machine Learning. Vol. 235. PMLR, 21–27 Jul 2024. P. 54590–54613. Proceedings of Machine Learning Research.
Xi Z., Chen W., Guo X., et al. The rise and potential of large language model based agents: a survey. Science China Information Sciences. 2025. Vol. 68, no. 2. P. 121101. DOI: 10.1007/s11432-024-4222-0.
Pallagani V., Roy K., Muppasani B., et al. On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). Proceedings of the International Conference on Automated Planning and Scheduling. Vol. 34. 2024. P. 432–444. DOI: 10.1609/icaps.v34i1.31503.
Kambhampati S., Valmeekam K., Guan L., et al. Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks. Proceedings of the 41st International Conference on Machine Learning. Vol. 235. PMLR, 2024. P. 22895–22907. Proceedings of Machine Learning Research. DOI: 10.48550/arXiv.2402.01817.
Huang C., Zhang L. On the Limit of Language Models as Planning Formalizers. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) / ed. by W. Che, J. Nabende, E. Shutova, M.T. Pilehvar. Vienna, Austria: Association for Computational Linguistics, 2025. P. 4880–4904. DOI: 10.18653/v1/2025.acl-long.242.
Webb T., Mondal S.S., Momennejad I. A Brain-Inspired Agentic Architecture to Improve Planning with LLMs. Nature Communications. 2025. Vol. 16, no. 1. P. 8633. DOI: 10.1038/s41467-025-63804-5.
Corrêa A.B., Pereira A.G., Seipp J. Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code. Advances in Neural Information Processing Systems 38 (NeurIPS 2025). 2025. DOI: 10.48550/arXiv.2503.18809.
Fox M., Long D. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research. 2003. Vol. 20. P. 61–124. DOI: 10.1613/jair.1129.
Gerevini A., Long D. Preferences and Soft Constraints in PDDL3. ICAPS Workshop on Planning with Preferences and Soft Constraints. 2006. P. 46–53. URL: https://artificial-intelligence.unibs.it/gerevini/papers/WS-PREF-ICAPS06.pdf.
Kristof-Brown A.L., Zimmerman R.D., Johnson E.C. Consequences of Individuals’ Fit at Work: A Meta-Analysis of Person-Job, Person-Organization, Person-Group, and Person-Supervisor Fit. Personnel Psychology. 2005. Vol. 58, no. 2. P. 281–342. DOI: 10.1111/j.1744-6570.2005.00672.x.
Ruiz-Torres A.J., Ablanedo-Rosas J.H., Mukhopadhyay S., Paletta G. Scheduling Workers: A Multi-Criteria Model Considering Their Satisfaction. Computers & Industrial Engineering. 2019. Vol. 128. P. 747–754. DOI: 10.1016/j.cie.2018.12.070.
McCarthy J. Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I. Communications of the ACM. 1960. Vol. 3, no. 4. P. 184–195. DOI: 10.1145/367177.367199.
Browning T.R., Yassine A.A. Resource-Constrained Multi-Project Scheduling: Priority Rule Performance Revisited. International Journal of Production Economics. 2010. Vol. 126, no. 2. P. 212–228. DOI: 10.1016/j.ijpe.2010.03.009.
Bold M., Goerigk M. A Compact Reformulation of the Two-Stage Robust Resource-Constrained Project Scheduling Problem. Computers & Operations Research. 2021. Vol. 130. P. 105232. DOI: 10.1016/j.cor.2021.105232.
Ranjbar M., Khalilzadeh M., Kianfar F., Etminani K. An Optimal Procedure for Minimizing Total Weighted Resource Tardiness Penalty Costs in the Resource-Constrained Project Scheduling Problem. Computers & Industrial Engineering. 2012. Vol. 62, no. 1. P. 264–270. DOI: 10.1016/j.cie.2011.09.013.
Khoshjahan Y., Najafi A.A., Afshar-Nadjafi B. Resource Constrained Project Scheduling Problem with Discounted Earliness-Tardiness Penalties: Mathematical Modeling and Solving Procedure. Computers & Industrial Engineering. 2013. Vol. 66, no. 2. P. 293–300. DOI: 10.1016/j.cie.2013.06.017.
Goetz N., Wald A. Employee Performance in Temporary Organizations: The Effects of Person-Environment Fit and Temporariness on Task Performance and Innovative Performance. European Management Review. 2020. Vol. 18, no. 2. P. 25–41. DOI: 10.1111/emre.12438.
Sackett P.R. Reflections on a Career Studying Individual Differences in the Workplace. Annual Review of Organizational Psychology and Organizational Behavior. 2021. Vol. 8, no. 1. P. 1–18. DOI: 10.1146/annurev-orgpsych-012420-061939.
Chowdhury S., Schulz E., Milner M., Voort D.V.D. Core Employee Based Human Capital and Revenue Productivity in Small Firms: An Empirical Investigation. Journal of Business Research. 2014. Vol. 67, no. 11. P. 2473–2479. DOI: 10.1016/j.jbusres.2014.03.007.
Hart P., Nilsson N., Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics. 1968. Vol. 4, no. 2. P. 100–107. DOI: 10.1109/TSSC.1968.300136.
Richter S., Helmert M. Preferred Operators and Deferred Evaluation in Satisficing Planning. Proceedings of the Nineteenth International Conference on Automated Planning and Scheduling (ICAPS 2009), Thessaloniki, Greece, September 19–23, 2009. AAAI Press, 2009. P. 273–280. DOI: 10.1609/icaps.v19i1.13345.
Perron L., Didier F., Gay S. The CP-SAT-LP Solver. 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informatik. P. 3:1–3:2. Leibniz International Proceedings in Informatics (LIPIcs). DOI: 10.4230/LIPIcs.CP.2023.3.
Rafikov T.R., Poptsov A.V., Oliseenko V.D., Abramov M.V. formalizplanning: repository containing source code and computational experiment data. 2026. URL: https://github.com/dscspro/formalizplanning (accessed: 14.07.2026).
Kolisch R., Sprecher A. PSPLIB – A Project Scheduling Problem Library: OR Software – ORSEP Operations Research Software Exchange Program. European Journal of Operational Research. 1997. Vol. 96, no. 1. P. 205–216. DOI: 10.1016/S0377-2217(96)00170-1.
