Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins

• Autorzy: Sinisi Stefano , Alimguzhin Vadim , Mancini Toni , Tronci Enrico , Mari Federico , Leeners Brigitte

Identyfikatory

DOI

10.3233/FI-2020-1943

• Języki publikacji: EN

Abstrakty

EN

In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.

Słowa kluczowe

EN

artificial intelligence; ISCT; in silico clinical trials; in silico treatment optimisation; personalised medicine; simulation; virtual physiological human

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2020
• Tom: Vol. 174, nr 3/4
• Strony: 283--310
• Opis fizyczny: Bibliogr. 79 poz., rys., wykr.

Twórcy

autor

Sinisi Stefano

• Computer Science Department, Sapienza University of Rome, Italy

autor

Alimguzhin Vadim

• Computer Science Department, Sapienza University of Rome, Italy

autor

Mancini Toni

• Computer Science Department, Sapienza University of Rome, Italy

autor

Tronci Enrico

• Computer Science Department, Sapienza University of Rome, Italy

autor

Mari Federico

• Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Italy

autor

Leeners Brigitte

• Division of Reproductive Endocrinology, University Hospital Zurich, Switzerland

Bibliografia

• [1] Mancini T, Mari F, Massini A, Melatti I, Salvo I, Sinisi S, Tronci E, Ehrig R, Röblitz S, Leeners B. Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction. In: Proceedings of 25th RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2018), volume 2271 of CEUR Workshop Proceedings. CEUR-WS.org, 2018. URL https://doi.org/10.29007/g864.
• [2] Mould D, Upton R. Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development. CPT: Pharmacometrics & Systems Pharmacology, 2012. 1(9):1-14.
• [3] Clapworthy G, Kohl P, Gregerson H, Thomas S, Viceconti M, Hose D, Pinney D, Fenner J, McCormack K, Lawford P, Van Sint Jan S, Waters S, Coveney P. Digital Human Modelling: A Global Vision and a European Perspective. In: Duffy V (ed.), Digital Human Modeling. Springer, Berlin, Heidelberg, 2007 pp. 549-558. ISBN 978-3-540-73321-8.
• [4] Trayanova N, Boyle P, Nikolov P. Personalized imaging and modeling strategies for arrhythmia prevention and therapy. Current Opinion in Biomedical Engineering, 2018. 5:21-28. doi:10.1016/j.cobme.2017.11.007.
• [5] Cox L, Loerakker S, Rutten M, De Mol B, Van De Vosse F. A Mathematical Model to Evaluate Control Strategies for Mechanical Circulatory Support. Artificial Organs, 2009. 33(8):593-603. doi:10.1111/j.1525-1594.2009.00755.x.
• [6] Röblitz S, Stötzel C, Deuflhard P, Jones H, Azulay DO, van der Graaf P, Martin S. A Mathematical Model of the Human Menstrual Cycle for the Administration of GnRH Analogues. Journal of Theoretical Biology, 2013. 321:8-27. doi:http://dx.doi.org/10.1016/j.jtbi.2012.11.020.
• [7] Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Čajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F. A Tumor Growth Inhibition Model for Low-Grade Glioma Treated with Chemotherapy or Radiotherapy. Clinical Cancer Research, 2012. 18(18):5071-5080. doi:10.1158/1078-0432.CCR-12-0084.
• [8] Jackson P, Juliano J, Hawkins-Daarud A, Rockne R, Swanson K. Patient-Specific Mathematical Neuro-Oncology: Using a Simple Proliferation and Invasion Tumor Model to Inform Clinical Practice. Bulletin of Mathematical Biology, 2015. 77(5):846-856. doi:10.1007/s11538-015-0067-7.
• [9] Roy P, Roy K. Molecular docking and QSAR studies of aromatase inhibitor androstenedione derivatives. Journal of Pharmacy and Pharmacology, 2010. 62(12):1717-1728. doi:10.1111/j.2042-7158.2010. 01154.x.
• [10] Bächler M, Menshykau D, De Geyter C, Iber D. Species-Specific Differences in Follicular Antral Sizes Result from Diffusion-Based Limitations on the Thickness of the Granulosa Cell Layer. Molecular Human Reproduction, 2014. 20(3):208-221. doi:10.1093/molehr/gat078.
• [11] Iarosz K, Borges F, Batista A, Baptista M, Siqueira R, Viana R, Lopes S. Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatment. Journal of Theoretical Biology, 2015. 368:113-121. doi:10.1016/j.jtbi.2015.01.006.
• [12] Yalcinkaya F, Kizilkaplan E, Erbas A. Mathematical modelling of human heart as a hydroelectromechanical system. In: Proceedings of 8th International Conference on Electrical and Electronics Engineering (ELECO 2013). IEEE, 2013 pp. 362-366. doi:10.1109/ELECO.2013.6713862.
• [13] Müller LO, Toro EF. A global multiscale mathematical model for the human circulation with emphasis on the venous system. International Journal for Numerical Methods in Biomedical Engineering, 2014. 30(7):681-725. doi:10.1002/cnm.2622.
• [14] Kanderian S, Weinzimer S, Voskanyan G, Steil G. Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes. Journal of Diabetes Science and Technology, 2009. 3:1047-1057. doi:10.1177/193229680900300508.
• [15] Herrero P, Georgiou P, Oliver N, Reddy M, Johnston D, Toumazou C. A Composite Model of Glucagon-Glucose Dynamics for In Silico Testing of Bihormonal Glucose Controllers. Journal of Diabetes Science and Technology, 2013. 7:941-951. doi:10.1177/193229681300700416.
• [16] Dalla Man C, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C. The UVA/Padova Type 1 Diabetes Simulator: New Features. Journal of Diabetes Science and Technology, 2014. 8:26-34. doi:10.1177/1932296813514502.
• [17] Schaller S, Willmann S, Lippert J, Schaupp L, Pieber T, Schuppert A, Eissing T. A generic integrated physiologically based whole-body model of the glucose-insulin-glucagon regulatory system. CPT: Pharmacometrics & Systems Pharmacology, 2013. 2(8):1-10. doi:10.1038/psp.2013.40.
• [18] Schlender JF, Meyer M, Thelen K, Krauss M, Willmann S, Eissing T, Jaehde U. Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals. Clinical Pharmacokinetics, 2016. 55(12):1573-1589.
• [19] Hester R, Brown A, Husband L, Iliescu R, Pruett W, Summers R, Coleman T. HumMod: a modeling environment for the simulation of integrative human physiology. Frontiers in physiology, 2011. 2:12. doi:10.3389/fphys.2011.00012.
• [20] Mateják M, Kofránek J. Physiomodel - An integrative physiology in Modelica. In: Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015). IEEE, 2015 pp. 1464-1467.
• [21] Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J. The Systems Biology Markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 2003. 19(4):524-531. doi:10.1093/bioinformatics/btg015.
• [22] Asai Y, Abe T, Oka H, Okita M, Hagihara K, Ghosh S, Matsuoka Y, Kurachi Y, Nomura T, Kitano H. A Versatile Platform for Multilevel Modeling of Physiological Systems: SBML-PHML Hybrid Modeling and Simulation. Advances Biomedical Engineering, 2014. 3:50-58. doi:10.14326/abe.3.50.
• [23] Fritzson P, Ulfhielm E, Belic A, Fransson M, Grèen H. Biochemical Mathematical Modeling with Modelica and the BioChem Library. In: Proceedings of 6th International Conference on Applied Mathematics (APLIMAT 2007). 2007 pp. 147-159. ID:16642810.
• [24] Maggioli F, Mancini T, Tronci E. SBML2Modelica: Integrating Biochemical Models within Open-Standard Simulation Ecosystems. Bioinformatics, 2020. 36(7):2165-2172. doi:10.1093/bioinformatics/btz860.
• [25] Avicenna Project. In silico Clinical Trials: How Computer Simulation will Transform the Biomedical Industry. URL http://avicenna-isct.org/wp-content/uploads/2016/01/AvicennaRoadmapPDF-27-01-16.pdf, 2016.
• [26] Pappalardo F, Russo G, Tshinanu F, Viceconti M. In silico clinical trials: concepts and early adoptions. Briefings in Bioinformatics, 2018. doi:10.1093/bib/bby043.
• [27] Weld D. Recent Advances in AI Planning. AI Magazine, 1999. 20(2):93-93.
• [28] Rossi F, Van Beek P, Walsh T (eds.). Handbook of Constraint Programming. Elsevier, 2006.
• [29] Biere A, Heule M, van Maaren H (eds.). Handbook of Satisfiability, volume 185. IOS Press, 2009. ISBN-10:1586039296, 13:978-1586039295.
• [30] Sontag E. Mathematical Control Theory: Deterministic Finite Dimensional Systems (2nd Ed.). Springer, 1998. ISBN-0-387-984895.
• [31] Alur R, Belta C, Ivančić F, Kumar V, Mintz M, Pappas GJ, Rubin H, Schug J. Hybrid Modeling and Simulation of Biomolecular Networks. In: Proceedings of 4th International Workshop on Hybrid Systems: Computation and Control (HSCC 2001), volume 2034 of Lecture Notes in Computer Science. Springer, 2003 pp. 19-32. doi:10.1007/3-540-45351-2_6.
• [32] Bartocci E, Lió P. Computational modeling, formal analysis, and tools for systems biology. PLoS Computational Biology, 2016. 12(1):e1004591. doi:10.1371/journal.pcbi.1004591.
• [33] Wang Q, Clarke EM. Formal modeling of biological systems. In: Proceedings of 2016 IEEE International High Level Design Validation and Test Workshop (HLDVT). IEEE, 2016 pp. 178-184. doi:10.1109/HLDVT.2016.7748273.
• [34] Mancini T, Mari F, Massini A, Melatti I, Salvo I, Tronci E. On Minimising the Maximum Expected Verification Time. Information Processing Letters, 2017. 122:8-16. doi:10.1016/j.ipl.2017.02.001.
• [35] Tronci E, Mancini T, Salvo I, Sinisi S, Mari F, Melatti I, Massini A, Davi’ F, Dierkes T, Ehrig R, Röblitz S, Leeners B, Krüger T, Egli M, Ille F. Patient-Specific Models from Inter-Patient Biological Models and Clinical Records. In: Proceedings of 14th International Conference on Formal Methods in Computer-Aided Design (FMCAD 2014). IEEE, 2014 pp. 207-214. doi:10.1109/FMCAD.2014.6987615.
• [36] Mancini T, Tronci E, Salvo I, Mari F, Massini A, Melatti I. Computing Biological Model Parameters by Parallel Statistical Model Checking. In: Proceedings of 3rd International Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2015), volume 9044 of Lecture Notes in Computer Science. Springer, 2015 pp. 542-554. doi:10.1007/978-3-319-16480-9_52.
• [37] Calabrese A, Mancini T, Massini A, Sinisi S, Tronci E. Generating T1DM Virtual Patients for In Silico Clinical Trials via AI-Guided Statistical Model Checking. In: Proceedings of 26th RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2019), volume 2538 of CEUR Workshop Proceedings. CEUR-WS.org, 2019. ID:210936680.
• [38] Leeners B, Kruger T, Geraedts K, Tronci E, Mancini T, Ille F, Egli M, Roeblitz S, Saleh L, Spanaus K, Schippert C, Zhang Y, Hengartner M. Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles. Frontiers in Behavioral Neuroscience, 2017. 11. doi:10.3389/fnbeh.2017.00120.
• [39] Hengartner M, Kruger T, Geraedts K, Tronci E, Mancini T, Ille F, Egli M, Roeblitz S, Ehrig R, Saleh L, Spanaus K, Schippert C, Zhang Y, Leeners B. Negative Affect is Unrelated to Fluctuations in Hormone Levels Across the Menstrual Cycle: Evidence from a Multisite Observational Study across Two Successive Cycles. Journal of Psychosomatic Research, 2017. 99:21-27. doi:10.1016/j.jpsychores.2017.05.018.
• [40] Leeners B, Krüger T, Geraedts K, Tronci E, Mancini T, Egli M, Röblitz S, Saleh L, Spanaus K, Schippert C, Zhang Y, Ille F. Associations Between Natural Physiological and Supraphysiological Estradiol Levels and Stress Perception. Frontiers in Psychology, 2019. 10:1296. doi:10.3389/fpsyg.2019.01296.
• [41] Mancini T, Mari F, Massini A, Melatti I, Merli F, Tronci E. System Level Formal Verification via Model Checking Driven Simulation. In: Proceedings of 25th International Conference on Computer Aided Verification (CAV 2013), volume 8044 of Lecture Notes in Computer Science. Springer, 2013 pp. 296-312. doi:10.1007/978-3-642-39799-8_21.
• [42] Mancini T, Mari F, Massini A, Melatti I, Tronci E. Anytime System Level Verification via Parallel Random Exhaustive Hardware in the Loop Simulation. Microprocessors and Microsystems, 2016. 41:12-28. doi:10.1016/j.micpro.2015.10.010.
• [43] Mancini T, Mari F, Massini A, Melatti I, Tronci E. Anytime System Level Verification via Random Exhaustive Hardware In The Loop Simulation. In: Proceedings of 17th Euromicro Conference on Digital System Design (DSD 2014). IEEE, 2014 pp. 236-245.
• [44] Mancini T, Mari F, Massini A, Melatti I, Tronci E. SyLVaaS: System Level Formal Verification as a Service. In: Proceedings of 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). IEEE, 2015 pp. 476-483.
• [45] Mancini T, Mari F, Massini A, Melatti I, Tronci E. System Level Formal Verification via Distributed Multi-Core Hardware in the Loop Simulation. In: Proceedings of 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2014). IEEE, 2014 pp. 734-742. doi:10.1109/PDP.2014.32.
• [46] Mancini T, Mari F, Massini A, Melatti I, Tronci E. SyLVaaS: System Level Formal Verification as a Service. Fundamenta Informaticae, 2016. 1-2:101-132. doi:10.3233/FI-2016-1444.
• [47] Mancini T, Flener P, Pearson J. Combinatorial Problem Solving over Relational Databases: View Synthesis through Constraint-Based Local Search. In: Proceedings of ACM Symposium on Applied Computing (SAC 2012). ACM, 2012 pp. 80-87. doi:10.1145/2245276.2245295.
• [48] Glover F, Kochenberger G (eds.). Handbook of Metaheuristics, volume 57. Springer, 2006.
• [49] Gottlob G, Greco G, Mancini T. Conditional Constraint Satisfaction: Logical Foundations and Complexity. In: Proceedings of 20th International Joint Conference on Artificial Intelligence (IJCAI 2007). 2007 pp. 88-93.
• [50] Mancini T, Cadoli M, Micaletto D, Patrizi F. Evaluating ASP and Commercial Solvers on the CSPLib. Constraints, 2008. 13(4):407-436.
• [51] Bordeaux L, Cadoli M, Mancini T. A Unifying Framework for Structural Properties of CSPs: Definitions, Complexity, Tractability. Journal of Artificial Intelligence Research, 2008. 32:607-629. doi:10.1613/jair.2538.
• [52] Bordeaux L, Cadoli M, Mancini T. Generalizing Consistency and other Constraint Properties to Quantified Constraints. ACM Transactions on Computational Logic, 2009. 10(3):17:1-17:25. doi:10.1145/1507244.1507247.
• [53] Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2014. 2(1):3. doi:10.1186/2047-2501-2-3.
• [54] Fogliata A, Belosi F, Clivio A, Navarria P, Nicolini G, Scorsetti M, Vanetti E, Cozzi L. On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiotherapy & Oncology, 2014. 113(3):385-391. doi:10.1016/j.radonc.2014.11.009.
• [55] Willmann S, Lippert J, Sevestre M, Solodenko J, Fois F, Schmitt W. PK-Sim^®: a physiologically based pharmacokinetic “whole-body” model. BIOSILICO, 2003. 1(4):121-124. doi:10.1016/S1478-5382(03)02342-4.
• [56] Jeena P, Bishai W, Pasipanodya J, Gumbo T. In Silico children and the glass mouse model: clinical trial simulations to identify and individualize optimal isoniazid doses in children with tuberculosis. Antimicrobial Agents and Chemotherapy, 2011. 55(2):539-545. doi:10.1128/AAC.00763-10.
• [57] van Dijkman S, Wicha S, Danhof M, Della Pasqua O. Individualized dosing algorithms and therapeutic monitoring for antiepileptic drugs. Clinical Pharmacology & Therapeutics, 2018. 103(4):663-673. doi:10.1002/cpt.777.
• [58] Le Novère N. Quantitative and logic modelling of molecular and gene networks. Nature Reviews Genetics, 2015. 16(3):146-158. doi:10.1038/nrg3885.
• [59] Fox M, Long D. Modelling Mixed Discrete-Continuous Domains for Planning. Journal of Artificial Intelligence Research, 2006. 27:235-297. doi: 10.1613/jair.2044.
• [60] Vallati M, Magazzeni D, De Schutter B, Chrpa L, McCluskey T. Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach. In: Proceedings of 30th National Conference on Artificial Intelligence (AAAI 2016). AAAI, 2016 pp. 3188-3194.
• [61] Coles A, Coles A. PDDL+ Planning with Events and Linear Processes. In: Proceedings of 24th International Conference on Automated Planning and Scheduling (ICAPS 2014). AAAI, 2014.
• [62] Bryce D, Gao S, Musliner D, Goldman R. SMT-Based Nonlinear PDDL+ Planning. In: Proceedings of 29th National Conference on Artificial Intelligence (AAAI 2015). AAAI, 2015 pp. 3247-3253.
• [63] Bogomolov S, Magazzeni D, Podelski A, Wehrle M. Planning as Model Checking in Hybrid Domains. In: Proceedings of 28th National Conference on Artificial Intelligence (AAAI 2014). AAAI, 2014 pp. 2228-2234.
• [64] Bogomolov S, Magazzeni D, Minopoli S, Wehrle M. PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior. In: Proceedings of 25th International Conference on Automated Planning and Scheduling (ICAPS 2015). AAAI, 2015 pp. 42-46.
• [65] Della Penna G, Magazzeni D, Mercorio F, Intrigila B. UPMurphi: A Tool for Universal Planning on PDDL+ Problems. In: Proceedings of 19th International Conference on Automated Planning and Scheduling (ICAPS 2009). AAAI, 2009.
• [66] Della Penna G, Intrigila B, Magazzeni D, Mercorio F. Planning for Autonomous Planetary Vehicles. In: Proceedings of 6th International Conference on Autonomic and Autonomous Systems (ICAS 2010). IEEE, 2010 pp. 131-136. doi:10.1109/ICAS.2010.26.
• [67] Della Penna G, Intrigila B, Magazzeni D, Melatti I, Tronci E. Cgmurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems. European Journal of Control, 2013. 19(1):14-36. URL https://doi.org/10.1016/j.ejcon.2013.02.001.
• [68] Alimguzhin V, Mari F, Melatti I, Salvo I, Tronci E. Linearizing Discrete-Time Hybrid Systems. IEEE Transactions on Automatic Control, 2017. 62(10):5357-5364. doi:10.1109/TAC.2017.2694559.
• [69] Mari F, Melatti I, Salvo I, Tronci E. Model Based Synthesis of Control Software from System Level Formal Specifications. ACM Transactions on Software Engineering and Methodology, 2014. 23(1):1-42. URL https://doi.org/10.1145/2559934.
• [70] Mazo M, Davitian A, Tabuada P. PESSOA: A tool for embedded controller synthesis. In: Proceedings of 22nd International Conference on Computer Aided Verification (CAV 2010), volume 6174 of Lecture Notes in Computer Science. Springer, 2010 pp. 566-569. doi:10.1007/978-3-642-14295-6_49.
• [71] Rungger M, Zamani M. SCOTS: A Tool for the Synthesis of Symbolic Controllers. In: Proceedings of 19th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2016). ACM, 2016 pp. 99-104. doi:10.1145/2883817.2883834.
• [72] Li H, Williams B. Generative Planning for Hybrid Systems Based on Flow Tubes. In: Proceedings of 18th International Conference on Automated Planning and Scheduling (ICAPS 2008). AAAI, 2008 pp. 206-213. ID:10626542.
• [73] Mancini T, Mari F, Melatti I, Salvo I, Tronci E, Gruber J, Hayes B, Prodanovic M, Elmegaard L. Demand-Aware Price Policy Synthesis and Verification Services for Smart Grids. In: Proceedings of 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014). IEEE, 2014 pp. 794-799. doi:10.1109/SmartGridComm.2014.7007745.
• [74] Mancini T, Mari F, Melatti I, Salvo I, Tronci E, Gruber J, Hayes B, Prodanovic M, Elmegaard L. User Flexibility Aware Price Policy Synthesis for Smart Grids. In: Proceedings of 18th Euromicro Conference on Digital System Design (DSD 2015). IEEE, 2015 pp. 478-485. doi:10.1109/DSD.2015.35.
• [75] Hayes B, Melatti I, Mancini T, Prodanovic M, Tronci E. Residential Demand Management using Individualised Demand Aware Price Policies. IEEE Transactions on Smart Grid, 2017. 8(3). doi:10.1109/TSG.2016.2596790.
• [76] Lipovetzky N, Ramirez M, Geffner H. Classical Planning with Simulators: Results on the Atari Video Games. In: Proceedings of 24th International Join Conference on Artificial Intelligence (IJCAI 2015), volume 15. 2015 pp. 1610-1616. ID:418270.
• [77] Ramirez M, Papasimeon M, Benke L, Lipovetzky N, Miller T, Pearce A. Real-Time UAV Maneuvering via Automated Planning in Simulations. In: Proceedings of 26th International Join Conference on Artificial Intelligence (IJCAI 2017). 2017 pp. 5243-5245. URL https://doi.org/10.24963/ijcai.2017/778.
• [78] Frances G, Ramírez Jávega M, Lipovetzky N, Geffner H. Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators. In: Proceedings of 26th International Joint Conference on Artificial Intelligence (IJCAI 2017). 2017 pp. 4294-4301. doi:10.24963/ijcai.2017/600.
• [79] Lipovetzky N, Geffner H. Width and Serialization of Classical Planning Problems. In: Proceedings of 20th European Conference on Artificial Intelligence (ECAI 2012). IOS Press, 2012 pp. 540-545. classical-width-ecai12.pdf.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).