Top

Published in:

2023 | OriginalPaper | Chapter

Symbolic Artificial Intelligence Methods for Prescriptive Analytics

Authors : Gerhard Friedrich, Martin Gebser, Erich C. Teppan

Published in: Digital Transformation

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Prescriptive analytics in supply chain management and manufacturing addresses the question of “what” should happen “when”, where good recommendations require the solving of decision and optimization problems in all stages of the product life cycle at all decision levels. Artificial intelligence (AI) provides general methods and tools for the automated solving of such problems.

We start our contribution with a discussion of the relation between AI and analytics techniques. As many decision and optimization problems are computationally complex, we present the challenges and approaches for solving such hard problems by AI methods and tools. As a running example for the introduction of general problem-solving frameworks, we employ production planning and scheduling.

First, we present the fundamental modeling and problem-solving concepts of constraint programming (CP), which has a long and successful history in solving practical planning and scheduling tasks. Second, we describe highly expressive methods for problem representation and solving based on answer set programming (ASP), which is a variant of logic programming. Finally, as the application of exact algorithms can be prohibitive for very large problem instances, we discuss some methods from the area of local search aiming at near-optimal solutions. Besides the introduction of basic principles, we point out available tools and practical showcases.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Process Mining—Discovery, Conformance, and Enhancement of Manufacturing Processes

next chapter Machine Learning for Cyber-Physical Systems

The concept of operation types is not needed to define the FJSSP. However, this concept is often given in real environments representing machine abilities.

https://www.ibm.com/products/ilog-cplex-optimization-studio

In OPL, it is also possible to write (mixed) integer linear programs, quadratic or continuous programs.

This is similar to structs in the C language.

https://www.minizinc.org/challenge.html

https://www.ibm.com/analytics/cplex-cp-optimizer

Abels, D., Jordi, J., Ostrowski, M., Schaub, T., Toletti, A., Wanko, P.: Train scheduling with hybrid ASP. In: Balduccini, M., Lierler, Y., Woltran, S. (eds.) Proceedings of the Fifteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’19). Lecture Notes in Artificial Intelligence, vol. 11481, pp. 3–17. Springer-Verlag (2019)

Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley (1995)

Alviano, M., Amendola, G., Dodaro, C., Leone, N., Maratea, M., Ricca, F.: Evaluation of disjunctive programs in WASP. In: Balduccini, M., Lierler, Y., Woltran, S. (eds.) Proceedings of the Fifteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’19). Lecture Notes in Artificial Intelligence, vol. 11481, pp. 241–255. Springer-Verlag (2019)

Alviano, M., Dodaro, C., Maratea, M.: Nurse (re)scheduling via answer set programming. Intelligenza Artificiale 12(2), 109–124 (2018)

Balduccini, M.: Industrial-size scheduling with ASP+CP. In: Delgrande, J., Faber, W. (eds.) Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’11). Lecture Notes in Artificial Intelligence, vol. 6645, pp. 284–296. Springer-Verlag (2011)

Banbara, M., Inoue, K., Kaufmann, B., Okimoto, T., Schaub, T., Soh, T., Tamura, N., Wanko, P.: teaspoon: Solving the curriculum-based course timetabling problems with answer set programming. Annals of Operations Research 275(1), 3–37 (2019)

Baral, C., Gelfond, M.: Logic programming and knowledge representation. Journal of Logic Programming 19/20, 73–148 (1994)

Beck, H., Bierbaumer, B., Dao-Tran, M., Eiter, T., Hellwagner, H., Schekotihin, K.: Rule-based stream reasoning for intelligent administration of content-centric networks. In: Michael, L., Kakas, A. (eds.) Proceedings of the Fifteenth European Conference on Logics in Artificial Intelligence (JELIA’16). Lecture Notes in Artificial Intelligence, vol. 10021, pp. 522–528. Springer-Verlag (2016)

Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability, Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press (2009)

10.

Blazewicz, J., Ecker, K., Pesch, E., Schmidt, G., Weglarz, J.: Handbook on Scheduling: From Theory to Applications. Springer-Verlag (2014)

11.

Booth, K., Nejat, G., Beck, C.: A constraint programming approach to multi-robot task allocation and scheduling in retirement homes. In: Rueher, M. (ed.) Proceedings of the Twenty-second International Conference on Principles and Practice of Constraint Programming (CP’16). Lecture Notes in Computer Science, vol. 9892, pp. 539–555. Springer-Verlag (2016)

12.

Calimeri, F., Dodaro, C., Fuscà, D., Perri, S., Zangari, J.: Efficiently coupling the I-DLV grounder with ASP solvers. Theory and Practice of Logic Programming 20(2), 205–224 (2020)

13.

Calimeri, F., Ianni, G., Pacenza, F., Perri, S., Zangari, J.: Incremental answer set programming with overgrounding. Theory and Practice of Logic Programming 19(5-6), 957–973 (2019)

14.

Cordier, M., Dague, P., Lévy, F., Montmain, J., Staroswiecki, M., Travé-Massuyès, L.: Conflicts versus analytical redundancy relations: A comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(5), 2163–2177 (2004)

15.

Cuteri, B., Dodaro, C., Ricca, F., Schüller, P.: Overcoming the grounding bottleneck due to constraints in ASP solving: Constraints become propagators. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI’20). pp. 1688–1694. ijcai.org (2020)

16.

Da Col, G., Teppan, E.: Learning constraint satisfaction heuristics for configuration problems. In: Zhang, L., Haag, A. (eds.) Proceedings of the Nineteenth International Configuration Workshop. pp. 8–11. IESEG School of Management (2017)

17.

Da Col, G., Teppan, E.: Google vs IBM: A constraint solving challenge on the job-shop scheduling problem. In: Bogaerts, B., Erdem, E., Fodor, P., Formisano, A., Ianni, G., Inclezan, D., Vidal, G., Villanueva, A., de Vos, M., Yang, F. (eds.) Technical Communications of the Thirty-fifth International Conference on Logic Programming (ICLP’19). Electronic Proceedings in Theoretical Computer Science, vol. 306. Open Publishing Association (2019)

18.

Da Col, G., Teppan, E.: Industrial size job shop scheduling tackled by present day CP solvers. In: Schiex, T., de Givry, S. (eds.) Proceedings of the Twenty-fifth International Conference on Principles and Practice of Constraint Programming (CP’19). Lecture Notes in Computer Science, vol. 11802, pp. 144–160. Springer-Verlag (2019)

19.

De Cat, B., Bogaerts, B., Bruynooghe, M., Janssens, G., Denecker, M.: Predicate logic as a modeling language: The IDP system. In: Kifer, M., Liu, A. (eds.) Declarative Logic Programming: Theory, Systems, and Applications. pp. 279–323. ACM / Morgan and Claypool Publishers (2018)

20.

De Raedt, L., Passerini, A., Teso, S.: Learning constraints from examples. In: McIlraith, S., Weinberger, K. (eds.) Proceedings of the Thirty-Second National Conference on Artificial Intelligence (AAAI’18), the Thirtieth Conference on Innovative Applications of Artificial Intelligence (IAAI’18), and the Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’18). pp. 7965–7970. AAAI Press (2018)

21.

Dechter, R.: Constraint Processing. Morgan Kaufmann Publishers (2003)

22.

Delen, D., Demirkan, H.: Data, information and analytics as services. Decision Support Systems 55(1), 359–363 (2013)

23.

Dodaro, C., Gasteiger, P., Leone, N., Musitsch, B., Ricca, F., Schekotihin, K.: Combining answer set programming and domain heuristics for solving hard industrial problems. Theory and Practice of Logic Programming 16(5-6), 653–669 (2016)

24.

Dodaro, C., Leone, N., Nardi, B., Ricca, F.: Allotment problem in travel industry: A solution based on ASP. In: ten Cate, B., Mileo, A. (eds.) Proceedings of the Ninth International Conference on Web Reasoning and Rule Systems (RR’15). Lecture Notes in Computer Science, vol. 9209, pp. 77–92. Springer-Verlag (2015)

25.

Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)

26.

Erdem, E., Gelfond, M., Leone, N.: Applications of ASP. AI Magazine 37(3), 53–68 (2016)

27.

Erdem, E., Patoglu, V.: Applications of ASP in robotics. Künstliche Intelligenz 32(2-3), 143–149 (2018)

28.

Falkner, A., Friedrich, G., Haselböck, A., Schenner, G., Schreiner, H.: Twenty-five years of successful application of constraint technologies at Siemens. AI Magazine 37(4), 67–80 (2016)

29.

Falkner, A., Friedrich, G., Schekotihin, K., Taupe, R., Teppan, E.: Industrial applications of answer set programming. Künstliche Intelligenz 32(2-3), 165–176 (2018)

30.

Felfernig, A., Burke, R.: Constraint-based recommender systems: Technologies and research issues. In: Fensel, D., Werthner, H. (eds.) Proceedings of the Tenth International Conference on Electronic Commerce (ICEC’08). ACM International Conference Proceeding Series, vol. 342, pp. 3:1–3:10. ACM (2008)

31.

Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Intelligent support for interactive configuration of mass-customized products. In: Monostori, L., Váncza, J., Ali, M. (eds.) Proceedings of the Fourteenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE’01). Lecture Notes in Computer Science, vol. 2070, pp. 746–756. Springer-Verlag (2001)

32.

Felfernig, A., Teppan, E., Friedrich, G., Isak, K.: Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applications. In: Bradshaw, J., Lieberman, H., Staab, S. (eds.) Proceedings of the Thirteenth International Conference on Intelligent User Interfaces (IUI’08). pp. 217–226. ACM (2008)

33.

Fleischanderl, G., Friedrich, G., Haselböck, A., Schreiner, H., Stumptner, M.: Configuring large systems using generative constraint satisfaction. IEEE Intelligent Systems and their Applications 13(4), 59–68 (1998)

34.

Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Co. (1979)

35.

Gazzaneo, L., Padovano, A., Umbrello, S.: Designing smart operator 4.0 for human values: A value sensitive design approach. Procedia Manufacturing 42, 219–226 (2020)

36.

Gebser, M., Janhunen, T., Rintanen, J.: Answer set programming as SAT modulo acyclicity. In: Schaub, T., Friedrich, G., O’Sullivan, B. (eds.) Proceedings of the Twenty-first European Conference on Artificial Intelligence (ECAI’14). pp. 351–356. IOS Press (2014)

37.

Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T., Thiele, S., Wanko, P.: Potassco User Guide. University of Potsdam (2019)

38.

Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Wanko, P.: Theory solving made easy with clingo 5. In: Carro, M., King, A. (eds.) Technical Communications of the Thirty-second International Conference on Logic Programming (ICLP’16). Open Access Series in Informatics (OASIcs), vol. 52, pp. 2:1–2:15. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik (2016)

39.

Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan and Claypool Publishers (2012)

40.

Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Multi-shot ASP solving with clingo. Theory and Practice of Logic Programming 19(1), 27–82 (2019)

41.

Gebser, M., Kaufmann, B., Otero, R., Romero, J., Schaub, T., Wanko, P.: Domain-specific heuristics in answer set programming. In: desJardins, M., Littman, M. (eds.) Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI’13). pp. 350–356. AAAI Press (2013)

42.

Gebser, M., Kaufmann, B., Schaub, T.: Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187–188, 52–89 (2012)

43.

Gebser, M., Leone, N., Maratea, M., Perri, S., Ricca, F., Schaub, T.: Evaluation techniques and systems for answer set programming: A survey. In: Lang, J. (ed.) Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence (IJCAI’18). pp. 5450–5456. ijcai.org (2018)

44.

Gebser, M., Maratea, M., Ricca, F.: The seventh answer set programming competition: Design and results. Theory and Practice of Logic Programming 20(2), 176–204 (2020)

45.

Gebser, M., Obermeier, P., Schaub, T., Ratsch-Heitmann, M., Runge, M.: Routing driverless transport vehicles in car assembly with answer set programming. Theory and Practice of Logic Programming 18(3-4), 520–534 (2018)

46.

Gebser, M., Ryabokon, A., Schenner, G.: Combining heuristics for configuration problems using answer set programming. In: Calimeri, F., Ianni, G., Truszczyński, M. (eds.) Proceedings of the Thirteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’15). Lecture Notes in Artificial Intelligence, vol. 9345, pp. 384–397. Springer-Verlag (2015)

47.

Gebser, M., Maratea, M., Ricca, F.: What’s hot in the answer set programming competition. In: Schuurmans, D., Wellman, M. (eds.) Proceedings of the Thirtieth National Conference on Artificial Intelligence (AAAI’16). pp. 4327–4329. AAAI Press (2016)

48.

Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Proceedings of the Fifth International Conference and Symposium of Logic Programming (ICLP’88). pp. 1070–1080. MIT Press (1988)

49.

Gelfond, M.: Answer sets. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation. Foundations of Artificial Intelligence, vol. 3, pp. 285–316. Elsevier (2008)

50.

Gendreau, M., Potvin, J.: Handbook of Metaheuristics. Springer-Verlag (2010)

51.

Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)

52.

Hoos, H., Stützle, T.: Stochastic Local Search: Foundations & Applications. Elsevier/Morgan Kaufmann Publishers (2004)

53.

Hungerländer, P., Maier, K., Pöcher, J., Rendl, A., Truden, C.: Solving an on-line capacitated vehicle routing problem with structured time windows. In: Fink, A., Fügenschuh, A., Geiger, M. (eds.) Selected Papers of the Annual International Conference of the German Operations Research Society (GOR’16). pp. 127–132. Operations Research Proceedings, Springer-Verlag (2018)

54.

Janhunen, T., Kaminski, R., Ostrowski, M., Schaub, T., Schellhorn, S., Wanko, P.: Clingo goes linear constraints over reals and integers. Theory and Practice of Logic Programming 17(5-6), 872–888 (2017)

55.

Janhunen, T., Niemelä, I.: Compact translations of non-disjunctive answer set programs to propositional clauses. In: Balduccini, M., Son, T. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning: Essays Dedicated to Michael Gelfond on the Occasion of His 65th Birthday. Lecture Notes in Computer Science, vol. 6565, pp. 111–130. Springer-Verlag (2011)

56.

Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group. acatech – National Academy of Science and Engineering (2013)

57.

Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks (ICNN’95). pp. 1942–1948. IEEE Computer Society Press (1995)

58.

Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

59.

Korbicz, J., Kowalczuk, Z., Koácielny, J., Cholewa, W. (eds.): Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag (2004)

60.

Laborie, P., Rogerie, J., Shaw, P., Vilím, P.: IBM ILOG CP optimizer for scheduling – 20+ years of scheduling with constraints at IBM/ILOG. Constraints 23(2), 210–250 (2018)

61.

Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G.: Prescriptive analytics: Literature review and research challenges. International Journal of Information Management 50, 57–70 (2020)

62.

Lifschitz, V.: Answer set programming and plan generation. Artificial Intelligence 138(1-2), 39–54 (2002)

63.

Lifschitz, V.: Answer Set Programming. Springer-Verlag (2019)

64.

Liu, G., Janhunen, T., Niemelä, I.: Answer set programming via mixed integer programming. In: Brewka, G., Eiter, T., McIlraith, S. (eds.) Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (KR’12). pp. 32–42. AAAI Press (2012)

65.

Maratea, M., Pulina, L., Ricca, F.: Multi-engine ASP solving with policy adaptation. Journal of Logic and Computation 25(6), 1285–1306 (2015)

66.

McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A., Di Gaspero, L., Qu, R., Burke, E.: Setting the research agenda in automated timetabling: The second international timetabling competition. INFORMS Journal on Computing 22(1), 120–130 (2010)

67.

Morgado, A., Heras, F., Liffiton, M., Planes, J., Marques-Silva, J.: Iterative and core-guided MaxSAT solving: A survey and assessment. Constraints 18(4), 478–534 (2013)

68.

Nguyen, V., Obermeier, P., Son, T., Schaub, T., Yeoh, W.: Generalized target assignment and path finding using answer set programming. In: Sierra, C. (ed.) Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI’17). pp. 1216–1223. IJCAI/AAAI Press (2017)

69.

Nogueira, M., Balduccini, M., Gelfond, M., Watson, R., Barry, M.: An A-prolog decision support system for the space shuttle. In: Ramakrishnan, I. (ed.) Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages (PADL’01). Lecture Notes in Computer Science, vol. 1990, pp. 169–183. Springer-Verlag (2001)

70.

Perron, L., Furnon, V.: OR-Tools https://developers.google.com/optimization/

71.

Pham, T., Germano, S., Mileo, A., Kümper, D., Intizar Ali, M.: Automatic configuration of smart city applications for user-centric decision support. In: Crespi, N., Manzalini, A., Secci, S. (eds.) Proceedings of the Twentieth Conference on Innovations in Clouds, Internet and Networks (ICIN’17). pp. 360–365. IEEE Computer Society Press (2017)

72.

Priore, P., Gómez, A., Pino, R., Rosillo, R.: Dynamic scheduling of manufacturing systems using machine learning: An updated review. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(1), 83-97 (2014)

73.

Rabbouch, B., Saâdaoui, F., Mraihi, R.: Constraint programming based algorithm for solving large-scale vehicle routing problems. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) Proceedings of the Fourteenth International Conference on Hybrid Artificial Intelligence Systems (HAIS’19). Lecture Notes in Computer Science, vol. 11734, pp. 526–539. Springer-Verlag (2019)

74.

Rahim, M., Musirin, I., Abidin, I., Othman, M., Joshi, D.: Congestion management based optimization technique using bee colony. In: Proceedings of the Fourth International Conference on Power Engineering and Optimization (PEOCO’10). pp. 184–188. IEEE Computer Society Press (2010)

75.

Reiter, R.: On closed world data bases. In: Gallaire, H., Minker, J. (eds.) Logic and Databases. pp. 55–76. Plenum Press (1978)

76.

Reynolds, C.: Flocks, herds, and schools: A distributed behavioral model. In: Stone, M. (ed.) Proceedings of the Fourteenth Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’87). pp. 25–34. ACM (1987)

77.

Ricca, F., Grasso, G., Alviano, M., Manna, M., Lio, V., Iiritano, S., Leone, N.: Team-building with answer set programming in the Gioia-Tauro seaport. Theory and Practice of Logic Programming 12(3), 361–381 (2012)

78.

Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming. Elsevier (2006)

79.

Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education (2010)

80.

Salido, M., Garrido, A., Barták, R.: Introduction: Special issue on constraint satisfaction techniques for planning and scheduling problems. Engineering Applications of Artificial Intelligence 21(5), 679–682 (2008)

81.

Shmueli, G., Koppius, O.: Predictive analytics in information systems research. MIS Quarterly 35(3), 553–572 (2011)

82.

Souza, G.: Supply chain analytics. Business Horizons 57(5), 595–605 (2014)

83.

Valouxis, C., Housos, E.: Constraint programming approach for school timetabling. Computers & Operations Research 30(10), 1555–1572 (2003)

84.

Weinzierl, A., Bogaerts, B., Bomanson, J., Eiter, T., Friedrich, G., Janhunen, T., Kaminski, T., Langowski, M., Leutgeb, L., Schenner, G., Taupe, R.: The Alpha solver for lazy-grounding answer-set programming. ALP Newsletter (2019)

85.

Xi, B., Liu, Z., Raghavachari, M., Xia, C., Zhang, L.: A smart hill-climbing algorithm for application server configuration. In: Feldman, S., Uretsky, M., Najork, M., Wills, C. (eds.) Proceedings of the Thirteenth International Conference on World Wide Web (WWW’04). pp. 287–296. ACM (2004)

86.

Zhang, L., Madigan, C., Moskewicz, M., Malik, S.: Efficient conflict driven learning in a Boolean satisfiability solver. In: Ernst, R. (ed.) Proceedings of the International Conference on Computer-Aided Design (ICCAD’01). pp. 279–285. IEEE Computer Society Press (2001)

Title: Symbolic Artificial Intelligence Methods for Prescriptive Analytics
Authors: Gerhard Friedrich
Martin Gebser
Erich C. Teppan
Publisher: Springer Berlin Heidelberg
Book: Digital Transformation
Print ISBN: 978-3-662-65003-5

Electronic ISBN: 978-3-662-65004-2

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-662-65004-2_16

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner