Skip to main content
SpringerLink
Log in
Book cover

A Guided Tour of Artificial Intelligence Research pp 153–183Cite as

Constraint Reasoning

  • Chapter
  • First Online:

Abstract

In this chapter, I briefly present constraint reasoning. Constraint reasoning has been a subfield of artificial intelligence (AI) that is nowadays more well-known as constraint programming (CP). The change of name occurred more or less when CP has started to be widely used for solving combinatorial problems in industrial applications. This also corresponds to the moment where CP was enriched by the contributions from logic programming for the aspects related to languages and from operation research for the propagation of complex constraints. Considering the topic of this book, I will stay on a AI-oriented presentation of CP.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    In the rest of this chapter we will represent an instantiation indifferently as a sequence or as a set of variable assignments. The assignment of value \(v_i\) to variable \(x_i\) is denoted by \((x_i,v_i)\).

  2. 2.

    The width of Freuder is a lower bound to the tree-width (Arnborg 1985).

  3. 3.

    The pigeon-hole problem is to assign n pigeons to \(n-1\) holes in such a way that no hole contains more than one pigeon.

References

  • Amilhastre J, Fargier H, Marquis P (2002) Consistency restoration and explanations in dynamic CSPs -application to configuration. Artif Intell 135:199–234

    Article  MathSciNet  MATH  Google Scholar 

  • Andersen H, Hadzic T, Hooker JN, Tiedemann P (2007) A constraint store based on multivalued decision diagrams. In: Proceedings CP’07, Providence, RI, pp 118–132

    Google Scholar 

  • Apt K (2003) Principles of constraint programming. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Arnborg S (1985) Efficient algorithms for combinatorial problems on graphs with bounded decomposability - a survey. BIT 25:2–23

    Article  MathSciNet  MATH  Google Scholar 

  • Beldiceanu N, Simonis H (2011) A constraint seeker: finding and ranking global constraints from examples. In: Proceedings CP’11, Perugia, Italy, pp 12–26

    Google Scholar 

  • Beldiceanu N, Carlsson M, Debruyne R, Petit T (2005a) Reformulation of global constraints based on constraints checkers. Constraints 10(4):339–362

    Article  MathSciNet  MATH  Google Scholar 

  • Beldiceanu N, Carlsson M, Rampon J (2005b) Global constraint catalog. Technical report T2005:08, Swedish Institute of Computer Science, Kista, Sweden

    Google Scholar 

  • Benhamou B (1994) Study of symmetry in constraint satisfaction problems. In: Proceedings PPCP’94, Seattle, WA, pp 249–257

    Google Scholar 

  • Benhamou F, Granvilliers L (2006) Continuous and interval constraints. In: Rossi F, van Beek P, Walsh T (eds) Handbook of constraint programming, chapter 16. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Bennaceur H, Affane M (2001) Partition-k-AC: an efficient filtering technique combining domain partition and arc consistency. In: Proceedings CP’01, Paphos, Cyprus, pp 560–564 (Short paper)

    Google Scholar 

  • Bergman D, Ciré AA, van Hoeve WJ (2014) MDD propagation for sequence constraints. J Artif Intell Res 50:697–722

    Article  MathSciNet  MATH  Google Scholar 

  • Berlandier P (1995) Improving domain filtering using restricted path consistency. In: Proceedings IEEE-CAIA’95, Los Angeles, CA

    Google Scholar 

  • Bertelé U, Brioshi F (1972) Nonserial dynamic programming. Academic Press, London

    MATH  Google Scholar 

  • Bessiere C, Régin J (1996) MAC and combined heuristics: two reasons to forsake FC (and CBJ?) on hard problems. In: Proceedings CP’96, Cambridge, MA, pp 61–75

    Google Scholar 

  • Bessiere C, Régin J (2001) Refining the basic constraint propagation algorithm. In: Proceedings IJCAI’01, Seattle, WA, pp 309–315

    Google Scholar 

  • Bessiere C, Freuder E, Régin J (1999) Using constraint metaknowledge to reduce arc consistency computation. Artif Intell 107:125–148

    Article  MathSciNet  MATH  Google Scholar 

  • Bessiere C, Régin J, Yap R, Zhang Y (2005) An optimal coarse-grained arc consistency algorithm. Artif Intell 165:165–185

    Article  MathSciNet  MATH  Google Scholar 

  • Bessiere C, Hebrard E, Hnich B, Walsh T (2007) The complexity of reasoning with global constraints. Constraints 12(2):239–259

    Article  MathSciNet  MATH  Google Scholar 

  • Bessiere C, Hebrard E, Hnich B, Kiziltan Z, Walsh T (2008) Slide: a useful special case of the cardpath constraint. In: Proceedings ECAI’08, Patras, Greece, pp 475–479

    Google Scholar 

  • Bessiere C, Katsirelos G, Narodytska N, Walsh T (2009) Circuit complexity and decompositions of global constraints. In: Proceedings IJCAI’09, Pasadena, CA, pp 412–418

    Google Scholar 

  • Bessiere C, Raedt LD, Guns T, Kotthoff L, Nanni M, Nijssen S, O’Sullivan B, Paparrizou A, Pedreschi D, Simonis H (2017) The inductive constraint programming loop. IEEE Intell Syst 32(5):44–52

    Article  Google Scholar 

  • Boussemart F, Hemery F, Lecoutre C, Sais L (2004) Boosting systematic search by weighting constraints. In: Proceedings ECAI’04, Valencia, Spain, pp 146–150

    Google Scholar 

  • Bulatov AA (2017) A dichotomy theorem for nonuniform CSPs. In: Proceedings IEEE-FOCS’17, Berkeley, CA, pp 319–330

    Google Scholar 

  • Bulatov AA, Dalmau V (2006) A simple algorithm for mal’tsev constraints. SIAM J Comput 36(1):16–27

    Article  MathSciNet  MATH  Google Scholar 

  • Cabon C, de Givry S, Lobjois L, Schiex T, Warners J (1999) Radio link frequency assignment. Constraints 4:79–89

    Article  MATH  Google Scholar 

  • Carbonnel C (2016) The dichotomy for conservative constraint satisfaction is polynomially decidable. In: Proceedings CP’16, Toulouse, France, pp 130–146

    Google Scholar 

  • Chen H, Dalmau V, Grußien B (2013) Arc consistency and friends. J Log Comput 23(1):87–108

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng BMW, Lee JH, Wu JCK (1997) A nurse rostering system using constraint programming and redundant modeling. IEEE Trans Inf Technol Biomed 1(1):44–54

    Article  Google Scholar 

  • Cohen DA, Jeavons PG (2017) The power of propagation: when GAC is enough. Constraints 22(1):3–23

    Article  MathSciNet  MATH  Google Scholar 

  • Cooper M (1997) Fundamental properties of neighbourhood substitution in constraint satisfaction problems. Artif Intell 90:1–24

    Article  MathSciNet  MATH  Google Scholar 

  • Cooper M, Cohen D, Jeavons P (1994) Characterising tractable constraints. Artif Intell 65:347–361

    Article  MathSciNet  MATH  Google Scholar 

  • Dao T, Duong K, Vrain C (2017) Constrained clustering by constraint programming. Artif Intell 244:70–94

    Article  MathSciNet  MATH  Google Scholar 

  • Debruyne R, Bessiere C (1997) From restricted path consistency to max-restricted path consistency. In: Proceedings CP’97, Linz, Austria, pp 312–326

    Google Scholar 

  • Debruyne R, Bessiere C (2001) Domain filtering consistencies. J Artif Intell Res 14:205–230

    Article  MathSciNet  MATH  Google Scholar 

  • Dechter R (1986) Learning while searching in constraint satisfaction problems. In: Proceedings AAAI’86, Philadelphia, PA, pp 178–183

    Google Scholar 

  • Dechter R (1990) Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition. Artif Intell 41:273–312

    Article  Google Scholar 

  • Dechter R, Dechter A (1988) Belief maintenance in dynamic constraint networks. In: Proceedings AAAI’88, St Paul MN, pp 37–42

    Google Scholar 

  • Dechter R, Pearl J (1988) Network-based heuristics for constraint-satisfaction problems. Artif Intell 34:1–38

    Article  MathSciNet  MATH  Google Scholar 

  • Dechter R, Pearl J (1989) Tree clustering for constraint networks. Artif Intell 38:353–366

    Article  MathSciNet  MATH  Google Scholar 

  • Dechter R, van Beek P (1997) Local and global relational consistency. Theor Comput Sci 173(1):283–308

    Article  MathSciNet  MATH  Google Scholar 

  • Demeulenaere J, Hartert R, Lecoutre C, Perez G, Perron L, Régin J-C, Schaus P (2016) Compact-table: efficiently filtering table constraints with reversible sparse bit-sets. In: Proceedings CP’16, Toulouse, France, pp 207–223

    Google Scholar 

  • Deville Y, Barette O, Van Hentenryck P (1999) Constraint satisfaction over connected row convex constraints. Artif Intell 109(1–2):243–271

    Article  MathSciNet  MATH  Google Scholar 

  • Dincbas M, Van Hentenryck P, Simonis H, Aggoun A (1988) The constraint logic programming language chip. In: Proceedings of the international conference on fifth generation computer systems, Tokyo, Japan, pp 693–702

    Google Scholar 

  • Faltings B, Macho-Gonzalez S (2005) Open constraint programming. Artif Intell 161(1–2):181–208

    Article  MathSciNet  MATH  Google Scholar 

  • Fargier H, Lang J, Schiex T (1996) Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge. In: Proceedings AAAI’96, Portland, OR, pp 175–180

    Google Scholar 

  • Feder T, Vardi M (1998) The computational structure of monotone monadic SNP and constraint satisfaction: a study through datalog and group theory. SIAM J Comput 28(1):57–104

    Article  MathSciNet  MATH  Google Scholar 

  • Fikes R (1970) REF-ARF: a system for solving problems stated as procedures. Artif Intell 1:27–120

    Article  MATH  Google Scholar 

  • Freuder E (1978) Synthesizing constraint expressions. Commun ACM 21(11):958–966

    Article  MathSciNet  MATH  Google Scholar 

  • Freuder E (1982) A sufficient condition for backtrack-free search. J ACM 29(1):24–32

    Article  MathSciNet  MATH  Google Scholar 

  • Freuder E (1985) A sufficient condition for backtrack-bounded search. J ACM 32(4):755–761

    Article  MathSciNet  MATH  Google Scholar 

  • Freuder E (1990) Complexity of k-tree structured constraint satisfaction problems. In: Proceedings AAAI’90, Boston, MA, pp 4–9

    Google Scholar 

  • Freuder E (1991) Eliminating interchangeable values in constraint satisfaction problems. In: Proceedings AAAI’91, Anaheim, CA, pp 227–233

    Google Scholar 

  • Freuder E, Elfe C (1996) Neighborhood inverse consistency preprocessing. In: Proceedings AAAI’96, Portland, OR, pp 202–208

    Google Scholar 

  • Freuder E, Wallace R (1992) Partial constraint satisfaction. Artif Intell 58:21–70

    Article  MathSciNet  Google Scholar 

  • Gaschnig J (1974) A constraint satisfaction method for inference making. In: Proceedings twelfth annual Allerton conference on circuit and system theory, pp 866–874

    Google Scholar 

  • Gaschnig J (1978) Experimental case studies of backtrack vs waltz-type vs new algorithms for satisficing assignment problems. In: Proceedings second Canadian conference on artificial intelligence, Canada, Toronto, pp 268–277

    Google Scholar 

  • Gaschnig J (1979) Performance measurement and analysis of certain search algorithms. Technical report CMU-CS-79-124, Carnegie-Mellon University, Pittsburgh, PA

    Google Scholar 

  • Gaspin C, Westhof E (1994) The determination of the secondary structures of ribonucleic acids as a constraint satisfaction problem. In: IEEE Colloquium on molecular bioinformatics, London, UK

    Google Scholar 

  • Gent I, Petrie K, Puget J (2006) Symmetry in constraint programming. In: Rossi F, van Beek P, Walsh T (eds) Handbook of constraint programming, chapter 10. Elsevier, Amsterdam

    Google Scholar 

  • Gervet C (1994) Conjunto: constraint logic programming with finite set domains. In: Proceedings ILPS’94, Ithaca, NY, pp 339–358

    Google Scholar 

  • Gervet C, Van Hentenryck P (2006) Length-lex ordering for set CSPs. In: Proceedings AAAI’06, Boston, MA, pp 48–53

    Google Scholar 

  • Golomb S, Baumert L (1965) Backtrack programming. J ACM 12(4):516–524

    Article  MathSciNet  MATH  Google Scholar 

  • Gomes C, Fernandez C, Selman B, Bessiere C (2005) Statistical regimes across constrainedness regions. Constraints 10(4):317–337

    Article  MathSciNet  MATH  Google Scholar 

  • Gomes C, Selman B, Crato N (1997) Heavy-tailed distributions in combinatorial search. In: Proceedings CP’97, Linz, Austria, pp 121–135

    Google Scholar 

  • Gottlob G, Leone N, Scarcello F (2000) A comparison of structural CSP decomposition methods. Artif Intell 124(2):243–282

    Article  MathSciNet  MATH  Google Scholar 

  • Guns T, Nijssen S, Raedt LD (2011) Itemset mining: a constraint programming perspective. Artif Intell 175(12–13):1951–1983

    Article  MathSciNet  MATH  Google Scholar 

  • Gyssens M, Jeavons P, Cohen D (1994) Decomposing constraint satisfaction problems using database techniques. Artif Intell 66:57–89

    Article  MathSciNet  MATH  Google Scholar 

  • Haralick R, Elliott G (1980) Increasing tree seach efficiency for constraint satisfaction problems. Artif Intell 14:263–313

    Article  Google Scholar 

  • Harvey W, Ginsberg M (1995) Limited discrepancy search. In: Proceedings IJCAI’95, Montréal, Canada, pp 607–613

    Google Scholar 

  • Hoda S, van Hoeve WJ, Hooker JN (2010) A systematic approach to MDD-based constraint programming. In: Proceedings CP’10, St Andrews, Scotland, pp 266–280

    Google Scholar 

  • ILOG (1997) User’s manual. ILOG Solver, 4.0 edition

    Google Scholar 

  • Janssen P, Vilarem M (1988) Problèmes de satisfaction de contraintes : techniques de résolution et application à la synthèse de peptides. Technical Report 54, CRIM, University of Montpellier, France

    Google Scholar 

  • Janssen P, Jégou P, Nouguier B, Vilarem MC (1989) A filtering process for general constraint-satisfaction problems: achieving pairewise-consistency using an associated binary representation. In: Proceedings IEEE-ICTAI’89, Fairfax, VA, pp 420–427

    Google Scholar 

  • Jeavons P, Cooper M (1995) Tractable constraints on ordered domains. Artif Intell 79(2):327–339

    Article  MathSciNet  MATH  Google Scholar 

  • Jeavons P, Cohen D, Cooper M (1998) Constraints, consistency and closure. Artif Intell 101:251–265

    Article  MathSciNet  MATH  Google Scholar 

  • Jégou P (1993) On the consistency of general constraint-satisfaction problems. In: Proceedings AAAI’93, Washington, DC, pp 114–119

    Google Scholar 

  • Jégou P, Terrioux C (2003) Hybrid backtracking bounded by tree-decomposition of constraint networks. Artif Intell 146(1):43–75

    Article  MathSciNet  MATH  Google Scholar 

  • Katsirelos G, Bacchus F (2003) Unrestricted nogood recording in CSP search. In: Proceedings CP’03, Kinsale, Ireland, pp 873–877

    Google Scholar 

  • Korf R (1996) Improved limited discrepancy search. In: Proceedings AAAI’96, Portland, OR, pp 286–291

    Google Scholar 

  • Laurière J-L (1978) A language and a program for stating and solving combinatorial problems. Artif Intell 10:29–127

    Article  MathSciNet  MATH  Google Scholar 

  • Lecoutre C (2011) STR2: optimized simple tabular reduction for table constraints. Constraints 16(4):341–371

    Article  MathSciNet  MATH  Google Scholar 

  • Lecoutre C, Hemery F (2007) A study of residual supports in arc consistency. In: Proceedings IJCAI’07, Hyderabad, India, pp 125–130

    Google Scholar 

  • Lecoutre C, Sais L, Tabary S, Vidal V (2006) Last conflict based reasoning. In: Proceedings ECAI’06, Riva del Garda, Italy, pp 133–137

    Google Scholar 

  • Lecoutre C, Sais L, Tabary S, Vidal V (2007) Nogood recording from restarts. In: Proceedings IJCAI’07, Hyderabad, India, pp 131–136

    Google Scholar 

  • Lecoutre C, Likitvivatanavong C, Yap RHC (2015) STR3: a path-optimal filtering algorithm for table constraints. Artif Intell 220:1–27

    Article  MathSciNet  MATH  Google Scholar 

  • Lhomme O (1993) Consistency techniques for numeric CSPs. In: Proceedings IJCAI’93, Chambéry, France, pp 232–238

    Google Scholar 

  • Lombardi M, Milano M, Bartolini A (2017) Empirical decision model learning. Artif Intell 244:343–367

    Article  MathSciNet  MATH  Google Scholar 

  • Luby M, Sinclair A, Zuckerman D (1993) Optimal speedup of Las Vegas algorithms. Inf Process Lett 47(4):173–180

    Article  MathSciNet  MATH  Google Scholar 

  • Mackworth A (1977a) Consistency in networks of relations. Artif Intell 8:99–118

    Article  MATH  Google Scholar 

  • Mackworth A (1977b) On reading sketch maps. In: Proceedings IJCAI’77, Cambridge, MA, pp 598–606

    Google Scholar 

  • Martin P, Shmoys D (1996) A new approach to computing optimal schedules for the job-shop scheduling problem. In: Proceedings IPCO’96, Vancouver, BC, pp 389–403

    Google Scholar 

  • Mehta D, O’Sullivan B, Quesada L (2011) Value ordering for finding all solutions: interactions with adaptive variable ordering. In: Proceedings CP’11, Perugia, Italy, pp 606–620

    Google Scholar 

  • Meseguer P (1997) Interleaved depth-first search. In: Proceedings IJCAI’97, Nagoya, Japan, pp 1382–1387

    Google Scholar 

  • Michel L, Van Hentenryck P (2012) Activity-based search for black-box constraint programming solvers. In: Proceedings CPAIOR’12, Nantes, France, pp 228–243

    Google Scholar 

  • Mohr R, Henderson T (1986) Arc and path consistency revisited. Artif Intell 28:225–233

    Article  Google Scholar 

  • Mohr R, Masini G (1988) Good old discrete relaxation. In: Proceedings ECAI’88, Munchen, FRG, pp 651–656

    Google Scholar 

  • Montanari U (1974) Networks of constraints: fundamental properties and applications to picture processing. Inf Sci 7:95–132

    Article  MathSciNet  MATH  Google Scholar 

  • Nadel B (1988) Tree search and arc consistency in constraint satisfaction algorithms. In: Kanal L, Kumar V (eds) Search in artificial intelligence. Springer, Berlin, pp 287–342

    Chapter  Google Scholar 

  • Ohrimenko O, Stuckey P, Codish M (2009) Propagation via lazy clause generation. Constraints 14(3):357–391

    Article  MathSciNet  MATH  Google Scholar 

  • Pesant G (2004) A regular language membership constraint for finite sequences of variables. In: Proceedings CP’04, Canada, Toronto, pp 482–495

    Google Scholar 

  • Prosser P (1993) Hybrid algorithms for the constraint satisfaction problem. Comput Intell 9(3):268–299

    Article  Google Scholar 

  • Refalo P (2004) Impact-based search strategies for constraint programming. In: Proceedings CP’04, Canada, Toronto, pp 557–571

    Google Scholar 

  • Régin J-C (1994) A filtering algorithm for constraints of difference in CSPs. In: Proceedings AAAI’94, Seattle, WA, pp 362–367

    Google Scholar 

  • Régin J-C (1996) Generalized arc consistency for global cardinality constraint. In: Proceedings AAAI’96, Portland, OR, pp 209–215

    Google Scholar 

  • Sabin D, Freuder E (1994) Contradicting conventional wisdom in constraint satisfaction. In: Proceedings PPCP’94, Seattle, WA

    Google Scholar 

  • Schaus P, Aoga JOR, Guns T (2017) Coversize: a global constraint for frequency-based itemset mining. In: Proceedings CP’17, Melbourne, Australia, pp 529–546

    Google Scholar 

  • Schiex T, Verfaillie G (1993) Nogood recording for static and dynamic CSPs. In: Proceedings IEEE-ICTAI’93, Boston, MA, pp 48–55

    Google Scholar 

  • Schiex T, Fargier H, Verfaillie G (1995) Valued constraint satisfaction problems: hard and easy problems. In: Proceedings IJCAI’95, Montréal, Canada, pp 631–637

    Google Scholar 

  • Shaw P (1998) Using constraint programming and local search methods to solve vehicle routing problems. In: Proceedings CP’98, Pisa, Italy, pp 417–431

    Google Scholar 

  • Siala M (2015) Search, propagation, and learning in sequencing and scheduling problems. Constraints 20(4):479–480

    Article  MathSciNet  Google Scholar 

  • Ugarte W, Boizumault P, Crémilleux B, Lepailleur A, Loudni S, Plantevit M, Raïssi C, Soulet A (2017) Skypattern mining: from pattern condensed representations to dynamic constraint satisfaction problems. Artif Intell 244:48–69

    Article  MathSciNet  MATH  Google Scholar 

  • Ullmann J (2007) Partition search for non-binary constraint satisfaction. Inf Sci 177(18):3639–3678

    Article  MathSciNet  MATH  Google Scholar 

  • van Beek P, Dechter R (1995) On the minimality and global consistency of row-convex constraint networks. J ACM 42(3):543–561

    Article  MathSciNet  MATH  Google Scholar 

  • Walsh T (1997) Depth-bounded discrepancy search. In: Proceedings IJCAI’97, Nagoya, Japan, pp 1388–1393

    Google Scholar 

  • Walsh T (2002) Stochastic constraint programming. In: Proceedings ECAI’02, Lyons, France, pp 111–115

    Google Scholar 

  • Waltz D (1972) Generating semantic descriptions from drawings of scenes with shadows. Technical report, MAC AI-271, MIT

    Google Scholar 

  • Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685

    Article  Google Scholar 

  • Zhuk D (2017) A proof of CSP dichotomy conjecture. In: Proceedings IEEE-FOCS’17, Berkeley, CA, pp 331–342

    Google Scholar 

Download references

Acknowledgements

I am very grateful to Anastasia Paparrizou and Thomas Schiex for their thorough reading of this chapter and for their suggestions. I also thank Clément Carbonnel, Michele Lombardi, and Michela Milano for their help in describing some of the contributions presented in this chapter.

Author information

Authors and Affiliations

  1. CNRS, University of Montpellier, Montpellier, France

    Christian Bessiere

Authors
  1. Christian Bessiere
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Bessiere .

Editor information

Editors and Affiliations

  1. CRIL-CNRS, Université d’Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bessiere, C. (2020). Constraint Reasoning. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06167-8_6

Download citation

Publish with us

Policies and ethics

Search

Navigation