1 符号主义人工智能：第 2 章参考文献
[1] McCarthy J. Programs with common sense[C]. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes, pages 75–91. London, 1959.
[2] Russell S, Norvig P. Artificial Intelligence: A Modern Approach[M]. Third Edition. New Jersey: Prentice Hall, 2009.
[3] van Harmelen F, Lifschitz V, Porter B. Handbook of Knowledge Representation[M]. Amsterdam: Elsevier Science, 2008.
[4] D'Agostino M, Gabbay DM, Hähnle R, Posegga J. Handbook of Tableau Methods[M]. Berlin: Springer press, 2003.
[5] Davis M, Putnam H. A computing procedure for quantification theory[J]. Journal of the ACM, 1960, 7(3): 201–215.
[6] Robinson JA. A machine oriented logic based on the resolution principle[J]. Computer Machine, 1965, 12(1):23–41.
[7] McCune W, Wos L. Otter: the CADE-13 competition incarnations[J]. Journal of Automated Reasoning, 1997, 18(2): 211–220.
[8] Beckert B, Hähnle R, Oel P, Sulzmann M. The tableau-based theorem prover: 3TAP Version 4.0[C]. In: Proc. of 13th International Conference on Automated Deduction (CADE 1996), New Brunswick, USA, 1996, 303–307.
[9] Kalvala S. Using Isabelle to Prove Simple Theorems[C]. In: Proc. of Higher Order Logic Theorem Proving and its Application, 6th International Workshop (HUG 1993), Vancouver, Canada, 1993, 514–517.
[10] Biere A, Heule M, van Maaren H, Walsh T. Handbook of
Satisfiability[M]. Amsterdam: IOS Press, 2009.
[11] Kautz H, Selman B. Planning as satisfiability[C]. In: Proceeding of the 10th European conference on Artificial intelligence, 1992: 359– 363.
[12] Biere A, Cimatti A, Clarke E, Zhu Y. Symbolic model checking without BDDs[C]. In: Proceedings of the Workshop on Tools and Algorithms for the Construction and Analysis of Systems, LNCS. Springer-Verlag, 1999: 193–207.
[13] Huang G, Jia X, Liau C, You J. Two-Literal Logic Programs and Satisfiability Representation of Stable Models: A Comparison[C]. Canadian Conference on AI 2002: 119–131.
[14] Selman B, Kautz H. Knowledge compilation and theory approximation[J]. Journal of the ACM, 1996, 43(2): 193–224.
[15] Cadoli M, Donini F. A survey on knowledge compilation[J]. AI Communications, 1997, 10(3–4): 137–150.
[16] Roth D. On the Hardness of Approximate Reasoning[J]. Artificial Intelligence, 1996, 82(1–2): 273–302.
[17] Chavira M, Darwiche A. On probabilistic inference by weighted model counting[J]. Artificial Intelligence, 2008, 172(6–7): 772–799.
[18] Domshlak C , Hoffmann J . Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting[C]. Journal of Artificial Intelligence Research 30. 2007: 565-620.
[19] Baluta T, Shen S, Shinde S, et al. Quantitative verification of neural networks and its security applications[C]. Proceedings of the 2019 ACM SIGSAC Conferenceon Computer and Communications Security. 2019: 1249-1264.
[20] Stumptner M. An Overview of Knowledge-Based Configuration[J].
AI Communications, 1997, 10(2): 111–125.
[21] Lavagno L, Martin G, Scheffer L. Electronic Design Automation For Integrated Circuits Handbook[M]. Jersey: CRC Press, 2006.
[22] Darwiche A. Decomposable negation normal form[J]. Journal of the ACM, 2001, 48(4): 608–647.
[23] Davis M, Logemann G, Loveland D. A machine program for theorem-proving[J]. Communications of the ACM, 1962, 5(7): 394– 397.
[24] João P. Marques Silva, Karem A. Sakallah. GRASP - a new search algorithm for satisfiability. ICCAD 1996: 220-227
[25] Eén N, Sörensson N. An Extensible SAT-solver[C]. In: Proceedings of 6th International Conference on Theory and Applications of Satisfiability Testing, 2003: 502–518.
[26] Biere A. Lingeling, Plingeling, PicoSAT and PrecoSAT at SAT Race 2010[R]. Technical Report 10/1, FMV Reports Series, Institute for Formal Models and Verification, Johannes Kepler University, Altenbergerstr. 69, 4040 Linz, Austria.
[27] Soos M, Nohl K, Castelluccia C. Extending SAT Solvers to Cryptographic Problems[C]. In: Proceedings of 12th International Conference on Theory and Applications of Satisfiability Testing, 2009: 244–257.
[28] Armin Biere and Mathias Fleury. Gimsatul, IsaSAT and Kissat entering the SAT Competition 2022. In Proc. of SAT Competition 2022.
[29] Selman B, Levesque HJ, Mitchell DG. A new method for solving hard satisfiability problems[C]. In: Proc. of 10th AAAI, pages 440– 446, San Jose, CA, July 1992.
[30] Selman B, Kautz H, Cohen B. Local search strategies for satisfiability testing[M]. In D.S. Johnson and M.A. Trick, editors. Cliques, Coloring, and Satisfiability: the Second DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26, pages 521–532. American Mathematical Society, 1996.
[31] Mézard M, Parisi G, Zecchina R. Analytic and algorithmic solution of random satisfiability problems[J]. Science, 2002, 297(5582):812–815.
[32] Cai S, Su K. Configuration checking with aspiration in local search for SAT[C]. In: Proc. of AAAI-2012, pp. 434-440.
[33] Birnbaum, E.; and Lozinskii, E. L. 1999. The good old Davis- Putnam procedure helps counting models. Journal of Artificial Intelligence Research 10: 457–477.
[34] Sang, T.; Bacchus, F.; Beame, P.; Kautz, H.; and Pitassi, T. 2004. Combining component caching and clause learning for effective model counting. In Proc. of SAT.
[35] Thurley M. sharpSAT-counting models with advanced component caching and implicit BCP[J]. SAT, 2006, 4121: 424-429.
[36] Sharma S , Roy S , Soos M , et al. Ganak: A Scalable Probabilistic Exact Model Counter[C]. Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19. 2019. vol 19: 1169-1176.
[37] Oztok U, Darwiche. A. On compiling CNF into Decision-DNNF[C]. International Conference on Principles and Practice of Constraint Programming. Springer, Cham, 2014: 42-57.
[38] Lai Y, Meel K S, Yap R H C. The power of literal equivalence in
model counting[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(5): 3851-3859.
[39] Korhonen, Tuukka, and Matti Järvisalo. "SharpSAT-TD Participating in Model Counting Competition 2021." (2021). The Model Counting Competition 2021[J]. 2021.
[40] Bryant R E. Symbolic boolean manipulation with ordered binary- decision diagrams[J]. ACM Computing Surveys (CSUR), 1992, 24(3): 293-318.
[41] DAdnan Darwiche. New Advances in Compiling CNF into Decomposable Negation Normal Form. ECAI 2004: 328-332.
[42] Muise C, McIlraith S A, Beck J C, et al. D sharp: fast d-DNNF compilation with sharpSAT[C]. Canadian Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, 2012: 356-361.
[43] Oztok U, Darwiche A. A top-down compiler for sentential decision diagrams[C]. Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015: 3141-3148.
[44] Lagniez J M , Marquis P . An Improved Decision-DNNF Compiler[C]. Twenty-sixth International Joint Conference on Artificial Intelligence. 2017.vol 17: 667-673.
[45] Dudek J, Phan V, Vardi M. ADDMC: weighted model counting with algebraic decision diagrams[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(02): 1468-1476.
[46] Dudek J M, Phan V H N, Vardi M Y. DPMC: weighted model counting by dynamic programming on project-join trees[C]. International Conference on Principles and Practice of Constraint Programming. Springer, Cham, 2020: 211-230.
[47] Chakraborty, S.; Meel, K. S.; and Vardi, M. Y. 2013. A Scalable
Approximate Model Counter. In Proc. of CP, 200–216
[48] Gomes, C. P.; Sabharwal, A.; and Selman, B. 2006. Model Counting: A New Strategy for Obtaining Good Bounds. In Proc. of AAAI, 54– 61
[49] Gomes, C. P.; Hoffmann, J.; Sabharwal, A.; and Selman, B. 2007. From Sampling to Model Counting. In Veloso, M. M., ed., Proceedings of the 20th International Joint Conference on Artifcial Intelligence, 2293–2299.
[50] Gogate, V.; and Dechter, R. 2011. SampleSearch: Importance sampling in presence of determinism. Artifcial Intelligence, 175: 694–729.
[51] Ermon, S.; Gomes, C. P.; and Selman, B. 2012. Uniform Solution Sampling Using a Constraint Solver As an Oracle. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artifcial Intelligence, 255–264.
[52] Darwiche A, Marquis P. A knowledge compilation map[J]. Journal of Artificial Intelligence Research, 2002, 17: 229–264.
[53] Clarke E, Grumberg O, Peled D. Model Checking[D]. Massachusetts: The MIT Press. 2000.
[54] Sztipanovits J, Misra A. Diagnosis of Discrete Event Systems Using Ordered Binary Decision Diagrams[C]. In: Proceedings of the Seventh International Workshop on Principles of Diagnosis. Val Morin, Québec, Oct. 1996.
[55] Sinz C. Knowledge Compilation for Product Configuration[C]. In: Proceedings of the Workshop on Configuration at ECAI. Lyon, France, 2002: 23–26.
[56] Cimatti A, Roveri M. Conformant planning via symbolic model
checking[J]. Journal of Artificial Intelligence Research, 2000, 13: 305–338.
[57] Jha AK, Suciu D: Knowledge compilation meets database theory: compiling queries to decision diagrams[C]. In: Proceedings of ICDT, 2011: 162–173.
[58] Loekito E, Bailey J, Pei J. A binary decision diagram based approach for mining frequent subsequences[J]. Knowledge and Information Systems, 2010, 24(2):235–268.
[59] Horn A. On sentences which are true of direct unions of algebras. Journal of Symbolic Logic, 1951, 16: 14–21.
[60] Roth JP. Computer Logic, Testing, and Verification[D]. Computer Science Press, Potomac, MD., 1980.
[61] Schrag R. Compilation for critically constrained knowledge bases[C]. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Menlo Park: AAAI press, 1996: 510–515.
[62] Marquis P. Knowledge compilation using theory prime implicates [C]. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), Menlo Park: AAAI press, 1995: 837–843.
[63] Murray NV, Rosenthal E. Linear response time for implicate and implicant queries[J]. Knowledge and Information Systems, 2010, 22(3): 287–317.
[64] Bienvenu M. Prime Implicates and Prime Implicants: From Propositional to Modal Logic[J]. Journal of Artificial Intelligence Research, 2009, 36: 71–128
[65] Lin H, Sun J. Knowledge Compilation Using Extension Rule [J].
Journal of Automated Reasoning, 2004, 32(2): 93-102.
[66] Bryant RE. Graph-based algorithms for Boolean function manipulation[J]. IEEE Transactions on Computers, 1986, C-35(8): 677–691.
[67] Sieling D, Wegener I. Reduction of OBDDs in linear time[J]. Information Processing Letters, 1993, 48(3):139–144.
[68] Martin Davis. The Early History of Automated Deduction. In John Alan Robinson, Andrei Voronkov: Handbook of Automated Reasoning (in 2 volumes). Elsevier and MIT Press 2001, 2001: 3- 15.
[69] Allen Newell, Herbert A. Simon. The logic theory machine-A complex information processing system. IRE Trans. Inf. Theory 2(3): 61-79 (1956)
[70] Dag Prawitz, Haåkan Prawitz, Neri Voghera. A Mechanical Proof Procedure and its Realization in an Electronic Computer. J. ACM 7(2): 102-128 (1960)
[71] Herbert L. Gelernter. Realization of a geometry theorem proving machine. IFIP Congress 1959: 273-281
[72] Paul C. Gilmore. A program for the production from axioms, of proofs for theorems derivable within the first order predicate calculus. IFIP Congress 1959: 265-272
[73] Hao Wang. Toward Mechanical Mathematics. IBM J. Res. Dev. 4(1): 2-22 (1960)
[74] Hao Wang. Proving Theorems by Pattern Recognition I. Commun. ACM 3(4): 220-234 (1960)
[75] A. H. Lightstone, Abraham Robinson. On the Representation of Herbrand Functions in Algebraically Closed Fields. J. Symb. Log.
22(2): 187-204 (1957)
[76] Martin Davis, Hilary Putnam. A Computing Procedure for Quantification Theory. J. ACM 7(3): 201-215 (1960)
[77] Martin Davis, George Logemann, Donald W. Loveland. A machine program for theorem-proving. Commun. ACM 5(7): 394-397 (1962)
[78] John Alan Robinson. A Machine-Oriented Logic Based on the Resolution Principle. J. ACM 12(1): 23-41 (1965)
[79] Larry Wos, Daniel F. Carson, George A. Robinson. The unit preference strategy in theorem proving. AFIPS Fall Joint Computing Conference (1) 1964: 615-621
[80] Larry Wos, George A. Robinson, Daniel F. Carson. Efficiency and Completeness of the Set of Support Strategy in Theorem Proving. J. ACM 12(4): 536-541 (1965)
[81] James R. Slagle. Automatic Theorem Proving With Renamable and Semantic Resolution. J. ACM 14(4): 687-697 (1967)
[82] Robert Anderson, W. W. Bledsoe. A Linear Format for Resolution With Merging and a New Technique for Establishing Completeness. J. ACM 17(3): 525-534 (1970)
[83] R. Kowalski andD. Kuehner. Linear Resolution with Selection Function. Artificial Intelligence. 2 (1971), 227–260.
[84] Chin-Liang Chang, Richard C. T. Lee. Symbolic logic and mechanical theorem proving. Computer science classics, Academic Press 1973
[85] Neil V. Murray. Completely Non-Clausal Theorem Proving. Artif. Intell. 18(1): 67-85 (1982)
[86] Larry Wos, Ross A. Overbeek, Lawrence J. Henschen. Hyperparamodulation: A Refinement of Paramodulation. CADE
1980: 208-219
[87] R. Nieuwenhuis and A. Rubio. Paramodulation-based theorem proving. In John Alan Robinson, Andrei Voronkov: Handbook of Automated Reasoning (in 2 volumes). Elsevier and MIT Press, 2001, 371–443.
[88] Leo Bachmair and Harald Ganzinger. Resolution theorem proving. In John Alan Robinson, Andrei Voronkov: Handbook of Automated Reasoning (in 2 volumes). Elsevier and MIT Press, 2001, 19–99.
[89] Franz Baader, Tobias Nipkow. Term rewriting and all that. Cambridge University Press, 1998, 1-301.
[90] Wolfgang Küchlin. A Theorem-Proving Approach to the Knuth-
Bendix Completion Algorithm. EUROCAM, 1982: 101-108
[93] https://www.tptp.org/CASC/29/
[95] William McCune. Solution of the Robbins problem. Journal of Automated Reasoning, 19(3):263–276, December 1997.
[96] https://vprover.github.io/team.html
[97] Sarah M. Loos, Geoffrey Irving, Christian Szegedy, Cezary Kaliszyk. Deep Network Guided Proof Search. LPAR 2017: 85-105
[98] Wu Wen-Tsün. Basic Principles of Mechanical Theorem Proving in Elementary Geometries. J. Autom. Reason. 2(3): 221-252 (1986)
[99] Wu Wen-Tsün. Mechanical Theorem Proving of Differential Geometries and Some of its Applications in Mechanics. J. Autom. Reason. 7(2): 171-191 (1991)
[100] Wu Wen-Tsün. Mechanical Theorem Proving in Geometries - Basic
Principles. Texts & Monographs in Symbolic Computation, Springer 1994, 1-288.
[101] 王湘浩，刘叙华，广义归结，计算机学报，2，1982,81-92.
[102] 刘叙华，基于归结方法的自动推理，科学出版社，北京，1994.
[103] Toni Bollinger. A Model Elimination Calculus for Generalized Clauses. IJCAI 1991: 126-131
[104] William McCune. Experiments with Discrimination-Tree Indexing and Path Indexing for Term Retrieval. J. Autom. Reason. (1992) 9(2): 147-167
[105] Eugene C. Freuder. In Pursuit of the Holy Grail. Constraints 2(1): 57-61 (1997)
[106] Sketchpad, A Man-Machine Graphical Communication System. Outstanding Dissertations in the Computer Sciences, Garland Publishing, New York 1963.
[107] Waltz, D.L.: Understanding line drawings of scenes with shadows, in: Psychology of Computer Vision, McGrawHill, New York, 1975.
[108] Francesca Rossi, Peter van Beek, Toby Walsh. editors. Handbook of constraint programming, Elsevier, 2006.
[109] Christophe Lecoutre. Constraint Networks: Techniques and Algorithms, Wiley, 2010.
[111] https://www.ibm.com/analytics/data-science/prescriptive- analytics/cplex-cp-optimizer
[112] G. Simonin, C. Artigues, E. Hebrard, P. Lopez. Scheduling scientific experiments for comet exploration. Constraints 20(1), 77-99, 2015.
[113] S. Chien, G. Rabideau, D. Tran, F. Nespoli, D. Frew, H. Metselaar,
F. Nespoli, D. Frew, H. Metselaar, M. Fernandez, M. Kueppers, L.
O'Rourke. Scheduling science campaigns for the Rosetta mission: A preliminary report. In Proceedings of the International Workshop on Planning and Scheduling for Space (IWPSS). Moffett Field, CA, March 2013.
[114] Freuder E C. Progress towards the Holy Grail. Constraints, 2017(7):1-14.
[115] Jean-Francois Puget. Constraint Programming Next Challenge: Simplicity of Use. In the Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming (CP), LNCS 3258, September 2004, 5-8, Toronto, Canada, 2004.
[116] 《新一代人工智能发展规划》，国务院，2017-7-8.
[117] Ke Xu, Frederic Boussemart, Fred Hemery and Christophe Lecoutre.Random Constraint Satisfaction: Easy Generation of Hard (Satisfiable) Instances. Artificial Intelligence, 171(2007):514-534.
[118] Jimmy H.M. Lee and Zichen Zhu. Towards Breaking More Composition Symmetries in Partial Symmetry Breaking, Artificial Intelligence, 252:51-82, November, 2017.
[119] 丁博,王怀民, 史殿习, 唐扬斌. 低约束密度分布式约束优化问
题的求解算法.软件学报, 22(4):625-639,2011.
[120] 段沛博，张长胜，张斌. 分布式约束优化方法研究进展.软件学院， 27 (2) :264-279，2016.
[121] 王秦辉,陈恩红,王煦法. 分布式约束满足问题研究及其进展. 软件学报，17(10):2029-2039, 2006.
[122] 季晓慧,黄拙,张健. 约束求解与优化技术的结合.计算机学报，
28(11):1790-1797, 2005.
[123] Feifei Ma, Xin Gao, Minghao Yin, Linjie Pan, Ji-Wei Jin, Hai Liu, Jian Zhang:Optimizing Shortwave Radio Broadcast Resource
Allocation via Pseudo-Boolean Constraint Solving and Local Search.
CP 2016: 650-665
[124] 孙吉贵,朱兴军,张永刚,李莹.一种基于预处理技术的约束满足问题求解算法.计算机学报，2008(06):919-926.
[125] 孙吉贵,高健,张永刚.一个基于最小冲突修补的动态约束满足求解算法.计算机研究与发展，2007(12):2078-2084.
[126] B. M. Smith, “Modelling,” in Handbook of Constraint Programming,
T. W. F. Rossi, P. van Beek, Ed. Elsevier, 2006, ch. 11, pp. 377–406.
[127] E. C. Freuder. Modeling: The Final Frontier. In Proceedings PACLP99, the 1st International Conference on the Practical Applications of Constraint Technologies and Logic Programming, pages 15–21, 1999. Keynote address.
[128] J.-F. Puget. Constraint programming next challenge: Simplicity of use. In M. Wallace, editor, Principles and Practice of Constraint Programming - CP 2004, LNCS 3258, pages 5 – 8. Springer, 2004. Invited talk.
[129] H. Simonis. Finite Domain Constraint Programming Methodology. Tutorial presented at the PACT 2000 conference. (Available as a Powerpoint presentation from the author.), 2000.
[130] R. Coletta, C. Bessi`ere, B. O’Sullivan, E. C. Freuder, S. O’Connell, and J. Quinqueton, “Semi-automatic modeling by constraint acquisition,” in CP, ser. Lecture Notes in Computer Science, F. Rossi, Ed., vol. 2833.Springer, 2003, pp. 812–816.
[131] C. Bessi`ere, R. Coletta, B. O’Sullivan, and M. Paulin, “Query- driven constraint acquisition,” in IJCAI, M. M. Veloso, Ed., 2007, pp. 50–55.
[132] Shchekotykhin, K., & Friedrich, G. (2009). Argumentation based
constraint acquisition. In Ninth IEEE international conference on data mining (pp. 476–482).
[133] Lallouet, A., Lopez, M., Martin, L., & Vrain, C. (2010). On learning constraint problems. In Proceedings of the 22nd IEEE international conference on tools for artificial intelligence, IEEE-ICTAI’10 (pp. 45–52).
[134] Picard-Cantin, ´ E., Bouchard, M., Quimper, C., & Sweeney, J. (2016). Learning parameters for the Sequence constraint from solutions. In Principles and practice of constraint programming (pp.405–420). Springer LNCS 9892.
[135] Bessiere, C., De Raedt, L., Guns, T., Kotthoff, L., Nanni, M., Nijssen, S., O’Sullivan, B., Paparrizou, A., Pedreschi, D., & Simonis, H. (2016). The inductive constraint programming loop. In Data mining and constraint programming (pp. 303–309). Springer LNAI 10101.
[136] David Wolpert and William G. Macready. No Free Lunch Theorems for Optimization. IEEE Trans. Evolutionary Computation, 1(1):67– 82, 1997.
[137] Amadini, R., Gabbrielli, M., & Mauro, J. (2016). An extensive evaluation of portfolio approachesfor constraint satisfaction problems. International Journal of Interactive Multimedia and Artificial Intelligence, 3(7), 81–86.
[138] Thanasis Balafoutis and Kostas Stergiou. Evaluating and Improving Modern Variable and Revision Ordering Strategies in CSPs. Fundam. Inform. 102(3-4): 229-261, 2010.
[139] Zhang Z and Epstein S L. Learned Value-Ordering Heuristics for Constraint Satisfaction. In the Proceedings of STAIR-08 Workshop at AAAI-2008.
[140] Chu, G., & Stuckey, P. (2015). Learning value heuristics for constraint programming. In Integration of AI and OR techniques in constraint programming (pp. 108–123). Springer LNCS 9075.
[141] Ortiz-Bayliss, J., Terashima-Mar´ın, H., & Conant-Pablos, S. (2015). Lifelong learning selection hyper-heuristics for constraint satisfaction problems. In Mexican international conference on artificial intelligence (pp. 190–201). Springer LNCS 9413.
[142] Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro. An Empirical Evaluation of Portfolios Approaches for solving CSPs. CoRR abs/1212.0692, 2012.
[143] Kostas Stergiou:Heuristics for dynamically adapting propagation in constraint satisfaction problems. AI Commun. 22(3): 125-141 (2009)
[144] Balafrej, A., Bessiere, C., Paparrizou, A., & 2015. Multi-armed bandits for adaptive constraint propagation. In Proceedings of the twenty-fourth international joint conference on artificial intelligence (pp.290–296).
[145] http://www.cril.univ-artois.fr/~lecoutre/benchmarks.html
[146] O’Sullivan, B. (2010). Automated modelling and solving in constraint programming. In Proceedings ofthe twenty-fourth national conference on artificial intelligence (pp. 1493–1497).
[147] Hamscher W, Console L, De Kleer J. Readings in model-based diagnosis[M]. Morgan-Kaufmann Publishers, San Mateo, CA, USA, 1992.
[148] Console L, Dressler O. Model-based diagnosis in the real world: lessons learned and challenges remaining[C]//Proceedings of the 16th International Joint Conferences on Artificial Intelligence (IJCAI'99).1999:1393-1400.
[149] Struss P. Model-based diagnosis—progress and problems[M].Wissensbasierte Systeme. Springer Berlin Heidelberg, 1989: 320-331.
[150] Ali M F, Veneris A, Smith A, et al. Debugging sequential circuits using Boolean satisfiability[C]//Proceedings of the 2004 IEEE/ACM International Conference on Computer-Aided Design (ICCAD '04). 2004:204-209.
[151] Gao Z, Cecati C, Ding S X. A survey of fault diagnosis and fault- tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757-3767.
[152] zhu S C, Weissenbacher G, Malik S. Post-silicon fault localisation using maximum satisfiability and backbones[C]//Proceedings of the 11th on Formal Methods in Computer-Aided Design (FMCAD'11). 2011:63-66.
[153] Deorio A, Li J L, Bertacco V. Bridging pre-and post-silicon debugging with BiPeD[C]//Proceedings of the International Conference on Computer-Aided Design (ICCAD'12). 2012:95-100.
[154] Liu M,Ouyang D T,Cai S W,Zhang L M. Efficient zonal diagnosis with maximum satisfiability [J]. Science China Information
Sciences，2018，61(11)：112101.
[155] 周慧思,欧阳丹彤, 刘梦, 田乃予,张立明.一种结合结构特征求解诊断问题的 PMS 方法[J].中国科学:信息科学,2019,49(6):685- 697.
[156] Feldman A, Provan G M, Van Gemund A J C., Computing minimal diagnoses by greedy stochastic search[C]. Proc of 23rd AAAI Conference on Artificial Intelligence, Chicago：AAAI Press, 2008:
911–918
[157] Metodi A, Stern R, Kalech M, et al, A novel sat-based approach to model based diagnosis[J]. Journal of Artificial Intelligence Research, 2014, 51:377–411
[158] J Marques-Silva, M Janota, A Ignatiev, and A Morgado., Efficient model based diagnosis with maximum satisfiability[C]. Proc of 24th International Joint Conference on Artificial Intelligence, Buenos
Aires：AAAI Press, 2015: 1966–1972
[159] Zhou Huisi, Ouyang Dantong, Zhao Xiangfu, Zhang Liming*. Two Compacted Models for Efficient Model-based Diagnosis[C]//Proceedings of the 36th Conference on Artificial Intelligence (AAAI’22),2022:3885-3893.
[160] Manhaeve R, Dumancic S, Kimmig A, Demeester T, Raedt LD. Deepproblog: Neural probabilistic logic programming. In Advances in Neural Information Processing Systems, 2018,3749–3759.
[161] Evans R, Grefenstette E. Learning explanatory rules from noisy data. Journal of Artificial Intelligence Research, 2018,61:1–64.
[162] Martires PZD, Derkinderen V, Manhaeve R, et al. Transforming probabilistic programs into algebraic circuits for inference and learning, 2019.
[163] Kalyan A, Mohta A, Polozov O, et al. Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples. International Conference on Learning Representations, 2018,1-18.
[164] Ellis KM, Morales LE, Sable-Meyer M, et al. Learning libraries of subroutines for neurally–guided bayesian program induction. Neural Information Processing Systems, 2018.
[165] Ellis KM, Morales LE, Sable-Meyer M, et al. Learning libraries of
subroutines for neurally–guided bayesian program induction. Neural Information Processing Systems, 2018.
[166] Si X, Raghothaman M, Heo K, et al. Synthesizing datalog programs using numerical relaxation. International Conference on Machine Learning, 2019,1-9.
[167] Bonjak M , Rocktschel T , Naradowsky J , et al. Programming with a differentiable forth interpreter. International Conference on Machine Learning, 2017,1-18.
[168] Lise G, Ben T. Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA,2007,1-6.
[169] Cohen WW, Yang F, Mazaitis KR. Tensorlog: Deep learning meets probabilistic dbs. CoRR, abs/1707.05390, 2017,1-28.
[170] Xu J , Zhang Z , Friedman T , et al. A semantic loss function for deep learning with symbolic knowledge. International Conference on Machine Learning, 2018,1-10.
[171] Donadello I, Serafini L, Garcez AD. Logic Tensor Networks for Semantic Image Interpretation. Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017,1-14.
[172] Chen R, Chen T, Hui X, et al. Knowledge Graph Transfer Network for Few-Shot Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(7):10575-10582.
[173] Marra G, Giannini F, Diligenti M, Gori M. Integrating learning and reasoning with deep logic models. Machine Learning and Knowledge Discovery in Databases, 2020,1197: 517-532.
[174] Zhou ZH. Abductive learning: towards bridging machine learning and logical reasoning. Science China Information Sciences, 2019,62(7):76101.
[175] J. Tian, Y. Li, W. Chen, L. Xiao, H. He, and Y. Jin, “Weakly supervised neural symbolic learning for cognitive tasks,” in AAAI, 2022.
[176] D. Yu, B. Yang, Q. Wei, A. Li, and S. Pan, “A probabilistic graphical model based on neural-symbolic reasoning for visual relationship detection,” in CVPR, 2022, pp. 10 609–10 618.
[177] Qu M, Tang J, et al. Probabilistic logic neural networks for reasoning. Neural Information Processing Systems, 2019,32.
[178] Zhang Y, Chen X, Yang Y, Ramamurthy A, Li B, Qi Y, and Song L, Efficient probabilistic logic reasoning with graph neural networks. International Conference on Learning Representations, 2020,1–20.
[179] Marra G, O Kuželka. Neural markov logic networks. arXiv preprint arXiv:1905.13462, 2019,1-30.
[180] Marra G, Diligenti M, Giannini F, Gori M, Maggini M. Relational neural machines. In European Conference on Artificial Intelligence, 2020,11-8.
[181] Galarraga L, Teflioudi C, Hose K, Suchanek FK. Fast rule mining in ontological logical knowledge bases with amie. The VLDB JournalThe International Journal on Very Large Data Bases, 2015,24(6):707–730.
[182] Campero A, Pareja A, Klinger T, Tenenbaum J, Riedel S. Logical rule induction and theory learning using neural theorem proving. arXiv preprint arXiv:1809.02193, 2018. 1-11.
[183] Rocktaschel T, Riedel S. End-to-end differentiable proving. In Advances in Neural Information Processing Systems, 2017, 3788– 3800.
[184] Payani A, Fekri F. Inductive logic programming via differentiable
deep neural logic networks. arXiv preprint arXiv:1906.03523, 2019. 1-12.
[185] Yang Y, Song L. Learn to explain efficiently via neural logic inductive learning. International Conference on Learning Representations, 2020,1-15.
[186] Diligenti M, Gori M, Sacca C. Semantic-based regularization for learning and inference. Artificial Intelligence, 2015,244:143-165.
[187] Xu J , Zhang Z , Friedman T , et al. A semantic loss function for deep learning with symbolic knowledge. International Conference on Machine Learning, 2018,1-10.
[188] Hu, ZT, et al. Harnessing Deep Neural Networks with Logic Rules. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016,1.
[189] Xie Y, Xu Z, Kankanhalli M S, et al. Embedding Symbolic Knowledge into Deep Networks. Neural Information Processing Systems, 2019,4233–4243.
[190] Luo, R., Zhang, N., Han, B., & Yang, L. Context-Aware Zero-Shot Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(07):11709-11716.
[191] Li A, Luo T, Lu Z, Xiang T, Wang L. Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 7212–7220.
[192] Chen R, Chen T, Hui X, et al. Knowledge Graph Transfer Network for Few-Shot Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(7):10575-10582.
[193] Dai W Z, Xu Q, Yu Y, et al. Bridging machine learning and logical
reasoning by abductive learning[J]. Advances in Neural Information Processing Systems, 2019, 32.
[194] Tian J, Li Y, Chen W, et al. Weakly supervised neural symbolic learning for cognitive tasks[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(5): 5888-5896.
[195] Yu D, Yang B, Wei Q, et al. A probabilistic graphical model based on neural-symbolic reasoning for visual relationship detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 10609-10618.
[196] Khot T, Natarajan S, Kersting K, Shavlik J. Learning markov logic networks via functional gradient boosting. In 2011 IEEE 11th International Conference on Data Mining, 2011,320–329.
[197] Bach SH, Broecheler M, Huang B, Getoor L. Hinge-loss markov random fields and probabilistic soft logic. Journal of Machine Learning Research. 2017,18(109):1-67.
[198] Liao GQ, Lan TM, Huang XM, Chen H, Wan CX, Liu DX, Liu XP. Survey on recommendation systems in event based social networks. Ruan Jian Xue Bao/Journal of Software, 2021,32(2):424−444.
[199] Ghoul AE, Sahbi H. Semi-supervised learning using a graph-based phase field model for imbalanced data set classification. IEEE International Conference on Acoustics, 2014,11:2942-2946.
[200] M. Shi, Y. Tang, X. Zhu, D. Wilson, and J. Liu. Multi-class imbalanced graph convolutional network learning. International Joint Conferences on Artificial Intelligence Organization, 2020,2879–2885.