A Finite Representation of the Narrowing Space

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-14125-1_4Narrowing basically extends rewriting by allowing free variables in terms and by replacing matching with unification. As a consequence, the search space of narrowing becomes usually infinite, as in logic programming. In this paper, we introduce the use of some operators that allow one to always produce a finite data structure that still represents all the narrowing derivations. Furthermore, we extract from this data structure a novel, compact equational representation of the (possibly infinite) answers computed by narrowing for a given initial term. Both the finite data structure and the equational representation of the computed answers might be useful in a number of areas, like program comprehension, static analysis, program transformation, etc.Nishida, N.; Vidal, G. (2013). A Finite Representation of the Narrowing Space. En Logic-Based Program Synthesis and Transformation. Springer. 54-71. doi:10.1007/978-3-319-14125-1_4S5471Albert, E., Vidal, G.: The Narrowing-Driven Approach to Functional Logic Program Specialization. New Generation Computing 20(1), 3–26 (2002)Alpuente, M., Falaschi, M., Vidal, G.: Partial Evaluation of Functional Logic Programs. ACM Transactions on Programming Languages and Systems 20(4), 768–844 (1998)Alpuente, M., Falaschi, M., Vidal, G.: Compositional Analysis for Equational Horn Programs. In: Rodríguez-Artalejo, M., Levi, G. (eds.) ALP 1994. LNCS, vol. 850, pp. 77–94. Springer, Heidelberg (1994)Antoy, S., Ariola, Z.: Narrowing the Narrowing Space. In: Hartel, P.H., Kuchen, H. (eds.) PLILP 1997. LNCS, vol. 1292, pp. 1–15. Springer, Heidelberg (1997)Arts, T., Giesl, J.: Termination of term rewriting using dependency pairs. Theoretical Computer Science 236(1–2), 133–178 (2000)Arts, T., Zantema, H.: Termination of Logic Programs Using Semantic Unification. In: Proietti, M. (ed.) LOPSTR 1995. LNCS, vol. 1048, pp. 219–233. Springer, Heidelberg (1996)Baader, F., Nipkow, T.: Term Rewriting and All That. Cambridge University Press (1998)Bae, K., Escobar, S., Meseguer, J.: Abstract Logical Model Checking of Infinite-State Systems Using Narrowing. In: Proceedings of the 24th International Conference on Rewriting Techniques and Applications. LIPIcs, vol. 21, pp. 81–96. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2013)De Schreye, D., Glück, R., Jørgensen, J., Leuschel, M., Martens, B., Sørensen, M.: Conjunctive partial deduction: foundations, control, algorihtms, and experiments. Journal of Logic Programming 41(2&3), 231–277 (1999)Escobar, S., Meadows, C., Meseguer, J.: A rewriting-based inference system for the NRL Protocol Analyzer and its meta-logical properties. Theoretical Computer Science 367(1–2), 162–202 (2006)Escobar, S., Meseguer, J.: Symbolic Model Checking of Infinite-State Systems Using Narrowing. In: Baader, F. (ed.) RTA 2007. LNCS, vol. 4533, pp. 153–168. Springer, Heidelberg (2007)Fribourg, L.: SLOG: A Logic Programming Language Interpreter Based on Clausal Superposition and Rewriting. In: Proceedings of the Symposium on Logic Programming, pp. 172–185. IEEE Press (1985)Gnaedig, I., Kirchner, H.: Proving weak properties of rewriting. Theoretical Computer Science 412(34), 4405–4438 (2011)Hanus, M.: The integration of functions into logic programming: From theory to practice. Journal of Logic Programming 19&20, 583–628 (1994)Hanus, M. (ed.): Curry: An integrated functional logic language (vers. 0.8.3) (2012). http://www.curry-language.orgHermenegildo, M., Rossi, F.: On the Correctness and Efficiency of Independent And-Parallelism in Logic Programs. In: Lusk, E., Overbeck, R. (eds.) Proceedings of the 1989 North American Conf. on Logic Programming, pp. 369–389. The MIT Press, Cambridge (1989)Hölldobler, S. (ed.): Foundations of Equational Logic Programming. LNCS, vol. 353. Springer, Heidelberg (1989)Meseguer, J., Thati, P.: Symbolic Reachability Analysis Using Narrowing and its Application to Verification of Cryptographic Protocols. Electronic Notes in Theoretical Computer Science 117, 153–182 (2005)Middeldorp, A., Okui, S.: A Deterministic Lazy Narrowing Calculus. Journal of Symbolic Computation 25(6), 733–757 (1998)Nishida, N., Sakai, M., Sakabe, T.: Generation of Inverse Computation Programs of Constructor Term Rewriting Systems. IEICE Transactions on Information and Systems J88–D–I(8), 1171–1183 (2005) (in Japanese)Nishida, N., Sakai, M., Sakabe, T.: Partial Inversion of Constructor Term Rewriting Systems. In: Giesl, J. (ed.) RTA 2005. LNCS, vol. 3467, pp. 264–278. Springer, Heidelberg (2005)Nishida, N., Vidal, G.: Program inversion for tail recursive functions. In: Schmidt-Schauß, M. (ed.) Proceedings of the 22nd International Conference on Rewriting Techniques and Applications. LIPIcs, vol. 10, pp. 283–298. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2011)Nishida, N., Vidal, G.: Computing More Specific Versions of Conditional Rewriting Systems. In: Albert, E. (ed.) LOPSTR 2012. LNCS, vol. 7844, pp. 137–154. Springer, Heidelberg (2013)Nutt, W., Réty, P., Smolka, G.: Basic Narrowing Revisited. Journal of Symbolic Computation 7(3/4), 295–317 (1989)Ohlebusch, E.: Advanced Topics in Term Rewriting. Springer, London, UK (2002)Palamidessi, C.: Algebraic Properties of Idempotent Substitutions. In: Paterson, M. (ed.) ICALP 1990. LNCS, vol. 443, pp. 386–399. Springer, Heidelberg (1990)Ramos, J.G., Silva, J., Vidal, G.: Fast Narrowing-Driven Partial Evaluation for Inductively Sequential Systems. In: Danvy, O., Pierce, B.C. (eds.) Proceedings of the 10th ACM SIGPLAN International Conference on Functional Programming, pp. 228–239. ACM Press (2005)Slagle, J.R.: Automated theorem-proving for theories with simplifiers, commutativity and associativity. Journal of the ACM 21(4), 622–642 (1974

Concolic Execution and Test Case Generation in Prolog

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-17822-6_10Symbolic execution extends concrete execution by allowing symbolic input data and then exploring all feasible execution paths. It has been defined and used in the context of many different programming languages and paradigms. A symbolic execution engine is at the heart of many program analysis and transformation techniques, like partial evaluation, test case generation or model checking, to name a few. Despite its relevance, traditional symbolic execution also suffers from several drawbacks. For instance, the search space is usually huge (often infinite) even for the simplest programs. Also, symbolic execution generally computes an overapproximation of the concrete execution space, so that false positives may occur. In this paper, we propose the use of a variant of symbolic execution, called concolic execution, for test case generation in Prolog. Our technique aims at full statement coverage. We argue that this technique computes an underapproximation of the concrete execution space (thus avoiding false positives) and scales up better to medium and large Prolog applications.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economía y Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEO/2011/052.Vidal Oriola, GF. (2015). Concolic Execution and Test Case Generation in Prolog. En Logic-Based Program Synthesis and Transformation. Springer. 167-181. https://doi.org/10.1007/978-3-319-17822-6_10S167181Albert, E., Arenas, P., Gómez-Zamalloa, M., Rojas, J.M.: Test case generation by symbolic execution: basic concepts, a CLP-based instance, and actor-based concurrency. In: Bernardo, M., Damiani, F., Hähnle, R., Johnsen, E.B., Schaefer, I. (eds.) SFM 2014. LNCS, vol. 8483, pp. 263–309. Springer, Heidelberg (2014)Belli, F., Jack, O.: Implementation-based analysis and testing of Prolog programs. In: ISSTA, pp. 70–80. ACM (1993)Clarke, L.A.: A program testing system. In: Proceedings of the 1976 Annual Conference (ACM’76), Houston, pp. 488–491 (1976)De Schreye, D., Glück, R., Jørgensen, J., Leuschel, M., Martens, B., Sørensen, M.H.: Conjunctive partial deduction: foundations, control, algorithms, and experiments. J. Log. Program. 41(2&3), 231–277 (1999)Giesl, J., Ströder, T., Schneider-Kamp, P., Emmes, F., Fuhs, C.: Symbolic evaluation graphs and term rewriting: a general methodology for analyzing logic programs. In: PPDP’12, pp. 1–12. ACM (2012)Godefroid, P., Klarlund, N., Sen, K.: DART: directed automated random testing. In: Proceedings of PLDI’05, pp. 213–223. ACM (2005)Godefroid, P., Levin, M.Y., Molnar, D.A.: Sage: whitebox fuzzing for security testing. Commun. ACM 55(3), 40–44 (2012)Gómez-Zamalloa, M., Albert, E., Puebla, G.: Test case generation for object-oriented imperative languages in CLP. TPLP 10(4–6), 659–674 (2010)King, J.C.: Symbolic execution and program testing. Commun. ACM 19(7), 385–394 (1976)Leuschel, M.: The DPPD (Dozens of Problems for Partial Deduction) Library of Benchmarks. http://www.ecs.soton.ac.uk/mal/systems/dppd.html (2007)Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Berlin (1987)Lloyd, J.W., Shepherdson, J.C.: Partial evaluation in logic programming. J. Log. Program. 11, 217–242 (1991)Martens, B., Gallagher, J.: Ensuring global termination of partial deduction while allowing flexible polyvariance. In: Proceedings of ICLP’95, pp. 597–611. MIT Press (1995)Pasareanu, C.S., Rungta, N.: Symbolic PathFinder: symbolic execution of Java bytecode. In: Pecheur, C., Andrews, J., Di Nitto, E. (eds.) ASE, pp. 179–180. ACM (2010)Rojas, J.M., Gómez-Zamalloa, M.: A framework for guided test case generation in constraint logic programming. In: Albert, E. (ed.) Proceedings of LOPSTR. LNCS, vol. 7844, pp. 176–193. Springer, Heidelberg (2013)Sen, K., Marinov, D., Agha, G.: CUTE: a concolic unit testing engine for C. In: Proceedings of ESEC/SIGSOFT FSE 2005, pp. 263–272. ACM (2005)Ströder, T., Emmes, F., Schneider-Kamp, P., Giesl, J., Fuhs, C.: A linear operational semantics for termination and complexity analysis of $$\sf ISO$$ $$\sf Prolog$$ . In: Vidal, G. (ed.) LOPSTR’11. LNCS, vol. 7225, pp. 237–252. Springer, Heidelberg (2012

On the social utility of symbolic logic: Lewis Carroll against ‘The Logicians’

Symbolic logic faced great difficulties in its early stage of development in order to acquire recognition of its utility for the needs of science and society. The aim of this paper is to discuss an early attempt by the British logician Lewis Carroll (1832–1898) to promote symbolic logic as a social good. This examination is achieved in three phases: first, Carroll’s belief in the social utility of logic, broadly understood, is demonstrated by his numerous interventions to fight fallacious reasoning in public debates. Then, Carroll’s attempts to promote symbolic logic, specifically, are revealed through his work on a treatise that would make the subject accessible to a wide and young audience. Finally, it is argued that Carroll’s ideal of logic as a common good influenced the logical methods he invented and allowed him to tackle more efficiently some problems that resisted to early symbolic logicians

Inference of termination conditions for numerical loops in Prolog

We present a new approach to termination analysis of numerical computations in logic programs. Traditional approaches fail to analyse them due to non well-foundedness of the integers. We present a technique that allows overcoming these difficulties. Our approach is based on transforming a program in a way that allows integrating and extending techniques originally developed for analysis of numerical computations in the framework of query-mapping pairs with the well-known framework of acceptability. Such an integration not only contributes to the understanding of termination behaviour of numerical computations, but also allows us to perform a correct analysis of such computations automatically, by extending previous work on a constraint-based approach to termination. Finally, we discuss possible extensions of the technique, including incorporating general term orderings.Comment: To appear in Theory and Practice of Logic Programming. To appear in Theory and Practice of Logic Programmin

A hybrid approach to conjunctive partial evaluation of logic programs

Conjunctive partial deduction is a well-known technique for the partial evaluation of logic programs. The original formulation follows the so called online approach where all termination decisions are taken on-the-fly. In contrast, offline partial evaluators first analyze the source program and produce an annotated version so that the partial evaluation phase should only follow these annotations to ensure the termination of the process. In this work, we introduce a lightweight approach to conjunctive partial deduction that combines some of the advantages of both online and offline styles of partial evaluation. © 2011 Springer-Verlag.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant ACOMP/2010/042.Vidal Oriola, GF. (2011). A hybrid approach to conjunctive partial evaluation of logic programs. En Logic-Based Program Synthesis and Transformation. Springer Verlag (Germany). 6564:200-214. https://doi.org/10.1007/978-3-642-20551-4_13S2002146564Ben-Amram, A., Codish, M.: A SAT-Based Approach to Size Change Termination with Global Ranking Functions. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 218–232. Springer, Heidelberg (2007)Bruynooghe, M., De Schreye, D., Martens, B.: A General Criterion for Avoiding Infinite Unfolding during Partial Deduction of Logic Programs. In: Saraswat, V., Ueda, K. (eds.) Proc. 1991 Int’l Symp. on Logic Programming, pp. 117–131 (1991)Christensen, N.H., Glück, R.: Offline Partial Evaluation Can Be as Accurate as Online Partial Evaluation. ACM Transactions on Programming Languages and Systems 26(1), 191–220 (2004)Codish, M., Taboch, C.: A Semantic Basis for the Termination Analysis of Logic Programs. Journal of Logic Programming 41(1), 103–123 (1999)De Schreye, D., Glück, R., Jørgensen, J., Leuschel, M., Martens, B., Sørensen, M.H.: Conjunctive Partial Deduction: Foundations, Control, Algorihtms, and Experiments. Journal of Logic Programming 41(2&3), 231–277 (1999)Hruza, J., Stepánek, P.: Speedup of logic programs by binarization and partial deduction. TPLP 4(3), 355–380 (2004)Jones, N.D., Gomard, C.K., Sestoft, P.: Partial Evaluation and Automatic Program Generation. Prentice-Hall, Englewood Cliffs (1993)Leuschel, M.: Homeomorphic Embedding for Online Termination of Symbolic Methods. In: Mogensen, T.Æ., Schmidt, D.A., Sudborough, I.H. (eds.) The Essence of Computation. LNCS, vol. 2566, pp. 379–403. Springer, Heidelberg (2002)Leuschel, M.: The DPPD (Dozens of Problems for Partial Deduction) Library of Benchmarks (2007), http://www.ecs.soton.ac.uk/~mal/systems/dppd.htmlLeuschel, M., Elphick, D., Varea, M., Craig, S., Fontaine, M.: The Ecce and Logen Partial Evaluators and Their Web Interfaces. In: Proc. of PEPM 2006, pp. 88–94. IBM Press (2006)Leuschel, M., Vidal, G.: Fast Offline Partial Evaluation of Large Logic Programs. In: Hanus, M. (ed.) LOPSTR 2008. LNCS, vol. 5438, pp. 119–134. Springer, Heidelberg (2009)Lloyd, J.W., Shepherdson, J.C.: Partial Evaluation in Logic Programming. Journal of Logic Programming 11, 217–242 (1991)Somogyi, Z.: A System of Precise Modes for Logic Programs. In: Shapiro, E.Y. (ed.) Proc. of Third Int’l Conf. on Logic Programming, pp. 769–787. The MIT Press, Cambridge (1986

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm

Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling