A Modular Order-sorted Equational Generalization Algorithm

Generalization, also called anti-unification, is the dual of unification. Given terms t and t , a generalizer is a term t of which t and t are substitution instances. The dual of a most general unifier (mgu) is that of least general generalizer (lgg). In this work, we extend the known untyped generalization algorithm to, first, an order-sorted typed setting with sorts, subsorts, and subtype polymorphism; second, we extend it to work modulo equational theories, where function symbols can obey any combination of associativity, commutativity, and identity axioms (including the empty set of such axioms); and third, to the combination of both, which results in a modular, order-sorted equational generalization algorithm. Unlike the untyped case, there is in general no single lgg in our framework, due to order-sortedness or to the equational axioms. Instead, there is a finite, minimal and complete set of lggs, so that any other generalizer has at least one of them as an instance. Our generalization algorithms are expressed by means of inference systems for which we give proofs of correctness. This opens up new applications to partial evaluation, program synthesis, and theorem proving for typed equational reasoning systems and typed rulebased languages such as ASF+SDF, Elan, OBJ, Cafe-OBJ, and Maude. © 2014 Elsevier Inc. All rights reserved. 1.M. Alpuente, S. Escobar, and J. Espert have been partially supported by the EU (FEDER) and the Spanish MEC/MICINN under grant TIN 2010-21062-C02-02, and by Generalitat Valenciana PROMETEO2011/052. J. Meseguer has been supported by NSF Grants CNS 09-04749, and CCF 09-05584.Alpuente Frasnedo, M.; Escobar Román, S.; Espert Real, J.; Meseguer, J. (2014). A Modular Order-sorted Equational Generalization Algorithm. Information and Computation. 235:98-136. https://doi.org/10.1016/j.ic.2014.01.006S9813623

ACUOS: A System for Order-Sorted Modular ACU Generalization

[ES] La generalización, también denominada anti-unificación, es la operación dual de la unificación. Dados dos términos t y t' , un generalizador es un término t'' del cual t y t' son instancias de sustitución. El concepto dual del unificador más general (mgu) es el de generalizador menos general (lgg). En esta tesina extendemos el conocido algoritmo de generalización sin tipos a, primero, una configuración order-sorted con sorts, subsorts y polimorfismo de subtipado; en segundo lugar, la extendemos para soportar generalización módulo teorías ecuacionales, donde los símbolos de función pueden obedecer cualquier combinación de axiomas de asociatividad, conmutatividad e identidad (incluyendo el conjunto vacío de dichos axiomas); y, en tercer lugar, a la combinación de ambos, que resulta en un algoritmo modular de generalización order-sorted ecuacional. A diferencia de las configuraciones sin tipos, en nuestro marco teórico en general el lgg no es único, lo que se debe tanto al tipado como a los axiomas ecuacionales. En su lugar, existe un conjunto finito y mínimo de lggs, tales que cualquier otra generalización tiene a alguno de ellos como instancia. Nuestros algoritmos de generalización se expresan mediante reglas de inferencia para las cuales damos demostraciones de corrección. Ello abre la puerta a nuevas aplicaciones en campos como la evaluación parcial, la síntesis de programas, la minería de datos y la demostración de teoremas para sistemas de razonamiento ecuacional y lenguajes tipados basados en reglas tales como ASD+SDF, Elan, OBJ, CafeOBJ y Maude. Esta tesis también describe una herramienta para el cómputo automatizado de los generalizadores de un conjunto dado de estructuras en un lenguaje tipado módulo un conjunto de axiomas dado. Al soportar la combinación modular de atributos ecuacionales de asociatividad, conmutatividad y existencia de elemento neutro (ACU) para símbolos de función arbitrarios, la generalización ACU modular aporta suficiente poder expresivo a la generalización ordinaria para razonar sobre estructuras de datos tipadas tales como listas, conjuntos y multiconjuntos. La técnica ha sido implementada con generalidad y eficiencia en el sistema ACUOS y puede ser fácilmente integrada con software de terceros.[EN] Generalization, also called anti-uni cation, is the dual of uni cation. Given terms t and t 0 , a generalization is a term t 00 of which t and t 0 are substitution instances. The dual of a most general uni er (mgu) is that of least general generalization (lgg). In this thesis, we extend the known untyped generalization algorithm to, rst, an order-sorted typed setting with sorts, subsorts, and subtype polymorphism; second, we extend it to work modulo equational theories, where function symbols can obey any combination of associativity, commutativity, and identity axioms (includ- ing the empty set of such axioms); and third, to the combination of both, which results in a modular, order-sorted equational generalization algo- rithm. Unlike the untyped case, there is in general no single lgg in our framework, due to order-sortedness or to the equational axioms. Instead, there is a nite, minimal set of lggs, so that any other generalization has at least one of them as an instance. Our generalization algorithms are expressed by means of inference systems for which we give proofs of cor- rectness. This opens up new applications to partial evaluation, program synthesis, data mining, and theorem proving for typed equational rea- soning systems and typed rule-based languages such as ASF+SDF, Elan, OBJ, Cafe-OBJ, and Maude. This thesis also describes a tool for automatically computing the gen- eralizers of a given set of structures in a typed language modulo a set of axioms. By supporting the modular combination of associative, com- mutative and unity (ACU) equational attributes for arbitrary function symbols, modular ACU generalization adds enough expressive power to ordinary generalization to reason about typed data structures such as lists, sets and multisets. The ACU generalization technique has been generally and e ciently implemented in the ACUOS system and can be easily integrated with third-party software.Espert Real, J. (2012). ACUOS: A System for Order-Sorted Modular ACU Generalization. http://hdl.handle.net/10251/1921

ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11558-0_40Computing generalizers is relevant in a wide spectrum of automated reasoning areas where analogical reasoning and inductive inference are needed. The ACUOS system computes a complete and minimal set of semantic generalizers (also called “anti-unifiers”) of two structures in a typed language modulo a set of equational axioms. By supporting types and any (modular) combination of associativity (A), commutativity (C), and unity (U) algebraic axioms for function symbols, ACUOS allows reasoning about typed data structures, e.g. lists, trees, and (multi-)sets, and typical hierarchical/structural relations such as is a and part of. This paper discusses the modular ACU generalization tool ACUOS and illustrates its use in a classical artificial intelligence problem.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2010-21062-C02-02 and TIN 2013-45732-C4-1-P, by Generalitat Valenciana PROMETEO2011/052, and by NSF Grant CNS 13-10109. J. Espert has also been supported by the Spanish FPU grant FPU12/06223.Alpuente Frasnedo, M.; Escobar Román, S.; Espert Real, J.; Meseguer, J. (2014). ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance. En Logics in Artificial Intelligence. Springer. 573-581. https://doi.org/10.1007/978-3-319-11558-0_40S573581Alpuente, M., Escobar, S., Espert, J., Meseguer, J.: A Modular Order-sorted Equational Generalization Algorithm. Information and Computation 235, 98–136 (2014)Alpuente, M., Escobar, S., Meseguer, J., Ojeda, P.: A Modular Equational Generalization Algorithm. In: Hanus, M. (ed.) LOPSTR 2008. LNCS, vol. 5438, pp. 24–39. Springer, Heidelberg (2009)Alpuente, M., Escobar, S., Meseguer, J., Ojeda, P.: Order–Sorted Generalization. ENTCS 246, 27–38 (2009)Alpuente, M., Espert, J., Escobar, S., Meseguer, J.: ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance. Tech. rep., DSIC-UPV (2013), http://www.dsic.upv.es/users/elp/papers.htmlArmengol, E.: Usages of Generalization in Case-Based Reasoning. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 31–45. Springer, Heidelberg (2007)Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C. (eds.): All About Maude - A High-Performance Logical Framework. LNCS, vol. 4350. Springer, Heidelberg (2007)Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.L.: Reflection, metalevel computation, and strategies. In: All About Maude [6], pp. 419–458Gentner, D.: Structure-Mapping: A Theoretical Framework for Analogy*. Cognitive Science 7(2), 155–170 (1983)Krumnack, U., Schwering, A., Gust, H., Kühnberger, K.-U.: Restricted higher order anti unification for analogy making. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 273–282. Springer, Heidelberg (2007)Kutsia, T., Levy, J., Villaret, M.: Anti-Unification for Unranked Terms and Hedges. Journal of Automated Reasoning 520, 155–190 (2014)Meseguer, J.: Conditioned rewriting logic as a united model of concurrency. Theor. Comput. Sci. 96(1), 73–155 (1992)Muggleton, S.: Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic. Artif. Intell. 114(1-2), 283–296 (1999)Ontañón, S., Plaza, E.: Similarity measures over refinement graphs. Machine Learning 87(1), 57–92 (2012)Plotkin, G.: A note on inductive generalization. In: Machine Intelligence, vol. 5, pp. 153–163. Edinburgh University Press (1970)Pottier, L.: Generalisation de termes en theorie equationelle: Cas associatif-commutatif. Tech. Rep. INRIA 1056, Norwegian Computing Center (1989)Schmid, U., Hofmann, M., Bader, F., Häberle, T., Schneider, T.: Incident Mining using Structural Prototypes. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part II. LNCS, vol. 6097, pp. 327–336. Springer, Heidelberg (2010

Higher-Order Equational Pattern Anti-Unification

We consider anti-unification for simply typed lambda terms in associative, commutative, and associative-commutative theories and develop a sound and complete algorithm which takes two lambda terms and computes their generalizations in the form of higher-order patterns. The problem is finitary: the minimal complete set of generalizations contains finitely many elements. We define the notion of optimal solution and investigate special fragments of the problem for which the optimal solution can be computed in linear or polynomial time

E-Generalization Using Grammars

We extend the notion of anti-unification to cover equational theories and present a method based on regular tree grammars to compute a finite representation of E-generalization sets. We present a framework to combine Inductive Logic Programming and E-generalization that includes an extension of Plotkin's lgg theorem to the equational case. We demonstrate the potential power of E-generalization by three example applications: computation of suggestions for auxiliary lemmas in equational inductive proofs, computation of construction laws for given term sequences, and learning of screen editor command sequences.Comment: 49 pages, 16 figures, author address given in header is meanwhile outdated, full version of an article in the "Artificial Intelligence Journal", appeared as technical report in 2003. An open-source C implementation and some examples are found at the Ancillary file

Interpretable Graph Networks Formulate Universal Algebra Conjectures

The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Yet, the use of AI in Universal Algebra (UA) -- one of the fields laying the foundations of modern mathematics -- is still completely unexplored. This work proposes the first use of AI to investigate UA's conjectures with an equivalent equational and topological characterization. While topological representations would enable the analysis of such properties using graph neural networks, the limited transparency and brittle explainability of these models hinder their straightforward use to empirically validate existing conjectures or to formulate new ones. To bridge these gaps, we propose a general algorithm generating AI-ready datasets based on UA's conjectures, and introduce a novel neural layer to build fully interpretable graph networks. The results of our experiments demonstrate that interpretable graph networks: (i) enhance interpretability without sacrificing task accuracy, (ii) strongly generalize when predicting universal algebra's properties, (iii) generate simple explanations that empirically validate existing conjectures, and (iv) identify subgraphs suggesting the formulation of novel conjectures