Generalization, also called anti-unification, is the dual of unification. Given terms t and t , a generalization 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 generalization (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 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 correctness. This opens up new applications to partial evaluation, program synthesis, and theorem proving for typed equational reasoning systems and typed rule-based languages such as ASF+SDF, Elan, OBJ, Cafe-OBJ, and Maude.
Abstract. Computing 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.
Web-TLR is a software tool designed for model-checking Web applications which is based on rewriting logic. Web applications are expressed as rewrite theories which can be formally verified by using the Maude built-in LTLR model-checker. Web-TLR is equipped with a user-friendly, graphical Web interface that shields the user from unnecessary information. Whenever a property is refuted, an interactive slideshow is generated that allows the user to visually reproduce, step by step, the erroneous navigation trace that underlies the failing model checking computation. This provides deep insight into the system behavior, which helps to debug Web applications
WEB-TLR is a Web verification engine that is based on the well-established Rewriting LogicMaude/LTLR tandem for Web system specification and model-checking. In WEB-TLR, Web applications are expressed as rewrite theories that can be formally verified by using the Maude built-in LTLR model-checker. Whenever a property is refuted, a counterexample trace is delivered that reveals an undesired, erroneous navigation sequence. Unfortunately, the analysis (or even the simple inspection) of such counterexamples may be unfeasible because of the size and complexity of the traces under examination. In this paper, we endow WEB-TLR with a new Web debugging facility that supports the efficient manipulation of counterexample traces. This facility is based on a backward trace-slicing technique for rewriting logic theories that allows the pieces of information that we are interested to be traced back through inverse rewrite sequences. The slicing process drastically simplifies the computation trace by dropping useless data that do not influence the final result. By using this facility, the Web engineer can focus on the relevant fragments of the failing application, which greatly reduces the manual debugging effort and also decreases the number of iterative verifications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.