Formalising nominal C-unification generalised with protected variables

This work extends a rule-based specification of nominal C-unification formalised in Coq to include ‘protected variables’ that cannot be instantiated during the unification process. By introducing protected variables, we are able to reuse the C-unification simplification rules to solve nominal C-matching (as well as equality check) problems. From the algorithmic point of view, this extension is sufficient to obtain a generalised C-unification procedure; however, it cannot be formally checked by simple reuse of the original formalisation. This paper describes the additional effort necessary in order to adapt the specification of the inference rules and reuse previous formalisations. We also generalise a functional recursive nominal C-unification algorithm specified in PVS with protected variables, effectively adapting this algorithm to the tasks of nominal C-matching and nominal equality check. The PVS formalisation is applied to test the correctness of a Python manual implementation of the algorithm.

Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

Abstarct This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.

A Mechanized Proof of a Textbook Type Unification Algorithm

Unification is the core of type inference algorithms for modern functional programming languages, like Haskell and SML. As a first step towards a formalization of a type inference algorithm for such programming languages, we present a formalization in Coq of a type unification algorithm that follows classic algorithms presented in programming language textbooks. We also report on the use of such formalization to build a correct type inference algorithm for the simply typed λ-calculus.

Knowledge Processing Using EKRL for Robotic Applications

This article describes a semantic framework that demonstrates an approach for modeling and reasoning based on environment knowledge representation language (EKRL) to enhance interaction between robots and their environment. Unlike EKRL, standard Binary approaches like OWL language fails to represent knowledge in an expressive way. The authors show in this work how to: model environment and interaction in an expressive way with first-order and second-order EKRL data-structures, and reason for decision-making thanks to inference capabilities based on a complex unification algorithm. This is with the understanding that robot environments are inherently subject to noise and partial observability, the authors extended EKRL framework with probabilistic reasoning based on Markov logic networks to manage uncertainty.

A framework for improving error messages in dependently-typed languages

Dependently-typed programming languages provide a powerful tool for establishing code correctness. However, it can be hard for newcomers to learn how to employ the advanced type system of such languages effectively. For simply-typed languages, several techniques have been devised to generate helpful error messages and suggestions for the programmer. We adapt these techniques to dependently-typed languages, to facilitate their more widespread adoption. In particular, we modify a higher-order unification algorithm that is used to resolve and type-check implicit arguments. We augment this algorithm with replay graphs, allowing for a global heuristic analysis of a unification problem-set, error-tolerant typing, which allows type-checking to continue after errors are found, and counter-factual unification, which makes error messages less affected by the order in which types are checked. A formalization of our algorithm is presented with an outline of its correctness. We implement replay graphs, and compare the generated error messages to those from existing languages, highlighting the improvements we achieved.

A Resolution Calculus for Second-order Logic with Eager Unification

The efficiency of the first-order resolution calculus is impaired when lifting it to higher-order logic. The main reason for that is the semi-decidability and infinitary natureof higher-order unification algorithms, which requires the integration of unification within the calculus and results in a non-efficient search for refutations.We present a modification of the constrained resolution calculus (Huet'72) which uses an eager unification algorithm while retaining completeness. Thealgorithm is complete with regard to bounded unification only, which for many cases, does not pose a problem in practice.

Is it possible to unify sequential programs?

We introduce a first-order model of imperative sequential programs and set up formally the unification problem in this model: given a pair of programs π<sub>1</sub> and π<sub>2</sub> find a pair of substitutions (θ<sub>1</sub>,θ<sub>2</sub>) such that the instances π<sub>1</sub>θ<sub>1</sub> and π<sub>2</sub>θ<sub>2</sub> of these programs are equivalent, i.e. compute the same function. Since functional equivalence of programs is undecidable, we choose its decidable approximation --- a strong equivalence, --- which is well-known in theory of program schemata. Our main result is a polynomial time unification algorithm for sequential programs w.r.t. strong equivalence of programs.