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- Ekaterina Komendantskaya, John Power
- CSL
- 2011

Coalgebra may be used to provide semantics for SLD-derivations, both finite and infinite. We first give such semantics to classical SLD-derivations, proving results such as adequacy, soundness and completeness. Then, based upon coalgebraic semantics, we propose a new sound and complete algorithm for parallel derivations. We analyse this new algorithm in… (More)

- Ekaterina Komendantskaya, John Power
- CALCO
- 2011

Every variable-free logic program induces a P f P f-coalgebra on the set of atomic formulae in the program. The coalgebra p sends an atomic formula A to the set of the sets of atomic formulae in the antecedent of each clause for which A is the head. In an earlier paper, we identified a variable-free logic program with a P f P f-coalgebra on Set and showed… (More)

- Ekaterina Komendantskaya, John Power, Martin Schmidt
- J. Log. Comput.
- 2016

Coinductive definitions, such as that of an infinite stream, may often be described by elegant logic programs, but ones for which SLD-refutation is of no value as SLD-derivations fall into infinite loops. Such definitions give rise to questions of lazy corecursive derivations and parallelism, as execution of such logic programs can have both recursive and… (More)

We present ML4PG — a machine learning extension for Proof General. It allows users to gather proof statistics related to shapes of goals, sequences of applied tactics, and proof tree structures from the libraries of interactive higher-order proofs written in Coq and SSReflect. The gathered data is clustered using the state-of-the-art machine learning… (More)

- Ekaterina Komendantskaya, Kacper Lichota
- ICANN
- 2012

We propose a new method of feature extraction that allows to apply pattern-recognition abilities of neural networks to data-mine automated proofs. We propose a new algorithm to represent proofs for first-order logic programs as feature vectors; and present its implementation. We test the method on a number of problems and implementation scenarios, using… (More)

- Yves Bertot, Ekaterina Komendantskaya
- Electr. Notes Theor. Comput. Sci.
- 2008

In Constructive Type Theory, recursive and corecursive definitions are subject to syntactic restrictions which guarantee termination for recursive functions and productivity for corecursive functions. However, many terminating and productive functions do not pass the syntactic tests. Bove proposed in her thesis an elegant reformulation of the method of… (More)

- Ekaterina Komendantskaya, Guy McCusker, John Power
- AMAST
- 2010

Logic programming, a class of programming languages based on first-order logic, provides simple and efficient tools for goal-oriented proof-search. Logic programming supports recursive computations, and some logic programs resemble the inductive or coinductive definitions written in functional programming languages. In this paper, we give a coalgebraic… (More)

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen… (More)

- Yves Bertot, Ekaterina Komendantskaya
- TYPES
- 2008

We propose a (limited) solution to the problem of constructing stream values defined by recursive equations that do not respect the guardedness condition. The guardedness condition is imposed on definitions of corecursive functions in Coq, AGDA, and other higher-order proof assistants. In this paper, we concentrate in particular on those non-guarded… (More)

We demonstrate the interplay between learning and deductive algorithms in logic and neural networks. In particular, we show how learning algorithms recognised in neurocomputing can be used to built connectionist neural networks which simulate the work of semantic operator for logic programs with uncertainty and conventional algorithm of SLD-resolution.