23,491 research outputs found
Transformations of Logic Programs on Infinite Lists
We consider an extension of logic programs, called \omega-programs, that can
be used to define predicates over infinite lists. \omega-programs allow us to
specify properties of the infinite behavior of reactive systems and, in
general, properties of infinite sequences of events. The semantics of
\omega-programs is an extension of the perfect model semantics. We present
variants of the familiar unfold/fold rules which can be used for transforming
\omega-programs. We show that these new rules are correct, that is, their
application preserves the perfect model semantics. Then we outline a general
methodology based on program transformation for verifying properties of
\omega-programs. We demonstrate the power of our transformation-based
verification methodology by proving some properties of Buechi automata and
\omega-regular languages.Comment: 37 pages, including the appendix with proofs. This is an extended
version of a paper published in Theory and Practice of Logic Programming, see
belo
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa
Proving theorems by program transformation
In this paper we present an overview of the unfold/fold proof method, a method for proving theorems about programs, based on program transformation. As a metalanguage for specifying programs and program properties we adopt constraint logic programming (CLP), and we present a set of transformation rules (including the familiar unfolding and folding rules) which preserve the semantics of CLP programs. Then, we show how program transformation strategies can be used, similarly to theorem proving tactics, for guiding the application of the transformation rules and inferring the properties to be proved. We work out three examples: (i) the proof of predicate equivalences, applied to the verification of equality between CCS processes, (ii) the proof of first order formulas via an extension of the quantifier elimination method, and (iii) the proof of temporal properties of infinite state concurrent systems, by using a transformation strategy that performs program specialization
Transforming floundering into success
We show how logic programs with "delays" can be transformed to programs
without delays in a way which preserves information concerning floundering
(also known as deadlock). This allows a declarative (model-theoretic),
bottom-up or goal independent approach to be used for analysis and debugging of
properties related to floundering. We rely on some previously introduced
restrictions on delay primitives and a key observation which allows properties
such as groundness to be analysed by approximating the (ground) success set.
This paper is to appear in Theory and Practice of Logic Programming (TPLP).
Keywords: Floundering, delays, coroutining, program analysis, abstract
interpretation, program transformation, declarative debuggingComment: Number of pages: 24 Number of figures: 9 Number of tables: non
Program transformation for development, verification, and synthesis of programs
This paper briefly describes the use of the program transformation methodology for the development of correct and efficient programs. In particular, we will refer to the case of constraint logic programs and, through some examples, we will show how by program transformation, one can improve, synthesize, and verify programs
Correctness and completeness of logic programs
We discuss proving correctness and completeness of definite clause logic
programs. We propose a method for proving completeness, while for proving
correctness we employ a method which should be well known but is often
neglected. Also, we show how to prove completeness and correctness in the
presence of SLD-tree pruning, and point out that approximate specifications
simplify specifications and proofs.
We compare the proof methods to declarative diagnosis (algorithmic
debugging), showing that approximate specifications eliminate a major drawback
of the latter. We argue that our proof methods reflect natural declarative
thinking about programs, and that they can be used, formally or informally, in
every-day programming.Comment: 29 pages, 2 figures; with editorial modifications, small corrections
and extensions. arXiv admin note: text overlap with arXiv:1411.3015. Overlaps
explained in "Related Work" (p. 21
Reverse Engineering from Assembler to Formal Specifications via Program Transformations
The FermaT transformation system, based on research carried out over the last
sixteen years at Durham University, De Montfort University and Software
Migrations Ltd., is an industrial-strength formal transformation engine with
many applications in program comprehension and language migration. This paper
is a case study which uses automated plus manually-directed transformations and
abstractions to convert an IBM 370 Assembler code program into a very
high-level abstract specification.Comment: 10 page
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