1,607 research outputs found
Analytical learning and term-rewriting systems
Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques
The exp-log normal form of types
Lambda calculi with algebraic data types lie at the core of functional
programming languages and proof assistants, but conceal at least two
fundamental theoretical problems already in the presence of the simplest
non-trivial data type, the sum type. First, we do not know of an explicit and
implemented algorithm for deciding the beta-eta-equality of terms---and this in
spite of the first decidability results proven two decades ago. Second, it is
not clear how to decide when two types are essentially the same, i.e.
isomorphic, in spite of the meta-theoretic results on decidability of the
isomorphism.
In this paper, we present the exp-log normal form of types---derived from the
representation of exponential polynomials via the unary exponential and
logarithmic functions---that any type built from arrows, products, and sums,
can be isomorphically mapped to. The type normal form can be used as a simple
heuristic for deciding type isomorphism, thanks to the fact that it is a
systematic application of the high-school identities.
We then show that the type normal form allows to reduce the standard beta-eta
equational theory of the lambda calculus to a specialized version of itself,
while preserving the completeness of equality on terms. We end by describing an
alternative representation of normal terms of the lambda calculus with sums,
together with a Coq-implemented converter into/from our new term calculus. The
difference with the only other previously implemented heuristic for deciding
interesting instances of eta-equality by Balat, Di Cosmo, and Fiore, is that we
exploit the type information of terms substantially and this often allows us to
obtain a canonical representation of terms without performing sophisticated
term analyses
Introduction to the 28th International Conference on Logic Programming Special Issue
We are proud to introduce this special issue of the Journal of Theory and
Practice of Logic Programming (TPLP), dedicated to the full papers accepted for
the 28th International Conference on Logic Programming (ICLP). The ICLP
meetings started in Marseille in 1982 and since then constitute the main venue
for presenting and discussing work in the area of logic programming
Generating reversible circuits from higher-order functional programs
Boolean reversible circuits are boolean circuits made of reversible
elementary gates. Despite their constrained form, they can simulate any boolean
function. The synthesis and validation of a reversible circuit simulating a
given function is a difficult problem. In 1973, Bennett proposed to generate
reversible circuits from traces of execution of Turing machines. In this paper,
we propose a novel presentation of this approach, adapted to higher-order
programs. Starting with a PCF-like language, we use a monadic representation of
the trace of execution to turn a regular boolean program into a
circuit-generating code. We show that a circuit traced out of a program
computes the same boolean function as the original program. This technique has
been successfully applied to generate large oracles with the quantum
programming language Quipper.Comment: 21 pages. A shorter preprint has been accepted for publication in the
Proceedings of Reversible Computation 2016. The final publication is
available at http://link.springer.co
New Equations for Neutral Terms: A Sound and Complete Decision Procedure, Formalized
The definitional equality of an intensional type theory is its test of type
compatibility. Today's systems rely on ordinary evaluation semantics to compare
expressions in types, frustrating users with type errors arising when
evaluation fails to identify two `obviously' equal terms. If only the machine
could decide a richer theory! We propose a way to decide theories which
supplement evaluation with `-rules', rearranging the neutral parts of
normal forms, and report a successful initial experiment.
We study a simple -calculus with primitive fold, map and append operations on
lists and develop in Agda a sound and complete decision procedure for an
equational theory enriched with monoid, functor and fusion laws
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