296,745 research outputs found
Combining Models of Approximation with Partial Learning
In Gold's framework of inductive inference, the model of partial learning
requires the learner to output exactly one correct index for the target object
and only the target object infinitely often. Since infinitely many of the
learner's hypotheses may be incorrect, it is not obvious whether a partial
learner can be modifed to "approximate" the target object.
Fulk and Jain (Approximate inference and scientific method. Information and
Computation 114(2):179--191, 1994) introduced a model of approximate learning
of recursive functions. The present work extends their research and solves an
open problem of Fulk and Jain by showing that there is a learner which
approximates and partially identifies every recursive function by outputting a
sequence of hypotheses which, in addition, are also almost all finite variants
of the target function.
The subsequent study is dedicated to the question how these findings
generalise to the learning of r.e. languages from positive data. Here three
variants of approximate learning will be introduced and investigated with
respect to the question whether they can be combined with partial learning.
Following the line of Fulk and Jain's research, further investigations provide
conditions under which partial language learners can eventually output only
finite variants of the target language. The combinabilities of other partial
learning criteria will also be briefly studied.Comment: 28 page
Didactiques de l’intercompréhension et enseignement du français en contexte plurilingue
Intercomprehension is an innovative technique for teaching and learning based on the ability of speakers quickly to master techniques for transferring competences between related languages, principally with respect to comprehension. This methodology relies on activities contrasting with those communicative practices that have become the rule in the area of teaching language and cultures, such as translation, contrastive grammar, the importance of writing. The methodological common denominator is that of a plurilingual and pluricultural pedagogy. Learning French thus opens a door to a range of romance languages spoken by more than 500 million people throughout the world. For French-speakers, intercomprehension represents a means of rapid access to related languages and cultures, at the same time encouraging reflexive observation of the first language. The practice of intercomprehension educates for plurilingualism. It targets the development of a new relationship with languages, by means of an active practice of observation, which makes it possible to justify the acquisition of partial competencies in a language as a valid goal for learning
Computabilities of Validity and Satisfiability in Probability Logics over Finite and Countable Models
The -logic (which is called E-logic in this paper) of
Kuyper and Terwijn is a variant of first order logic with the same syntax, in
which the models are equipped with probability measures and in which the
quantifier is interpreted as "there exists a set of measure
such that for each , ...." Previously, Kuyper and
Terwijn proved that the general satisfiability and validity problems for this
logic are, i) for rational , respectively
-complete and -hard, and ii) for ,
respectively decidable and -complete. The adjective "general" here
means "uniformly over all languages."
We extend these results in the scenario of finite models. In particular, we
show that the problems of satisfiability by and validity over finite models in
E-logic are, i) for rational , respectively
- and -complete, and ii) for , respectively
decidable and -complete. Although partial results toward the countable
case are also achieved, the computability of E-logic over countable
models still remains largely unsolved. In addition, most of the results, of
this paper and of Kuyper and Terwijn, do not apply to individual languages with
a finite number of unary predicates. Reducing this requirement continues to be
a major point of research.
On the positive side, we derive the decidability of the corresponding
problems for monadic relational languages --- equality- and function-free
languages with finitely many unary and zero other predicates. This result holds
for all three of the unrestricted, the countable, and the finite model cases.
Applications in computational learning theory, weighted graphs, and neural
networks are discussed in the context of these decidability and undecidability
results.Comment: 47 pages, 4 tables. Comments welcome. Fixed errors found by Rutger
Kuype
Inductive Inference and Reverse Mathematics
The present work investigates inductive inference from the perspective
of reverse mathematics. Reverse mathematics is a framework which relates
the proof strength of theorems and axioms throughout many areas of
mathematics in an interdisciplinary way. The present work looks at
basic notions of learnability including Angluin\u27s tell-tale condition and its variants for learning in the limit and for conservative learning. Furthermore, the more general criterion of partial learning is investigated. These notions are studied in the reverse mathematics context for uniformly and weakly represented families of languages. The results are stated in terms of axioms referring to domination and induction strength
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a
high-level formal specification. In recent times, effective formal synthesis
methods have been proposed based on the use of inductive learning. We refer to
this class of methods that learn programs from examples as formal inductive
synthesis. In this paper, we present a theoretical framework for formal
inductive synthesis. We discuss how formal inductive synthesis differs from
traditional machine learning. We then describe oracle-guided inductive
synthesis (OGIS), a framework that captures a family of synthesizers that
operate by iteratively querying an oracle. An instance of OGIS that has had
much practical impact is counterexample-guided inductive synthesis (CEGIS). We
present a theoretical characterization of CEGIS for learning any program that
computes a recursive language. In particular, we analyze the relative power of
CEGIS variants where the types of counterexamples generated by the oracle
varies. We also consider the impact of bounded versus unbounded memory
available to the learning algorithm. In the special case where the universe of
candidate programs is finite, we relate the speed of convergence to the notion
of teaching dimension studied in machine learning theory. Altogether, the
results of the paper take a first step towards a theoretical foundation for the
emerging field of formal inductive synthesis
- …