73,215 research outputs found
Logical Reduction of Metarules
International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times
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
Answer Sets for Consistent Query Answering in Inconsistent Databases
A relational database is inconsistent if it does not satisfy a given set of
integrity constraints. Nevertheless, it is likely that most of the data in it
is consistent with the constraints. In this paper we apply logic programming
based on answer sets to the problem of retrieving consistent information from a
possibly inconsistent database. Since consistent information persists from the
original database to every of its minimal repairs, the approach is based on a
specification of database repairs using disjunctive logic programs with
exceptions, whose answer set semantics can be represented and computed by
systems that implement stable model semantics. These programs allow us to
declare persistence by defaults and repairing changes by exceptions. We
concentrate mainly on logic programs for binary integrity constraints, among
which we find most of the integrity constraints found in practice.Comment: 34 page
A Potpourri of Reason Maintenance Methods
We present novel methods to compute changes to materialized
views in logic databases like those used by rule-based reasoners.
Such reasoners have to address the problem of changing axioms in the
presence of materializations of derived atoms. Existing approaches have
drawbacks: some require to generate and evaluate large transformed programs
that are in Datalog - while the source program is in Datalog and
significantly smaller; some recompute the whole extension of a predicate
even if only a small part of this extension is affected by the change.
The methods presented in this article overcome these drawbacks and derive
additional information useful also for explanation, at the price of an
adaptation of the semi-naive forward chaining
Refinement Calculus of Reactive Systems
Refinement calculus is a powerful and expressive tool for reasoning about
sequential programs in a compositional manner. In this paper we present an
extension of refinement calculus for reactive systems. Refinement calculus is
based on monotonic predicate transformers, which transform sets of post-states
into sets of pre-states. To model reactive systems, we introduce monotonic
property transformers, which transform sets of output traces into sets of input
traces. We show how to model in this semantics refinement, sequential
composition, demonic choice, and other semantic operations on reactive systems.
We use primarily higher order logic to express our results, but we also show
how property transformers can be defined using other formalisms more amenable
to automation, such as linear temporal logic (suitable for specifications) and
symbolic transition systems (suitable for implementations). Finally, we show
how this framework generalizes previous work on relational interfaces so as to
be able to express systems with infinite behaviors and liveness properties
Automated Termination Proofs for Logic Programs by Term Rewriting
There are two kinds of approaches for termination analysis of logic programs:
"transformational" and "direct" ones. Direct approaches prove termination
directly on the basis of the logic program. Transformational approaches
transform a logic program into a term rewrite system (TRS) and then analyze
termination of the resulting TRS instead. Thus, transformational approaches
make all methods previously developed for TRSs available for logic programs as
well. However, the applicability of most existing transformations is quite
restricted, as they can only be used for certain subclasses of logic programs.
(Most of them are restricted to well-moded programs.) In this paper we improve
these transformations such that they become applicable for any definite logic
program. To simulate the behavior of logic programs by TRSs, we slightly modify
the notion of rewriting by permitting infinite terms. We show that our
transformation results in TRSs which are indeed suitable for automated
termination analysis. In contrast to most other methods for termination of
logic programs, our technique is also sound for logic programming without occur
check, which is typically used in practice. We implemented our approach in the
termination prover AProVE and successfully evaluated it on a large collection
of examples.Comment: 49 page
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