278 research outputs found
Subsumer: A Prolog theta-subsumption engine
No full textState-of-the-art theta-subsumption engines like Django (C) and Resumer2 (Java) are implemented in imperative languages. Since theta-subsumption is inherently a logic problem, in this paper we explore how to efficiently implement it in Prolog.
theta-subsumption is an important problem in computational logic and particularly relevant to the Inductive Logic Programming (ILP) community as it is at the core of the hypotheses coverage test which is often the bottleneck of an ILP system. Also, since most of those systems are implemented in Prolog, they can immediately take advantage of a Prolog based theta-subsumption engine.
We present a relatively simple (~1000 lines in Prolog) but efficient and general theta-subsumption engine, Subsumer. Crucial to Subsumer's performance is the dynamic and recursive decomposition of a clause in sets of independent components. Also important are ideas borrowed from constraint programming that empower Subsumer to efficiently work on clauses with up to several thousand literals and several dozen distinct variables.
Using the notoriously challenging Phase Transition dataset we show that, cputime wise, Subsumer clearly outperforms the Django subsumption engine and is competitive with the more sophisticated, state-of-the-art, Resumer2. Furthermore, Subsumer's memory requirements are only a small fraction of those engines and it can handle arbitrary Prolog clauses whereas Django and Resumer2 can only handle Datalog clauses
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 17382 "Approaches and Applications of Inductive Programming". After a short introduction to the state of the art to
inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given
05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis
No full textA short report on the Dagstuhl seminar on Probabilistic, Logical and Relational Learning – Towards a Synthesis is given
05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis
No full textFrom 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202)
No full textIn this report the program and the outcomes of Dagstuhl Seminar 19202 "Approaches and Applications of Inductive Programming" is documented. After a short introduction to the state of the art to inductive programming research, an overview of the introductory tutorials, the talks, program demonstrations, and the outcomes of discussion groups is given
Compression, Significance and Accuracy
No full textInductive Logic Programming (ILP) involves learning relational concepts from examples and background knowledge. To date all ILP learning systems make use of tests inherited from propositional and decision tree learning for evaluating the significance of hypotheses. None of these significance tests take account of the relevance or utility of the background knowledge. In this paper we describe a method, called HP-compression, of evaluating the significance of a hypothesis based on the degree to which it allows compression of the observed data with respect to the background knowledge. This can be measured by comparing the lengths of the input and output tapes of a reference Turing machine which will generate the examples from the hypothesis and a set of derivational proofs. The model extends an earlier approach of Muggleton by allowing for noise. The truth values of noisy instances are switched by making use of correction codes. The utility of compression as a significance measure is eval..
07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis
No full textFrom April 14 – 20, 2007, the Dagstuhl Seminar 07161 ``Probabilistic, Logical and Relational Learning - A Further Synthesis'' was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
An investigation into theory completion techniques in inductive logic programming
No full textTraditional Inductive Logic Programming (ILP) focuses on the setting where the target theory is a generalisation of the observations. This is known as Observational Predicate Learning (OPL). In the Theory Completion setting the target theory is not in the same predicate as the observations (non-OPL). This thesis investigates two alternative simple extensions to traditional ILP to perform non-OPL or Theory Completion. Both techniques perform extraction-case abduction from an existing background theory and one seed observation. The first technique -- Logical Back-propagation -- modifies the existing background theory so that abductions can be achieved by a form of constructive negation using a standard SLD-resolution theorem prover. The second technique -- SOLD-resolution -- modifies the theorem prover, and leaves the existing background theory unchanged. It is shown that all abductions produced by Logical Back-propagation can also be generated by SOLD-resolution; but the reverse does not hold. The implementation using the SOLD-resolution technique -- the ALECTO system -- was applied to the problems of completing context free and context dependant grammars; and learning Event Calculus programs. It was successfully able to learn an Event Calculus program to control the navigation of a real-life robot. The Event Calculus is a formalism to represent common-sense knowledge. It follows that the discovery of some common-sense knowledge was produced with the assistance of a machine
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