1,801 research outputs found
Sketched Answer Set Programming
Answer Set Programming (ASP) is a powerful modeling formalism for
combinatorial problems. However, writing ASP models is not trivial. We propose
a novel method, called Sketched Answer Set Programming (SkASP), aiming at
supporting the user in resolving this issue. The user writes an ASP program
while marking uncertain parts open with question marks. In addition, the user
provides a number of positive and negative examples of the desired program
behaviour. The sketched model is rewritten into another ASP program, which is
solved by traditional methods. As a result, the user obtains a functional and
reusable ASP program modelling her problem. We evaluate our approach on 21 well
known puzzles and combinatorial problems inspired by Karp's 21 NP-complete
problems and demonstrate a use-case for a database application based on ASP.Comment: 15 pages, 11 figures; to appear in ICTAI 201
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H2 2 has universal Turing expressivity though H2 2 is decidable given a finite signature. Additionally we show that Knuth–Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation MetagolD to PAC-learn minimal cardinality H2 2 definitions. This result is consistent with our experiments which indicate that MetagolD efficiently learns compact H2 2 definitions involving predicate invention for learning robotic strategies, the East–West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs
This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined as
ordered lists of clauses each ending in a cut. The method, called FOIDL, is
based on FOIL (Quinlan, 1990) but employs intensional background knowledge and
avoids the need for explicit negative examples. It is particularly useful for
problems that involve rules with specific exceptions, such as learning the
past-tense of English verbs, a task widely studied in the context of the
symbolic/connectionist debate. FOIDL is able to learn concise, accurate
programs for this problem from significantly fewer examples than previous
methods (both connectionist and symbolic).Comment: See http://www.jair.org/ for any accompanying file
Inductive Logic Programming in Databases: from Datalog to DL+log
In this paper we address an issue that has been brought to the attention of
the database community with the advent of the Semantic Web, i.e. the issue of
how ontologies (and semantics conveyed by them) can help solving typical
database problems, through a better understanding of KR aspects related to
databases. In particular, we investigate this issue from the ILP perspective by
considering two database problems, (i) the definition of views and (ii) the
definition of constraints, for a database whose schema is represented also by
means of an ontology. Both can be reformulated as ILP problems and can benefit
from the expressive and deductive power of the KR framework DL+log. We
illustrate the application scenarios by means of examples. Keywords: Inductive
Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid
Knowledge Representation and Reasoning Systems. Note: To appear in Theory and
Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables
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How does predicate invention affect human comprehensibility?
During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols
Logic Programs as Declarative and Procedural Bias in Inductive Logic Programming
Machine Learning is necessary for the development of Artificial Intelligence, as pointed out by Turing in his 1950 article ``Computing Machinery and Intelligence''. It is in the same article that Turing suggested the use of computational logic and background knowledge for learning. This thesis follows a logic-based machine learning approach called Inductive Logic Programming (ILP), which is advantageous over other machine learning approaches in terms of relational learning and utilising background knowledge. ILP uses logic programs as a uniform representation for hypothesis, background knowledge and examples, but its declarative bias is usually encoded using metalogical statements. This thesis advocates the use of logic programs to represent declarative and procedural bias, which results in a framework of single-language representation.
We show in this thesis that using a logic program called the top theory as declarative bias leads to a sound and complete multi-clause learning system MC-TopLog. It overcomes the entailment-incompleteness of Progol, thus outperforms Progol in terms of predictive accuracies on learning grammars and strategies for playing Nim game. MC-TopLog has been applied to two real-world applications funded by Syngenta, which is an agriculture company.
A higher-order extension on top theories results in meta-interpreters, which allow the introduction of new predicate symbols. Thus the resulting ILP system Metagol can do predicate invention, which is an intrinsically higher-order logic operation. Metagol also leverages the procedural semantic of Prolog to encode procedural bias, so that it can outperform both its ASP version and ILP systems without an equivalent procedural bias in terms of efficiency and accuracy. This is demonstrated by the experiments on learning Regular, Context-free and Natural grammars. Metagol is also applied to non-grammar learning tasks involving recursion and predicate invention, such as learning a definition of staircases and robot strategy learning. Both MC-TopLog and Metagol are based on a -directed framework, which is different from other multi-clause learning systems based on Inverse Entailment, such as CF-Induction, XHAIL and IMPARO. Compared to another -directed multi-clause learning system TAL, Metagol allows the explicit form of higher-order assumption to be encoded in the form of meta-rules.Open Acces
Constructive approaches to Program Induction
Search is a key technique in artificial intelligence, machine learning and Program Induction. No
matter how efficient a search procedure, there exist spaces that are too large to search effectively
and they include the search space of programs. In this dissertation we show that in the context
of logic-program induction (Inductive Logic Programming, or ILP) it is not necessary to search
for a correct program, because if one exists, there also exists a unique object that is the most
general correct program, and that can be constructed directly, without a search, in polynomial
time and from a polynomial number of examples. The existence of this unique object, that we
term the Top Program because of its maximal generality, does not so much solve the problem
of searching a large program search space, as it completely sidesteps it, thus improving the
efficiency of the learning task by orders of magnitude commensurate with the complexity of a
program space search.
The existence of a unique Top Program and the ability to construct it given finite resources
relies on the imposition, on the language of hypotheses, from which programs are constructed,
of a strong inductive bias with relevance to the learning task. In common practice, in machine
learning, Program Induction and ILP, such relevant inductive bias is selected, or created,
manually, by the human user of a learning system, with intuition or knowledge of the problem
domain, and in the form of various kinds of program templates. In this dissertation we show
that by abandoning the reliance on such extra-logical devices as program templates, and instead
defining inductive bias exclusively as First- and Higher-Order Logic formulae, it is possible to
learn inductive bias itself from examples, automatically, and efficiently, by Higher-Order Top
Program construction.
In Chapter 4 we describe the Top Program in the context of the Meta-Interpretive Learning
approach to ILP (MIL) and describe an algorithm for its construction, the Top Program
Construction algorithm (TPC). We prove the efficiency and accuracy of TPC and describe
its implementation in a new MIL system called Louise. We support theoretical results with
experiments comparing Louise to the state-of-the-art, search-based MIL system, Metagol, and
find that Louise improves Metagol’s efficiency and accuracy. In Chapter 5 we re-frame MIL as
specialisation of metarules, Second-Order clauses used as inductive bias in MIL, and prove that
problem-specific metarules can be derived by specialisation of maximally general metarules, by
MIL. We describe a sub-system of Louise, called TOIL, that learns new metarules by MIL and
demonstrate empirically that the metarules learned by TOIL match those selected manually,
while maintaining the accuracy and efficiency of learning.
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