71 research outputs found
Louise: A Meta-Interpretive Learner for Efficient Multi-clause Learning of Large Programs
We present Louise, a new Meta-Interpretive Learner that performs efficient multi-clause learning, implemented in Prolog. Louise is efficient enough to learn programs that are too large to be learned with the current state-of-the-art MIL system, Metagol. Louise learns by first constructing the most general program in the hypothesis space of a MIL problem and then reducing this "Top program" by Plotkin's program reduction algorithm. In this extended abstract we describe Louise's learning approach and experimentally demonstrate that Louise can learn programs that are too large to be learned by our implementation of Metagol, Thelma
Inductive Acquisition of Expert Knowledge
Expert systems divide neatly into two categories: those in which ( 1) the expert decisions result in
changes to some external environment (control systems), and (2) the expert decisions merely seek
to describe the environment (classification systems). Both the explanation of computer-based
reasoning and the "bottleneck" (Feigenbaum, 1979) of knowledge acquisition are major issues in
expert systems research. We have contributed to these areas of research in two ways. Firstly, we
have implemented an expert system shell, the Mugol environment, which facilitates knowledge
acquisition by inductive inference and provides automatic explanation of run-time reasoning on
demand. RuleMaster, a commercial version of this environment, has been used to advantage
industrially in the construction and testing of two large classification systems. Secondly, we have
investigated a new technique called sequence induction which can be used in the construction of
control systems. Sequence induction is based on theoretical work in grammatical learning. We
have improved existing grammatical learning algorithms as well as suggesting and theoretically
characterising new ones. These algorithms have been successfully applied to the acquisition of
knowledge for a diverse set of control systems, including inductive construction of robot plans and
chess end-game strategies
Explanatory machine learning for sequential human teaching
The topic of comprehensibility of machine-learned theories has recently drawn
increasing attention. Inductive Logic Programming (ILP) uses logic programming
to derive logic theories from small data based on abduction and induction
techniques. Learned theories are represented in the form of rules as
declarative descriptions of obtained knowledge. In earlier work, the authors
provided the first evidence of a measurable increase in human comprehension
based on machine-learned logic rules for simple classification tasks. In a
later study, it was found that the presentation of machine-learned explanations
to humans can produce both beneficial and harmful effects in the context of
game learning. We continue our investigation of comprehensibility by examining
the effects of the ordering of concept presentations on human comprehension. In
this work, we examine the explanatory effects of curriculum order and the
presence of machine-learned explanations for sequential problem-solving. We
show that 1) there exist tasks A and B such that learning A before B has a
better human comprehension with respect to learning B before A and 2) there
exist tasks A and B such that the presence of explanations when learning A
contributes to improved human comprehension when subsequently learning B. We
propose a framework for the effects of sequential teaching on comprehension
based on an existing definition of comprehensibility and provide evidence for
support from data collected in human trials. Empirical results show that
sequential teaching of concepts with increasing complexity a) has a beneficial
effect on human comprehension and b) leads to human re-discovery of
divide-and-conquer problem-solving strategies, and c) studying machine-learned
explanations allows adaptations of human problem-solving strategy with better
performance.Comment: Submitted to the International Joint Conference on Learning &
Reasoning (IJCLR) 202
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
The goal of ILP is to induce a hypothesis (a logic program) that generalises
given training examples and background knowledge. As ILP turns 30, we survey
recent work in the field. In this survey, we focus on (i) new meta-level search
methods, (ii) techniques for learning recursive programs that generalise from
few examples, (iii) new approaches for predicate invention, and (iv) the use of
different technologies, notably answer set programming and neural networks. We
conclude by discussing some of the current limitations of ILP and discuss
directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text
overlap with arXiv:2002.11002, arXiv:2008.0791
05051 Abstracts Collection -- Probabilistic, Logical and Relational Learning - Towards a Synthesis
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis\u27\u27 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
Inductive programming meets the real world
© Gulwani, S. et al. | ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Communications of the ACM, http://dx.doi.org/10.1145/2736282[EN] Since most end users lack programming skills they often
spend considerable time and effort performing tedious and
repetitive tasks such as capitalizing a column of names manually.
Inductive Programming has a long research tradition
and recent developments demonstrate it can liberate users
from many tasks of this kind.Gulwani, S.; Hernández-Orallo, J.; Kitzelmann, E.; Muggleton, SH.; Schmid, U.; Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM. 58(11):90-99. doi:10.1145/2736282S90995811Bengio, Y., Courville, A. and Vincent, P. Representation learning: A review and new perspectives.Pattern Analy. Machine Intell. 35, 8 (2013), 1798--1828.Bielawski, B. Using the convertfrom-string cmdlet to parse structured text.PowerShell Magazine, (Sept. 9, 2004); http://www.powershellmagazine.com/2014/09/09/using-the-convertfrom-string-cmdlet-to-parse-structured-text/Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka-Jr, E.R. and T.M. Mitchell, T.M. Toward an architecture for never-ending language learning. InAAAI, 2010.Chandola, V., Banerjee, A. and V. Kumar, V. Anomaly detection: A survey.ACM Computing Surveys 41, 3 (2009), 15.Cypher, A. 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Deep knowledge: Inductive programming as an answer, Dagstuhl TR 13502, 2013.Hofmann, M. and Kitzelmann, E. I/O guided detection of list catamorphisms---towards problem specific use of program templates in IP. InACM SIGPLAN PEPM, 2010.Jha, J., Gulwani, S., Seshia, S. and Tiwari, A. Oracle-guided component-based program synthesis. InProceedings of the ICSE, 2010.Katayama, S. Efficient exhaustive generation of functional programs using Monte-Carlo search with iterative deepening. InProceedings of PRICAI, 2008.Kitzelmann, E. Analytical inductive functional programming.LOPSTR 2008, LNCS 5438.Springer, 2009, 87--102.Kitzelmann, E. Inductive programming: A survey of program synthesis techniques. InAAIP, Springer, 2010, 50--73.Kitzelmann, E. and Schmid, U. Inductive synthesis of functional programs: An explanation based generalization approach.J. Machine Learning Research 7, (Feb. 2006), 429--454.Kotovsky, K., Hayes, J.R. and Simon, H.A. Why are some problems hard? 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On the definition of a general learning system with user-defined operators.arXiv preprint arXiv:1311.4235, 2013.Menon, A., Tamuz, O., Gulwani, S., Lampson, B. and Kalai, A. A machine learning framework for programming by example. InProceedings of the ICML, 2013.Miller, R.C. and Myers, B.A. Multiple selections in smart text editing. InProceedings of IUI, 2002, 103--110.Muggleton, S.H. Inductive Logic Programming.New Generation Computing 8, 4 (1991), 295--318.Muggleton, S.H. and Lin, D. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited.IJCAI 2013, 1551--1557.Muggleton, S.H., Lin, D., Pahlavi, N. and Tamaddoni-Nezhad, A. Meta-interpretive learning: application to grammatical inference.Machine Learning 94(2014), 25--49.Muggleton, S.H., De Raedt, L., Poole, D., Bratko, I., Flach, P. and Inoue, P. ILP turns 20: Biography and future challenges.Machine Learning 86, 1 (2011), 3--23.Olsson, R. Inductive functional programming using incremental program transformation.Artificial Intelligence 74, 1 (1995), 55--83.Perelman, D., Gulwani, S., Grossman, D. and Provost, P. Test-driven synthesis.PLDI, 2014.Raza, M., Gulwani, S. and Milic-Frayling, N. Programming by example using least general generalizations.AAAI, 2014.Schmid, U. and Kitzelmann, E. Inductive rule learning on the knowledge level.Cognitive Systems Research 12, 3 (2011), 237--248.Schmid, U. and Wysotzki, F. Induction of recursive program schemes.ECML 1398 LNAI(1998), 214--225.Shapiro, E.Y. An algorithm that infers theories from facts.IJCAI(1981), 446--451.Solar-Lezama, A.Program Synthesis by Sketching.Ph.D thesis, UC Berkeley, 2008.Summers, P.D. A methodology for LISP program construction from examples.JACM 24, 1 (1977), 162--175.Tenenbaum, J.B., Griffiths, T.L. and Kemp, C. Theory-based Bayesian models of inductive learning and reasoning.Trends in Cognitive Sciences 10, 7 (2006), 309--318.Young, S. 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Recommended from our members
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
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 Mitchell’s, 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 two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks
An important application of Synthetic Biology is the engineering of the host
cell system to yield useful products. However, an increase in the scale of the
host system leads to huge design space and requires a large number of
validation trials with high experimental costs. A comprehensible machine
learning approach that efficiently explores the hypothesis space and guides
experimental design is urgently needed for the Design-Build-Test-Learn (DBTL)
cycle of the host cell system. We introduce a novel machine learning framework
ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive
logical reasoning and actively learns from training examples. In contrast to
numerical models, ILP-iML1515 is built on comprehensible logical
representations of a genome-scale metabolic model and can update the model by
learning new logical structures from auxotrophic mutant trials. The ILP-iML1515
framework 1) allows high-throughput simulations and 2) actively selects
experiments that reduce the experimental cost of learning gene functions in
comparison to randomly selected experiments.Comment: Invited presentation for AAAI Spring Symposium Series 2023 on
Computational Scientific Discover
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