Learning First Order Logic Time Series Classifiers
- Juan J. Rodríguez; Carlos J. Alonso; Henrik Boström
A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as "always", and distance based, such as the euclidean distance. Special
Learning First Order Logic Time Series Classifiers
- Juan J. Rodríguez; Carlos J. Alonso; Henrik Boström
A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as "always", and distance based, such as the euclidean distance.
Logic: deductive and inductive. By Alexander Bain ...
- Bain, Alexander, 1818-1903.
New and rev. ed.
A manual of logic, deductive and inductive. By H.H. Munro ...
- Munro, H. H.
2 p. l., xii, 237 p.
Elements of inductive logic. By Noah K. Davis.
- Davis, Noah Knowles, 1830- [from old catalog]
viii, 204 p. ;
Elementary lessons in logic: deductive and inductive, with copious questions and examples, and a vocabulary of logical terms, by W. Stanley Jevons.
- Jevons, William Stanley, 1835-1882.
lambda-Subsumption and Its Application to Learning from Positive-only Examples
- Zdravko Markov
. The general aim of the present paper is to show the advantages of the model-theoretic approach to Inductive Logic Programming. The paper introduces a new generality ordering between Horn clauses, called -subsumption. It is stronger than `-subsumption and weaker than generalized subsumption. Most importantly -subsumption allows to compare clauses in a local sense, i.e. with respect to a partial interpretation. This allows to define a non-trivial upper bound in the - subsumption lattice without the use of negative examples. An algorithm for concept learning from positive-only examples, based on these ideas, is described and its performance is empirically evaluated...
Efficient Spatial and Temporal Learning Procedures and Relational Evidence Theory
- Adrian Pearce; Terry Caelli; Walter F. Bischof
We present a relational and evidence-based approach to building systems which can learn various identification, location and planning tasks in spatial and temporal domains. This machine learning problem is a difficult one because it involves, in addition to database operations such as indexing, the abilitytogeneralize over training samples from continuous and relational data types. Relational evidence theory integrates methods from inductive logic programming with those from evidence theory and evaluates the symbolic representations formed. Generalization methods are combined with causal modeling and dynamic constraint satisfaction to optimize both the representation bias and search strategy used during learning. The approach is...
Planning to learn: Recent Developments and Future Directions
The talk will cover my lab’s recent research concerning planning to learn and discuss its relationships to relevant work of other researchers. I will first introduce a machine-learning application that had motivated us to explore how knowledge-discovery workflows could be designed automatically using a data-mining ontology. In particular, we mined product-engineering data such as CAD documents for structural design patterns . This task entailed the orchestration of numerous data-preprocessing and machine-learning algorithms in surprisingly complex workflows. The involved technique of sorted refinement  lead to non-linear, non-tree knowledge discovery workflows, in that the data flow was forked into individually processed...
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention Revisited ∗
- Stephen Muggleton; Dianhuan Lin
In recent years Predicate Invention has been underexplored 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 abduction with respect to a meta-interpreter. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. 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 H 2 2 has universal...
Inducing Evolution-Robust Pointcuts
- Mathieu Braem; Kris Gybels; Andy Kellens; Wim Vanderperren
One of the problems in Aspect-Oriented Software Development is specifying pointcuts that are robust with respect to evolution of the base program. We propose to use Inductive Logic Programming, and more specifically the FOIL algorithm, to automatically discover intensional pattern-based pointcuts. In this paper we demonstrate this approach using several experiments in Java, where we successfully induce a pointcut from a given set of joinpoints. Furthermore, we present the tool chain and IDE that supports our approach. 1.
Parameter screening and optimisation for ilp using designed experiments
- Ashwin Srinivasan; Ganesh Ramakrishnan; De Raedt
Reports of experiments conducted with an Inductive Logic Programming system rarely describe how specific values of parameters of the system are arrived at when constructing models. Usually, no attempt is made to identify sensitive parameters, and those that are used are often given “factory-supplied ” default values, or values obtained from some non-systematic exploratory analysis. The immediate consequence of this is, of course, that it is not clear if better models could have been obtained if some form of parameter selection and optimisation had been performed. Questions follow inevitably on the experiments themselves: specifically, are all algorithms being treated fairly,...
- Er K. Jetten; W. G. J. Beek Msc; Dr. B. Bredeweg
The creation of qualitative models of dynamic systems is a difficult and time consuming task, requiring skills domain experts often lack. In this thesis Inductive Logic Programming (ILP) is used in order to automate the creation of these models in the Garp3 formalism. Model primitives from Garp3 are implemented as background knowledge, and it is shown ILP can learn patterns widely used by domain experts in qualitative models, as well as more complex models using these model primitives. Learning of these patterns and models is extensively tested when confronted with different types of noise. ILP shows to be very robust...
A Link-Based Method for Propositionalization
- Quang-thang Dinh; Matthieu Exbrayat; Christel Vrain
Abstract. Propositionalization, a popular technique in Inductive Logic Programming, aims at converting a relational problem into an attributevalue one. An important facet of propositionalization consists in building a set of relevant features. To this end we propose a new method, based on a synthetic representation of the database, modeling the links between connected ground atoms. Comparing it to two state-of-the-art logicbasedpropositionalization techniquesonthreebenchmarks,we showthat our method leads to good results in supervised classification. 1
MicroRNAs Analysis by Hypothesis Finding
- Andrei Doncescu; Katsumi Inoue; Anne Pradine
Abstract. The cell is an entity composed of several thousand types of interacting proteins. Our goal is to comprehend the cancer regulation mechanisms using the microRNAs. MicroRNAs are present in almost all genetic regulatory networks acting as inhibitors targeting mRNAs. In this paper, it is shown how the Artificial Intelligence description method functioning on the basis of Inductive Logic Programming can be used successfully to describe essential aspects of cancer mechanisms. The results obtained show new microRNAs markers for melanoma metastasis. 1
Towards logic-based representations of musical harmony for classification, retrieval and knowledge discovery
- Amélie Anglade; Simon Dixon
We present a logic-based framework using a relational description of musical data and logical inference for automatic characterisation of music. It is intended to be an alternative to the bag-of-frames approach for classification tasks but is also suitable for retrieval and musical knowledge discovery. We present the first results obtained with such a system using Inductive Logic Programming as inference method to characterise the Beatles and Real Book harmony. We conclude with a discussion of the knowledge representation problems we faced during these first tests. 1.
Computational Modelling of Harmony
- Simon Dixon
Abstract. Many computational models for processing music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. In this talk I describe two recent approaches to modelling musical harmony which attempt to reduce the gap between computational models and human understanding of music. The first is a chord transcription system which uses a high-level model of musical context in which chord, key, metric position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, achieving state-of-the-art performance. The second approach uses inductive logic programming to...
Probabilistic and Logic-Based Modelling of Harmony
- Simon Dixon; Matthias Mauch; Amélie Anglade
Abstract. Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent approaches to modelling musical harmony, using a probabilistic and a logic-based framework respectively, which attempt to reduce the gap between computational models and human understanding of music. The first is a chord transcription system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, achieving state-of-the-art performance. The second approach uses...
F.M.: AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases
- Luis Galárraga; Christina Teflioudi; Katja Hose; Fabian M. Suchanek
Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today’s KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data...
INTERNATIONALIZATION OF ENTREPRENEURIAL FIRMS
- Christopher B. Bingham; Kathleen M. Eisenhardt
While much research suggests that organizational processes are learned from experience, surprisingly little is known about the content of what is learned and how that content is developed. Using an inductive research logic and in-depth nested case studies, we explore how organizations learn key processes from heterogeneous experience. Specifically, we study how organizations learn to internationalize. The setting is six entrepreneurial firms with headquarters in three culturally distinct countries (i.e., Finland, United States, Singapore). We show that learning a key process originates with cognitive templates that leaders use to seed initial experience, not with pure experiential learning as much research...