Interactive Concept-Learning and Constructive Induction by Analogy
Luc De Raedt,Maurice Bruynooghe +1 more
TL;DR: A novel approach to interactive concept-learning and constructive induction that better fits the requirements imposed by the learning apprentice paradigm is presented and incorporated in the system Clint-Cia, which integrates several user-friendly features into one working whole.
read more
Abstract: The available concept-learners only partially fulfill the needs imposed by the learning apprentice generation of learners. We present a novel approach to interactive concept-learning and constructive induction that better fits the requirements imposed by the learning apprentice paradigm. The approach is incorporated in the system Clint-Cia, which integrates several user-friendly features into one working whole: it is interactive, generates examples, shifts its bias, identifies concepts in the limit, copes with indirect relevance, recovers from errors, performs constructive induction and invents new concepts by analogy to previously learned ones.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Inductive Logic Programming : Theory and Methods
Stephen Muggleton,Luc De Raedt +1 more
TL;DR: The most important theories and methods of Inductive Logic Programming, a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge, are surveyed.
1.7K
Deep transfer via second-order Markov logic
Jesse Davis,Pedro Domingos +1 more
- 14 Jun 2009
TL;DR: The algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain, and has successfully transferred learned knowledge among molecular biology, social network and Web domains.
•Posted Content
Inductive logic programming at 30: a new introduction
TL;DR: The necessary logical notation and the main ILP learning settings are introduced, the main building blocks of an ILP system are described, and several ILP systems on several dimensions are compared.
Learning programs by learning from failures
Andrew Cropper,Rolf Morel +1 more
TL;DR: Popper is introduced, an ILP system that implements this approach by combining answer set programming and Prolog, and shows that constraints drastically improve learning performance, and Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.
Inductive logic programming: derivations, successes and shortcomings
TL;DR: It is argued that algorithms can be directly derived from the formal specifications of ILP, which provides a common basis for Inverse Resolution, Explanation-Based Learning, Abduction and Relative Least General Generalisation.
References
•Book
Machine Learning: An Artificial Intelligence Approach
Ryszard S. Michalski,Jaime G. Carbonell,Tom M. Mitchell +2 more
- 03 Oct 2013
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
3.1K
A theory and methodology of inductive learning
TL;DR: The presented theory views inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements.
1.8K
Developing Multiagent Systems with agentTool
Scott A. DeLoach,Mark F. Wood +1 more
- 07 Jul 2000
TL;DR: MaSE guides a designer from an initial system specification to implementation by guiding the designer through a set of inter-related graphically based system models as envisioned by MaSE.
1.7K
Generalization as search
TL;DR: The problem of concept learning, or forming a general description of a class of objects given a set of examples and non-examples, is viewed here as a search problem.
1.6K
Explanation-based generalization: a unifying view
TL;DR: This paper proposed a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization, which is illustrated in the context of several example problems, and used to contrast several existing systems for explanation based generalization.
Related Papers (5)
Stephen Muggleton,Wray Buntine +1 more
- 12 Jun 1988
Ehud Shapiro
- 14 Apr 1983
John W. Lloyd
- 01 Jan 1984