A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background...
: Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learni...
In this paper we describe an approach for integrating abduction and induction in the ILP setting of learning from interpretations with the aim of solving the problem of incomplete...
Inductive Logic Programming (ILP) deals with inducing clausal theories from examples basically through generalization or specialization. The specialization and generalization oper...
The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995) to track individual users’ knowledge f...
Interactive tools to help users author plans or processes are essential in a variety of domains. KANAL helps users author sound plans by simulating them, checking for a variety of...
Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful ...
This paper presents a framework for user-oriented text mining. It is then illustrated with an example of discovering knowledge from competitors’ websites. The knowledge to be di...
Abstract. In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP...
Stephen Muggleton, Huma Lodhi, Ata Amini, Michael ...
We show how chemical background knowledge can be used to improve the prediction performance in structureactivitity relationships (SARs) for non-congeneric compounds. The goal of t...