This paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enable...
We present algorithms for exactly learning unknown environments that can be described by deterministic nite automata. The learner performs a walk on the target automaton, where at...
Abstract. Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to pot...
Abstract. This paper reports our research work in the new field of humancomputer collaborative learning (HCCL). The general architecture of an HCCL is defined. An HCCL system, call...
This paper describes a method for learner modelling for use within simulation-based learning environments. The goal of the learner modelling system is to provide the learner with a...
This paper addresses the problem of learning from highly structured data. Speci cally, it describes a procedure, called decomposition, that allows a learner to access automatically...
Our work explores an interactive open learner modelling (IOLM) approach where learner diagnosis is considered as an interactive process involving both a computer system and a learn...
Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to potential ap...
Developing a learner model containing an accurate representation of a learner’s knowledge is made more difficult in distributed learning environments where the learner uses mult...
Christopher A. Brooks, Mike Winter, Jim E. Greer, ...
This article introduces an approach to adaptive wayfinding support for lifelong learners based on self-organisation theory. It describes an architecture which supports the recordin...