MSIP: Agents embodying a category-based learning process for the ITS tutor to self-improve its instructional plans

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volume

3157

First Page

114

Last Page

123

Publication Date

1-1-2004

Abstract

We have conceived of a Multi-agent Self-improving Planner (MSIP) within the tutor module of an intelligent tutoring system (ITS). It embodies a learning process that utilizes knowledge about different student categories to adapt and improve its instructional plans on the level of these categories. In this sense, the categories become recipients and effectors of effective plans. The fundamental reason for introducing agents as learning elements is their intrinsic capability to learn and perform autonomously during on-line interaction. This paper discusses each agent's learning task and the representation of the knowledge each acquires. Empirical results drawn from performing the agents' tasks using recorded teaching scenarios validate the MSIP's learning process. © Springer-Verlag Berlin Heidelberg 2004.

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Digitial Object Identifier (DOI)

10.1007/978-3-540-28633-2_14

Disciplines

Computer Sciences

Keywords

Intelligent tutoring systems; Multiagent systems

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