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Conference or Workshop Item

Evaluation of Fairness Characteristics in an Intelligent System For Academic Course Selection
http://jb4-2.eprints-hosting.org/2261


Ali Aseere
David E. Millard
Enrico Gerding

Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning
http://jb4-2.eprints-hosting.org/2260

Agents are autonomous software components that work with one another in a decentralized fashion to achieve some end. Agent systems have been used in Technology Enhanced Learning (TEL) before, but these applications seldom take advantage of the fact that each agent may have its own goals and strategies, which makes agent systems an attractive way of providing personalized learning. In particular, since agents can solve problems in a decentralized way, this makes them an attractive way of supporting informal learning. In this paper we use scenarios to examine how common problem solving techniques from the agents world (voting, coalition formation and auction systems) map to significant challenges for personalized and informal learning in the TEL world. Through an agent simulation we then show how an agent system might perform in one of those scenarios and explore how different agent strategies might influence the outcome. Based on this work we argue that agent systems provide a way of providing ultra-personalization of the learning process in a decentralized way and highlight equitability and scrutability as two key challenges for future investigation.

Ali Aseere
David Millard
Enrico Gerding

This list was generated on Tue Aug 27 22:53:24 2019 UTC.