Applications of intelligent agents
Agent technology
Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Intelligent agents for educational computer-aided systems
Journal of Interactive Learning Research - Special issue on intelligent agents for educational computer-aided systems
A multi-agent system for computer science education
Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Agent-based cooperative learning: a proof-of-concept experiment
Proceedings of the 35th SIGCSE technical symposium on Computer science education
Negotiation in multi-agent systems
The Knowledge Engineering Review
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Information Sciences: an International Journal
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Software requirements to support qos in collaborative m-learning activities
CRIWG'12 Proceedings of the 18th international conference on Collaboration and Technology
Hi-index | 0.00 |
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.