Learning Resources Brokerage Systems: an Agent-based Virtual Market Model

1. Learning Resources Brokerage Systems: an Agent-based Virtual Market Model Nikos Manouselis, Demetrios G. Sampson e-mail:{nikosm,sampson}@iti.gr

2. short overview • Rationale: learning resources brokerage systems • open issues: pricing and trading aspects • Market modeling of learning resources brokerage systems • actors involved • Agent architecture of market-based brokerage system • agent types • Prototype implementation: auction-based market • scenario of use • interactions protocol • Conclusions

3. general framework • Education and learning sector • major producer and consumer of digital learning resources with intellectual properties • A number of initiatives around the world launched • to facilitate learning resources publication, search, identification, delivery

4. different technological aspects • Technologically advanced architectures and systems for learning resources indexing, storing, search, retrieval and delivery • Interoperability frameworks between heterogeneous learning resources • Development of intelligent mechanisms for automation of activities • i.e. related with learning resources management • Development of mechanisms for addressing the commercial aspect of learning resources publication, sharing, trading and reuse • i.e. digital rights management and variable pricing policies

5. approaches • Learning resources managements systems • EM2 (Sampson & Karampiperis, 2003) • Brokerage systems for sharing learning resources • LOMster (Ternier, Duval & Vandepitte, 2002) • Brokerage systems for exchange and trading learning resources • UNIVERSAL (Brantner, Enzi, Guth, Neumann & Simon, 2001)

6. exchange and trading: open issues • In typical learning resources brokerage systems human effort is required to perform several tasks • Comparing provision and delivery costs • Negotiate upon specific provision terms • Although users are profiled • Consumer-related preferences are not modeled and exploited • Mechanisms for management of property rights • Study effects on the pricing of learning resources

7. approach under study… • Market-based modeling of learning resources brokerage system • Actors are players with personal strategies and goals • Market design defines how actors interact • Agent-based analysis, design and development of the brokerage system • Allows for complex software systems implemented as a collection of interactive, autonomous agents • Facilitates the development of automated negotiation, brokering, trading tasks that can save human labor time

8. market model

9. participating actors • Producers: learning resources providers who publish learning object advertisements to the mediator role • Consumers: learning resources users (learners, tutors, content providers) who are searching for learning objects • Mediators: representing producers and consumers behaviors and requests in the brokerage system, can play different roles • Simple facilitators of interactions • Proactive brokers of content, such as auctioneers

10. participating agent types • Assistant Agents: responsible for user requests elicitation, formulation of offers and requests into messages understandable by brokers • Consumer Assistant Agents / Producer Assistant Agents • Broker Agents: represent users in the virtual market, facilitate collection and evaluation of requests and offers, interact with other brokers, responsible for negotiation among parties • Consumer Broker Agents / Producer Broker Agents • Matchmaker: provides mediating services, informing agents about other participants, roles and availability; can also interfere with inter-agents relations, balance workload, coordinate digital rights management, etc.

11. agent analysis and design

12. case of study: auction market • Auctions provide a popular, easily analysable market mechanism design • Mechanism for selling goods to members of a pool of bidders • Outcome determines who gets the goods and how much they pay • Simplest form of auction: sealed bid auction • Each bidder makes a single bid and communicates it to the auctioneer • The ‘best’ bid wins (e.g. lower price)

13. example scenario • One consumer is communicating a request for a learning object to several producers: • User expresses initial request to Consumer Assistant • Consumer Assistant communicates request to Consumer Broker • Consumer Broker requests from Matchmaker Producer Brokers that can serve request • Consumer Broker initiates auction requesting offers from all Producer Brokers • Each Producer Broker replies with a sealed bid offer • Consumer Broker selects the best offer and informs the Consumer Assistant on the proposed offer

14. user interfaces

15. interactions

16. protocol steps

17. conclusions • Introduced market modeling for learning resources brokerage systems • Introduced agent-based analysis and design • Prototype implementation • Auction-based mechanism • Agents trading learning objects • Promising approach for brokerage systems • Can provide intelligent tools for pricing and digital rights management • Can provide autonomous system components acting on behalf of the user • Prototype should be tested in the context of a real brokerage environment for learning resources

18. Contact Details Nikos Manouselis and Prof. Demetrios Sampson nikosm@iti.gr, sampson@iti.gr Informatics and Telematics Institute, Centre for Research and Technology Hellas, 42, Arkadias Street,Athens,GR-15234,Greece and Department of Technology Education and Digital Systems, University of Piraeus 150, Androutsou Street, Piraeus, GR-18534 Greece