Abstract
In this chapter we discuss how semantic technologies in general and specific Semantic Web standards in particular can contribute to the goal of achieving interoperability between independent, loosely coupled, heterogeneous, autonomous software components (i.e. agents) and for the realization of open interaction systems. In particular we will discuss how those technologies have been used for the definition of the semantics of agent communication languages, for the definition of norms and policies used to regulate interactions in open frameworks, and for defining efficient mechanisms for matching demands (i.e., content they need) to supplies (i.e., available content) in telecommunication networks. In particular regarding this last type of application we describe a techno-economic approach for solving the matching problem, by means of a multi-agent system representing an electronic marketplace. Its functionality is realized by applying a semantic-aware content discovery model with two-level filtering in order to finally recommend a ranked set of eligible content to the users in response to their requests. The filtering processes not only consider the semantic information associated with the available content, but also ratings regarding the actual performance of businesses that act as content providers as well as the prices paid by businesses for advertising their content.
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Notes
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W3C list of reasoners, editors, development environments, APIs: http://www.w3.org/2007/OWL/wiki/Implementations.
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The notion of budget is very important in the SPPCA scenario because it enables content providers to specify their spend limits for the current auction round. Note that one content provider can have multiple content advertisements simultaneously participating in the same SPPCA auction. If such is the case, all advertisements of the same content provider potentially have different bid values since a content provider can advertise only one content advertisement per BID message, and yet all the advertisements share the same budget. Thus, when multiple BID messages for the same auction round from a single content provider are received, budget values are cumulatively added to a budget balance unique for all advertisements originating from the same content provider. This way, content providers do not need to use complex optimization techniques to optimally distribute their budget among their multiple content advertisements. The advertisements of every content provider are monitored during the auction round and potentially all advertisements of a certain content provider become inactive until the end of that round if this content provider’s budget is spent before the auction round is over. Consequently, this content provider’s advertisements are not considered in any of the subsequent content discovery processes during that round.
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EWMA-based learning cannot calculate the overall ratings when a content provider is participating for the first time and does not have a history of customer feedback (i.e., there is no entry for the content provider in the Provider Database). Therefore, when a content provider sends a BID message for the first time, the broker not only puts the information about new content advertisement into the Content Database, but also creates a new entry in the Provider Database where the initial quality and price ratings of this new content provider are set to the average values of quality and price ratings of all content providers whose entries already exist in the Provider Database. In such a manner we counter the problem of cold start inherent to EWMA-based learning method.
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Acknowledgements
This work was partially supported by the Hasler Foundation project n. 11115-KG and by the Swiss State Secretariat for Education and Research project n. C08.0114, as well as projects 036-0362027-1639 “Content Delivery and Mobility of Users and Services in New Generation Networks”, supported by the Ministry of Science, Education and Sports of the Republic of Croatia, and “Agent-based Service & Telecom Operations Management”, supported by Ericsson Nikola Tesla, Croatia.
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Fornara, N., Ježić, G., Kušek, M., Lovrek, I., Podobnik, V., Tržec, K. (2013). Semantics in Multi-agent Systems. In: Ossowski, S. (eds) Agreement Technologies. Law, Governance and Technology Series, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5583-3_7
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