English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Ambient Intelligence aims to offer personalized services and easier ways ofinteraction between people and systems. Since several users and systems maycoexist in these environments, it is quite possible that entities with opposingpreferences need to cooperate to reach their respective goals. Automatednegotiation is pointed as one of the mechanisms that may provide a solution tothis kind of problems. In this article, a multi-issue bilateral bargainingmodel for Ambient Intelligence domains is presented where it is assumed thatagents have computational bounded resources and do not know their opponents'preferences. The main goal of this work is to provide negotiation models thatobtain efficient agreements while maintaining the computational cost low. Aniching genetic algorithm is used before the negotiation process to sampleone's own utility function (self-sampling). During the negotiation process,genetic operators are applied over the opponent's and one's own offers in orderto sample new offers that are interesting for both parties. Results show thatthe proposed model is capable of outperforming similarity heuristics which onlysample before the negotiation process and of obtaining similar results tosimilarity heuristics which have access to all of the possible offers.

Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions