Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS

Structural relations established among agents influence the performance of decentralized service discovery process in multiagent systems. Moreover, distributed systems should be able to adapt their structural relations to changes in environmental conditions. In this article, we present a service-oriented multiagent systems, where agents initially self-organize their structural relations based on the similarity of their services. During the service discovery process, agents integrate a mechanism that facilitates the self-organization of their structural relations to adapt the structure of the system to the service demand. This mechanism facilitates the task of decentralized service discovery and improves its performance. Each agent has local knowledge about its direct neighbors and the queries received during discovery processes. With this information, an agent is able to analyze its structural relations and decide when it is more appropriate to modify its direct neighbors and select the most suitable acquaintances to replace them. The experimental evaluation shows how this self-organization mechanism improves the overall performance of the service discovery process in the system when the service demand changesThis work is partially supported by the Spanish Ministry of Science and Innovation through grants CSD2007-0022 (CONSOLIDER-INGENIO 2010), TIN2012-36586-C03-01, TIN2012-36586-C03-01, TIN2012-36586-C03-02, PROMETEOII/2013/019, and FPU grant AP-2008-00601 awarded to E. Del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Vasirani, M.; Fernández, A. (2014). Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS. ACM Transactions on Autonomous and Adaptive Systems. 9(3):1-24. https://doi.org/10.1145/2651423S12493Sherief Abdallah and Victor Lesser. 2007. 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Ανανεώνοντας τις συνδέσεις των κόμβων σε σημασιολογικά δίκτυα επικάλυψης

Today's content providers are naturally distributed and produce large amounts of information every day, making peer-to-peer data management a promis- ing approach o®ering scalability, adaptivity to dynamics and failure resilience. The management of large volumes of data in peer-to-peer networks has generated ad- ditional interest in methods for e®ective network organisation based on providers' contents and consequently, in methods supporting information retrieval. Overlay architectures (or overlay networks) are being introduced as tools for the organisa- tion and sharing of information residing in a network of peers. Moreover, semantic overlay networks partition the overlay layer by clustering the data sources. This is achieved through a rewiring protocol that is executed independently by each peer. The purpose of this protocol is to establish connections among peers being semanti- cally, thematically or socially similar (i.e., peers sharing similar interests). By this, the problem of ?nding the most relevant resources is reduced to one of locating the clusters similar to the query. In this thesis, we present iCluster, a generic architecture for supporting full- °edged information retrieval in large-scale peer-to-peer networks, along with its associated organisation and query forwarding protocols. The main focus of this work is on rewiring. We study the functional issues related to peer rewiring and discuss a number of choices in designing the rewiring strategy. In addition, we introduce the concept of clustering e±ciency, a measure that quanti?es the quality of network organisation by exploiting the underlying network structure. Clustering e±ciency is used for evaluating the performance of rewiring. We study the system performance and identify the rewiring protocol that proves e±cient under peer churn. Finally, we apply our architecture to a digital library use case and support searching and - xiii - ?ltering functionality using the same infrastructure. The experimental evaluation with real-word data and queries demonstrates that iCluster achieves signi?cant performance improvements (in terms of communication load and retrieval accuracy) over a well-known state-of-the-art peer-to-peer clus- tering method. Compared to exhaustive search by °ooding, iCluster exchanged a small loss in retrieval accuracy for much less message °ow. In addition, the proposed distributed protocols are proven e±cient under peer churn, shown to achieve high retrieval accuracy and scale up well for large networks. Our experimental results con?rm the dependency between rewiring strategies and retrieval performance, and give insights on the trade-o®s involved in the selection of a rewiring strategy. -

A measure for cluster cohesion in semantic overlay networks

Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups by means of a rewiring procedure that is periodically executed by each peer. Rewiring proceeds by establishing new connections to similar peers, and by discarding connections that are outdated or pointing to dissimilar peers. This process aims at improving cluster quality (how well peers with similar content are clustered together) and by this, at improving the flow of information in the network by reducing the number of messages that are exchanged. Therefore, measuring the quality of clustering is an important issue by itself. This is exactly the issue this work is dealing with. In this paper, we introduce a new clustering measure that takes into account the whole neighborhood of a peer (rather than its direct neighbors) thus, providing better insight on the quality of the underlying clustered organisation. Our experimental evaluation with real-word data and queries confirms our assumption that the new measure is better suited for measuring clustering quality than other known measures, such as the (generalised) clustering coefficient

Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods