Ontology Alignment Evaluation in the Context of Multi-Agent Interactions

Abstract The most prominent way to assess the quality of an ontology alignment is to compute its precision and recall with respect to another alignment taken as reference. These measures determine, respectively, the proportion of found mappings that belong to the reference alignment and the proportion of the reference alignment that was found. The use of these values has been criticised arguing that they fail to reflect important semantic aspects. In addition, they rely on the existence of a reference alignment. In this work we discuss the evaluation of alignments when they are used to facilitate communication between heterogeneous agents. We introduce the notion of pragmatic alignment to refer to the mappings that let agents understand each other, and we propose new versions of precision and recall that measure how useful mappings are for a particular interaction. We then discuss practical applications of these new measures and how they can be estimated dynamically by interacting agents

Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

Proceedings of the 11th European Agent Systems Summer School Student Session

This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

A formal foundation for ontology alignment interaction models

Ontology alignment foundations are hard to find in the literature. The abstract nature of the topic and the diverse means of practice makes it difficult to capture it in a universal formal foundation. We argue that such a lack of formality hinders further development and convergence of practices, and in particular, prevents us from achieving greater levels of automation. In this article we present a formal foundation for ontology alignment that is based on interaction models between heterogeneous agents on the Semantic Web. We use the mathematical notion of information flow in a distributed system to ground our three hypotheses of enabling semantic interoperability and we use a motivating example throughout the article: how to progressively align two ontologies of research quality assessment through meaning coordination. We conclude the article with the presentation---in an executable specification language---of such an ontology-alignment interaction model

Meaning Management: A Framework for Leadership Ontology

Leadership is a multifaceted and complex subject of research and demands a sound ontological stance that guides studies for the development of more integrative leadership theories. In this paper, I propose the leadership ontology PVA (perception formation – value creation – achievement realization) and associate it with the two existing leadership ontologies: TRIPOD (leader – member – shared goals) and DAC (direction – alignment – commitment). The leadership ontology PVA, based on a new theory called “meaning management,” consists of three circularly supporting functions: cognitive function to form perception, creative function to generate value, and communicative function to realize higher levels of achievement. The PVA is an epistemology-laden ontology since the meaning management theory allows one to make propositions that explicitly link its three functions with the leadership outcomes: perception, value, and achievement. Moreover, the PVA leadership ontology transcends and includes both the conventional TRIPOD ontology and the DAC ontology

On the integration of trust with negotiation, argumentation and semantics

Agreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing. © 2013 Cambridge University Press.This work was partially supported by the Agreement Technology COST action (IC0801). The authors would like to thank for helpful discussions and comments all participants in the panel on >Trust, Argumentation and Semantics> on 16 December 2009, Agia Napa, CyprusPeer Reviewe

Trust Strategies for the Semantic Web

Everyone agrees on the importance of enabling trust on the SemanticWebto ensure more efficient agent interaction. Current research on trust seems to focus on developing computational models, semantic representations, inference techniques, etc. However, little attention has been given to the plausible trust strategies or tactics that an agent can follow when interacting with other agents on the Semantic Web. In this paper we identify five most common strategies of trust and discuss their envisaged costs and benefits. The aim is to provide some guidelines to help system developers appreciate the risks and gains involved with each trust strategy