An OSA-CBM Multi-Agent Vehicle Health Management Architecture for Self-Health Awareness

Integrated Vehicle Health Management (IVHM) systems on modern aircraft or autonomous unmanned vehicles should provide diagnostic and prognostic capabilities with lower support costs and amount of data traffic. When mission objectives cannot be reached for the control system since unanticipated operating conditions exists, namely a failure, the mission plan must be revised or altered according to the health monitoring system assessment. Representation of the system health knowledge must facilitate interaction with the control system to compensate for subsystem degradation. Several generic architectures have been described for the implementation of health monitoring systems and their integration with the control system. In particular, the Open System Architecture - Condition-Based Maintenance (OSA-CBM) approach is considered in this work as initial point, and it is evolved in the sense of self-health awareness, by defining an appropriated multi-agent smart health management architecture based on smart device models, communication agents and a distributed control system. A case study about its application on fuel-cells as auxiliary power generator will demonstrate the integration.Postprint (published version

A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agent

Moving recommender systems from on-line commerce to retail stores

The increasing diversity of consumers' demand, as documented by the debate on the long tail of the distribution of sales volume across products, represents a challenge for retail stores. Recommender systems offer a tool to cope with this challenge. The recent developments in information technology and ubiquitous computing makes it feasible to move recommender systems from the on-line commerce, where they are widely used, to retail stores. In this paper, we aim to bridge the management literature and the computer science literature by analysing a number of issues that arise when applying recommender systems to retail stores: these range from the format of the stores that would benefit most from recommender systems to the impact of coverage and control of recommender systems on customer loyalty and competition among retail store

Research opportunities for argumentation in social networks

Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.This work summarises results of the authors joint research, funded by an STMS of the Agreement Technologies COST Action 0801, by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Atkinson, KM.; Botti Navarro, VJ.; Grasso, F.; Julian Inglada, VJ.; Mcburney, PJ. (2013). Research opportunities for argumentation in social networks. Artificial Intelligence Review. 39(1):39-62. doi:10.1007/s10462-012-9389-0S3962391Amgoud L (2009) Argumentation for decision making. Argumentation in artificial intelligence. Springer, BerlinAnderson P (2007) What is Web 2.0? Ideas, technologies and implications for education. JISC Iechnology and Standards Watch reportBentahar J, Meyer CJJ, Moulin B (2007) Securing agent-oriented systems: an argumentation and reputation-based approach. In: Proceedings of the 4th international conference on information technology: new generations (ITNG 2007), IEEE Computer Society, pp 507–515Buckingham Shum S (2008) Cohere: towards Web 2.0 argumentation. In: Proceedings of the 2nd international conference on computational models of argument, COMMA, pp 28–30Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370Cartwright D, Atkinson K (2008) Political engagement through tools for argumentation. In: Proceedings of the second international conference on computational models of argument (COMMA 2008), pp 116–127Chesñevar C, McGinnis J, Modgil S, Rahwan I, Reed C, Simari G, South M, Vreeswijk G, Willmott S (2006) Towards an argument interchange format. Knowl Eng Rev 21(4):293–316Chesñevar CI, Maguitman AG, Gonzàlez MP (2009) Empowering recommendation technologies through argumentation. Argumentation in artificial intelligence. Springer, Berlin, pp 403–422García AJ, Dix J, Simari GR (2009) Argument-based logic programming. Argumentation in artificial intelligence. Springer, BerlinGolbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the fourth international conference on trust management, LNCS, vol 3986, 93–104Gordon T, Prakken H, Walton D (2007) The Carneades model of argument and burden of proof. Artif Intell 171(10–15):875–896Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagating trust and distrust. In: Proceedings of the 13th international conference on, World Wide Web, pp 403–412Heras S, Navarro M, Botti V, Julián V (2009) Applying dialogue games to manage recommendation in social networks. In: Proceedings of the 6th international workshop on argumentation in multi-agent aystems, ArgMASHeras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010a) How argumentation can enhance dialogues in social networks. In: Proceedings of the 3rd international conference on computational models of argument, COMMA, vol 216, pp 267–274Heras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010b) Applying argumentation to enhance dialogues in social networks. In: ECAI 2010 workshop on computational models of natural argument, CMNA, pp 10–17Karacapilidis N, Tzagarakis M (2007) Web-based collaboration and decision making support: a multi-disciplinary approach. Web-Based Learn Teach Technol 2(4):12–23Kim D, Benbasat I (2003) Trust-related arguments in internet stores: a framework for evaluation. J Electron Commer Res 4(2):49–64Kim D, Benbasat I (2006) The effects of trust-assuring arguments on consumer trust in internet stores: application of Toulmin’s model of argumentation. Inf Syst Rese 17(3):286–300Laera L, Tamma V, Euzenat J, Bench-Capon T, Payne T (2006) Reaching agreement over ontology alignments. In: Proceedings of the 5th international semantic web conference (ISWC 2006)Lange C, Bojãrs U, Groza T, Breslin J, Handschuh S (2008) Expressing argumentative discussions in social media sites. In: Social data on the web (SDoW2008) workshop at the 7th international semantic web conferenceLinden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80Linden G, Hong J, Stonebraker M, Guzdial M (2009) Recommendation algorithms, online privacy and more. Commun ACM, 52(5)Mika P (2007) Ontologies are us: a unified model of social networks and semantics. J Web Semant 5(1):5–15Montaner M, López B, de la Rosa JL (2002) Opinion-based filtering through trust. In: Cooperative information agents VI, LNCS, vol 2446, pp 127–144Ontañón S, Plaza E (2008) Argumentation-based information exchange in prediction markets. In: Proceedings of the 5th international workshop on argumentation in multi-agent systems, ArgMASPazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, LNCS, vol 4321, pp 325–341Rahwan I, Zablith F, Reed C (2007) Laying the foundations for a world wide argument web. Artif Intell 171(10–15):897–921Rahwan I, Banihashemi B (2008) Arguments in OWL: a progress report. In: Proceedings of the 2nd international conference on computational models of argument (COMMA), pp 297–310Reed C, Walton D (2007) Argumentation schemes in dialogue. In: Dissensus and the search for common ground, OSSA-07, volume CD-ROM, pp 1–11Sabater J, Sierra C (2002) Reputation and social network analysis in multi-agent systems. In: Proceedings of the 1st international joint conference on autonomous agents and multiagent systems, vol 1, pp 475–482Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5:115–153Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web, LNCS, vol 4321, pp 291–324Schneider J, Groza T, Passant A (2012) A review of argumentation for the aocial semantic web. Semantic web-interoperability, usability, applicability. IOS Press, Washington, DCTempich C, Pinto HS, Sure Y, Staab S (2005) An argumentation ontology for distributed, loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT). In: Proceedings of the 2nd European semantic web conference, ESWC, pp 241–256Toulmin SE (1958) The uses of argument. Cambridge University Press, Cambridge, UKTrojahn C, Quaresma P, Vieira R, Isaac A (2009) Comparing argumentation frameworks for composite ontology matching. in: Proceedings of the 6th international workshop on argumentation in multi-agent systems, ArgMASTruthMapping. http://truthmapping.com/Walter FE, Battiston S, Schweitzer F (2007) A model of a trust-based recommendation system on a social network. J Auton Agents Multi-Agent Syst 16(1):57–74Walton D, Krabbe E (1995) Commitment in dialogue: basic concepts of interpersonal reasoning. State University of New York Press, New York, NYWalton D, Reed C, Macagno F (2008) Argumentation schemes. Cambridge University Press, CambridgeWells S, Gourlay C, Reed C (2009) Argument blogging. Computational models of natural argument, CMNAWyner A, Schneider J (2012) Arguing from a point of view. In: Proceedings of the first international conference on agreement technologie

Intelligent Recovery Architecture for Personalized Educational Content

Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This work proposes a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents. The model allows the development of an open and flexible architecture that supports the services necessary to conduct a search for distributed digital content dynamically. AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is based on the proposed model; it is a multi-agent architecture that can search and integrate heterogeneous educational content through a recovery model that uses a federated search. A major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR).The model and the technologies presented in this research are an example of the potential for developing recovery systems for digital content based on the paradigm of virtual organizations of agents. The advantages of the proposed architecture are its flexibility, customization, integrative solution and efficiency

CBR Proposal for Personalizing Educational Content

A major challenge in searching and retrieval digital content is to efficiently find the most suitable for the users. This paper proposes a new approach to filter the educational content retrieved based on Case-Based Reasoning (CBR). AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) is a multi-agent architecture that can search and integrate heterogeneous educational content within the CBR model proposes. The recommendation model and the technologies reported in this research applied to educational content are an example of the potential for personalizing labeled educational content recovered from heterogeneous environments.

Recomendador de usuarios en una plataforma colaborativa en base a su perfil y reputación

El uso de plataformas colaborativas en los procesos de enseñanza es muy usual hoy en día. Las mismas facilitan la comunicación entre docentes y estudiantes que pueden estar distantes físicamente. Pero uno de los problemas detectados son los nuevos usuarios, no saben quién es la persona más idónea para comenzar a interactuar sobre alguna temática, muchas veces las interacciones no son exitosas debido a que la persona con quien interactuó resulta no ser la idónea. En este artículo se presenta un sistema recomendador que recomienda a una persona un usuario idóneo con quien interactuar. Para demostrar la validez de este sistema recomendador se realizaron experimentos cuyos resultados demuestran que usuarios que interactuaron con usuarios recomendados de forma personalizada (en base a su perfil y la reputación del mismo dentro la plataforma colaborativa) fueron más exitosas.Sociedad Argentina de Informática e Investigación Operativ