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. 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AI-enabled adaptive learning systems: A systematic mapping of the literature

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.publishedVersio

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans’ trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users’ retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

Programmatic advertising: An exegesis of consumer concerns

Programmatic advertising is a nascent and rapidly growing information technology phenomenon that reacts to, and impacts upon, consumers and their behavior. Despite its popularity and widespread use, research in the area remains scant and our current knowledge is based upon a preponderance of practitioner-generated literature. This study contributes to our understanding of this technology by unpacking the means by which it functions and interacts with consumers. The study draws upon paradox theory to deconstruct programmatic advertising's inherent tensions as dilemmas and dialectics. Adopting organisations are faced with the dilemma of pursuing the acquisition of increasingly detailed information in order to provide more personalized offerings, yet doing so increases the likelihood of creating a sense of fear and distrust among consumers. The automation of personalized advertising appears attractive yet presents the dilemma that adverts may be inappropriately placed. Finally, the true cost/benefit of programmatic advertising is unclear, and adopters, platform providers and developers need to engage in dialectic in order to fully understand and communicate its financial implications. Through identifying these fundamental constraints, the study affords pathways for programmatic system actors to ameliorate their, and their customers' concerns

Towards Designing AI-Enabled Adaptive Learning Systems

Paper I, III, IV and V are not available as a part of the dissertation due to the copyright.Among the many innovations driven by artificial intelligence (AI) are more advanced learning systems known as AI-enabled adaptive learning systems (AI-ALS). AI-ALS are platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks based on the abilities of students. These systems support adaptive learning, which is the personalization of learning for students in a learning system, such that the system can deal with individual differences in aptitude. AI-ALS are gaining traction due to their ability to deliver learning content and adapt to individual student needs. While the potential and importance of such systems have been well documented, the actual implementation of AI-ALS and other AI-based learning systems in real-world teaching and learning settings has not reached the effectiveness envisaged on the level of theory. Moreover, AI-ALS lack transferable insights and codification of knowledge on their design and development. The reason for this is that many previous studies were experimental. Thus, this dissertation aims to narrow the gap between experimental research and field practice by providing practical design statements that can be implemented in effective AI-ALSs.publishedVersio

Advances in Technology Enhanced Learning

‘Advances in Technology Enhanced Learning’ presents a range of research projects which aim to explore how to make engagement in learning (and teaching) more passionate. This interactive and experimental resource discusses innovations which pave the way to open collaboration at scale. The book introduces methodological and technological breakthroughs via twelve chapters to learners, instructors, and decision-makers in schools, universities, and workplaces. The Open University's Knowledge Media Institute and the EU TELMap project have brought together the luminaries from the European research area to showcase their vision of the future of learning with technology via their recent research project work. The projects discussed range widely over the Technology Enhanced Learning area from: environments for responsive open learning, work-based reflection, work-based social creativity, serious games and many more