Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Reclaiming human machine nature

Extending and modifying his domain of life by artifact production is one of the main characteristics of humankind. From the first hominid, who used a wood stick or a stone for extending his upper limbs and augmenting his gesture strength, to current systems engineers who used technologies for augmenting human cognition, perception and action, extending human body capabilities remains a big issue. From more than fifty years cybernetics, computer and cognitive sciences have imposed only one reductionist model of human machine systems: cognitive systems. Inspired by philosophy, behaviorist psychology and the information treatment metaphor, the cognitive system paradigm requires a function view and a functional analysis in human systems design process. According that design approach, human have been reduced to his metaphysical and functional properties in a new dualism. Human body requirements have been left to physical ergonomics or "physiology". With multidisciplinary convergence, the issues of "human-machine" systems and "human artifacts" evolve. The loss of biological and social boundaries between human organisms and interactive and informational physical artifact questions the current engineering methods and ergonomic design of cognitive systems. New developpment of human machine systems for intensive care, human space activities or bio-engineering sytems requires grounding human systems design on a renewed epistemological framework for future human systems model and evidence based "bio-engineering". In that context, reclaiming human factors, augmented human and human machine nature is a necessityComment: Published in HCI International 2014, Heraklion : Greece (2014

Developing an emotional-based application for human-agent societies

The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2289-5The purpose of this paper is to present an emotional-based application for human-agent societies. This kind of applications are those where virtual agents and humans coexist and interact transparently into a fully integrated environment. Specifically, the paper presents an application where humans are immersed into a system that extracts and analyzes the emotional states of a human group trying to maximize the welfare of those humans by playing the most appropriate music in every moment. This system can be used not only online, calculating the emotional reaction of people in a bar to a new song, but also in simulation, to predict the people s reaction to changes in music or in the bar layout.This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI Grant AP2013-01276 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2016). Developing an emotional-based application for human-agent societies. Soft Computing. 20(11):4217-4228. https://doi.org/10.1007/s00500-016-2289-5S421742282011Ali F, Amin M (2013) The influence of physical environment on emotions, customer satisfaction and behavioural intentions in chinese resort hotel industry. In: KMITL-AGBA conference Bangkok, pp 15–17Barella A, Ricci A, Boissier O, Carrascosa C (2012) MAM5: Multi-agent model for intelligent virtual environments. In: 10th European workshop on multi-agent systems (EUMAS 2012), pp 16–30Becker-Asano C, Wachsmuth I (2010) Affective computing with primary and secondary emotions in a virtual human. Auton Agents Multi-Agent Syst 20(1):32–49. doi: 10.1007/s10458-009-9094-9Billhardt H, Julián V, Corchado J, Fernández A (2014) An architecture proposal for human-agent societies. In: Highlights of practical applications of heterogeneous multi-agent systems. The PAAMS collection, Communications in Computer and Information Science, vol 430, Springer, pp 344–357, doi: 10.1007/978-3-319-07767-3_31Billhardt H, Julián V, Corchado JM, Fernández A (2015) Human-agent societies: challenges and issues. Int J Artif Intell 13(1):28–44Broekens J (2007) Emotion and reinforcement: Affective facial expressions facilitate robot learning. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 4451 LNAI:113–132. doi: 10.1007/978-3-540-72348-6_6Canento F, Fred A, Silva H, Gamboa H, Lourenço A (2011) Multimodal biosignal sensor data handling for emotion recognition. In: Sensors, 2011 IEEE, pp 647–650Delac K, Grgic M, Grgic S (2005) Statistics in face recognition: analyzing probability distributions of PCA, ICA and LDA performance results. In: ISPA 2005 proceedings of the 4th international symposium on image and signal processing and analysis, 2005, pp 289–294. doi: 10.1109/ISPA.2005.195425Esparcia S, Sánchez-Anguix V, Aydogan R (2013) A negotiation approach for energy-aware room allocation systems. In: 1st Workshop on conflict resolution in decision making (COREDEMA 2013), Springer, vol 365, pp 280–291GOELEVEN E, De Raedt R, LEYMAN L, Verschuere B (2008) The karolinska directed emotional faces: a validation study. Cognit Emot 22(6):1094–1118Gouaïch A, Michel F, Guiraud Y (2005) MIC*: a deployment environment for autonomous agents. Springer, BerlinHale K, Stanney K (2002) Handbook of virtual environments: design, implementation, and applications. Human Factors and Ergonomics, Taylor and Francis, OxfordshireHan DM, Lim JH (2010) Smart home energy management system using IEEE 802.15. 4 and zigbee. Consumer Electronics, IEEE Transactions on 56(3):1403–1410. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5606276Holzapfel A, Stylianou Y (2007) A statistical approach to musical genre classification using non-negative matrix factorization. In: ICASSP 2007, IEEE international conference on, acoustics, speech and signal processing, 2007. IEEE, vol 2, pp 2–693Intille SS (2002) Designing a home of the future. IEEE Pervasive Comput 1(2):76–82Ioannou SV, Raouzaiou AT, Tzouvaras VA, Mailis TP, Karpouzis KC, Kollias SD (2005) Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw 18(4):423–435. doi: 10.1016/j.neunet.2005.03.004Jain D, Kobti Z (2011) Simulating the effect of emotional stress on task performance using OCC. Adv Artif Intell, Springer, pp 204–209. http://link.springer.com/chapter/10.1007/978-3-642-21043-3_24Journal I, Technological F, Singla K (2014) Audio noise reduction using different filters 1,2 1(11):1373–1375Kim MH, Joo YH, Park JB (2005) Emotion detection algorithm using frontal face image. In: International conference on computer application in shipbuildingLeon E, Clarke G, Callaghan V, Doctor F (2010) Affect-aware behaviour modelling and control inside an intelligent environment. Pervasive Mob Comput 6(5):559–574. doi: 10.1016/j.pmcj.2009.12.002Mangina E, Carbo J, Molina JM (2009) Agent-based ubiquitous computing. Atlantis Press : World Scientific, Amsterdam; Paris. doi: 10.2991/978-94-91216-31-2Masthoff J (2011) Group recommender systems: Combining individual models. Recommender systems handbook. Springer, Berlin, pp 677–702McCarthy JF, Anagnost TD (1998) Musicfx: An arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM conference on computer supported cooperative work, ACM, New York, NY, USA, CSCW ’98, pp 363–372Mehrabian A (1997) Analysis of affiliation-related traits in terms of the PAD temperament model. J Psychol 131(1):101–117. doi: 10.1080/00223989709603508Ortony A (1990) The cognitive structure of emotions. Cambridge University Press, CambridgePiana S, Odone F, Verri A, Camurri A (2014) Real-time Automatic Emotion Recognition from Body Gestures. arXiv preprint pp 1–7. arXiv:1402.5047Richert W, Coelho LP (2013) Building machine learning systems with python. Packt Publishing, BirminghamRincon J, Carrascosa C, Garcia E (2014a) Developing Intelligent Virtual Environments using MAM5 Meta-Model. In: International conference on practical applications of agents and multi-agent systems, Springer, pp 379–382Rincon J, Julian V, Carrascosa C (2015) Social emotional model. In: 13th International conference on practical applications of agents and multi-agent systems, LNAI, vol 9086, pp 199–210Rincon JA, Garcia E, Julian V, Carrascosa C (2014b) Developing adaptive agents situated in intelligent virtual environments. In: International conference on hybrid artificial intelligence systems, 8480 in LNCS, Springer, pp 98–109Sánchez-Anguix V, Julian V, Botti V, García-Fornes A (2013) Studying the impact of negotiation environments on negotiation teams’ performance. Inf Sci 219:17–40Sánchez-Anguix V, Aydogan R, Julian V, Jonker C (2014) Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electron Commer Res Appl 13(4):243–265Satyanarayanan M (2002) A catalyst for mobile and ubiquitous computing. Pervasive Computing, IEEE 1(1):2–5. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=993138Saunier J, Carrascosa C, Galland S, Kanmeugne PS (2015) Agent bodies: An interface between agent and environment. In: Agent environments for multi-agent systems IV, Springer, pp 25–40Scott T, Green WB, Stuart A (2005) Interactive effects of low-pass filtering and masking noise on word recognition. J Am Acad Audiol 114(11):867–878Senechal T, Rapp V, Prevost L (2011) Facial feature tracking for emotional dynamic analysis. In: Blanc-Talon, J, Kleihorst, R, Philips, W, Popescu, D, Scheunders, P (eds.) Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol 6915, pp 495–506Thalmann D, Musse SR, Braun A (2007) Crowd simulation, vol 1. Springer, BerlinTsonos D, Stavropoulou P, Kouroupetroglou G, Deligiorgi D, Papatheodorou N (2014) Emotional prosodic model evaluation for greek expressive text-to-speech synthesis. Lecture Notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 8514 LNCS(PART 2):166–174. doi: 10.1007/978-3-319-07440-5_16Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302. doi: 10.1109/TSA.2002.800560Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154. doi: 10.1023/B:VISI.0000013087.49260.fbVisutsak P (2012) Emotion classification using adaptive SVMs. Int J Comput Commun Eng 1(3):279–282Vukadinovic D, Pantic M (2005) Fully automatic facial feature point detection using Gabor feature based boosted classifiers. In: IEEE International conference on, systems, man and cybernetics, 2005 IEEE, vol 2, pp 1692–169

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

On the convergence of autonomous agent communities

This is the post-print version of the final published paper that is available from the link below. Copyright @ 2010 IOS Press and the authors.Community is a common phenomenon in natural ecosystems, human societies as well as artificial multi-agent systems such as those in web and Internet based applications. In many self-organizing systems, communities are formed evolutionarily in a decentralized way through agents' autonomous behavior. This paper systematically investigates the properties of a variety of the self-organizing agent community systems by a formal qualitative approach and a quantitative experimental approach. The qualitative formal study by applying formal specification in SLABS and Scenario Calculus has proven that mature and optimal communities always form and become stable when agents behave based on the collective knowledge of the communities, whereas community formation does not always reach maturity and optimality if agents behave solely based on individual knowledge, and the communities are not always stable even if such a formation is achieved. The quantitative experimental study by simulation has shown that the convergence time of agent communities depends on several parameters of the system in certain complicated patterns, including the number of agents, the number of community organizers, the number of knowledge categories, and the size of the knowledge in each category

Social Emotions in Multiagent Systems

Tesis por compendioA lo largo de los últimos años, los sistemas multi-agente (SMA) han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos. Este potencial no se debe principalmente a sus características individuales (como son su autonomía, su capacidad de percepción, reacción y de razonamiento), sino que también a la capacidad de comunicación y cooperación a la hora de conseguir un objetivo. De hecho, su capacidad social es la que más llama la atención, es este comportamiento social el que dota de potencial a los sistemas multi-agente. Estas características han hecho de los SMA, la herramienta de inteligencia artificial (IA) más utilizada para el diseño de entornos virtuales inteligentes (IVE), los cuales son herramientas de simulación compleja basadas en agentes. Sin embargo, los IVE incorporan restricciones físicas (como gravedad, fuerzas, rozamientos, etc.), así como una representación 3D de lo que se quiere simular. Así mismo, estas herramientas no son sólo utilizadas para la realización de simulaciones. Con la aparición de nuevas aplicaciones como \emph{Internet of Things (IoT)}, \emph{Ambient Intelligence (AmI)}, robot asistentes, entre otras, las cuales están en contacto directo con el ser humano. Este contacto plantea nuevos retos a la hora de interactuar con estas aplicaciones. Una nueva forma de interacción que ha despertado un especial interés, es el que se relaciona con la detección y/o simulación de estados emocionales. Esto ha permitido que estas aplicaciones no sólo puedan detectar nuestros estados emocionales, sino que puedan simular y expresar sus propias emociones mejorando así la experiencia del usuario con dichas aplicaciones. Con el fin de mejorar la experiencia humano-máquina, esta tesis plantea como objetivo principal la creación de modelos emocionales sociales, los cuales podrán ser utilizados en aplicaciones MAS permitiendo a los agentes interpretar y/o emular diferentes estados emocionales y, además, emular fenómenos de contagio emocional. Estos modelos permitirán realizar simulaciones complejas basadas en emociones y aplicaciones más realistas en dominios como IoT, AIm, SH.Over the past few years, multi-agent systems (SMA) have proven to be a powerful and versatile paradigm, with great potential for solving complex problems in dynamic and distributed environments. This potential is not primarily due to their individual characteristics (such as their autonomy, their capacity for perception, reaction and reasoning), but also the ability to communicate and cooperate in achieving a goal. In fact, its social capacity is the one that draws the most attention, it is this social behavior that gives potential to multi-agent systems. These characteristics have made the SMA, the artificial intelligence (AI) tool most used for the design of intelligent virtual environments (IVE), which are complex agent-based simulation tools. However, IVE incorporates physical constraints (such as gravity, forces, friction, etc.), as well as a 3D representation of what you want to simulate. Also, these tools are not only used for simulations. With the emergence of new applications such as \emph {Internet of Things (IoT)}, \emph {Ambient Intelligence (AmI)}, robot assistants, among others, which are in direct contact with humans. This contact poses new challenges when it comes to interacting with these applications. A new form of interaction that has aroused a special interest is that which is related to the detection and / or simulation of emotional states. This has allowed these applications not only to detect our emotional states, but also to simulate and express their own emotions, thus improving the user experience with those applications. In order to improve the human-machine experience, this thesis aims to create social emotional models, which can be used in MAS applications, allowing agents to interpret and / or emulate different emotional states, and emulate phenomena of emotional contagion. These models will allow complex simulations based on emotions and more realistic applications in domains like IoT, AIm, SH.Al llarg dels últims anys, els sistemes multi-agent (SMA) han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexos en entorns dinàmics i distribuïts. Aquest potencial no es deu principalment a les seues característiques individuals (com són la seua autonomia, la seua capacitat de percepció, reacció i de raonament), sinó que també a la capacitat de comunicació i cooperació a l'hora d'aconseguir un objectiu. De fet, la seua capacitat social és la que més crida l'atenció, és aquest comportament social el que dota de potencial als sistemes multi-agent. Aquestes característiques han fet dels SMA, l'eina d'intel·ligència artificial (IA) més utilitzada per al disseny d'entorns virtuals intel·ligents (IVE), els quals són eines de simulació complexa basades en agents. No obstant això, els IVE incorporen restriccions físiques (com gravetat, forces, fregaments, etc.), així com una representació 3D del que es vol simular. Així mateix, aquestes eines no són només utilitzades per a la realització de simulacions. Amb l'aparició de noves aplicacions com \emph{Internet of Things (IOT)}, \emph{Ambient Intelligence (AmI)}, robot assistents, entre altres, les quals estan en contacte directe amb l'ésser humà. Aquest contacte planteja nous reptes a l'hora d'interactuar amb aquestes aplicacions. Una nova forma d'interacció que ha despertat un especial interès, és el que es relaciona amb la detecció i/o simulació d'estats emocionals. Això ha permès que aquestes aplicacions no només puguen detectar els nostres estats emocionals, sinó que puguen simular i expressar les seues pròpies emocions millorant així l'experiència de l'usuari amb aquestes aplicacions. Per tal de millorar l'experiència humà-màquina, aquesta tesi planteja com a objectiu principal la creació de models emocionals socials, els quals podran ser utilitzats en aplicacions MAS permetent als agents interpretar i/o emular diferents estats emocionals i, a més, emular fenòmens de contagi emocional. Aquests models permetran realitzar simulacions complexes basades en emocions i aplicacions més realistes en dominis com IoT, AIM, SH.Rincón Arango, JA. (2018). Social Emotions in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/98090TESISCompendi

A multi-agent system with application in project scheduling

The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.

Joining the conspiracy? Negotiating ethics and emotions in researching (around) AIDS in southern Africa

AIDS is an emotive subject, particularly in southern Africa. Among those who have been directly affected by the disease, or who perceive themselves to be personally at risk, talking about AIDS inevitably arouses strong emotions - amongst them fear, distress, loss and anger. Conventionally, human geography research has avoided engagement with such emotions. Although the ideal of the detached observer has been roundly critiqued, the emphasis in methodological literature on 'doing no harm' has led even qualitative researchers to avoid difficult emotional encounters. Nonetheless, research is inevitably shaped by emotions, not least those of the researchers themselves. In this paper, we examine the role of emotions in the research process through our experiences of researching the lives of 'Young AIDS migrants' in Malawi and Lesotho. We explore how the context of the research gave rise to the production of particular emotions, and how, in response, we shaped the research, presenting a research agenda focused more on migration than AIDS. This example reveals a tension between universalised ethics expressed through ethical research guidelines that demand informed consent, and ethics of care, sensitive to emotional context. It also demonstrates how dualistic distinctions between reason and emotion, justice and care, global and local are unhelpful in interpreting the ethics of research practice

The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling