1,237 research outputs found
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
Adding real data to detect emotions by means of smart resource artifacts in MAS
[EN] This article proposes an application of a social emotional model, which allows to
extract, analyse, represent and manage the social emotion of a group of entities.
Specifically, the application is based on how music can influence in a positive or
negative way over emotional states. The proposed approach employs the JaCalIVE
framework, which facilitates the development of this kind of environments. A physical
device called smart resource offers to agents processed sensor data as a service. So
that, agents obtain real data from a smart resource. MAS uses the smart resource as an
artifact by means of a specific communications protocol. The framework includes a
design method and a physical simulator. In this way, the social emotional model allows
the creation of simulations over JaCalIVE, in which the emotional states are used in
the decision-making of the agents.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.Ricon, JA.; Poza-Lujan, J.; Posadas-Yagüe, J.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2016). Adding real data to detect emotions by means of smart resource artifacts in MAS. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 5(4):85-92. https://doi.org/10.14201/ADCAIJ2016548592S85925
Human Factors in Agile Software Development
Through our four years experiments on students' Scrum based agile software
development (ASD) process, we have gained deep understanding into the human
factors of agile methodology. We designed an agile project management tool -
the HASE collaboration development platform to support more than 400 students
self-organized into 80 teams to practice ASD. In this thesis, Based on our
experiments, simulations and analysis, we contributed a series of solutions and
insights in this researches, including 1) a Goal Net based method to enhance
goal and requirement management for ASD process, 2) a novel Simple Multi-Agent
Real-Time (SMART) approach to enhance intelligent task allocation for ASD
process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and
morale management for ASD process, 4) the first large scale in-depth empirical
insights on human factors in ASD process which have not yet been well studied
by existing research, and 5) the first to identify ASD process as a
human-computation system that exploit human efforts to perform tasks that
computers are not good at solving. On the other hand, computers can assist
human decision making in the ASD process.Comment: Book Draf
Smart territories
The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success.
The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process.
Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing
Artificial Intelligence in the development of modern infrastructures
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform tasks as human beings. Most of the examples of AI you hear about today - from computers playing chess to autonomous driving cars - rely heavily on deep learning and natural language processing
DeepTech - AI Models in Engineering Solutions
Artificial Intelligence revived in the last decade. The need for progress, the growing
processing capacity and the low cost of the Cloud have facilitated the development of new,
powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep
Learning and Convolutional Networks is transforming the way we work and is opening new
horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to
today’s problems. Nevertheless, our success is not entirely based on algorithms, it also
comes from our ability to follow our “gut” when choosing the best combination of algorithms
for an intelligent artefact. It's about approaching engineering with a lot of knowledge and
tact. This involves the use of both connectionist and symbolic systems, and of having a full
understanding of the algorithms used. Moreover, to address today’s problems we must
work with both historical and real-time data. We must fully comprehend the problem, its
time evolution, as well as the relevance and implications of each piece of data, etc. It is also
important to consider development time, costs and the ability to create systems that will
interact with their environment, will connect with the objects that surround them and will
manage the data they obtain in a reliable manner
Efficient Deployment of DeepTech AI Models in Engineering Solutions
The blockchain system, appeared in 2009 together with the virtual currency bitcoin, is a record of
digital transactions based on a huge database in which all financial operations carried out with
electronic currency are registered. The Blockchain (or chain of blocks) is a shared database that
works as a book for the record of purchase-sale operations or any other transaction. It is the
technological base of the operation of bitcoin, for example. It consists of a set of notes that are in a
shared online database in which operations, quantities, dates and participants are registered by
means of codes. By using cryptographic keys and being distributed by many computers (people),
it presents security advantages against manipulation and fraud. A modification in one of the
copies would be useless, but the change must be made in all the copies because the database is
open and public
The role of the AIoT and deepint.net
AIoT is a term, also known as intelligence of things, which refers to the new wave of the
future of technology that combines two major platforms, very present in today's market:
Artificial Intelligence (AI) and the Internet of things (IoT). As IoT devices will generate
large amounts of data, Artificial Intelligence is going to be functionally necessary to deal
with these huge volumes if we are to have any chance of making sense of the data. This
whole process will be called connected intelligence. To take this step forward and
definitively enter the era of Intelligence of Things, we will need to enable to a greater or
lesser part these cognitive and executive capacities towards objects. To do this, we are
going to talk more and more about the concept of Edge Computing (or “edge computing”),
which is nothing more than the ability to process data, analyze situations, evaluate
possible scenarios and make decisions from the object itself and not from a server
hundreds or thousands of miles away
Smart Buildings
This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques
Managing smart cities with deepint.net
In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities
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