A Distributed Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

This chapter presents ALZ-MAS (Alzheimer multi-agent system), an ambient intelligence (AmI)-based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by AmI. ALZ-MAS makes use of a services oriented multi-agent architecture, called flexible user and services oriented multi-agent architecture, to distribute resources and enhance its performance. It is demonstrated that a SOA approach is adequate to build distributed and highly dynamic AmI-based multi-agent systems

Improving an Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care using Wireless Sensor Networks

This paper describes last improvements made on ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by Ambient Intelligence. Among these context-aware technologies we have Wireless Sensor Networks. In this sense, ALZ-MAS is currently being improved by the use of a new platform of ZigBee devices that provides the system with new telemonitoring and locating engine

EMon : embodied monitorization

Serie : Lecture Notes in Computer Science, vol. 5859The amount of seniors in need of constant care is rapidly rising: an evident consequence of population ageing. There are already some monitorization environments which aim to monitor these persons while they remain at home. This, however, although better than delocalizing the elder to some kind of institution, may not still be the ideal solution, as it forces them to stay inside the home more than they wished, as going out means lack of accompaniment and a consequent sensation of fear. In this paper we propose EMon: a monitorization device small enough to be worn by its users, although powerful enough to provide the higher level monitorization systems with vital information about the user and the environment around him. We hope to allow the representation of an intelligent environment to move with its users, instead of being static, mandatorily associated to a single physical location. The first prototype of EMon, as presented in this paper, provides environmental data as well as GPS coordinates and pictures that are useful to describe the context of its user

Enhancing the role of multi-agent systems in the development of intelligent environments

Springer - Series Advances in Intelligent and Soft Computing, vol. 71The development of Intelligent Environments is a complex challenge. This complexity arises, in part, from the amount of different devices that need to be seamlessly integrated in a common and homogeneous environment, despite the fact of each device having its own characteristics. This heterogeneity of devices is particularly risky when one passes from the specification to the implementation phase, where all unexpected things start to happen. Multi-agent systems are the paradigm par excellence for implementing Intelligent Environments. However, traditionally, agents are only used at the implementation phase. In this paper we propose a new 3 step approach in which agents are used during all the development process, playing undoubtedly a much more preponderant role and making the path from the specification to the implementation a much easier and controllable one, always having in mind the challenges of the development of Intelligent Environments

Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios

In This Paper An Agent-Based Simulation Is Developed In Order To Evaluate An Ambient Intelligence Scenario Based On Agents. Many Ami Applications Are Implemented Through Agents But They Are Not Compared With Any Other Existing Alternative In Order To Evaluate The Relative Benefits Of Using Them. The Proposed Simulation Environment Analyses Such Benefits Using Two Evaluation Criteria: First, Measuring Agent Satisfaction Of Different Types Of Desires Along The Execution. Second, Measuring Time Savings Obtained Through A Correct Use Of Context Information. In This Paper An Existing Agent Architecture, An Ontology And A 12-Steps Protocol To Provide Ami Services In Airports, Is Evaluated Using The Netlogo Simulation Environment. In Our Netlogo Model We Are Considering Scalability Issues Of This Application Domain But Using Fipa And Bdi Extensions To Be Coherent With Our Previous Works And Our Previous Jade Implementation Of Them. The Netlogo Model Simulates An Airport With Agent &#39 Passengers&#39 Passing Through Several Zones Located In A Specific Order In A Map: Passport Controls, Check-In Counters Of Airline Companies, Boarding Gates, Different Types Of Shopping. Although The Initial Data In Each Simulation Is Generated Randomly, And The Model Is Just An Approximation Of Real-World Airports, The Definition Of This Case Of Use Of Ami Through Netlogo Agents Opens An Interesting Way To Evaluate The Benefits Of Using Ami, Which Is A Significant Contribution To The Final Development Of Ami Systems.This work was partially funded by CNPq PVE Project 314017/2013-5, FAPERJ APQ1 Project 211.500/2015 and by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-0

Ambient Intelligence Agent for Health Care.

This paper presents an autonomous intelligent agent developed for ambient intelligence health care in geriatric residences. The paper focuses on Ambient Intelligence (AmI) Technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. Monitoring within the care process is a vital function requiring AmI solutions. An autonomous deliberative case-based planner agent, AGALZ (Autonomous aGent for monitoring ALZheimer patients), is designed to facilitate the nurses’ integration into a multi-agent intelligent environment, named ALZ-MAS (ALZheimer Multi-Agent System), capable of obtaining information about the environment through RFID technology. The agent operates in wireless devices and is integrated with complementary agents

Multi-Agent Systems

[EN] With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.Julian Inglada, VJ.; Botti V. (2019). Multi-Agent Systems. Applied Sciences. 9(7):1-7. https://doi.org/10.3390/app9071402S1797Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi:10.18564/jasss.2661Baldoni, M., Baroglio, C., May, K., Micalizio, R., & Tedeschi, S. (2018). Computational Accountability in MAS Organizations with ADOPT. Applied Sciences, 8(4), 489. doi:10.3390/app8040489Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761. doi:10.1016/j.scico.2011.10.004Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., & Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8(5), 688. doi:10.3390/app8050688Challenger, M., Demirkol, S., Getir, S., Mernik, M., Kardas, G., & Kosar, T. (2014). On the use of a domain-specific modeling language in the development of multiagent systems. Engineering Applications of Artificial Intelligence, 28, 111-141. doi:10.1016/j.engappai.2013.11.012Boztepe, İ., & Erdur, R. (2018). Linked Data Aware Agent Development Framework for Mobile Devices. Applied Sciences, 8(10), 1831. doi:10.3390/app8101831Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365-377. doi:10.1016/j.artint.2006.02.006Duan, K., Fong, S., Zhuang, Y., & Song, W. (2018). Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences, 8(6), 862. doi:10.3390/app8060862Zhang, Q., Yao, J., Yin, Q., & Zha, Y. (2018). Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution. Applied Sciences, 8(7), 1077. doi:10.3390/app8071077Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Kranz, M., Holleis, P., & Schmidt, A. (2010). Embedded Interaction: Interacting with the Internet of Things. IEEE Internet Computing, 14(2), 46-53. doi:10.1109/mic.2009.141Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Costa, A., Novais, P., Corchado, J. M., & Neves, J. (2011). Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL, 20(4), 689-698. doi:10.1093/jigpal/jzr021Tapia, D. I., & Corchado, J. M. (2009). An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care. International Journal of Ambient Computing and Intelligence, 1(1), 15-26. doi:10.4018/jaci.2009010102Barriuso, A., De la Prieta, F., Villarrubia González, G., De La Iglesia, D., & Lozano, Á. (2018). MOVICLOUD: Agent-Based 3D Platform for the Labor Integration of Disabled People. Applied Sciences, 8(3), 337. doi:10.3390/app8030337Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences, 8(3), 446. doi:10.3390/app8030446Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2018). Cognitive Assistants—An Analysis and Future Trends Based on Speculative Default Reasoning. Applied Sciences, 8(5), 742. doi:10.3390/app8050742SATOH, K. (2005). Speculative Computation and Abduction for an Autonomous Agent. IEICE Transactions on Information and Systems, E88-D(9), 2031-2038. doi:10.1093/ietisy/e88-d.9.2031Miyashita, K. (2017). Incremental Design of Perishable Goods Markets through Multi-Agent Simulations. Applied Sciences, 7(12), 1300. doi:10.3390/app7121300Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Roscia, M., Longo, M., & Lazaroiu, G. C. (2013). Smart City by multi-agent systems. 2013 International Conference on Renewable Energy Research and Applications (ICRERA). doi:10.1109/icrera.2013.6749783Lozano, Á., De Paz, J., Villarrubia González, G., Iglesia, D., & Bajo, J. (2018). Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems. Applied Sciences, 8(1), 67. doi:10.3390/app8010067Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Billhardt, H., Fernández, A., Lujak, M., & Ossowski, S. (2018). Agreement Technologies for Coordination in Smart Cities. Applied Sciences, 8(5), 816. doi:10.3390/app805081

Improving Context-Awareness in a Healthcare Multi-Agent System

Context-aware technologies allow Ambient Assisted Living systems and applications to automatically obtain information from users and their environment in a distributed and ubiquitous way. One of the most important technologies used to provide context-awareness to a system is Wireless Sensor Networks. This paper describes last improvements made on ALZ-MAS, an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and healthcare for Alzheimer patients. In this sense, a new ZigBee platform is used to improve ALZ-MAS. This platform provides the system with new telemonitoring and locating engines that facilitate the integration of context-awareness into it

Context-Aware Multi-Agent Planning in intelligent environments

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use. Multi-agent planning generalizes the problem of planning in domains where several agents plan and act together, and share resources, activities, and goals. This contribution presents a practical extension of a formal theoretical model for Context-Aware Multi-Agent Planning based upon an argumentationbased defeasible logic. Our framework, named CAMAP, is implemented on a platform for open multiagent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. CAMAP is based on a multi-agent partial-order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible contextual reasoning to support their own beliefs and refute the beliefs of the others according to their context knowledge during the plan search process. CAMAP shows to be an adequate approach to tackle ambient intelligence problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.This work is mainly supported by the Spanish Ministry of Science and Education under the FPU Grant Reference AP2009-1896 awarded to Sergio Pajares Ferrando, and Projects, TIN2011-27652-C03-01, and Consolider Ingenio 2010 CSD2007-00022.Pajares Ferrando, S.; Onaindia De La Rivaherrera, E. (2013). Context-Aware Multi-Agent Planning in intelligent environments. Information Sciences. 227:22-42. https://doi.org/10.1016/j.ins.2012.11.021S224222

Multi-agent personal memory assistant

Springer - Series Advances in Intelligent and Soft Computing, vol. 71Memory is one of our most precious goods has it gives us the ability to store, retain and recall information thus giving a meaning to our past and help us to envision our future, dreams and expectations. However, ageing decreases the capacity of remembering and the capacity to store new memories, thus affecting our life quality. These presented problems configure a social and human dilemma. With the presented work we intend to address some of these problems, thru the use of the Personal Memory Assistant (PMA) concept in order to help its user to remember things and occurrences in a proactive manner. We will also address socialization and relaxation events that should be part of the user's life. With the use of a Multi-Agent System to implement the PMA, the objectives can be achieved in a ubiquitous and highly configurable manner. It is presented here the platform concept, scheme and the agent characteristics and their contribution to each and every agent