A mechanism for solving conflicts in ambient intelligent environments

Ambient Intelligence scenarios describe situations in which multitude of devices and agents live together. In this kind of scenarios is frequent to see the appearance of conflicts when modifying the state of a device as for example a lamp. Those problems are not as much of sharing of resources as of conflict of orders coming from different agents. This coexistence must deal also with the desire of privacy of the different users over their personal information such as where they are, what their preferences are or to whom this information should be available. When facing incompatible orders over the state of a device it turns necessary to make a decision. In this paper we propose a centralised mechanism based on prioritized FIFO queues to decide the order in which the control of a device is granted. The priority of the commands is calculated following a policy that considers issues such as the commander's role, command's type, context's state and commander-context and commander-resource relations. Finally we propose a set of particular policies for those resources that do not adjust to the general policy. In addition we present a model pretending to integrate privacy through limiting and protecting contextual information

Easing the smart home: Translating human hierarchies to intelligent environments

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02478-8_137Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain.Ubiquitous computing research have extended traditional environments in the so–called Intelligent Environments. All of them use their capabilities for pursuing their inhabitants’s satisfaction, but the ways of getting it are most of the times unclear and frequently unshared among different users. This last problem becomes patent in shared environments in which users with different preferences live together. This article presents a solution translating human hierarchies to the Ubicomp domain, in a continuing effort for leveraging the control capabilities of the inhabitants in their on–growing capable environments. This mechanism, as a natural ubicomp extension of the coordination mechanism used daily by humans, has been implemented over a real environment: a iving room equipped with ambient intelligence capabilities, and installed in two more: an intelligent classroom and an intelligent secure room.This work was partially funded by the Spanish Ministry of Science and Technology through the HADA project(TIN2007-64718) and by the chair UAM–Indra of Ambient Intelligenc

SAT based Enforcement of Domotic Effects in Smart Environments

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. `Domotic Effects' is a user goal modeling framework, which provides Ambient Intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architectur

Ubiquitous computing and ambient intelligence: New challenges for computing

The IST Advisory Group (ISTAG) of the European Union had a vision of "Ambient Intelligence" (AmI) in 1999. It refers to "an exciting new paradigm of information technology, in which people are empowered through a digital environment that is aware of their presence and context sensitive, adaptive and responsive to their needs, habits, gestures and emotions". In AmI the technology will become invisible, embedded, present whenever we need it, enabled by simple interactions, attuned to all our senses and adaptive to users and contexts (Scenarios for Ambient Intelligence). AmI proposes a shift in computing from the traditional computer to a whole set of devices placed around us providing users with an intelligent background

Towards a ubiquitous end-user programming system for smart spaces

This article presents a rule–based agent mechanism as the kernel of a ubiquitous end–user, UI–independent programming system. The underlying goal of our work is to allow end–users to control and program their environments in a uniform, application–independent way. The heterogeneity of environments, users and programming skills, as well as the coexistence of different users and domains of automation in the same environment are some of the main challenges analyzed. For doing so, we present our system and describe some of the real–environments, user studies and experiences we have had in the development process.This work has been partially funded by the following projects: HADA (Ministerio de Ciencia y Educación de España, TIN2007-64718), Vesta (Ministerio de Industria, Turismo y Comercio de España, TSI-020100-2009-828) y eMadrid (Comunidad de Madrid, S2009/TIC-1650)

Development of ambient intelligence systems based on collaborative task models

So far, the Ambient Intelligence (AmI) paradigm has been applied to the development of a great variety of real systems. They use advanced technologies such as ubiquitous computing, natural interaction and active spaces, which become part of social environments. In the design of AmI systems, the inherent collaboration among users (with the purpose of achieving common goals) is usually represented and treated in an ad-hoc manner. However, the development of this kind of systems can take advantage of rich design models which embrace concepts in the domain of collaborative systems in order to provide the adequate support for explicit or implicit collaboration. Thereby, relevant requirements to be satisfied, such as an effective coordination of human activities by means of task scheduling, demand to dynamically manage and provide group- and context-awareness information. This paper addresses the integration of both proactive and collaborative aspects into a unique design model for the development of AmI systems; in particular, the proposal has been applied to a learning system. Furthermore, the implementation of this system is based on a blackboardbased architecture, which provides a well-defined high-level interface to the physical layer.This research is partially supported by a Spanish R&D Project TIN2004-03140, Ubiquitous Collaborative Adaptive Training (U-CAT)

Using argumentation to solve conflicting situations in users' preferences in ambient assisted living

Preferences are fundamental in decision making, so understanding preference management is key in developing systems that guide the choices of the users. These choices can be decided through argument(s) which are known to have various strengths, as one argument can rely on more certain or vital information than the other. We explored argumentation technique from a previous study, and validated its potentials by applying to it several real life scenarios. The exploration demonstrates the usefulness of argumentation in handling conflicting preferences and inconsistencies, and provides effective ways to manage, reason and represents users' preferences. Using argumentation, we provide a practical implementation of a system to manage conflicting situations, and a simple interface that aids the flow of preferences from users to the system. We illustrated using the interface, how the changes in users' preferences can effect system output in a smart home. This article describes the functionalities of the implemented system, and illustrates the functions by solving some of the complexities in users' preferences in a real smart home. The system detects potential conflicts, and tries solve them using a redefined precedence order among some preference criteria. We also show how our system is capable of interacting with external sources data. The system was used to access and use live data of a UK supermarket chain store, through their application programming interface (API) and provide users suggestions on their eating habits, based on their set preference(s). The system was used to filter specific products from the live data, and check the product description, before advising the user accordingly

Defeasible Argumentation for Cooperative Multi-Agent Planning

Tesis por compendio[EN] Multi-Agent Systems (MAS), Argumentation and Automated Planning are three lines of investigations within the field of Artificial Intelligence (AI) that have been extensively studied over the last years. A MAS is a system composed of multiple intelligent agents that interact with each other and it is used to solve problems whose solution requires the presence of various functional and autonomous entities. Multi-agent systems can be used to solve problems that are difficult or impossible to resolve for an individual agent. On the other hand, Argumentation refers to the construction and subsequent exchange (iteratively) of arguments between a group of agents, with the aim of arguing for or against a particular proposal. Regarding Automated Planning, given an initial state of the world, a goal to achieve, and a set of possible actions, the goal is to build programs that can automatically calculate a plan to reach the final state from the initial state. The main objective of this thesis is to propose a model that combines and integrates these three research lines. More specifically, we consider a MAS as a team of agents with planning and argumentation capabilities. In that sense, given a planning problem with a set of objectives, (cooperative) agents jointly construct a plan to satisfy the objectives of the problem while they defeasibly reason about the environmental conditions so as to provide a stronger guarantee of success of the plan at execution time. Therefore, the goal is to use the planning knowledge to build a plan while agents beliefs about the impact of unexpected environmental conditions is used to select the plan which is less likely to fail at execution time. Thus, the system is intended to return collaborative plans that are more robust and adapted to the circumstances of the execution environment. In this thesis, we designed, built and evaluated a model of argumentation based on defeasible reasoning for planning cooperative multi-agent system. The designed system is independent of the domain, thus demonstrating the ability to solve problems in different application contexts. Specifically, the system has been tested in context sensitive domains such as Ambient Intelligence as well as with problems used in the International Planning Competitions.[ES] Dentro de la Inteligencia Artificial (IA), existen tres ramas que han sido ampliamente estudiadas en los últimos años: Sistemas Multi-Agente (SMA), Argumentación y Planificación Automática. Un SMA es un sistema compuesto por múltiples agentes inteligentes que interactúan entre sí y se utilizan para resolver problemas cuya solución requiere la presencia de diversas entidades funcionales y autónomas. Los sistemas multiagente pueden ser utilizados para resolver problemas que son difíciles o imposibles de resolver para un agente individual. Por otra parte, la Argumentación consiste en la construcción y posterior intercambio (iterativamente) de argumentos entre un conjunto de agentes, con el objetivo de razonar a favor o en contra de una determinada propuesta. Con respecto a la Planificación Automática, dado un estado inicial del mundo, un objetivo a alcanzar, y un conjunto de acciones posibles, el objetivo es construir programas capaces de calcular de forma automática un plan que permita alcanzar el estado final a partir del estado inicial. El principal objetivo de esta tesis es proponer un modelo que combine e integre las tres líneas anteriores. Más específicamente, nosotros consideramos un SMA como un equipo de agentes con capacidades de planificación y argumentación. En ese sentido, dado un problema de planificación con un conjunto de objetivos, los agentes (cooperativos) construyen conjuntamente un plan para resolver los objetivos del problema y, al mismo tiempo, razonan sobre la viabilidad de los planes, utilizando como herramienta de diálogo la Argumentación. Por tanto, el objetivo no es sólo obtener automáticamente un plan solución generado de forma colaborativa entre los agentes, sino también utilizar las creencias de los agentes sobre la información del contexto para razonar acerca de la viabilidad de los planes en su futura etapa de ejecución. De esta forma, se pretende que el sistema sea capaz de devolver planes colaborativos más robustos y adaptados a las circunstancias del entorno de ejecución. En esta tesis se diseña, construye y evalúa un modelo de argumentación basado en razonamiento defeasible para un sistema de planificación cooperativa multiagente. El sistema diseñado es independiente del dominio, demostrando así la capacidad de resolver problemas en diferentes contextos de aplicación. Concretamente el sistema se ha evaluado en dominios sensibles al contexto como es la Inteligencia Ambiental y en problemas de las competiciones internacionales de planificación.[CA] Dins de la intel·ligència artificial (IA), hi han tres branques que han sigut àmpliament estudiades en els últims anys: Sistemes Multi-Agent (SMA), Argumentació i Planificació Automàtica. Un SMA es un sistema compost per múltiples agents intel·ligents que interactúen entre si i s'utilitzen per a resoldre problemas la solución dels quals requereix la presència de diverses entitats funcionals i autònomes. Els sistemes multiagente poden ser utilitzats per a resoldre problemes que són difícils o impossibles de resoldre per a un agent individual. D'altra banda, l'Argumentació consistiex en la construcció i posterior intercanvi (iterativament) d'arguments entre un conjunt d'agents, amb l'objectiu de raonar a favor o en contra d'una determinada proposta. Respecte a la Planificació Automàtica, donat un estat inicial del món, un objectiu a aconseguir, i un conjunt d'accions possibles, l'objectiu és construir programes capaços de calcular de forma automàtica un pla que permeta aconseguir l'estat final a partir de l'estat inicial. El principal objectiu d'aquesta tesi és proposar un model que combine i integre les tres línies anteriors. Més específicament, nosaltres considerem un SMA com un equip d'agents amb capacitats de planificació i argumentació. En aquest sentit, donat un problema de planificació amb un conjunt d'objectius, els agents (cooperatius) construeixen conjuntament un pla per a resoldre els objectius del problema i, al mateix temps, raonen sobre la viabilitat dels plans, utilitzant com a ferramenta de diàleg l'Argumentació. Per tant, l'objectiu no és només obtindre automàticament un pla solució generat de forma col·laborativa entre els agents, sinó també utilitzar les creences dels agents sobre la informació del context per a raonar sobre la viabilitat dels plans en la seua futura etapa d'execució. D'aquesta manera, es pretén que el sistema siga capaç de tornar plans col·laboratius més robustos i adaptats a les circumstàncies de l'entorn d'execució. En aquesta tesi es dissenya, construeix i avalua un model d'argumentació basat en raonament defeasible per a un sistema de planificació cooperativa multiagent. El sistema dissenyat és independent del domini, demostrant així la capacitat de resoldre problemes en diferents contextos d'aplicació. Concretament el sistema s'ha avaluat en dominis sensibles al context com és la inte·ligència Ambiental i en problemes de les competicions internacionals de planificació.Pajares Ferrando, S. (2016). Defeasible Argumentation for Cooperative Multi-Agent Planning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/60159TESISCompendi

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

Developing dynamic conflict resolution models based on the interpretation of personal conflict styles

Proceedings of the 15th Portuguese conference on Artificial Intelligence - (EPIA 2011), Lisboa, Portugal, 2011.Conflict resolution is a classic field of Social Science research. However, with conflicts now also emerging in virtual environments, a new field of research has been developing in which Artificial Intelligence and particularly Ambient Intelligence are interesting. As result, the field of Online Dispute Resolution emerged as the use (in part or entirely) of technological tools to solve disputes. In this paper we focus on developing conflict resolution models that are able to adapt strategies in real time according to changes in the personal conflict styles of the parties. To do it we follow a novel approach in which an intelligent environment supports the lifecycle of the conflict resolution model with the provision of important context knowledge. The presented framework is able to react to important changes in the context of interaction, resulting in a conflict resolution approach that is able to perceive the parties and consequently achieve better outcomes.The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal