The Ambient Intelligence (AmI) paradigm envisions systems whose central entity is the user. AmI integrates technologies such as Artificial Intelligence, implicit Human Computer Interaction, and ...Ubiquitous Services. Each capability of AmI systems is oriented towards assistance of humans at work, in the classroom, or even at home. In consequence, the AmI development process usually incorporates the final user since the first stages. However, having users available during all this long process is not always possible. Agent-based social simulations where the users׳ role is played by simulated entities can be used to make the AmI development process faster and more effective. In this scenario, the modelling of CMHBs (Computational Models of Human Behaviour) is a major challenge. To address this issue, this paper proposes a methodology whose main contributions are: (1) the use of domain experts׳ knowledge to create CMHBs; (2) a common methodological framework to develop CMHBs by combining information obtained from sensors׳ perceptions and experts׳ experiences; and, (3) open source tools to support this development paradigm. The paper also presents a full case of study in a hospital which illustrates: the number of recommendations made by the methodology; the techniques proposed (mainly the use of ontologies and temporal reasoning); and, the potential of the methodology to model the personnel in a hospital.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. ...This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well, this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment ...(MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.
Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.
Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.
This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
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•Machine learning identified key molecules associated with Alzheimer (AD) and cognition (MCI).•Mental state test, language score and oleamide differentiates AD, MCI and controls.•In-vitro microglia experiments show oleamide to be excreted through exosomes.•Tau, SNCA and inflammation proteins differentiate between MCI stable and converted to AD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper proposes a new trust and reputation model to assist decision making process into agents in P2P environments, taking WSMO as the base for definition of tasks to contract. This work shows ...the integration of trust and reputation model and WSMO in two ways: 1) how agents use WSMO as ontology to define their requirements, responses, domain-dependent features and metrics; and 2) how the Web services discovery process in WSMO may be improved using trust and reputation criteria given by the model from data stored by consumer agents in previous interactions.
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FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Testing interactions in multi-agent systems is a complex task because of several reasons. Agents are distributed and can move through different nodes in a network, so their interactions can occur ...concurrently and from many different sites. Also, agents are autonomous entities with a variety of possible behaviours, which can evolve during their lives by adapting to changes in the environment and new interaction patterns. Furthermore, the number of agents can vary during system execution, from a few dozens to thousands or more. Therefore, the number of interactions can be huge and it is difficult to follow up their occurrence and relationships. In order to solve these issues we propose the use of a set of data mining tools, the ACLAnalyser, which processes the results of the execution of large scale multi-agent systems in a monitored environment. This has been integrated with an agent development toolset, the INGENIAS Development Kit, in order to facilitate the verification of multi-agent system models at the design level rather than at the programming level.
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FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Nowadays, most people are used to driving their own vehicles to accomplish certain routines like commuting, go shopping, and the like. Taking into account the increasing number of sensors vehicles ...are provided with, the present work states that it is possible to perceive the context of a vehicle by processing and fusioning the data of some of them. As a result, an on-board context-aware application that processes the usual itineraries of the Ego Vehicle as part of the vehicular context has been implemented. Particularly, the system follows a Complex Event Processing (CEP) approach, and it is able to detect the vehicular occupancy along with the meaningful points of the frequent itineraries whereby a density-based-cluster algorithm. Test results from simulations and real environments show the accuracy of the system when it comes to detect different types of itineraries.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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