1,010 research outputs found

    An intelligent peer-to-peer multi-agent system for collaborative management of bibliographic databases

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    This paper describes the design of a peer-to-peer system for collaborative management of distributed bibliographical databases. The goal of this system is twofold: firstly, it aims at providing help for users to manage their local bibliographical databases. Secondly, it offers the possibility to exchange bibliographical data among like-minded user groups in an implicit and intelligent manner. Each user is assisted by a personal agent that provides help such as: filling in bibliographical records, verifying the correctness of information entered and more importantly, recommendation of relevant bibliographical references. To do this, the personal agent needs to collaborate with its peers in order to get relevant recommendations. Each agent applies a case-based reasoning approach in order to provide peers with requested recommendations. The paper focuses mainly on describing the recommendation computation approach

    DeepTech - AI Models in Engineering Solutions

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    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

    Agile AI development for Real World Solutions

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    This keynote will analyse the importance of IoT, Blockchain and Edge Computing as contributors to the development of distributed intelligent systems that have the capacity to interact with the environment "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, IoT and Blockchain in an Edge Computing model or elsewhere, offers a world of possibilities and opportunities

    Phase I, Dose-Escalation, Two-Part Trial of the PARP Inhibitor Talazoparib in Patients with Advanced Germline BRCA1/2 Mutations and Selected Sporadic Cancers

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    Talazoparib inhibits PARP catalytic activity, trapping PARP1 on damaged DNA and causing cell death in BRCA1/2-mutated cells. We evaluated talazoparib therapy in this two-part, phase I, first-in-human trial. Antitumor activity, MTD, pharmacokinetics, and pharmacodynamics of once-daily talazoparib were determined in an open-label, multicenter, dose-escalation study (NCT01286987). The MTD was 1.0 mg/day, with an elimination half-life of 50 hours. Treatment-related adverse events included fatigue (26/71 patients; 37%) and anemia (25/71 patients; 35%). Grade 3 to 4 adverse events included anemia (17/71 patients; 24%) and thrombocytopenia (13/71 patients; 18%). Sustained PARP inhibition was observed at doses ≥0.60 mg/day. At 1.0 mg/day, confirmed responses were observed in 7 of 14 (50%) and 5 of 12 (42%) patients with BRCA mutation–associated breast and ovarian cancers, respectively, and in patients with pancreatic and small cell lung cancer. Talazoparib demonstrated single-agent antitumor activity and was well tolerated in patients at the recommended dose of 1.0 mg/day

    Organization based multiagent architecture for distributed environments

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    [EN]Distributed environments represent a complex field in which applied solutions should be flexible and include significant adaptation capabilities. These environments are related to problems where multiple users and devices may interact, and where simple and local solutions could possibly generate good results, but may not be effective with regards to use and interaction. There are many techniques that can be employed to face this kind of problems, from CORBA to multi-agent systems, passing by web-services and SOA, among others. All those methodologies have their advantages and disadvantages that are properly analyzed in this documents, to finally explain the new architecture presented as a solution for distributed environment problems. The new architecture for solving complex solutions in distributed environments presented here is called OBaMADE: Organization Based Multiagent Architecture for Distributed Environments. It is a multiagent architecture based on the organizations of agents paradigm, where the agents in the architecture are structured into organizations to improve their organizational capabilities. The reasoning power of the architecture is based on the Case-Based Reasoning methology, being implemented in a internal organization that uses agents to create services to solve the external request made by the users. The OBaMADE architecture has been successfully applied to two different case studies where its prediction capabilities have been properly checked. Those case studies have showed optimistic results and, being complex systems, have demonstrated the abstraction and generalizations capabilities of the architecture. Nevertheless OBaMADE is intended to be able to solve much other kind of problems in distributed environments scenarios. It should be applied to other varieties of situations and to other knowledge fields to fully develop its potencial.[ES]Los entornos distribuidos representan un campo de conocimiento complejo en el que las soluciones a aplicar deben ser flexibles y deben contar con gran capacidad de adaptación. Este tipo de entornos está normalmente relacionado con problemas donde varios usuarios y dispositivos entran en juego. Para solucionar dichos problemas, pueden utilizarse sistemas locales que, aunque ofrezcan buenos resultados en términos de calidad de los mismos, no son tan efectivos en cuanto a la interacción y posibilidades de uso. Existen múltiples técnicas que pueden ser empleadas para resolver este tipo de problemas, desde CORBA a sistemas multiagente, pasando por servicios web y SOA, entre otros. Todas estas mitologías tienen sus ventajas e inconvenientes, que se analizan en este documento, para explicar, finalmente, la nueva arquitectura presentada como una solución para los problemas generados en entornos distribuidos. La nueva arquitectura aquí se llama OBaMADE, que es el acrónimo del inglés Organization Based Multiagent Architecture for Distributed Environments (Arquitectura Multiagente Basada en Organizaciones para Entornos Distribuidos). Se trata de una arquitectura multiagente basasa en el paradigma de las organizaciones de agente, donde los agentes que forman parte de la arquitectura se estructuran en organizaciones para mejorar sus capacidades organizativas. La capacidad de razonamiento de la arquitectura está basada en la metodología de razonamiento basado en casos, que se ha implementado en una de las organizaciones internas de la arquitectura por medio de agentes que crean servicios que responden a las solicitudes externas de los usuarios. La arquitectura OBaMADE se ha aplicado de forma exitosa a dos casos de estudio diferentes, en los que se han demostrado sus capacidades predictivas. Aplicando OBaMADE a estos casos de estudio se han obtenido resultados esperanzadores y, al ser sistemas complejos, se han demostrado las capacidades tanto de abstracción como de generalización de la arquitectura presentada. Sin embargo, esta arquitectura está diseñada para poder ser aplicada a más tipo de problemas de entornos distribuidos. Debe ser aplicada a más variadas situaciones y a otros campos de conocimiento para desarrollar completamente el potencial de esta arquitectura

    Contributions to artificial intelligence: the IIIA perspective

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    La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years

    A Phase IB open-label, dose-escalation study of NUC 1031 in combination with carboplatin for recurrent ovarian cancer

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    Funding: The study was funded and the investigational drug NUC-1031 was supplied by NuCana plc.Purpose: NUC-1031 is a first-in-class ProTide modification of gemcitabine. In PRO-002, NUC‑1031 was combined with carboplatin in recurrent ovarian cancer (OC). Experimental Design: NUC-1031 was administered on days 1 & 8 with carboplatin on day 1 every 3 weeks for up to 6 cycles. Four dose cohorts of NUC-1031 (500, 625 and 750 mg/m2) with carboplatin (AUC4 or 5) were investigated. Primary endpoint was RP2CD. Secondary endpoints included safety, investigator-assessed objective response rate (ORR), clinical benefit rate (CBR), progression-free survival (PFS) and pharmacokinetics (PK). Results: 25 women with recurrent OC, a mean of 3.8 prior lines of chemotherapy and a median platinum-free interval (PFI) of 5 months (range: 7 - 451 days) were enrolled, 15/25 (60%) platinum-resistant; 9 (36%) partially platinum-sensitive and 1 (4%) platinum-sensitive. Of the 23 response-evaluable: there was 1 confirmed complete response (CR, 4%), 5 partial responses (PR, 17%) and 8 (35%) stable disease (SD). The ORR was 26% and CBR was 74% across all doses and 100% in the RP2CD cohort. Median PFS was 27.1 weeks. NUC-1031 was stable in the plasma and rapidly generated high intracellular dFdCTP levels that were unaffected by carboplatin. Conclusions: NUC-1031 combined with carboplatin is well tolerated in recurrent OC. Highest efficacy was observed at the RP2CD of 500 mg/m2 NUC-1031 on days 1 & 8 with AUC5 carboplatin day 1, every 3 weeks for 6 cycles. The ability to deliver carboplatin at AUC5 and the efficacy of this schedule even in patients with platinum-resistant disease makes this an attractive therapeutic combination.PostprintPeer reviewe

    Multi-robot coordination using flexible setplays : applications in RoboCup's simulation and middle-size leagues

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    Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
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