729 research outputs found

    Contextual and Possibilistic Reasoning for Coalition Formation

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    In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics

    Distributed First Order Logic

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    Distributed First Order Logic (DFOL) has been introduced more than ten years ago with the purpose of formalising distributed knowledge-based systems, where knowledge about heterogeneous domains is scattered into a set of interconnected modules. DFOL formalises the knowledge contained in each module by means of first-order theories, and the interconnections between modules by means of special inference rules called bridge rules. Despite their restricted form in the original DFOL formulation, bridge rules have influenced several works in the areas of heterogeneous knowledge integration, modular knowledge representation, and schema/ontology matching. This, in turn, has fostered extensions and modifications of the original DFOL that have never been systematically described and published. This paper tackles the lack of a comprehensive description of DFOL by providing a systematic account of a completely revised and extended version of the logic, together with a sound and complete axiomatisation of a general form of bridge rules based on Natural Deduction. The resulting DFOL framework is then proposed as a clear formal tool for the representation of and reasoning about distributed knowledge and bridge rules

    The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

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    In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

    Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique

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    [EN] Background: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes-the division of populations of patients into more meaningful subgroups driven by clinical features-and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. Objective: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. Methods: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. Results: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. Conclusions: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.We sincerely thank the different types of clinical institutions and the Mexican government, which made a huge effort to make these data publicly available. We also thank the clinicians and epidemiologists from the Servicios de Salud de Nayarit for the useful discussions on specific aspects of the medical attention to hospitalized patients and the reporting of epidemiological data processes related to COVID-19. Furthermore, we would also like to thank Francisco Tomas Garcia Ruiz for his valuable help in data visualization design. This work was supported by Universitat Politecnica de Valencia contract no. UPV-SUB.2-1302 and FONDO SUPERA COVID-19 by CRUE-Santander Bank grant: "Severity Subgroup Discovery and Classification on COVID-19 Real World Data through Machine Learning and Data Quality assessment (SUBCOVERWD-19) ." The authors thank the Institute for Information and Communication Technologies (ITACA) at the Universitat Politecnica de Valencia for its support in the publication of this manuscript.Zhou, L.; Romero-Garcia, N.; Martínez-Miranda, J.; Conejero, JA.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2022). Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique. JMIR Public Health and Surveillance. 8(3):1-21. https://doi.org/10.2196/300321218

    Enhancing innovation in organization by management control systems

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    Innovation is vital to organization’s success in today’s highly competitive and constantly changing business environment. Organizations need to drive value creating activities to be able to guarantee their long-term success and improve performance. This increasing need for change and growth provides a great deal of opportunity and many possibilities for businesses to renew themselves. However, innovation is a complex phenomenon, that involves high degree of uncertainty, high investments, and long-term approach. This has created a need for organizations to establish procedures that enable to control their processes and ensure the efficient use of their limited resources, balancing between the need for control and need to remain flexible and creative. Hence, the characteristics of management control systems and their suitability for innovation has raised an interest of organizational participants and researchers. This thesis aims to explore how management control systems can enhance innovation in organization. This study is based on the assumption that management control systems support innovation in organizational set-up and controls applied to innovation may have enabling and coercive design. Additionally, the use of management control systems is not limited to management but covers all organizational participants. Management control systems were studied as a package according to framework by Malmi & Brown (2008). This study was conducted as a qualitative study, having semi-structured interviews as chosen data collection method. Four interviews were carried out in one company´s innovation unit. The data gathered from interviews was analysed using thematic analysis. According to findings of this study organizations could benefit from comparing their management control systems to factors affecting their innovation management practises and balance the usage of enabling and coercive forms of control on them. Innovation is favouring the use of enabling control, but the balance is based on optimal fit. Management control systems applied to innovation should offer advanced visibility, communication, information sharing and collaboration, which are characteristics supported by enabling design of management control systems. The role of coercive controls is providing necessary limits to action and compliance. Management control systems package can also be applied to advance measuring innovation performance and generate metrics for assessing the value of innovation, which have been considered challenging tasks. This study’s empirical findings support the theoretical framework of this study at large extent. The results of this study should be reflected when considering how management control systems can enhance innovation in organization

    Dual PECCS: A Cognitive System for Conceptual Representation and Categorization

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    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation

    A Fishery Manager's Guidebook, Second Edition

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    A Fishery Managers' Guidebook was first published as an FAO Fisheries Technical Paper in 2002 to meet the need for information and guidance on the broad and often complex task of fisheries management. Based on subsequent experience and feedback gained from publication of the first edition, this new volume, has been expanded to provide broader coverage of the key elements of the task and updated in order to keep track of the rapid developments in theory and practice as academics and practitioners struggle to confront the many challenges facing modern fisheries management
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