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Resultados de búsqueda para Decision Making

Injecting domain knowledge in multi-objective optimization problems:A semantic approach

In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker’s preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheuristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal optimization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, we start from defining an ontology representing the domain semantics for a city (e.g., building, bridges, point of interest, routes, etc.) that allows defining a-priori preferences by a decision maker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Network Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. This proposal aims at representing a step forward towards the automatic modeling and adaptation of optimization problems guided by semantics, where the annotation of a human expert can be now considered during the optimization process.

Autores: Cristobal Barba-Gonzalez / Antonio J. Nebro / José García-Nieto / Maria Del Mar Roldan-Garcia / Ismael Navas-Delgado / Jose F Aldana Montes / 
Palabras Clave: Decision Making - domain knowledge - Metaheuristics - multi-objective optimization - Ontology - Semantic web technologies

An Autonomous-UAV Architecture for Remote Sensing and Intelligent Decision-making (Abstract)

Recently, the US Department of Transportations Federal Aviation Administration and other international organizations have proposed a set of requirements for small unmanned aerial vehicles (UAVs) to operate for nonrecreational purposes. However, existing UAV architectures fulfill only some of the established requirements, and not all in one solution. This paper presents an unprecedented event-driven service-oriented architecture that allows autonomous UAVs to satisfy all these requirements and to detect critical situations, performing real-time decision making. The core of this architecture is based on the use of complex event processing (CEP) onboard. The results obtained involve advances in terms of the number of events processed per second, response time, ease of use for nontechnological users, and code reconfiguration before or during the UAV flight. These results have been validated by implementing the architecture.

Autores: Juan Boubeta-Puig / Enrique Moguel / Fernando Sánchez-Figueroa / Juan Hernandez / Juan Carlos Preciado / 
Palabras Clave: Autonomous vehicles - Computer architecture - Decision Making - FAA - Real-time systems - Unmanned aerial vehicles

Identification and analysis of the elements required to manage technical debt by means of a systematic mapping study (Artículo relevante)

Technical debt, a metaphor for the long-term consequences of weak software development, must be managed to keep it under control. The main goal of this article is to identify and analyze the elements required to manage technical debt. The research method used to identify the elements is a systematic mapping, including a synthesis step to synthesize the elements definitions. Our perspective differs from previous literature reviews because it focused on the elements required to manage technical debt and not on the phenomenon of technical debt or the activities used in performing technical debt management. Additionally, the rigor and relevance for industry of the current techniques used to manage technical debt are studied. The elements were classified into three groups (basic decision-making factors, cost estimation techniques, practices and techniques for decision-making) and mapped according three stakeholders’ points of view (engineering, engineering management, and business-organizational management). The definitions, classification, and analysis of the elements provide a framework that can be deployed to help in the development of models that are adapted to the specific stakeholders’ interests to assist the decision-making required in technical debt management and to assess existing models and methods. The analysis indicated that technical debt management is context dependent. Artículo publicado en Journal of Systems and Software (JSS). JSS es clasificada como Q1 en JCR 2017.

Autores: Carlos Fernández-Sánchez / Juan Garbajosa / Agustín Yagüe / Jennifer Perez / 
Palabras Clave: Basic decision-making factors - Business-orga - Cost estimation techniques - Decision Making - Engineering - Engineering management - Practices and techniques for decision-making - Stakeholders’ points of view - Systematic mapping - technical debt - Technical debt management

Towards a Comprehensive Purchasing Model for Cloud Services

The Cloud Service Market has evolved into a complex landscape that challenges the decision making of users as they develop their purchasing process. In particular, we explore the case of cloud infrastructure (IaaS) providers as an example of heterogeneous variety of purchasing options and discounts; this variability represents an important drawback during the decision making process where there is a need to compare and select the best option. In this work, we define a common model to describe purchasing models from different providers taking into account such heterogeneity. This purchasing model represents a first step towards the automated support of decision making problems during the purchasing process. In order to illustrate our approach we apply the model in a real case study of IaaS purchasing.

Autores: Octavio Martín-Díaz / Pablo Fernandez / José María García / Antonio Ruiz-Cortés / 
Palabras Clave: Cloud Services - Decision Making - Purchasing Options

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