The Experts below are selected from a list of 16947 Experts worldwide ranked by ideXlab platform
Kshirasagar Naik - One of the best experts on this subject based on the ideXlab platform.
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a practical task offloading Decision Engine for mobile devices to use energy as a service eaas
World Congress on Services, 2014Co-Authors: Majid Altamimi, Kshirasagar NaikAbstract:In this paper, we propose a practical Decision Engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the Decision Engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the Decision Engine. The practicality of the Decision Engine is demonstrated by means of real-world computing scenarios.
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SERVICES - A Practical Task Offloading Decision Engine for Mobile Devices to Use Energy-as-a-Service (EaaS)
2014 IEEE World Congress on Services, 2014Co-Authors: Majid Altamimi, Kshirasagar NaikAbstract:In this paper, we propose a practical Decision Engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the Decision Engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the Decision Engine. The practicality of the Decision Engine is demonstrated by means of real-world computing scenarios.
Patrick Meyer - One of the best experts on this subject based on the ideXlab platform.
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Integrating Operators' Preferences into Decisions of Unmanned Aerial Vehicles: Multi-layer Decision Engine and Incremental Preference Elicitation
2019Co-Authors: Arwa Khannoussi, Alexandru Liviu Olteanu, Christophe Labreuche, Pritesh Narayan, Catherine Dezan, Jean-philippe Diguet, Jacques Petit-frère, Patrick MeyerAbstract:Due to the nature of autonomous Unmanned Aerial Vehicles (UAV) missions, it is important that the Decisions of a UAV stay consistent with the priorities of an operator, while at the same time allowing them to be easily audited and explained. We therefore propose a multi-layer Decision Engine that follows the logic of an operator and integrates its preferences through a Multi-Criteria Decision Aiding model. We also propose an incremental approach to elicit the operator's preferences, in view of minimizing his/her cognitive fatigue during this task.
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ADT - Integrating Operators’ Preferences into Decisions of Unmanned Aerial Vehicles: Multi-layer Decision Engine and Incremental Preference Elicitation
Annales Des Télécommunications, 2019Co-Authors: Arwa Khannoussi, Alexandru Liviu Olteanu, Christophe Labreuche, Pritesh Narayan, Catherine Dezan, Jean-philippe Diguet, Jacques Petit-frère, Patrick MeyerAbstract:Due to the nature of autonomous Unmanned Aerial Vehicles (UAV) missions, it is important that the Decisions of a UAV stay consistent with the priorities of an operator, while at the same time allowing them to be easily audited and explained. We therefore propose a multi-layer Decision Engine that follows the logic of an operator and integrates its preferences through a Multi-Criteria Decision Aiding model. We also propose an incremental approach to elicit the operator’s preferences, in view of minimizing his/her cognitive fatigue during this task.
Majid Altamimi - One of the best experts on this subject based on the ideXlab platform.
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a practical task offloading Decision Engine for mobile devices to use energy as a service eaas
World Congress on Services, 2014Co-Authors: Majid Altamimi, Kshirasagar NaikAbstract:In this paper, we propose a practical Decision Engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the Decision Engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the Decision Engine. The practicality of the Decision Engine is demonstrated by means of real-world computing scenarios.
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SERVICES - A Practical Task Offloading Decision Engine for Mobile Devices to Use Energy-as-a-Service (EaaS)
2014 IEEE World Congress on Services, 2014Co-Authors: Majid Altamimi, Kshirasagar NaikAbstract:In this paper, we propose a practical Decision Engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the Decision Engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the Decision Engine. The practicality of the Decision Engine is demonstrated by means of real-world computing scenarios.
Carlos M. Fernandes - One of the best experts on this subject based on the ideXlab platform.
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Effect of noisy fitness in real-time strategy games player behaviour optimisation using evolutionary algorithms
Journal of Computer Science and Technology, 2012Co-Authors: Antonio M. Mora, Antonio Fernández-ares, Pablo García-sánchez, Juan Julián Merelo, Carlos M. FernandesAbstract:This paper investigates the performance and the results of an Evolutionary Algorithm (EA) specifically designed for evolving the Decision Engine of a program (which, in this context, is called bot) that plays Planet Wars. This game, which was chosen for the Google Artificial Intelligence Challenge in 2010, requires the bot to deal withmultiple target planets, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The Decision Engine of the bot is initially based on a set of rules that have been defined after an empirical study, and a Genetic Algorithm (GA) is used for tuning the set of constants, weights and probabilities that those rules include, and therefore, the general behaviour of the bot. Then, the bot is supplied with the evolved Decision Engine and the results obtained when competing with other bots (a bot offered by Google as a sparring partner, and a scripted bot with a pre-established behaviour) are thoroughly analysed. The evaluation of the candidate solutions is based on the result of non-deterministic battles (and environmental interactions) against other bots, whose outcome depends on random draws as well as on the opponents’ actions. Therefore, the proposed GA is dealing with a noisy fitness function. After analysing the effects of the noisy fitness, we conclude that tackling randomness via repeated combats and reevaluations reduces this effect and makes the GA a highly valuable approach for solving this problem.
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IEEE Congress on Evolutionary Computation - Optimizing player behavior in a real-time strategy game using evolutionary algorithms
2011 IEEE Congress of Evolutionary Computation (CEC), 2011Co-Authors: Antonio Fernández-ares, Pablo García-sánchez, Antonio M. Mora, Juan Julián Merelo, Carlos M. FernandesAbstract:This paper describes an Evolutionary Algorithm for evolving the Decision Engine of a bot designed to play the Planet Wars game. This game, which has been chosen for the Google Artificial Intelligence Challenge in 2010, requires that the artificial player is able to deal with multiple objectives, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The Decision Engine of the bot is based on a set of rules that have been defined after an empirical study. Then, an Evolutionary Algorithm is used for tuning the set of constants, weights and probabilities that define the rules, and, therefore, the global behavior of the bot. The paper describes the Evolutionary Algorithm and the results attained by the Decision Engine when competing with other bots. The proposed bot defeated a baseline bot in most of the playing environments and obtained a ranking position in top-20% of the Google Artificial Intelligence competition.
Jian Hu - One of the best experts on this subject based on the ideXlab platform.
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a hybrid architecture of cognitive Decision Engine based on particle swarm optimization algorithms and case database
Annales Des Télécommunications, 2014Co-Authors: Hang Zhang, Jian HuAbstract:The architecture of cognitive Decision Engine should enable fast Decision making, long-term knowledge accumulating based on past operating experience, and capabilities of knowledge updating to adapt to new situations. In this paper, a hybrid architecture of cognitive Decision Engine based on particle swarm optimization algorithms and case database is proposed. Considering the user’s quality of service preferences and the wireless situations, how to determine the radio’s link parameters such as modulation type, symbol rate, and transmit power can be formulated as a multi-objective optimization problem. In the architecture, this problem is solved by using particle swarm optimization algorithms, which make cognitive radio have the fast Decision-making ability when facing unknown wireless situations. The case database, which stores the past running experiences of the cognitive radio is also integrated into the proposed architecture to improve the radio’s response speed and endows the radio with the ability of learning from its previous operating experiences. Simulation results show the effectiveness of the architecture, and the proposed cognitive Decision Engine can dynamically and properly reconfigure the radio according to the changes in wireless environment.
