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Scopus Q: Q3Subject: Artificial Intelligence

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    Citation - WoS: 11
    Citation - Scopus: 13
    Choice Functions for Autonomous Search in Constraint Programming: Ga Vs. Pso
    (Univ Osijek, Tech Fac, 2013) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Palma, Wenceslao; Monfroy, Eric; Castro, Carlos; Paredes, Fernando; Computer Engineering
    The variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.
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    AI Trustworthiness and Student Pilots: Exploring Attitudes, Anxieties, and Adaptation Performance
    (Elsevier B.V., 2025) Ceken, S.; Yilmaz, A.A.; Acar, A.B.
    This research explores the attitudes of student pilots toward artificial intelligence (AI) applications within the aviation sector, with a focus on their adaptation processes and potential challenges. The recent release of the "EASA AI Roadmap 2.0"by the European Union Aviation Safety Agency (EASA) underscores the growing impact of AI on aviation, driving the emergence of new business models and emphasizing a human-centric approach to AI integration within the aviation industry. This study addresses a significant gap in the literature by examining student pilots' perspectives on AI, specifically focusing on AI trustworthiness, attitudes, anxieties, and adaptation performance. The study utilizes a quantitative research approach, collecting data from 150 student pilots through surveys. Preliminary results from 106 respondents indicate varied attitudes toward AI, with significant implications for AI-supported cockpit assistant systems and the broader aviation industry. The study sample consisted of 106 (Mage = 23.6, SDage= 4.64; 79% male) student pilots from of university pilot training departments and various flight school in Turkey. Collected data were analyzed on SPSS 29. The study revealed that Sociotechnical Blindness AI anxiety is a significant predictor of general attitudes toward AI among student pilots. This finding suggests that higher levels of anxiety related to the perceived complexity and potential unintended consequences of AI are associated with more positive general attitudes toward AI. The findings emphasize the need for a human-centric approach to AI integration, highlighting the importance of trust, transparency, and adaptive training in the successful adoption of AI technologies in aviation. © 2024 The Authors. Published by ELSEVIER B.V.