Inteligencia Artificial <p style="text-align: justify;"><span style="color: #000000;"><strong><em><a style="color: #003366; text-decoration: underline;" href="" target="_blank" rel="noopener">Inteligencia Artificial</a></em></strong><span id="result_box" class="" lang="en"> is an international open access journal promoted by <span class="">the Iberoamerican Society of</span> Artificial Intelligence (<a href="">IBERAMIA</a>). </span></span>Since 1997, the journal publishes high-quality original papers reporting theoretical or applied advances in all areas of Artificial Intelligence. <span style="color: rgba(0, 0, 0, 0.87); font-family: 'Noto Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: justify; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">There are no fees for subscription, publication nor editing tasks<span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">Articles can be written in English, Spanish or Portuguese and <a href="">will be subjected</a> to a double-blind peer review process.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">The journal is abstracted and indexed in several <a href="">data bases</a>.</span></span><br /></span></p> Sociedad Iberoamericana de Inteligencia Artificial (IBERAMIA) en-US Inteligencia Artificial 1137-3601 <p>Open Access publishing.<br />Lic. under <a href="">Creative Commons CC-BY-NC</a><br />Inteligencia Artificial (Ed. IBERAMIA)<br />ISSN: 1988-3064 (on line).<br />(C) IBERAMIA &amp; The Authors</p> Evaluating the impact of curriculum learning on the training process for an intelligent agent in a video game <p>We want to measure the impact of the curriculum learning technique on a reinforcement training setup, several experiments were designed with different training curriculums adapted for the video game chosen as a case study. Then all were executed on a selected game simulation platform, using two reinforcement learning algorithms, and using the mean cumulative reward as a performance measure. Results suggest that curriculum learning has a significant impact on the training process, increasing training times in some cases, and decreasing them up to 40% percent in some other cases.</p> Jorge E Camargo Rigoberto Sáenz Copyright (c) 2021 Iberamia & The Authors 2021-09-30 2021-09-30 24 68 1 20 10.4114/intartif.vol24iss68pp1-20 A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection <p>Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.</p> Yaming Cao ZHEN YANG CHEN GAO Copyright (c) 2021 Iberamia & The Authors 2021-09-30 2021-09-30 24 68 21 32 10.4114/intartif.vol24iss68pp21-32 Procedural Content Generation for General Video Game Level Generation <p>With the passage of time, video games are becoming more complex, and their development incurs greater<br>time and cost. The creation of video gaming content such as levels, maps, textures and so on represent a large<br>part of the overall cost of game development. Procedural Content Generation (PCG) is a method of generating<br>content via a pseudo-random process. Level generation has been the most signi cant and oldest problem in the<br>PCG domain. The majority of the PCG level generators are speci c to a particular game, content is generated<br>only for a suited single type and these generators are evaluated mostly by computational metrics, user studies<br>and tness functions. Considering, the grand goal of general Arti cial Intelligence, it would be bene cial to sculpt<br>solutions that are applicable to a general set of problems. For the level generation problem, this can be achieved<br>by constructing a level generator that generates levels for a set of games and not explicitly for a single game. In<br>this research, we have created four di erent type of generators for the GVG-LG framework. The generators follow<br>a distinct path and are able to solve multiple problems related to PCG including dynamic diculty adjustment,<br>creation of intelligent controllers, creating aesthetically appealing levels and using patterns as objectives for level<br>generation. In addition, we evaluated all the generators using a variety of techniques. The experimental results<br>show promising results and represent our attempt at general video game level generation.</p> Adeel Zafar Hasan Mujtaba Omer Beg Copyright (c) 2021 Iberamia & The Authors 2021-09-30 2021-09-30 24 68 33 36 10.4114/intartif.vol24iss68pp33-36 KeyFinder: An Efficient Minimal Keys Finding Algorithm For Relational Databases <p>In relational databases, it is essential to know all minimal keys since the concept of database normaliza-<br>tion is based on keys and functional dependencies of a relation schema. Existing algorithms for determining keys<br>or computing the closure of arbitrary sets of attributes are generally time-consuming. In this paper we present an<br>efficient algorithm, called KeyFinder, for solving the key-finding problem. We also propose a more direct method<br>for computing the closure of a set of attributes. KeyFinder is based on a powerful proof procedure for finding<br>keys called tableaux. Experimental results show that KeyFinder outperforms its predecessors in terms of search<br>space and execution time.</p> Moussa Demba Copyright (c) 2021 Iberamia & The Authors 2021-09-30 2021-09-30 24 68 37 52 10.4114/intartif.vol24iss68pp37-52 Applying ensemble neural networks to analyze industrial maintenance: Influence of Saharan dust transport on gas turbine axial compressor fouling <p>The planning of industrial maintenance associated with the production of electricity is vital, as it yields a current and future snapshot of an industrial component in order to optimize the human, technical and economic resources of the installation. This study focuses on the degradation due to fouling of a gas turbine in the Canary Islands, and analyzes fouling levels over time based on the operating regime and local meteorological variables. In particular, we study the relationship between degradation and the suspended dust that originates in the Sahara Desert. To this end, we use a computational procedure that relies on a set of artificial neural networks to build an ensemble, using a cross-validated committees approach, to yield the compressor efficiency. The use of trained models makes it possible to know in advance how the local fouling of an industrial rotating component will evolve, which is useful for maintenance planning and for calculating the relative importance of the variables that make up the system</p> D. Gonzalez-Calvo R.M. Aguilar C. Criado-Hernandez L.A. Gonzalez-Mendoza Copyright (c) 2021 Iberamia & The Authors 2021-09-30 2021-09-30 24 68 53 71 10.4114/intartif.vol24iss68pp53-71