{"id":7,"date":"2015-07-23T14:43:38","date_gmt":"2015-07-23T14:43:38","guid":{"rendered":"http:\/\/ajcastro.com.pt\/home\/?page_id=7"},"modified":"2023-06-10T12:21:36","modified_gmt":"2023-06-10T11:21:36","slug":"research-interests","status":"publish","type":"page","link":"http:\/\/ajcastro.com.pt\/home\/research-interests\/","title":{"rendered":"Research"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">I am a pro bono&nbsp;Artificial Intelligence Researcher at&nbsp;<a rel=\"noreferrer noopener\" href=\"https:\/\/liacc.fe.up.pt\/\" target=\"_blank\">LIACC (Artificial Intelligence and Computer Science Laboratory)<\/a>, <a rel=\"noreferrer noopener\" href=\"https:\/\/sigarra.up.pt\/up\/en\/WEB_BASE.GERA_PAGINA?p_pagina=home\" data-type=\"URL\" data-id=\"https:\/\/sigarra.up.pt\/up\/en\/WEB_BASE.GERA_PAGINA?p_pagina=home\" target=\"_blank\">University of Porto<\/a> in Portugal. I perform research on the following topics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Intelligent systems &amp; quantum computing<\/li>\n\n\n\n<li>Accountability, responsibility and trustworthiness in intelligent systems (ART)<\/li>\n\n\n\n<li>Explainable Artificial Intelligence (XAI)<\/li>\n\n\n\n<li>Machine learning in general &amp; reinforcement learning in particular<\/li>\n\n\n\n<li>Evolutionary computing<\/li>\n\n\n\n<li>Autonomy, automation, negotiation and decision taking<\/li>\n\n\n\n<li>Distributed systems &amp; multi-agent systems<\/li>\n\n\n\n<li>Organisation structure in distributed systems\/MAS<\/li>\n\n\n\n<li>Agent oriented software engineering<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I like to share knowledge and help others to achieve their potential. That is the reason why most of my research is done while supervising students in their thesis and help them reach their goals. The research projects listed below are examples of the research performed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I am passionate about space exploration and I believe that AI already is, and will be even more, a huge contribution to this endeavour. I want to contribute to a more useful, responsible and trustworthy artificial intelligence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To see a list of publications, including, papers and books, please consult the <a href=\"http:\/\/ajcastro.com.pt\/home\/publications\/\" data-type=\"page\" data-id=\"37\">Publication<\/a> menu.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Research Projects<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/github.com\/antoniopedrodantas\/spotify-recommender-system\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"645\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?resize=1024%2C645\" alt=\"woman in white shirt using silver macbook\" class=\"wp-image-750\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?resize=1024%2C645 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?resize=300%2C189 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?resize=768%2C484 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?resize=1536%2C967 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3794188-1.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Inferring User Preferences by Analyzing their Behavior on Streaming Platforms Using Inverse Reinforcement Learning<\/strong><br>We used Inverse Reinforcement Learning (IRL) to infer user preferences on a social network or streaming platform. It is complicated to tell a computer what we do or do not like, human nature is complicated and programming a reward function that tries to address it would be tedious and prone to errors. However, there is a possibility that by observing a user&#8217;s behavior an IRL model could come up with a reward function that predicts its preferences.<br>Spotify&#8217;s API was used to specify the group of inputs we want to analyse and then observe the user&#8217;s behavior over a period of time.<br>The data collected and the shown results can then be used for a plethora of goals. It can improve user recommendations on the platform and improve the overall app&#8217;s experience, it can enhance advertising algorithms and lead to a better understanding of what the user really wants. This work can also be extended to further areas of studies. Analyzing users&#8217; behavior with this type of algorithm can be used to predict how people interact with many types of web platforms and enhance the overall user-experience.<br><strong>Main Author<\/strong>: Ant\u00f3nio Pedro Dantas<br><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: <span style=\"color: initial;\">Machine Learning &#8211; Inverse Reinforcement learning, Reinforcement learning<\/span><br><strong>Technology &amp; Tools<\/strong>: Javascript, Python<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/antoniopedrodantas\/spotify-recommender-system\" target=\"_blank\" rel=\"noopener\" title=\"\">https:\/\/github.com\/antoniopedrodantas\/spotify-recommender-system <\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/github.com\/andremmori\/msc-thesis\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"682\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?resize=1024%2C682\" alt=\"flight schedule screen turned on\" class=\"wp-image-753\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?resize=1024%2C682 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?resize=768%2C512 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?resize=1536%2C1024 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-2833379.jpg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Replanning Flight Schedules Using Quantum Computing<\/strong><br>This work focuses on the Aircraft Recovery Problem (ARP), which involves decisions concerning aircraft to flight assignments in situations where unforeseen events have disrupted the existing flight schedule. One of the main difficulties of solving the ARP is the overall complexity since a single disruption may cause a chain effect that can disrupt many other subsequent flights. Computing the total solution space is not feasible for today&#8217;s classical computers due to the number of possible solutions and constraints while trying to minimize the impact of the disruption.<br>The Quadratic Unconstrained Binary Optimization (QUBO) model has been one of the primary options for solving optimization problems using Quantum Computing, with different companies such as IBM, D-wave and Microsoft building quantum computers dedicated to solving it.<br>In this study, the ARP was modeled as a QUBO and was solved by classical and hybrid solvers, comparing the final operational cost and resulting flight plan. Actual data from a major airline, was used to analyze the performance of the implementation in a real-case scenario. The implementation was compared to another previous classical implementation of an ARP solution in terms of execution time and the resulting operational plan. <br>This study concluded that modeling the ARP as a QUBO proved efficient and feasible. Solving the QUBO with hybrid computers provided a quick, feasible and low-cost solution.<br><strong>Main Author<\/strong>: Andr\u00e9 Mamprin Mori<br><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: <span style=\"color: initial;\"><span style=\"color: initial;\">Quantum intelligent systems,  Quantum computing,<\/span><\/span><br><strong>Technology &amp; Tools<\/strong>: IBM Quantum Experience, OpenQASM, Qiskit, Python, Jupyter notebooks<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/andremmori\/msc-thesis\" target=\"_blank\" rel=\"noopener\" title=\"\">https:\/\/github.com\/andremmori\/msc-thesis<\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"700\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?resize=1024%2C700\" alt=\"king chess piece\" class=\"wp-image-530\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?resize=1024%2C700 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?resize=300%2C205 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?resize=768%2C525 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?resize=1536%2C1051 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-59197.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Quantum Reinforcement Learning Applied to Games<\/strong><br><meta charset=\"utf-8\">We combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. The domain of board games provided a deterministic environment with perfect information, where we could focus on the learning process. After implementing the quantum algorithm, we performed an analysis of its performance and compared its results against a classical approach. We concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space.<br><strong>Main Author<\/strong>: Miguel Alexandre Brand\u00e3o Teixeira<br><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><meta charset=\"utf-8\"><strong>Research Topics<\/strong>: <span style=\"color: initial;\">Quantum intelligent systems,  Quantum computing, Machine Learning (Reinforcement learning<\/span>)<br><strong>Technology &amp; Tools<\/strong>: IBM Quantum Experience, OpenQASM, Qiskit, Python, Jupyter notebooks<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/ajmcastro\/quantum-reinforcement-learning.git\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/ajmcastro\/quantum-reinforcement-learning.git <\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/QUBO_Model_LuisNoitesMartins.pdf.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"671\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?resize=1024%2C671\" alt=\"miniature airplane and hand of person over drawn map\" class=\"wp-image-531\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?resize=1024%2C671 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?resize=300%2C197 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?resize=768%2C503 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?resize=1536%2C1007 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3769146.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Using Quantum Computing to Solve the Tail Assignment Problem<\/strong><br>The problem was set as a Quadratic Unconstrained Binary Optimisation (QUBO) model, using two different techniques and was solved using a classical and two hybrid solvers. Tests were run based on extractions from real-world data, analysing the performance of the implementation in terms of time, scalability, and quality (i.e., the lowest operational costs) of the obtained solutions.<br><strong>Main Author<\/strong>: Lu\u00eds Noites Martins<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><meta charset=\"utf-8\"><strong>Research Topics<\/strong>: <span style=\"color: initial;\">Quantum intelligent systems,  Quantum computing, Quantum Annealer, <\/span>Scheduling, Optimisation<br><strong>Technology &amp; Tools<\/strong>: D-Wave, Python<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/ajmcastro\/quantum-tail-assignment.git\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/ajmcastro\/quantum-tail-assignment.git<\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Chapter_6_GQN_Protocol.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"574\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/masdima_1.png?resize=1024%2C574\" alt=\"\" class=\"wp-image-532\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/masdima_1.png?resize=1024%2C574 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/masdima_1.png?resize=300%2C168 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/masdima_1.png?resize=768%2C431 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/masdima_1.png?w=1243 1243w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Generic Q-Negotiation Protocol<\/strong><br>We introduce an adaptive protocol for multi-attribute negotiation in a cooperative distributed problem solving environment with agents that are willing to cooperate but, at the same time, with some degree of self-interestedness and rationality. The agents are able to adapt their strategies during bid formulation, due to the inclusion of a Q-Learning algorithm. Our model is multidimensional where each agent represents and possesses expertise about a dimension and the set of the dimensions represents the complete solution of the problem. This protocol supports agents of two types (organiser and respondent), assuming different roles with different problem solving methods and bid strategies. Results show that the protocol gives better integrated solutions that are closer to the optimal one.<br><strong>Main Author<\/strong>: Ant\u00f3nio J. M. Castro<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Eug\u00e9nio Oliveira and Ana Paula Rocha<br><meta charset=\"utf-8\"><strong>Research Topics<\/strong>: Multi-agent systems, Negotiation, Autonomy, Decision making, Machine learning (Reinforcement learning)<br><strong>Technology &amp; Tools<\/strong>: Java, Jade, MySQL<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/DISS_Francisca_Entrega.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"575\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?resize=1024%2C575\" alt=\"\" class=\"wp-image-533\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?resize=1024%2C575 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?resize=300%2C169 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?resize=768%2C431 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?resize=1536%2C863 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/MASDIMA_UI_06.png?w=1595 1595w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Human-in-the-Loop and Learning in Automatic Negotiation<\/strong><br>Using reinforcement learning in automatic negotiation for the software agents to learn how to win the negotiation in an environment simultaneously cooperative and competitive. Additionally, the human-in-the-loop was introduced as a way of evaluating the winning deals. The goal was to improve the reaction of the system to the needs of the real context where the systems was being used as well as to reduce the social (user) reluctance regarding the use of an automatic system.<br><strong>Main Author<\/strong>: Paula Francisca Ferreira Teixeira<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Eug\u00e9nio Oliveira, Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><meta charset=\"utf-8\"><strong>Research Topics<\/strong>: Multi-agent systems, Negotiation, Autonomy, Decision making, Machine learning (Reinforcement learning)<br><strong>Technology &amp; Tools<\/strong>: Java, Jade, MySQL<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Tese_Aprender_com_Passado.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"808\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?resize=1024%2C808\" alt=\"retro cassette tape with small flower\" class=\"wp-image-534\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?resize=1024%2C808 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?resize=300%2C237 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?resize=768%2C606 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?resize=1536%2C1212 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5588391.jpeg?w=1647 1647w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Learning with the Past &#8211; Supporting Automatic Negotiation<br><\/strong>A new methodology called Case-based Reasoning for Dynamic Distributed Environments (CBR-DDE) was introduced, based on the Case-based Reasoning (CBR) methodology. The goal was to obtain solutions (the outcome of the automatic negotiation) that decrease the average response time of the system to a new disruption problem, increasing its degree of trustworthiness, while maintaining the level of quality of the solutions presented.<br><strong>Main Author<\/strong>: Jos\u00e9 Pedro Sobreiro Furtado da Silva<br><strong><meta charset=\"utf-8\"><strong>Supervisors<\/strong><\/strong>: Eug\u00e9nio Oliveira, Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><meta charset=\"utf-8\"><strong>Research Topics<\/strong>: Multi-agent systems, Negotiation, Autonomy, Decision making, Machine learning (Case-based reasoning)<br><strong>Technology &amp; Tools<\/strong>: Java, Jade, MySQL<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/PAAMS2017_37_argumentation-accommodation-airline_CameraReady.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"703\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?resize=1024%2C703\" alt=\"silhouette of person in airport\" class=\"wp-image-535\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?resize=1024%2C703 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?resize=300%2C206 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?resize=768%2C527 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?resize=1536%2C1054 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-227690.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Argumentation in the Resolution of Passenger Problems<\/strong><br><meta charset=\"utf-8\">A negotiation protocol with arguments was introduced allowing humans to interact with software agents to achieve the best deal when facing a disruption. The argumentation process includes an argument structure, with claims and reasons, and reasoning behaviours, especially in the software agent (in this case representing the airline company interests). The software agent understands the human arguments and formulate new arguments to rebut the received ones, presenting counterproposals.<br><strong>Main Author<\/strong>: Jorge Filipe Monteiro Lima<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Multi-agent systems, Negotiation, Argumentation <br><strong>Technology &amp; Tools<\/strong>: Ionic2, Java, Jade, Jetty, Servlets, Rest API, MongoDB<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Chapter_5_Porto_methodology.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?resize=1024%2C683\" alt=\"female engineer planning dam\" class=\"wp-image-536\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?resize=1024%2C683 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?resize=768%2C512 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?resize=1536%2C1025 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-3862379.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>PORTO An Improvement to GAIA<\/strong><br>We propose an improvement to the <em>GAIA <\/em>methodology called <em>PORTO<\/em>. This methodology includes the following phases: requirements analysis, analysis, architectural and detail design, implementation, testing and validation. It is the result of its application to the analysis, design and development of the MASDIMA &#8211; Multi-Agent System for Disruption Management. We present each phase of the methodology using as an example the MASDIMA system. This will allow the reader to better understand the concepts together with the proposed methodology. <br><strong>Main Author<\/strong>: Antonio J. M. Castro<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Eug\u00e9nio Oliveira, Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Agent-oriented software engineering, Multi-agent systems<br><strong>Technology &amp; Tools<\/strong>: UML, AUML, GAIA, Porto<br><strong>Code Repository<\/strong>: TBA<br><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/travel-recommendation-reinforcement.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"684\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?resize=1024%2C684\" alt=\"photo of coconut trees on seashore\" class=\"wp-image-537\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?resize=1024%2C684 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?resize=768%2C513 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?resize=1536%2C1025 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-1591373.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Travel Recommendation using Reinforcement Learning<\/strong><br><meta charset=\"utf-8\">The goal was to recommend flights to passengers, based on a destination selected by the passenger and considering the airline interests, which was to fulfil empty seats in every flight. Seven reinforcement learning approaches were made and compared to three collaborative filtering methods (item-based, user-based and matrix factorisation) using real data from a European airline.<br><strong>Main Author<\/strong>: Pedro Miguel Herdeiro Vaz de Moura<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Recommendation, Machine learning (reinforcement learning), <br><strong>Technology &amp; Tools<\/strong>: Android studio, Python, R, Django, MySQL<br><strong>Code Repository<\/strong>: TBA<br><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Dissertation_Predictive_Analysis_of_Flight_Times.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?resize=1024%2C576\" alt=\"window view of airplane\" class=\"wp-image-538\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?resize=1024%2C576 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?resize=300%2C169 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?resize=768%2C432 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?resize=1536%2C864 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-615060.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Predictive Analysis of Flight Times<\/strong><br><meta charset=\"utf-8\">Prediction of the aircraft movement times, namely, taxi-in, taxi-out, airtime and block time. These predicted times are expected to produce more accurate data and are to be used in an OCC (operational control center) decision support system to replace the use of scheduled or OCC-estimated times. The models were trained on data from a European Airline air traffic activity from 2013 to 2018, along with scraped weather data retrieved from Iowa Environmental Mesonet. The data was analysed and refactored, along with some feature engineering. After extensive experiments, the most successful models were built, making use of a stack of linear estimators with gradient boosting as meta-estimator.<br><strong>Main Author<\/strong>: Afonso Manuel Maia Lopes Salgado de Sousa<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Machine learning (prediction), Regression <br><strong>Technology &amp; Tools<\/strong>: Python<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/ajmcastro\/flight-time-prediction.git\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/ajmcastro\/flight-time-prediction.git<\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Thesis_AntonioMoura_20150211.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?resize=1024%2C683\" alt=\"animal tree lizard reptile\" class=\"wp-image-539\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?resize=1024%2C683 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?resize=768%2C512 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?resize=1536%2C1025 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-8014464.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Evolutionary Computation Methods applied to Airline Operations<\/strong><br><meta charset=\"utf-8\">Particle Swarm Optimisation, Ant Colony Optimisation and Genetic Algorithms were studied and developed to solve the aircraft recovery problem when solving disruption in the AOCC. A comparison was made with two previously implemented algorithms, namely, Hill Climbing and Simulated Annealing. An Artificial Bee Colony (ABC) algorithm was also developed for the optimisation of the Flight Schedule problem when faced with disruptions.<br><strong>Main Author<\/strong>: Ant\u00f3nio Jos\u00e9 Ferreira de Castro Moura<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Evolutionary computing, Optimisation<br><strong>Technology &amp; Tools<\/strong>: Java<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/ABCforSolvingFlightDisruptionProblem-Tanja_CameraReady_95.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"684\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?resize=1024%2C684\" alt=\"bees producing honey on honeycombs in yard\" class=\"wp-image-582\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?resize=1024%2C684 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?resize=768%2C513 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?resize=1536%2C1025 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-5247988.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Artificial Bee Colony Algorithm for Solving the Flight Disruption Problem<\/strong><br>We developed an Artificial Bee Colony (ABC) optimisation algorithm, firstly introduced by in 2005 and proposed for optimising numerical problems. ABC is the swarm-based meta-heuristic algorithm inspired by intelligent behaviour of honey bee colonies. ABC has been applied on solving the flight disruption problem, by swapping aircraft and\/or cancelling\/delaying flights, and its performance has been shown through experimentation. The environment and data for experiments are provided by MASDIMA, Multi-Agent System for DIsruption Management.<br><strong>Main Author:<\/strong> Tanja \u0160ar\u010devi\u0107<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Evolutionary computing, Optimisation<br><strong>Technology &amp; Tools<\/strong>: Java, MySQL<br><strong>Code Repository<\/strong> (Open Source): <a href=\"https:\/\/github.com\/ajmcastro\/artificial-bee-colony-flight-schedule.git\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/ajmcastro\/artificial-bee-colony-flight-schedule.git<\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/paams-2018-aaat_camera_ready_81.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"682\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?resize=1024%2C682\" alt=\"turned on monitor displaying frequency graph\" class=\"wp-image-540\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?resize=1024%2C682 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?resize=768%2C512 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?resize=1536%2C1024 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/pexels-photo-241544.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><meta charset=\"utf-8\"><strong>Crew and Aircraft Electronic Market<\/strong><br><meta charset=\"utf-8\">An electronic market modelled as a multi-agent system where airlines can negotiate and lease each other the required resources when solving disruption problems. The negotiation occurs in several rounds, where qualitative comments made by the buyer agent on proposals sent by the sellers enables these to learn how to calculate new proposals using a case-based reasoning (CBR) methodology.<br><meta charset=\"utf-8\"><strong>Main Author<\/strong>: Luis Brochado Pinto dos Reis<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ana Paula Rocha and Ant\u00f3nio J. M. Castro<br><strong>Research Topics<\/strong>: Electronic markets, Multi-agent systems, Negotiation, Machine learning (Case-based reasoning)<br><strong>Technology &amp; Tools<\/strong>: Java, MySQL<br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"http:\/\/ajcastro.com.pt\/home\/wp-content\/uploads\/2021\/08\/Chapter06_StudyingImpactOrganisationalStructure.pdf\" target=\"_blank\" rel=\"noopener\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"682\" src=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?resize=1024%2C682\" alt=\"close up photography of yellow green red and brown plastic cones on white lined surface\" class=\"wp-image-596\" srcset=\"https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?resize=1024%2C682 1024w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?resize=300%2C200 300w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?resize=768%2C512 768w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?resize=1536%2C1024 1536w, https:\/\/i0.wp.com\/ajcastro.com.pt\/home\/wp-content\/uploads\/2015\/07\/play-stone-network-networked-interactive-163064.jpeg?w=1880 1880w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Impact of the Organisational Structure on Operations Management<\/strong><br><meta charset=\"utf-8\">We have done the first simulation of a real airline operations control scenario, involving human actors, existing computerised systems, time and spatial location, operational activities and reasoning processes. Besides the faithful modelling of airline entities, it used pre- and post-real operational data to better reproduce workflow inception and disruption handling. Along with operational performance assessment, the conducted research also delved into the decision-making practices of the Airline Operational Control Centre specialists, performing a comparison between empirical probabilistic action and tangible solutions obtained through operational records analysis. The usage of learning techniques was demonstrated as a mean to optimize the reasoning accuracy within the simulation. In terms of tools, Brahms (BDI agents), a human-centered multi-agent environment was used to implement and simulate the conceptual representation of the airline operational entities.<br><meta charset=\"utf-8\"><strong>Main Author<\/strong>: Nuno Machado<br><meta charset=\"utf-8\"><strong>Supervisors<\/strong>: Ant\u00f3nio J. M. Castro and Eug\u00e9nio Oliveira<br><strong>Research Topics<\/strong>: Organisation structure, Multi-agent systems, Simulation, BDI agents<br><strong>Technology &amp; Tools<\/strong>: HTML5, Brahms, Java, Netty, <br><strong>Code Repository<\/strong>: TBA<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>I am a pro bono&nbsp;Artificial Intelligence Researcher at&nbsp;LIACC (Artificial Intelligence and Computer Science Laboratory), University of Porto in Portugal. I perform research on the following topics: I like to share knowledge and help others to achieve their potential. That is the reason why most of my research is done while supervising students in their thesis [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":281,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-7","page","type-page","status-publish","has-post-thumbnail","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.1.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"I am a pro bono Artificial Intelligence Researcher at LIACC (Artificial Intelligence and Computer Science Laboratory), University of Porto in Portugal. 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