Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities

TAIP: an anytime algorithm for allocating student teams to internship programs

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process. Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.Comment: 10 pages, 7 figure

An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08% accuracy

Combining Optimization and Machine Learning for the Formation of Collectives

This thesis considers the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility and cooperative learning). Such problems require fast approaches that can produce solutions of high quality for hundreds of agents. With this goal in mind, existing solutions for the formation of collectives focus on enhancing the optimization approach by exploiting the characteristics of a domain. However, the resulting approaches rely on specific domain knowledge and are not transferable to other collective formation problems. Therefore, approaches that can be applied to various problems need to be studied in order to obtain general approaches that do not require prior knowledge of the domain. Along these lines, this thesis proposes a general approach for the formation of collectives based on a novel combination of machine learning and an \emph{Integer Linear Program}. More precisely, a machine learning component is trained to generate a set of promising collectives that are likely to be part of a solution. Then, such collectives and their corresponding utility values are introduced into an \emph{Integer Linear Program} which finds a solution to the collective formation problem. In that way, the machine learning component learns the structure shared by ``good'' collectives in a particular domain, making the whole approach valid for various applications. In addition, the empirical analysis conducted on two real-world domains (i.e., ridesharing and team formation) shows that the proposed approach provides solutions of comparable quality to state-of-the-art approaches specific to each domain. Finally, this thesis also shows that the proposed approach can be extended to problems that combine the formation of collectives with other optimization objectives. Thus, this thesis proposes an extension of the collective formation approach for assigning pickup and delivery locations to robots in a warehouse environment. The experimental evaluation shows that, although it is possible to use the collective formation approach for that purpose, several improvements are required to compete with state-of-the-art approaches. Overall, this thesis aims to demonstrate that machine learning can be successfully intertwined with classical optimization approaches for the formation of collectives by learning the structure of a domain, reducing the need for ad-hoc algorithms devised for a specific application

Evaluar con juegos: herramientas y métodos para una evaluación diversificada en la ludificación

En este trabajo se presentan algunas herramientas y consideraciones metodológicas sobre la evaluación mediante actividades y herramientas ludificadas. Más específicamente, se defenderá la mezcla de dinámicas y diferentes elementos propios de la ludificación para lograr así una mayor atención a la diversidad en la evaluación. A tal efecto, se expone a modo de ejemplo cómo lograr una evaluación completa del alumnado mediante el empleo combinado de los sistemas de respuesta a cuestionarios (Kahoot!, Socrative,...), el aprendizaje basado en retos, juegos colaborativos, presentaciones orales y pequeños proyectos, que utilicen sistemas de puntuación, insignias y otros sistemas de reconocimiento que, de forma sencilla, se puedan trasladar a las valoraciones oficiales de las asignaturas y permitan a los docentes una evaluación diversificada de las competencias del alumnado, yendo más allá de las meras calificaciones y de los exámenes tradicionales que también pueden formar parte de una evaluación ludificada.Peer ReviewedPostprint (published version

Desenvolupament dels mòduls MP6 i MP7 del CFGS DAW amb metodologies ABP i SCRUM

Les metodologies àgils són cada vegada més populars en les empreses de desenvolupament de web i software. Per poder preparar l'alumnat de formació professional per aquestes metodologies emprades en el món laboral, és important incorporar-les al currículum. Metodologies actives com l'aprenentatge basat en projectes (ABP) han obtingut bons resultats però a vegades es veuen afectades per dinàmiques de grup o males implementacions. L'objectiu d'aquest treball és fer una proposta d'implementació de l'ABP en dos mòduls del cicle de grau superior de desenvolupament d'aplicacions web amb la metodologia àgil Scrum. Amb aquesta proposta connectem dos mòduls que es donen de forma independent quan són complementaris i intentem millorar l'adquisició d'aprenentatges per part de l'alumnat, a més d'enriquir les seves competències personals, socials i professionals

Disseny i adaptació del CFGS d'ASIX per orientar les seves AEA a especialitzar-se en Cloud Computing treballant amb Azure i AWS

En aquest treball es detecta la necessitat del disseny i implantació de noves activitats docents per incorporar el Cloud Computing als Cicles Formatius d'Informàtica. La incorporació dels nous continguts dirigits al Cloud es fa a través d'unes activitats PBL (Project Based Learning) que simula una situació real en un entorn professional. A més es detecta que les característiques del projecte fan que s'integri fàcilment en un entorn d'aprenentatge en llengua estrangera i s'adapta el projecte per treballar-lo utilitzant la metodologia AICLE

Visual Analytics of set data by generative modeling

本論文は集合データのVisual Analytics (VA)を実現する汎用的方法論を提案するものである．VAとは，人間とデータ分析システムが視覚的インタラクションを介してデータ分析・仮説検証・意思決定を行う過程を指す．すなわちVAはデータ駆動型意思決定における人間主導的アプローチであり，全自動化型のAI主導的アプローチの対極としてその重要性が高まっている．本論文はVAの中でも未開拓な領域である集合データのVAシステムを対象とする．集合データの典型例はビジネスやスポーツにおけるチームデータであり，既存チームの分析と新規チームのメンバー選択支援がVAシステムの主な用途となる．本研究の目的は，このような集合データVAシステムを実現する汎用的な方法論を確立することである．本論文の主な貢献は次の通りである．(1) 集合データVAシステムが満たすべき要件を明確化し，要件を満たす上で克服すべき困難点を明らかにした．(2) 多様体上の確率分布表現を用いた集合データのモデル化法を実現した．これにより既存チームの分析と新規チームの生成・予測が可能となった．(3) さまざまなデータ構造に適応可能な多様体ネットワークモデルを提案した．(4) ユーザの関心標的の指示により駆動されるインタラクティブな視覚的インタフェースを実現した．これにより複数要因が絡まったデータに対する対話的分析が可能になった．(1)(2)により集合データVAの方法論が確立され，(3)(4)により汎用的VAシステムの構築法が確立された．これらはVAの分野において今まで達成されなかった成果である．本論文の構成は次の通りである．第一章では序論として人間主導的データ駆動アプローチとしてのVAについて概説し，さらに集合データVAの必要性と課題，提案手法のキーアイディアと本論文の貢献について述べる．第二章では本研究の背景と関連研究について三つの観点から述べる．第一はVAの観点であり，VAの枠組みや現在までの研究動向について詳しく解説する．第二は機械学習の観点であり，集合データを扱う機械学習の困難点や既存のアプローチ，および最近の研究動向について解説する．とりわけ本研究と関わりの深い集合データの生成モデルについて詳しく述べる．第三はデータ駆動型のチーム編成支援の観点であり，既存の研究群を自動化型・情報提示型・VA型の三種に分類して概説する．第三章では集合データVAに求められる要件を定義する．集合データVAという概念自体が本研究独自のものであるため，その概念と要件を定義し，さらに実現上の技術的課題を明らかにする．具体的には集合データVAのシステムが満たすべき四つの要件と，システムの汎用的構築手法が満たすべき一つの要件を定義する．第四章では集合データVAシステムの構築法を提案する．まず多様体上の確率分布表現を用いた集合データのモデリング法を述べ，次に複雑なデータ構造に適応可能な多様体ネットワークモデリングへの拡張を述べる．さらに対話的な可視化を実現する視覚的インタフェースの構築法についても述べる．第五章は提案手法のデモンストレーションである．提案手法を用いてバスケットボールチームのVAシステムを構築し，過去のメンバー構成とゲーム成績のデータ分析や，新規ゲームにおけるメンバー選択支援などをデモンストレーションする．また比較手法となるVAシステムを構築し，提案システムが十分な能力を持つことを実証する．第六章は議論である．第三章で定義した要件の妥当性の検証や，提案手法のさらなる拡張についての検討を行う．また提案手法のデータモデリングの枠組みと，既存の機械学習のパラダイムとの関連についても述べている．第七章は総括として本論文をまとめる．以上，本論文では集合データVAシステムの実現を提案するとともに，汎用的なVAシステムの構築方法を提案した．本研究の意義は単一用途のVAシステムを開発したことではなく，集合データを含むさまざまなデータに適応可能な汎用的VAシステムの構築法を実現したことにある．これは用途特化型のVAシステム開発が多いVA研究領域においては稀有な試みといえる．すなわち本研究は人間主導型データ駆動アプローチの新たな基盤構築をめざしたものである．九州工業大学博士学位論文 学位記番号: 生工博甲第439号 学位授与年月日: 令和4年3月25日第1章 序論|第2章 背景と関連研究|第3章 集合データVAが満たすべき要件|第4章 生成的多様体ネットワークモデリング|第5章 デモンストレーション|第6章 議論|第7章 総括九州工業大学令和3年

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe