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

Artificial intelligence methods to support people management in organisations

Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Autónoma de Barcelona--07-05-2018-Excelente cum laudemOrganisations have shifted from work arranged around individual jobs to teambased work structures. A new generation of solutions for organisations must give support to team management by encouraging team effectiveness and introducing automation. In this dissertation, we tackle several different problems that are connected to team management in organisations. In particular, we contribute by proposing a people management workflow that addresses the problems connected to team composition as well as problems of accurate employee evaluation and task performance evaluation. First, we review the literature on team composition and formation from both the organisational psychology and computer science perspectives and we explore the connection between individuals’ attributes and team performance as well as the cross fertilization opportunities between those fields. Second, we review the most prominent tools to measure individuals’ attributes, as these measures are necessary inputs for team composition processes. In particular, we describe the dominant approaches in Organisational Psychology, Industrial Psychology and Human Resources and summarise they main findings to measure individual personality and competences. Third, we use our findings to propose a model to predict team performance given a task and based on individuals’ attributes (i.e. competences, personality and gender). We define the Synergistic Team Composition Problem (STCP) as the problem of finding a team partition constrained by size so that each team, and the whole partition of employees into teams, is balanced in terms of individuals’ competences, personality and gender. We propose two different algorithms to solve this problem: an optimal algorithm called STCPSolver that is effective for small instances of the problem, and an approximate algorithm called SynTeam that provides high-quality, but not necessarily optimal solutions. We present empirical results that we obtained when analysing student performance. Our results show the benefits of a more informed team composition that exploits individuals’ competences, personalities and gender. Fourth, we devise an algorithm called Collaborative Judgment (CJ) to fairly evaluate individuals’ and teams’ outcomes once tasks are performed. In particular, we want to diminish the importance of biases in the evaluation process by allowing evaluators to assess their peers, namely other evalutors. Our empirical results show the benefits of more informed assessment aggregation method.Peer reviewe

Arti cial intelligence methods to support people management in organisations

Las organizaciones han pasado del trabajo focalizado en individuos a estructuras de trabajo basadas en equipos. Una nueva generación de soluciones para las organizaciones debe proporcionar la gestión de equipos, fomentando la eficacia del equipo mediante la automatización. En esta tesis abordamos varios problemas centrales para la gestión de equipos en organizaciones. En particular, proponemos una solución que aborda los problemas relacionados con la composición del equipo, así como los problemas de evaluación de los empleados y la evaluación del rendimiento de las tareas. En primer lugar, revisamos la literatura sobre la composición y formación de equipos, tanto desde la perspectiva de la psicología organizacional como de las ciencias de la computación. Exploramos la conexión entre los atributos de los individuos y el rendimiento del equipo, así como las oportunidades de intercambio de ideas entre estos dos campos. En segundo lugar, revisamos las herramientas más destacadas para medir los atributos de los individuos, ya que estas medidas son necesarias para los procesos de composición de equipos. En particular, describimos los enfoques dominantes en Psicología Organizacional, Psicología Industrial y Recursos Humanos, resumiendo los principales hallazgos para medir la personalidad y las competencias de los individuos. En tercer lugar, utilizamos nuestros observaciones para proponer un modelo que predice el rendimiento de un equipo dada una tarea y en función de los atributos de los individuos (competencias, personalidad y género). Definimos el Synergistic Team Composition Problem (STCP) como el problema de encontrar una partición de equipos restringida por tamaño para que cada equipo, y toda la partición de empleados en equipos, sea equilibrada en términos de competencias individuales, personalidad y género. Proponemos dos algoritmos diferentes para resolver este problema: un algoritmo óptimo llamado STCPSolver que es efectivo para pequeñas instancias del problema, y ​​un algoritmo aproximado llamado SynTeam, que proporciona soluciones de alta calidad, pero no necesariamente óptimas. A continuación, presentamos los resultados empíricos que obtuvimos al analizar el rendimiento de nuestros algoritmos en un ámbito educativo. Nuestros resultados muestran los beneficios de una composición de equipos más informada utilizando las competencias, las personalidades y el género de las personas. En cuarto lugar, diseñamos un algoritmo llamado Collaborative Judgment (CJ) para evaluar de un modo justo los resultados de los individuos y el rendimiento de los equipos que han realizado las tareas. En particular, el objetivo del algoritmo es disminuir la importancia de los sesgos en el proceso de evaluación a través de la evaluación entre los propios evaluadores. Nuestros resultados empíricos muestran los beneficios de un método de agregación de evaluaciones más informado.Organisations have shifted from work arranged around individual jobs to team-based work structures. A new generation of solutions for organisations must give support to team management by encouraging team effectiveness and introducing automation. In this dissertation, we tackle several different problems that are connected to team management in organisations. In particular, we contribute by proposing a people management workflow that addresses the problems connected to team composition as well as problems of accurate employee evaluation and task performance evaluation. First, we review the literature on team composition and formation from both the organisational psychology and computer science perspectives and we explore the connection between individuals’ attributes and team performance as well as the cross fertilization opportunities between those fields. Second, we review the most prominent tools to measure individuals' attributes, as these measures are necessary inputs for team composition processes. In particular, we describe the dominant approaches in Organisational Psychology, Industrial Psychology and Human Resources and summarise they main findings to measure individual personality and competences. Third, we use our findings to propose a model to predict team performance given a task and based on individuals' attributes (i.e. competences, personality and gender). We define the Synergistic Team Composition Problem (STCP) as the problem of finding a team partition constrained by size so that each team, and the whole partition of employees into teams, is balanced in terms of individuals' competences, personality and gender. We propose two different algorithms to solve this problem: an optimal algorithm called STCPSolver that is effective for small instances of the problem, and an approximate algorithm called SynTeam that provides high-quality, but not necessarily optimal solutions. We present empirical results that we obtained when analysing student performance. Our results show the benefits of a more informed team composition that exploits individuals' competences, personalities and gender. Fourth, we devise an algorithm called Collaborative Judgment (CJ) to fairly evaluate individuals' and teams' outcomes once tasks are performed. In particular, we want to diminish the importance of biases in the evaluation process by allowing evaluators to assess their peers, namely other evalutors. Our empirical results show the benefits of more informed assessment aggregation method

Collaborative assessments in on-line classrooms

With massive open on-line courses (MOOCs) gaining momentum, it is now common for thousands of students to enrol in a course, making manual assessments by teachers simply unfeasible. Peer assessments is one way to go when auto-scoring approaches are not possible. Current on-line courses usually use a simple aggregation of peer assessments, but these suffer from two main pitfalls. First, simple aggregation does not take into consideration the reliability of a peer assessment. Second, simple aggregation calculates what the students think of an assignment as opposed to what the teacher thinks of it (the far more important opinion). This work proposes two different models to address these two different pitfalls. These models lay the foundation for future work, where we intend to combine both models into a single one that addresses both pitfalls at once. The aim is to build an automated assessment system that results from the collaboration of both students and teachers.The second author is supported by an Industrial PhD scholarship from the Generalitat de Catalunya. Furthermore, this work is supported by the Gencat 2014 SGR 118 project (funded by the Generalitat de Catalunya), and the Collective-Mind and Collectiveware projects (funded by the Spanish Ministry of Economy and Competitiveness, under grant numbers TEC2013-49430-EXP and TIN2015-66863-C2-1-R, respectively).Peer Reviewe

Collaborative Rankings

In this paper we introduce a new ranking algorithm, called Collaborative Judgement (CJ), that takes into account peer opinions of agents and/or humans on objects (e.g. products, exams, papers) as well as peer judgements over those opinions. The combination of these two types of information has not been studied in previous work in order to produce object rankings. Here we apply Collaborative Judgement to the use case of scientific paper assessment and we validate it over simulated data. The results show that the rankings produced by our algorithm improve current scientific paper ranking practice, which is based on averages of opinions weighted by their reviewers' self-assessments.Peer reviewe

Collaborative Judgement

In this paper we introduce a new ranking algorithm, called Collaborative Judgement (CJ), that takes into account peer opinions of agents and/or humans on objects (e.g. products, exams, papers) as well as peer judgements over those opinions. The combination of these two types of information has not been studied in previous work in order to produce object rankings. We apply CJ to the use case of scientific paper assessment and we validate it over simulated data. The results show that the rankings produced by our algorithm improve current scientific paper ranking practice based on averages of opinions weighted by their reviewers’ self-assessments. © Springer International Publishing Switzerland 2015.The first author is supported by an Industrial PhD scholarship from the Generalitat de Catalunya. This work is also supported by the CollectiveMind project (Spanish Ministry of Economy and Competitiveness, grant number TEC2013- 49430-EXP) and the COR project (TIN2012-38876-C02-01 )Peer Reviewe

A concise review on multi-agent teams: contributions and research opportunities

[EN]The composition and formation of effective teams is crucial for both companies, to assure their competitiveness, and for a broad range of emerging applications exploiting multiagent collaboration (e.g. human-agent teamwork, crowdsourcing). The aim of this article is to provide an integrative perspective on team composition, team formation and their relationship with team performance. Thus, we review and classify the contributions in the computer science literature dealing with these topics. Our purpose is twofold. First, we intend to identify the strengths and weaknesses of the contributions made so far. Second, we pursue to identify research gaps and opportunities. Given the volume of the existing literature, our review is not intended to be exhaustive. Instead, we focus on the most recent contributions that broke new ground to spur innovative researchWork supported by Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER), CollectiveMind (MINECO TEC2013-49430-EXP), SMA (201550E040), and Gencat 2014 SGR 118. Ewa Andrejczuk is supported by an Industrial PhD scholarship from the Generalitat de Catalunya.Peer reviewe

Optimising Congenial Teams

[EN] 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. One of the key factors influencing team performance is the personality of team members. In this paper, we introduce a new algorithm to partition a group of individuals into gender and psychologically-balanced problem-solving teams. With this purpose, we got inspiration from Wildes post-Jungian theory for team formation. Personality traits of people are obtained through a quantitative transformation of the Myers-Briggs Type Indicator (MBTI). The algorithm uses a greedy technique to balance the psychological traits of the members of teams so that each team gets the full range of problem-solving capabilities. Finally, we present some preliminary empirical results comparing the quality of the teams obtained by our algorithm and those proposed by a teacher.This work is supported by the CollectiveMind project (funded by the Spanish Ministry of Economy and Competitiveness, under grant number TEC2013- 49430- EXP) and the Collectiveware project (TIN2015-66863-C2-1-R). The first author is supported by an Industrial PhD scholarship from the Generalitat de Catalunya. We thank Rosa Maria Duran, tutor of group ‘3r B’, and Júlia Andrés, tutor of group ‘2n A’, for their collaboration in the evaluation of the method.Peer reviewe

AATEAM : achieving the ad hoc teamwork by employing the attention mechanism

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates’ behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates’ behaviour in real-time. We train one attention network per teammate type. The attention networks learn both to extract the temporal correlations from the sequence of states (i.e. contexts) and the mapping from contexts to actions. Each attention network also learns to predict a future state given the current context and its output action. The prediction accuracies help to determine which actions the ad hoc agent should take. We perform extensive experiments to show the effectiveness of our method.Accepted versio

ATSIS : achieving the ad hoc teamwork by sub-task inference and selection

In an ad hoc teamwork setting, the team needs to coordinate their activities to perform a task without prior agreement on how to achieve it. The ad hoc agent cannot communicate with its teammates but it can observe their behaviour and plan accordingly. To do so, the existing approaches rely on the teammates' behaviour models. However, the models may not be accurate, which can compromise teamwork. For this reason, we present Ad Hoc Teamwork by Sub-task Inference and Selection (ATSIS) algorithm that uses a sub-task inference without relying on teammates' models. First, the ad hoc agent observes its teammates to infer which sub-tasks they are handling. Based on that, it selects its own sub-task using a partially observable Markov decision process that handles the uncertainty of the sub-task inference. Last, the ad hoc agent uses the Monte Carlo tree search to find the set of actions to perform the sub-task. Our experiments show the benefits of ATSIS for robust teamwork.NRF (Natl Research Foundation, S’pore)Accepted versio