Theories and Models of Teams and Groups

This article describes some of the theoretical approaches used by social scientists as well as those used by computer scientists to study the team and group phenomena. The purpose of this article is to identify ways in which these different fields can share and develop theoretical models and theoretical approaches, in an effort to gain a better understanding and further develop team and group research

Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

Complex Systems in Engineering and Technology Education: A Mixed Methods Study Investigating the Role Computer Simulations Serve in Student Learning

This research was conducted to determine if students receiving complex systems instruction in the form of software simulations recognize patterns and underlying elements of complex systems more effectively than students receiving traditional instruction. Complex systems were investigated with an analytic (reductive) approach in a control group and with a synthesis approach in the treatment group. Exploration of this top-down approach to learning complex systems counters traditional bottom-up methodologies, investigating systems and subsystems at the component level. The hypothesis was that students experiencing complex systems scenarios in a computer-based learning environment would outperform their counterparts by constructing a greater number of explanations with emergent-like responses. A mixed method experimental, pretest posttest, control group triangulation design research study was designed for high school students enrolled in an Introduction to Technology and Engineering course. A pretest consisting of one open-ended near transfer problem and one far transfer problem was administered, investigating the generation of reductive (clockwork) and complex (emergent-like) mental models. A stratified sampling procedure was used to assign students to control or treatment groups. Following treatment, an analysis of covariance failed to reveal statistically significant evidence supporting the hypothesis. However, qualitative data in the form of student transcriptions, daily lab reports, and data entry worksheets revealed evidence of emergent-like response and behaviors

3D Sensing Character Simulation using Game Engine Physics

Creating visual 3D sensing characters that interact with AI peers and the virtual envi- ronment can be a difficult task for those with less experience in using learning algorithms or creating visual environments to execute an agent-based simulation. In this thesis, the use of game engines was studied as a tool to create and execute vi- sual simulations with 3D sensing characters, and train game ready bots. The idea was to make use of the game engine’s available tools to create highly visual simulations without requiring much knowledge in modeling or animation, as well as integrating exterior agent simulation libraries to create sensing characters without needing expertise in learning algorithms. These sensing characters, were be 3D humanoid characters that can perform the basic functions of a game character such as moving, jumping, and interacting, but also have simulated different senses in them. The senses that these characters can have include: touch using collision detection, vision using ray casts, directional sound, smell, and other imaginable senses. These senses are obtained using different game develop- ment techniques available in the game engine and can be used as input for the learning algorithm to help the character learn. This allows the simulation of agents using off-the- shelf algorithms and using the game engine’s motor for the visualizations of these agents. We explored the use of these tools to create visual bots for games, and teach them how to play the game until they reach a level where they can serve as adversaries for real-life players in interactive games. This solution was tested using both reinforcement learning and imitation learning algorithms in an attempt to compare how efficient both learning methods can be when used to teach sensing game bots in different game scenarios. These scenarios varied in both objective and environment complexity as well as the number of bots to access how each solution behaves in different scenarios. In this document is presented a related work on the agent simulation and game engine areas, followed by a more detailed solution and its implementation ending with practical tests and its results.Criar visualizações de personagens 3D com sentidos que interagem com colegas de IA e com o ambiente virtual pode ser uma tarefa difícil para programadores com menos experiência no uso de algoritmos de aprendizagem automática ou na criação de ambientes visuais para executar simulações baseadas em agentes. Nesta tese foi estudado o uso de motores de jogos como ferramenta para criar e execu- tar simulações visuais com personagens 3D, e treinar bots para jogos. A ideia foi usar as ferramentas disponíveis do motor de jogos para criar simulações visuais sem exigir muito conhecimento em modelação ou animação, para além de integrar bibliotecas de simulação de agentes externas para criar personagens com sentidos sem precisar de conhecimentos em algoritmos de aprendizagem automática. Estas personagens 3D são humanoides que podem desempenhar as funções básicas de uma personagem de um jogo como mover, saltar e interagir, mas também terão simulados neles diferentes sentidos. Os sentidos que estas personagens podem ter inclui: o tato, colisões, visão, som direcional, olfato e outros sentidos imagináveis. Estes sentidos são obtidos usando diferentes técnicas de desenvol- vimento de jogos disponíveis no motor de jogos, e podem ser usados como inputs para os algoritmos de aprendizagem automática para ajudar as personagens a aprender. Esta solução foi testada usando algoritmos de Reinforcement Learning e Imitation Le- arning, com o intuito de comparar a eficiência de ambos os métodos de aprendizagem quando usados para ensinar bots de jogos em diferentes cenários. Estes cenários variaram em complexidade de objetivo e ambiente, e também no número de bots para que se possa visualizar como cada algoritmo se comporta em diferentes cenários. Neste documento será apresentado um estado da arte nas áreas de simulação de agentes e motores de jogos, seguido de uma proposta de solução mais detalhada para este problema

Using Synthetic Worlds for Work and Learning

Synthetic worlds [Castronova 2005] are graphically-rich, three-dimensional (3D), electronic environments where members assume an embodied persona (i.e., avatars) and engage in socializing, competitive quests, and economic transactions with globally distributed others. Frequently categorized as technologies of play, synthetic worlds range from massively multiplayer online games (MMOGs) such as World of Warcraft, to virtual reality environments such as Second Life. Increasingly, educators, researchers and corporations are recognizing these 3D online spaces as legitimate communication media, thereby blurring the lines between work and play, and between reality and virtuality. In this panel, presented at the 2007 International Conference on Information Systems, we explore how the fluid work-play and reality-virtuality boundaries are negotiated and managed in practice. The panelists will rely on their research, conducted in educational, corporate and game environments, to address questions about learning, working and playing in these new media spaces