INVESTIGATING AGENT AND TASK OPENNESS IN ADHOC TEAM FORMATION

When deciding which ad hoc team to join, agents are often required to consider rewards from accomplishing tasks as well as potential benefits from learning when working with others, when solving tasks. We argue that, in order to decide when to learn or when to solve task, agents have to consider the existing agents’ capabilities and tasks available in the environment, and thus agents have to consider agent and task openness—the rate of new, previously unknown agents (and tasks) that are introduced into the environment. We further assume that agents evolve their capabilities intrinsically through learning by observation or learning by doing when working in a team. Thus, an agent will need to consider which task to do or which team to join would provide the best situation for such learning to occur. In this thesis, we develop an auction-based multiagent simulation framework, a mechanism to simulate openness in our environment, and conduct comprehensive experiments to investigate the impact of agent and task openness. We propose several agent task selection strategies to leverage the environmental openness. Furthermore, we present a multiagent solution for agent-based collaborative human task assignment when finding suitable tasks for users in complex environments is made especially challenging by agent openness and task openness. Using an auction-based protocol to fairly assign tasks, software agents model uncertainty in the outcomes of bids caused by openness, then acquire tasks for people that maximize both the user’s utility gain and learning opportunities for human users (who improve their abilities to accomplish future tasks through learning by experience and by observing more capable humans). Experimental results demonstrate the effects of agent and task openness on collaborative task assignment, the benefits of reasoning about openness, and the value of non-myopically choosing tasks to help people improve their abilities for uncertain future tasks

Lessons Learned from Comprehensive Deployments of Multiagent CSCL Applications I-MINDS and ClassroomWiki

Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, design the relevant pedagogical aspects (i.e., the learning objectives, assessment method, etc.) to overcome the associated challenges (e.g., free riding, student assessment, forming student groups that improve student learning, etc). We have used a multiagent-based system to develop a CSCL application and multiagent frameworks to form student groups that improve student collaborative learning. In this paper, we describe the contexts of our three generations of CSCL applications (i.e., I-MINDS and Classroom Wiki) and provide a set of lessons learned from our deployments in terms of the script, tool, and pedagogical aspects of using CSCL. We believe that our lessons would allow 1) the instructors and students to use intelligent CSCL applications more effectively and efficiently, and help to improve the design of such systems, and 2) the researchers to gain additional insights into the impact of collaborative learning theories when they are applied to real-world classrooms

ArqMAEC: um modelo arquitetural baseado em Agentes para monitorar, avaliar e estimular a colaboração em ambientes educacionais gamificados

Orientador : Prof. Dr. Andrey Ricardo PimentelTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 25/08/2016Inclui referências : f. 243-263Resumo: A Aprendizagem Colaborativa tem sido estudada e apresentada a comunidade acadêmica como um grande sucesso para o ensino-aprendizagem, porém são poucas as ferramentas automatizadas para monitorar, avaliar e estimular a colaboração entre os estudantes, principalmente em áreas onde os estudantes colaboram brincando, como os jogos educacionais colaborativos. Metodologias e ferramentas de pesquisa foram desenvolvidas, todavia o problema ainda continua. Este projeto de pesquisa tem como objetivo apresentar um modelo de arquitetura de sistema multiagente para ser incorporada a softwares educacionais colaborativos gamificados que monitore, avalie e estimule a colaboração realizada pelos participantes. Foi realizada uma pesquisa bibliográfica sobre os conceitos de Aprendizagem Colaborativa, Agentes inteligentes e Jogos colaborativos aplicados simultaneamente. Desenvolveu-se um modelo arquitetural de sistemas multiagentes para ser incorporado a softwares educacionais colaborativos gamificados, e descritos cenários para avaliação através de inspeção com especialistas. Na avaliação com especialistas em Educação à Distancia o modelo foi aprovado para monitorar, avaliar e estimular comportamentos colaborativos. Espera-se que com esta arquitetura possa-se desenvolver softwares capazes de monitorar o processo de colaboração a fim de avaliar os participantes e estimular a sua colaboração. Palavras-chave: Monitoramento, avaliação e estimulação do processo de colaboração, Sistemas multiagentes, ambientes de colaboração gamificados.Abstract: Collaborative Learning has been studied and presented to the academic community as a great success for teaching-learning, but there are few automated tools to monitor, assess, and stimullating collaboration among students, especially in areas where students collaborate by playing, such as Collaborative educational games. Methodologies and research tools have been developed, but the problem still continues. This research project aims to present a multi-agent system architecture model to be incorporated into gami_ed collaborative educational software that monitors, assesses and stimulates the collaboration performed by the participants. A bibliographic research was carried out on the concepts of Collaborative Learning, Intelligent Agents and Collaborative Games applied simultaneously. An architectural model of multi-agent systems was developed to be incorporated into gamfied collaborative educational software, and scenarios were described for evaluation through expert inspection. In the evaluation with specialists in Distance Education the model was approved to monitor, assess and stimulate collaborative behaviours. It is expected that with this architecture you can develop software capable of monitoring the collaboration process in order to assess the participants and stimulate their collaboration. Key words: Monitoring, assessment and stimulation of the collaboration process, Multi-agent systems, gamified collaborative environments