Vermont interdependent services team approach: A guide to coordinating educational support services

Vermont interdependent services team approach: A guide to coordinating educational support services (VISTA) is a guide to coordinating educationally related services (e.g., physical therapy, occupational therapy, speech-language pathology) for students with disabilities in inclusive schools

Multiagent Learning Through Indirect Encoding

Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding

Influence of teamwork aptitude and personal characteristics of team members on team effectiveness: How should we form effective teams?

This study examines the factors that are important for team success by exploring the best possible criteria for selecting members for teamwork. Two models of team composition were proposed, productivty and synergy. The findings are discussed for their implications for team satisfaction and productivity

Human and modeling approaches for humanitarian transportation planning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.Recent disasters have highlighted the need for more effective supply chain management during emergency response. Planning and prioritizing the use of trucks and helicopters to transport humanitarian aid to affected communities is a key logistics challenge. This dissertation explores ways to improve humanitarian transportation planning by building on the strengths of both humans and models. The changing, urgent, multi-objective context of humanitarian aid makes it challenging to formulate and deploy useful planning models. Humans are better able to understand the context, but struggle with the complexity of the problem. This research investigates the strengths and weaknesses of human transportation planners in comparison with models, with the goal of supporting both- better human decision-making and better models for humanitarian transportation planning. Chapter 2 investigates how experienced humanitarian logisticians build transportation plans in a simulated emergency response. Based on an ethnographic study of ten logistics response teams, I show how humans come to understand the problem and its objectives through sensemaking, and solve it through a search-like series of decisions guided by goal-oriented decision rules. I find that the definition of objectives is an important strength of the sensemaking process, and that the human reliance on greedy search may be a weakness of human problem-solving. Chapter 3 defines a performance measure for humanitarian transportation plans, by measuring the importance of the objectives identified in the ethnographic study. I use a conjoint analysis survey of expert humanitarian logisticians to quantify the importance of each objective and develop a utility function to value the performance of aid delivery plans. The results show that the amount of cargo delivered is the most important objective and cost the least; experts prefer to prioritize vulnerable communities and critical commodities, but not to the exclusion of others. Chapter 4 investigates the performance of human decision-making approaches in comparison to optimization models. The human decision-making processes found in Chapter 2 are modeled as heuristic algorithms and compared to a mixed-integer linear program. Results show that optimization models create better transportation plans, but that human decision processes could be nearly as effective if implemented consistently with the right decision rules.by Erica L. Gralla.Ph.D