Semantic Modeling for Group Formation

Group formation has always been a subject of interest in collaborative learning research. As it is concerned with assigning learners to the groups that maximize their benefits, computer-supported group formation can be viewed in this context as an active personalization for the individual as an entity within the group. While applying this personalization to all students in the class can cause conflicts due to the differences of needs and interests between the individuals, negotiating the allocations to groups to reach consensus can be a very challenging task. The automated process of grouping students while preserving the individual’s personalization needs to be supported by an appropriate learner model. In this paper, we propose a semantic learner model based on the Friend of Friend (FOAF) ontology, a vocabulary for mapping social networks. We discuss the model as we analyse the different types of groups and the learners’ features that need to be modeled for each of these types

Multilayered skill learning and movement coordination for autonomous robotic agents

With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent in both industrial and domestic settings. An increase in the number of robots, and the likely subsequent decrease in the ratio of people currently trained to directly control the robots, engenders a need for robots to be able to act autonomously. Larger numbers of robots present together provide new challenges and opportunities for developing complex autonomous robot behaviors capable of multirobot collaboration and coordination. The focus of this thesis is twofold. The first part explores applying machine learning techniques to teach simulated humanoid robots skills such as how to move or walk and manipulate objects in their environment. Learning is performed using reinforcement learning policy search methods, and layered learning methodologies are employed during the learning process in which multiple lower level skills are incrementally learned and combined with each other to develop richer higher level skills. By incrementally learning skills in layers such that new skills are learned in the presence of previously learned skills, as opposed to individually in isolation, we ensure that the learned skills will work well together and can be combined to perform complex behaviors (e.g. playing soccer). The second part of the thesis centers on developing algorithms to coordinate the movement and efforts of multiple robots working together to quickly complete tasks. These algorithms prioritize minimizing the makespan, or time for all robots to complete a task, while also attempting to avoid interference and collisions among the robots. An underlying objective of this research is to develop techniques and methodologies that allow autonomous robots to robustly interact with their environment (through skill learning) and with each other (through movement coordination) in order to perform tasks and accomplish goals asked of them. The work in this thesis is implemented and evaluated in the RoboCup 3D simulation soccer domain, and has been a key component of the UT Austin Villa team winning the RoboCup 3D simulation league world championship six out of the past seven years.Computer Science

Efficiency Gains from Team-Based Coordination: Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision-making so far. In reality, however, teams often have to coordinate with other teams. We present an experiment with 825 participants, using six different coordination games, where either individuals or teams interact with each other. We find that teams coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.coordination games, individual decision-making, team decision-making, experience-weighted attraction learning, experiment

Market Characteristics, Intra-Firm Coordination, and the Choice of Human Resource Management Systems: Evidence from New Japanese Data

This paper explores theoretically and empirically potentially important yet often-neglected linkage between task coordination within the organization and the structure of organization and bundling of HRMPs (Human Resource Management Practices). In so doing, we also provide fresh insights on the interplay between the firm’s technological and output market characteristics and its choice of HRMP system. We begin with constructing a team-theoretic model and derive three task coordination modes: vertical control, horizontal coordination, and hybrid coordination. The model provides rich implications about complementarity involving task coordination modes, HRMPs, training and hiring, and management strategies, and illustrates how such complementarity is affected by the firm’s technological and output market conditions. Guided by the theoretical exploration, we analyze unique data from a new survey of Japanese firms which provide for the first time data on newer forms of HRMPs adopted by Japanese firms (such as cross-functional offline teams and self-managed online teams). One novel finding (which is consistent with the theory) is that the adoption of both self-managed online teams and cross-functional offline teams usually arises in firms with shop-floor committees while the introduction of cross-functional offline teams alone often takes place in firms with joint labor-management committees. We also confirm implications from our theory that firms in more competitive markets are more likely to adopt both types of teams while firms facing more erratic price movement tend not to adopt self-managed online teams.

Efficiency Gains from Team-Based Coordination – Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision making so far. In reality, however, teams often have to coordinate with other teams. We present a series of coordination experiments with a total of 1,101 participants. We find that teams of three subjects each coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.

Efficiency Gains from Team-Based Coordination: Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision-making so far. In reality, however, teams often have to coordinate with other teams. We present an experiment with 825 participants, using six different coordination games, where either individuals or teams interact with each other. We find that teams coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.coordination games, individual decision-making, team decision-making, experience-weighted attraction learning, experiment

Buzz: Face-to-Face Contact and the Urban Economy

This paper argues that existing models of urban concentrations are incomplete unless grounded in the most fundamental aspect of proximity; face-to-face contact. Face-to-face contact has four main features; it is an efficient communication technology; it can help solve incentive problems; it can facilitate socialization and learning; and it provides psychological motivation. We discuss each of these features in turn, and develop formal economic models of two of them. Face-to-face is particularly important in environments where information is imperfect, rapidly changing, and not easily codified, key features of many creative activities.Agglomeration, clustering, urban economics, face-to-face

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

Using reputation and adaptive coalitions to support collaboration in competitive environments

Internet-based scenarios, like co-working, e-freelancing, or crowdsourcing, usually need supporting collaboration among several actors that compete to service tasks. Moreover, the distribution of service requests, i.e., the arrival rate, varies over time, as well as the service workload required by each customer. In these scenarios, coalitions can be used to help agents to manage tasks they cannot tackle individually. In this paper we present a model to build and adapt coalitions with the goal of improving the quality and the quantity of tasks completed. The key contribution is a decision making mechanism that uses reputation and adaptation to allow agents in a competitive environment to autonomously enact and sustain coalitions, not only its composition, but also its number, i.e., how many coalitions are necessary. We provide empirical evidence showing that when agents employ our mechanism it is possible for them to maintain high levels of customer satisfaction. First, we show that coalitions keep a high percentage of tasks serviced on time despite a high percentage of unreliable workers. Second, coalitions and agents demonstrate that they successfully adapt to a varying distribution of customers' incoming tasks. This occurs because our decision making mechanism facilitates coalitions to disband when they become non-competitive, and individual agents detect opportunities to start new coalitions in scenarios with high task demand. © 2015 Elsevier Ltd. All rights reserved.The first author thanks the grant Formación de Profesorado Universitario (FPU), reference AP2010-1742. Arcos and Rodriguez-Aguilar thank projects COR (TIN2012-38876-C02-01/02) and Generalitat of Catalunya (2014 SGR-118). Work supported by the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)Peer Reviewe