TeamWorker: An agent-based support system for mobile task execution

Traditional workflow management systems are considered insufficiently flexible to support autonomous job management via close team working. This paper proposes a multi-agent system approach to enhancing existing workflow management systems to enable team-based job management in the field of telecommunications service provision and maintenance. This paper adopts a component-based approach and explains how applications can be developed by customising the generic components provided by a multi-agent systems framework

A multi-agent system to support location based group decision making in mobile teams

This paper describes an agent-based approach for developing a location-based asynchronous group decision-support system for mobile teams. The approach maximises the use of reusable service components (GSCmas — generic service component for multi-agent systems) as the main interaction mechanism between agents to allow flexible support of a new group-decision process. The paper describes the architecture of a GSCmas and provides details of how the GSCmas is integrated within a decision support system. Finally a system (mPower) based on the proposed approach is introduced and applied to a location-based group decision problem

DocuDrama

This paper presents an approach combining concepts of virtual storytelling with cooperative processes. We will describe why storytelling is relevant in cooperation support applications. We will outline how storytelling concepts provide a new quality for groupware applications. Different prototypes illustrate a combination of a groupware application with various storytelling components in a Theatre of Work

Supporting awareness in heterogeneous collaboration environments

Rapid technological advancements have made it possible for humans to collaborate as never before. However demands of group work necessitate distributed collaboration in very heterogeneous environments. Heterogeneity as in various applications, platforms, hardware and communication infrastructure. User mobility, lack of availability and cost often make imposing a common collaboration environment infeasible. Awareness is essential for successful collaboration. Awareness is a key design criterion in groupware but often collaboration occurs with applications not designed to support useful awareness. This dissertation deals with the issue of effective group awareness support in heterogeneous environments.;Awareness propagation is effective if the appropriate amount of information, relevant to the user\u27s sphere of activity is delivered in a timely, unobtrusive fashion. Thus issues such as information overload, and distraction have to be addressed. Furthermore ability to establish the appropriate balance between awareness and privacy is essential. Enhanced forms of awareness such as intersubjectivity and historical awareness are often invaluable. Heterogeneous environments significantly impact the above quality factors impeding effective awareness propagation. Users are unable to tailor the quality of awareness received.;Heterogeneity issues that affect awareness quality are identified. An awareness framework is proposed that binds various sources of awareness information. However for effective awareness support, physical integration must be augmented by information integration. As a solution, an awareness model is proposed. Specification of the awareness model and framework\u27s architecture and features is the key contribution. The proposed model has been validated through simulations of realistic collaboration involving human participation. Scenarios created, have tested the model\u27s usefulness in enhancing the quality of group work by propagating effective awareness among users. To accomplish the same, an Awareness Simulator application has been created. In the validation process, efforts made to create an experimental methodology revealed some techniques related to awareness evaluation in CSCW, which are proposed. Various issues required to successfully engineer such awareness frameworks are identified and their impact on requirements such as security and performance, discussed. With various standards and technologies that can be harnessed to create awareness frameworks, there is great promise that barriers in heterogeneous collaboration environments can be overcome

Group and individual time management tools: what you get is not what you need

Some studies of diaries and scheduling systems have considered how individuals use diaries with a view to proposing requirements for computerised time management tools. Others have focused on the criteria for success of group scheduling systems. Few have paid attention to how people use a battery of tools as an ensemble. This interview study reports how users exploit paper, personal digital assistants (PDAs) and a group scheduling system for their time management. As with earlier studies, we find many shortcomings of different technologies, but studying the ensemble rather than individual tools points towards a different conclusion: rather than aiming towards producing electronic time management tools that replace existing paper-based tools, we should be aiming to understand the relative strengths and weaknesses of each technology and look towards more seamless integration between tools. In particular, the requirements for scheduling and those for more responsive, fluid time management conflict in ways that demand different kinds of support

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

InfoTech Update, Volume 4, Number 3, Spring 1995

https://egrove.olemiss.edu/aicpa_news/4952/thumbnail.jp

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks