Development of the infrastructure for a multi-agent traffic management system simulation

This document contains detailed description of the design and the implementation of a multi-agent application controlling traffic lights in a city together with a system for simulating traffic and testing. The goal of this thesis is to design and build a simplified intelligent and distributed solution to the problem with the traffic in the big cities following different good practices in order to allow future refining of the model of the real world. The problem of the traffic in the big cities is still a problem that cannot be solved. Not only is the increasing number of cars a reason for the traffic jams, but also the way the traffic is organized. Usually, the intersections with traffic lights are replaced by roundabouts or interchanges to increase the number of cars that can cross the intersection in certain time. But still there are places where the infrastructure cannot be changed and the traffic light semaphores are the only way to control the car flows. In real life, the traffic lights have a predefined plan for change or they receive information from a centralized system when and how they have to change. But what if the traffic lights can cooperate and decide on their own when and how to change? Using this problem, the purpose of the thesis is to explore different agent-based software engineering approaches to design and build a non-conventional distributed system. From the software engineering point of view, the goal of the thesis is to apply the knowledge and use the skills, acquired during the various courses of the master program in Software Engineering, while solving a practical and complex problem such as the traffic in the cities.

The HOSHIN KANRI TREE. Cross-Plant Lean Shopfloor Management

Shopfloor Management (SM) empowerment methodologies have traditionally focused on two aspects: goal achievement following rigid structures, such as SQDCME, or evolutional aspects of empowerment factors away from strategic goal achievement. Furthermore, SM Methodologies have been organized almost solely around the hierarchical structure of the organization, failing systematically to cope with the challenges that Industry 4.0 is facing. The latter include the growing complexity of value-stream networks, sustainable empowerment of the workforce (Learning Factory), an autonomous and intelligent process management (Smart Factory), the need to cope with the increasing complexity of value-stream networks (VSN) and the leadership paradigm shift to strategic alignment. This paper presents a novel Lean SM Method (LSM) called ?HOSHIN KANRI Tree? (HKT), which is based on standardization of the communication patterns among process owners (POs) by PDCA. The standardization of communication patterns by HKT technology should bring enormous benefits in value stream (VS) performance, speed of standardization and learning rates to the Industry 4.0 generation of organizations. These potential advantages of HKT are being tested at present in worldwide research.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Integrating intelligent transportation systems within the planning process: an interim handbook.

Federal Highway Administration, Office of Traffic Management and Intelligent Systems Applications, Washington, D.C.

Responsive multimodal transportation management strategies and IVHS. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

A comparable systems analysis: comparing automated highway systems to air traffic management. Final report.

Federal Highway Administration, Washington, D.C.

Review of and preliminary guidelines for integrating transit into transportation management centers. Final report.

Transportation Systems Center, Cambridge, Mass.

National Program Plan for Intelligent Vehicle-Highway Systems (IVHS).

Mode of access: Internet.

Department of Transportation's Intelligent transportation systems (ITS) projects book.

Mode of access: Internet.

Smart highways: challenges facing DOT's intelligent vehicle highway systems program.

Mode of access: Internet.

Developing freeway and incident management systems using the national ITS architecture.

Performing organizations: Mitretek Systems and PB Farradyne, Inc.

Intelligent transportation systems benefits: 2001 update.

Transportation Department, Joint Program Office for Intelligent Transportation Systems, Washington, D.C.

The young farmer's manual: detailing the manipulations of the farm in a plain and intelligent manner. With practical directions for laying out a farm and erecting buildings, fences, and farm gates. Embracing also the young farmer's workshop: giving full directions for the selection of good farm and shop tools, their use and manufacture, with numerous original illustrations of fences, gates, tools, etc., and for performing nearly every branch of farming operations.

Vol. 2 has title: The young farmer's manual; or, How to make farming pay, giving plain and practical details of general farm management; with a chapter on soils, and their cultivation, and much other valuable matter of a practical character.

Should the knowledge-based economy be a savant or a sage? Wisdom and socially intelligent innovation

Discourse about knowledge-based economies rarely moves beyond the commercialization of science and engineering, and is locked in the discursive limits of functionalism. We argue that these discourses limit the scope of what knowledge-based economies might achieve because they are uninformed by an adequate conception of knowledge. In particular, knowledge management and knowledge-based economy discourse has not included the axiological dimension of knowledge that leads to wisdom. Taking an axiological perspective, we can discuss policy frameworks aimed at producing the social structures needed to bring fully formed and fully functioning knowledge societies into being. We argue that while the dominant discourse of industrial modernity remains rationalist, functionalist, utilitarian and technocratic, knowledge-based economies will resemble a savant rather than a sage. A wisdom-based renaissance of humanistic epistemology is needed to avoid increasing social dysfunction and a lack of wisdom in complex technological societies.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.