Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

EmergencyGrid:Planning in Convergence Environments

Government agencies are often responsible for event handling, planning, coordination, and status reporting during emergency response in natural disaster events such as floods, tsunamis and earthquakes. Across such a range of emergency response scenarios, there is a common set of requirements that distributed intelligent computer systems generally address. To support the implementation of these requirements, some researchers are proposing the creation of grids, where final interface and processing nodes perform joint work supported by a network infrastructure. The aim of this project is to extend the concepts of emergency response grids, using a convergence scenario between web and other computational platforms. Our initial work focuses on the Interactive Digital TV platform, where we intend to transform individual TV devices into active final nodes, using a hierarchical planning structure. We describe the architecture of this approach and an initial prototype specification that is being developed to validate some concepts and illustrate the advantages of this convergence planning environment

Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler

Oswald Physical and Engineering Sciences Honorable Mention: Summer 2012 LIP6 Write Up

Planning under uncertainty is a central problem in developing intelligent autonomous systems. The traditional representation for these problems is a Markov Decision Process (MDP). The MDP model can be extended to a Multi-criteria MDP (MMDP) for planning under uncertainty while trying to optimize multiple criteria. However, due to the trade-offs involved in multi criteria problems there may be infinitely many optimal solutions. The focus of this project has been to find a method that efficiently computes a subset of solutions that represents the entire set of optimal solutions for bi-objective MDPs

The Use of Intelligent Systems for Planning and Scheduling of Product Development Projects

AbstractThe paper investigates the use of intelligent systems to identify the factors that significantly influence the duration of new product development. These factors are identified on the basis of an internal database of a production enterprise and further used to estimate the duration of phases in product development projects. In the paper, some models and methodologies of the knowledge discovery process are compared and a method of knowledge acquisition from an internal database is proposed. The presented approach is dedicated to industrial enterprises that develop modifications of previous products and are interested in obtaining more precise estimates for project planning and scheduling. The example contains four stages of the knowledge discovery process including data selection, data transformation, data mining, and interpretation of patterns. The example also presents a performance comparison of intelligent systems in the context of variable reduction and preprocessing. Among data mining techniques, artificial neural networks and the fuzzy neural system are chosen to seek relationships between the duration of project phase and other data stored in the information system of an enterprise

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

A checklist for planning and implementing Field Operational Tests of Intelligent Transport Systems

Improved knowledge is needed to understand how drivers use intelligent transport systems, the short- and long-term effects of these systems, and how system performance can be optimized. The Field Operational Test, or FOT, is a powerful evaluation method that can be used to answer these and other questions. Although large-scale FOTs conducted in the past have yielded important knowledge, there is scope for improving the design and implementation of them. In this paper a checklist is presented that outlines the critical steps and considerations involved in successfully planning and implementing a FOT. It derives from work previously undertaken in the European Commission (EC)-funded FESTA (Field opErational teSt support Action) project

Open PhD Workshop on Technology-Enhanced Learning and Semantics, Software and Services

The 7FP project SISTER focuses, especially, on strengthening the PhD and PostDoc level of education and training of researchers, and thus attracting more young scientists to the research profession and retaining them. The project SISTER is structured around two ICT strategic research areas - Software and services, and Intelligent Content and Semantics. The research workshops and seminars will support the research in the particular area through brainstorming sessions, discussions and strategic planning. Some of them will be of benefit to the PhD students and Post Docs and the advancement in their careers, while others will be devoted to further research collaboration in selected EU research programmes. The main research areas addressed are: Creation of digital libraries with intelligent content. Semantic annotation of digital content - Creation of ontologies for the digital content in the libraries. Semantic annotation of the learning materials in the repositories. The created ontologies and their semantic annotation will allow searching materials using semantic web techniques. Development of adaptive intelligent learning systems based on intelligent ontologies and digital learning materials. New innovative pedagogical approaches, assessment models and organisational models for lifelong competence development. Software for the effective support of users who create, store, use and exchange knowledge resources, learning activities, units of learning and competence development programmes within a learning network. Models and tools for competence development into a common, easy to use infrastructure. Training programs to learn users how to work with the infrastructure, and to train instructors and companies (specifically SMEs) to deliver services using the infrastructure. Responsive environments for technology-enhanced learning higher education and business organisations "that motivate, engage and inspire learners, and which can be embedded in the business processes and human resources management systems of organisations". Special attention will be given to using the research outcomes related to Intelligent Content and Semantics and Digital Libraries for building intelligent Adaptive and intuitive learning systems and Web 2.0 oriented applications. Development of a semantics-based reference frameworks for the conceptualisation of learning content, learning objectives, and teaching strategies, and the implementation of pedagogically-driven and semantically-enhanced adaptive learning systems. This will lead to consolidating existing theoretical and technological frameworks for explicitly modelling educational content, teaching strategies, and learner characteristics, and integrating them under a common semantic model.Open PhD Workshop on Technology-Enhanced Learning and Semantics, Software and Services in conjunction with the 13th International Conference on Artificial Intelligence: Methodology, Systems, Applications - AI@Work (AIMSA 2008) 04-06 September, Varna, Bulgari

What-if game simulation in agent-based strategic production planners

In the nowadays highly unstable manufacturing market, companies are faced, on a daily basis, with important strategic decisions, such as “does the company has the necessary capacity to accept a high volume order?” or “what measures need to be implemented if the product demand increases x% a year?”. Decision-makers, i.e. company’s managers, rely on their experience and insights supported by classical tools to take such decisions. Classical mathematical solvers or agent-based systems are typical architectural solutions to implement strategic planning tools to support decision-makers on this important task. Within the ARUM (Adaptive Production Management) project, a hybrid strategic planning tool was specified and developed, combining the optimization features of classical solvers with the flexibility and agility of agent systems. This paper briefly presents such architecture and focuses on the generation of the “what-if game” mechanism to support the generation of more intelligent and dynamic planning solutions.The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007- 2013 under grant agreement n° 314056.info:eu-repo/semantics/publishedVersio