Building an environment model using depth information

Modeling the environment is one of the most crucial issues for the development and research of autonomous robot and tele-perception. Though the physical robot operates (navigates and performs various tasks) in the real world, any type of reasoning, such as situation assessment, planning or reasoning about action, is performed based on information in its internal world. Hence, the robot's intentional actions are inherently constrained by the models it has. These models may serve as interfaces between sensing modules and reasoning modules, or in the case of telerobots serve as interface between the human operator and the distant robot. A robot operating in a known restricted environment may have a priori knowledge of its whole possible work domain, which will be assimilated in its World Model. As the information in the World Model is relatively fixed, an Environment Model must be introduced to cope with the changes in the environment and to allow exploring entirely new domains. Introduced here is an algorithm that uses dense range data collected at various positions in the environment to refine and update or generate a 3-D volumetric model of an environment. The model, which is intended for autonomous robot navigation and tele-perception, consists of cubic voxels with the possible attributes: Void, Full, and Unknown. Experimental results from simulations of range data in synthetic environments are given. The quality of the results show great promise for dealing with noisy input data. The performance measures for the algorithm are defined, and quantitative results for noisy data and positional uncertainty are presented

Use-cases on evolution

This report presents a set of use cases for evolution and reactivity for data in the Web and Semantic Web. This set is organized around three different case study scenarios, each of them is related to one of the three different areas of application within Rewerse. Namely, the scenarios are: “The Rewerse Information System and Portal”, closely related to the work of A3 – Personalised Information Systems; “Organizing Travels”, that may be related to the work of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources” related to the work of A2 – Towards a Bioinformatics Web

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Evaluation of strategic information systems planning (SISP) techniques: Driver perspective

Strategic Information Systems Planning (SISP) literature reviews with a focus on the global dimension are considered in this research. The paper counters the evaluation of SISP techniques through information system (IS) strategic drivers. These techniques can be vital contributors in the IS strategy (ISS) designing process. Therefore, categorisation of the techniques of ISS planning will be developed. Keeping in mind the global dimension, the planning team needs to identify how it can cluster an organization’s ISS drivers. This may be achieved by analysing the drivers that can have an effect on IS for the organization, which may support categorisation of drivers against techniques being classified to understand which are needed to fit specific drivers. The contribution of this research is the taxonomy of SISP techniques, with a case study for X international airlines. This classification can benefit evaluation of the ISS planning processes to support decision-makers through the planning process

References to past designs

Designing by adaptation is almost invariably a dominantambiguity feature of designing, and references to past designs are ubiquitous in design discourse. Object references serve as indices into designers' stocks of design concepts, in which memories for concrete embodiments and exemplars are tightly bound to solution principles. Thinking and talking by reference to past designs serves as a way to reduce the overwhelming complexity of complex design tasks by enabling designers to use parsimonious mental representations to which details can be added as needed. However object references can be ambiguous, and import more of the past design than is intended or may be desirable

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research