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The representation method, strategy schemata, possesses features that are similar to several other representation methodologies. It captures inter-strategy knowledge, such as structural and inheritance relationships that are useful in the acquisition and refinement processes outlined by the paradigm. The nature of these schemata coupled with the reasoning methods explicitly associated with them, differentiate them from other representation and associated reasoning methodologies."
Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application – exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.
An industrial robot routinely carrying out an assembly or welding task is an impressive sight. More important, when operated within its design conditions it is a reliable production machine which - depending on the manufacturing process being automated - is relatively quick to bring into operation and can often repay its capital cost within a year or two. Yet first impressions can be deceptive: if the workpieces deviate somewhat in size or position, or, worse; if a gripper slips or a feeder jams the whole system may halt and look very unimpressive indeed. This is mainly because the sum total of the system's knowledge is simply a list of a few variables describing a sequence of positions in space; the means of moving from one to the next; how to react to a few input signals; and how to give a few output commands to associated machines. The acquisition, orderly retention and effective use of knowledge are the crucial missing techniques whose inclusion over the coming years will transform today's industrial robot into a truly robotic system embodying the 'intelligent connection of perception to action'. The use of computers to implement these techniques is the domain of Artificial Intelligence (AI) (machine intelligence). Evidently, it is an essential ingredient in the future development of robotics; yet the relationship between AI practitioners and robotics engineers has been an uneasy one ever since the two disciplines were born.
The perception-action cycle is the circular flow of information that takes place between the organism and its environment in the course of a sensory-guided sequence of behaviour towards a goal. Each action causes changes in the environment that are analyzed bottom-up through the perceptual hierarchy and lead to the processing of further action, top-down through the executive hierarchy, toward motor effectors. These actions cause new changes that are analyzed and lead to new action, and so the cycle continues. The Perception-action cycle: Models, architectures and hardware book provides focused and easily accessible reviews of various aspects of the perception-action cycle. It is an unparalleled resource of information that will be an invaluable companion to anyone in constructing and developing models, algorithms and hardware implementations of autonomous machines empowered with cognitive capabilities. The book is divided into three main parts. In the first part, leading computational neuroscientists present brain-inspired models of perception, attention, cognitive control, decision making, conflict resolution and monitoring, knowledge representation and reasoning, learning and memory, planning and action, and consciousness grounded on experimental data. In the second part, architectures, algorithms, and systems with cognitive capabilities and minimal guidance from the brain, are discussed. These architectures, algorithms, and systems are inspired from the areas of cognitive science, computer vision, robotics, information theory, machine learning, computer agents and artificial intelligence. In the third part, the analysis, design and implementation of hardware systems with robust cognitive abilities from the areas of mechatronics, sensing technology, sensor fusion, smart sensor networks, control rules, controllability, stability, model/knowledge representation, and reasoning are discussed.
This volume constitutes selected papers presented during the First International Conference on Cognitive Computation and Systems, ICCCS 2022, held in Beijing, China, in October 2022. The 31 papers were thoroughly reviewed and selected from the 75 submissions. The papers are organized in topical sections on ​computer vision; decision making and cognitive computation; robot and autonomous vehicle.
This book constitutes the proceedings of the 8th International Conference on Intelligent Human Computer Interaction, IHCI 2016, held in Pilani, India, in December 2016. The 22 regular papers and 3 abstracts of invited talks included in this volume were carefully reviewed and selected from 115 initial submissions. They deal with intelligent interfaces; brain machine interaction; HCI applications and technology; and interface and systems.
The two volume set LNCS 12506 and 12507 constitutes the proceedings of the 19th International Semantic Web Conference, ISWC 2020, which was planned to take place in Athens, Greece, during November 2-6, 2020. The conference changed to a virtual format due to the COVID-19 pandemic. The papers included in this volume deal with the latest advances in fundamental research, innovative technology, and applications of the Semantic Web, linked data, knowledge graphs, and knowledge processing on the Web. They were carefully reviewed and selected for inclusion in the proceedings as follows: Part I: Features 38 papers from the research track which were accepted from 170 submissions; Part II: Includes 22 papers from the resources track which were accepted from 71 submissions; and 21 papers in the in-use track, which had a total of 46 submissions. Chapter “Transparent Integration and Sharing of Life Cycle Sustainability Data with Provenance ” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
These volumes of "Advances in Intelligent Systems and Computing" highlight papers presented at the "Third Iberian Robotics Conference (ROBOT 2017)". Held from 22 to 24 November 2017 in Seville, Spain, the conference is a part of a series of conferences co-organized by SEIDROB (Spanish Society for Research and Development in Robotics) and SPR (Portuguese Society for Robotics). The conference is focused on Robotics scientific and technological activities in the Iberian Peninsula, although open to research and delegates from other countries. Thus, it has more than 500 authors from 21 countries. The volumes present scientific advances but also robotic industrial applications, looking to promote new collaborations between industry and academia.
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily