A Framework for the Representation, Acquisition and Manipulation of Strategic Knowledge for Knowledge Based Systems


Book Description

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."




Cognitive Systems


Book Description

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.




Intelligent robotics


Book Description

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.




Perception-Action Cycle


Book Description

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.




Intelligent Human Computer Interaction


Book Description

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.




Deep Learning for Robot Perception and Cognition


Book Description

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis




Cognitive Computation and Systems


Book Description

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.




The Semantic Web – ISWC 2020


Book Description

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.




Handbook of Knowledge Representation


Book Description

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