The Experts below are selected from a list of 273918 Experts worldwide ranked by ideXlab platform
Michael Beetz - One of the best experts on this subject based on the ideXlab platform.
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know rob 2 0 a 2nd generation Knowledge Processing framework for cognition enabled robotic agents
International Conference on Robotics and Automation, 2018Co-Authors: Michael Beetz, Daniel Bessler, Andrei Haidu, Mihai Pomarlan, Asil Kaan Bozcuoglu, Georg BartelsAbstract:In this paper we present KnowRob2, a second generation Knowledge representation and reasoning framework for robotic agents. KnowRob2 is an extension and partial redesign of KnowRob, currently one of the most advanced Knowledge Processing systems for robots that has enabled them to successfully perform complex manipulation tasks such as making pizza, conducting chemical experiments, and setting tables. The Knowledge base appears to be a conventional first-order time interval logic Knowledge base, but it exists to a large part only virtually: many logical expressions are constructed on demand from data structures of the control program, computed through robotics algorithms including ones for motion planning and solving inverse kinematics problems, and log data stored in noSQL databases. Novel features and extensions of KnowRob2 substantially increase the capabilities of robotic agents of acquiring open-ended manipulation skills and competence, reasoning about how to perform manipulation actions more realistically, and acquiring commonsense Knowledge.
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open ease a Knowledge Processing service for robots and robotics ai researchers
International Conference on Robotics and Automation, 2015Co-Authors: Michael Beetz, Moritz Tenorth, Jan WinklerAbstract:Making future autonomous robots capable of accomplishing human-scale manipulation tasks requires us to equip them with Knowledge and reasoning mechanisms. We propose OPEN-EASE, a remote Knowledge representation and Processing service that aims at facilitating these capabilities. OPEN-EASE gives its users unprecedented access to the Knowledge of leading-edge autonomous robotic agents. It also provides the representational infrastructure to make inhomogeneous experience data from robots and human manipulation episodes semantically accessible, and is complemented by a suite of software tools that enable researchers and robots to interpret, analyze, visualize, and learn from the experience data. Using OPEN-EASE users can retrieve the memorized experiences of manipulation episodes and ask queries regarding to what the robot saw, reasoned, and did as well as how the robot did it, why, and what effects it caused.
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knowrob a Knowledge Processing infrastructure for cognition enabled robots
The International Journal of Robotics Research, 2013Co-Authors: Moritz Tenorth, Michael BeetzAbstract:Autonomous service robots will have to understand vaguely described tasks, such as âset the tableâ or âclean upâ. Performing such tasks as intended requires robots to fully, precisely, and appropriately parameterize their low-level control programs. We propose Knowledge Processing as a computational resource for enabling robots to bridge the gap between vague task descriptions and the detailed information needed to actually perform those tasks in the intended way. In this article, we introduce the KnowRob Knowledge Processing system that is specifically designed to provide autonomous robots with the Knowledge needed for performing everyday manipulation tasks. The system allows the realization of âvirtual Knowledge basesâ: collections of Knowledge pieces that are not explicitly represented but computed on demand from the robot's internal data structures, its perception system, or external sources of information. This article gives an overview of the different kinds of Knowledge, the different inference mechanisms, and interfaces for acquiring Knowledge from external sources, such as the robot's perception system, observations of human activities, Web sites on the Internet, as well as Web-based Knowledge bases for information exchange between robots. We evaluate the system's scalability and present different integrated experiments that show its versatility and comprehensiveness.
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everything robots always wanted to know about housework but were afraid to ask
Intelligent Robots and Systems, 2012Co-Authors: Daniel Nyga, Michael BeetzAbstract:In this paper we discuss the problem of action-specific Knowledge Processing, representation and acquisition by autonomous robots performing everyday activities. We report on a thorough analysis of the household domain, which has been performed on a large corpus of natural-language instructions from the Web and underlines the supreme need of action-specific Knowledge for robots acting in those environments. We introduce the concept of Probabilistic Robot Action Cores (PRAC) that are well-suited for encoding such Knowledge in a probabilistic first-order Knowledge base. We additionally show how such a Knowledge base can be acquired by natural language and we address the problems of incompleteness, underspecification and ambiguity of naturalistic action specifications and point out how PRAC models can tackle those.
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Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments
IEEE International Conference on Intelligent Robots and Systems, 2011Co-Authors: Nico Blodow, Lucian Cosmin Goron, Zoltan-csaba Marton, Moritz Tenorth, Thomas Rühr, Dejan Pangercic, Michael BeetzAbstract:In this work we report about our efforts to equip service robots with the capability to acquire 3D semantic maps. The robot autonomously explores indoor environments through the calculation of next best view poses, from which it assembles point clouds containing spatial and registered visual information. We apply various segmentation methods in order to generate initial hypotheses for furniture drawers and doors. The acquisition of the final semantic map makes use of the robot's proprioceptive capabilities and is carried out through the robot's interaction with the environment. We evaluated the proposed integrated approach in the real kitchen in our laboratory by measuring the quality of the generated map in terms of the map's applicability for the task at hand (e.g. resolving counter candidates by our Knowledge Processing system).
Patrick Doherty - One of the best experts on this subject based on the ideXlab platform.
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stream based reasoning support for autonomous systems
European Conference on Artificial Intelligence, 2010Co-Authors: Fredrik Heintz, Jonas Kvarnstrom, Patrick DohertyAbstract:For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based Knowledge Processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment. DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.
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bridging the sense reasoning gap dyknow stream based middleware for Knowledge Processing
Advanced Engineering Informatics, 2010Co-Authors: Fredrik Heintz, Jonas Kvarnstrom, Patrick DohertyAbstract:Engineering autonomous agents that display rational and goal-directed behavior in dynamic physical environments requires a steady flow of information from sensors to high-level reasoning components. However, while sensors tend to generate noisy and incomplete quantitative data, reasoning often requires crisp symbolic Knowledge. The gap between sensing and reasoning is quite wide, and cannot in general be bridged in a single step. Instead, this task requires a more general approach to integrating and organizing multiple forms of information and Knowledge Processing on different levels of abstraction in a structured and principled manner. We propose Knowledge Processing middleware as a systematic approach to organizing such Processing. Desirable properties are presented and motivated. We argue that a declarative stream-based system is appropriate for the required functionality and present DyKnow, a concrete implemented instantiation of stream-based Knowledge Processing middleware with a formal semantics. Several types of Knowledge processes are defined and motivated in the context of a UAV traffic monitoring application. In the implemented application, DyKnow is used to incrementally bridge the sense-reasoning gap and generate partial logical models of the environment over which metric temporal logical formulas are evaluated. Using such formulas, hypotheses are formed and validated about the type of vehicles being observed. DyKnow is also used to generate event streams representing for example changes in qualitative spatial relations, which are used to detect traffic violations expressed as declarative chronicles.
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a Knowledge Processing middleware framework and its relation to the jdl data fusion model
International Conference on Information Fusion, 2005Co-Authors: Fredrik Heintz, Patrick DohertyAbstract:Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative Knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose successful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a Knowledge Processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and Processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the revised JDL data fusion model.
Zhisheng Huang - One of the best experts on this subject based on the ideXlab platform.
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web kr 2014 the 5th international workshop on web scale Knowledge representation retrieval and reasoning
Conference on Information and Knowledge Management, 2014Co-Authors: Yi Zeng, Spyros Kotoulas, Zhisheng HuangAbstract:We organize and present the 5th version of the International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2014) as a continuous effort to discuss and provide possible theories and techniques to deal with the barriers for Knowledge Processing at Web scale. This workshop was held in conjunction with the 2014 ACM International Conference on Information and Knowledge Management (CIKM 2014) on November 3rd, 2014 in Shanghai, China. Compared to previous workshops under the same title, accepted papers of this workshop covers even wider topics in the field. The contributions focus on semantic Knowledge extraction, representation, Knowledge clustering, inconsistency checking, entity relatedness and linking, query suggestions, etc. Many new approaches are proposed to investigate these topics in the context of Web-scale resources. This summary introduces the major contributions of accepted papers in the Web-KR 2014 workshop.
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web kr 2013 the 4th international workshop on web scale Knowledge representation retrieval and reasoning
Conference on Information and Knowledge Management, 2013Co-Authors: Yi Zeng, Spyros Kotoulas, Zhisheng HuangAbstract:As a continuous effort for organizing discussions and providing possible theories and techniques to deal with the barriers for Knowledge Processing at Web scale, the 2013 International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2013) was held in conjunction with the 2013 ACM International Conference on Information and Knowledge Management (CIKM 2013) on November 1st, 2013 at Burlingame, CA, United States. This is the 4th version of the Web-KR workshop. As in previous workshops under the same title, accepted papers of this workshop cover many important topics in the field. This year, the contributions focus on multi-faceted understanding of Web Knowledge sources, Web entity linking, deep Web Knowledge acquisition, and Web-scale stream reasoning. Many new approaches are proposed to deal with these problems in the context of large scale Web resources. This summary introduces the major contributions of accepted papers in the Web-KR 2013 workshop.
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the 2012 international workshop on web scale Knowledge representation retrieval and reasoning
Conference on Information and Knowledge Management, 2012Co-Authors: Spyros Kotoulas, Yi Zeng, Zhisheng HuangAbstract:The rapid and perpetual growth of Knowledge on the Web has given rise to many grand challenges (such as scalability, inconsistency, uncertainty, distribution and dynamics) for traditional Knowledge Processing methods and systems. Knowledge representation, retrieval and reasoning methods need to evolve and adapt to the Web to face these challenges and make this vast, heterogenous Knowledge useful and accessible. In this light, the International Workshop on Web-scale Knowledge Representation, Retrieval, and Reasoning (Web-KR) is initiated. This workshop serves as the third one in this workshop series. This summary discusses the scope of Web-KR and introduces the advances in this field through the accepted papers in the Web-KR 2012 workshop, co-located with CIKM 2012.
Fredrik Heintz - One of the best experts on this subject based on the ideXlab platform.
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stream based reasoning support for autonomous systems
European Conference on Artificial Intelligence, 2010Co-Authors: Fredrik Heintz, Jonas Kvarnstrom, Patrick DohertyAbstract:For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based Knowledge Processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment. DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.
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bridging the sense reasoning gap dyknow stream based middleware for Knowledge Processing
Advanced Engineering Informatics, 2010Co-Authors: Fredrik Heintz, Jonas Kvarnstrom, Patrick DohertyAbstract:Engineering autonomous agents that display rational and goal-directed behavior in dynamic physical environments requires a steady flow of information from sensors to high-level reasoning components. However, while sensors tend to generate noisy and incomplete quantitative data, reasoning often requires crisp symbolic Knowledge. The gap between sensing and reasoning is quite wide, and cannot in general be bridged in a single step. Instead, this task requires a more general approach to integrating and organizing multiple forms of information and Knowledge Processing on different levels of abstraction in a structured and principled manner. We propose Knowledge Processing middleware as a systematic approach to organizing such Processing. Desirable properties are presented and motivated. We argue that a declarative stream-based system is appropriate for the required functionality and present DyKnow, a concrete implemented instantiation of stream-based Knowledge Processing middleware with a formal semantics. Several types of Knowledge processes are defined and motivated in the context of a UAV traffic monitoring application. In the implemented application, DyKnow is used to incrementally bridge the sense-reasoning gap and generate partial logical models of the environment over which metric temporal logical formulas are evaluated. Using such formulas, hypotheses are formed and validated about the type of vehicles being observed. DyKnow is also used to generate event streams representing for example changes in qualitative spatial relations, which are used to detect traffic violations expressed as declarative chronicles.
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dyknow a stream based Knowledge Processing middleware framework
2009Co-Authors: Fredrik HeintzAbstract:As robotic systems become more and more advanced the need to integrate existing deliberative functionalities such as chronicle recognition, motion planning, task planning, and execution monitoring ...
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a Knowledge Processing middleware framework and its relation to the jdl data fusion model
International Conference on Information Fusion, 2005Co-Authors: Fredrik Heintz, Patrick DohertyAbstract:Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative Knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose successful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a Knowledge Processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and Processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the revised JDL data fusion model.
Prem Kumar Singh - One of the best experts on this subject based on the ideXlab platform.
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Complex neutrosophic concept lattice and its applications to air quality analysis
2019Co-Authors: Prem Kumar SinghAbstract:In the current year, the precise measurement of uncertainty and fluctuation exists in a complex fuzzy attributes is addressed as computationally and mathematically expensive tasks with regard to its graphical analytics. To deal with this problem the calculus of complex neutrosophic sets are recently introduced to characterize the uncertainty and its changes based on its truth, indeterminacy, and falsity membership–value, independently. This given a way to represent the given data sets in form of complex neutrosophic matrix for further analysis towards Knowledge Processing tasks. In this process, a major problem arises when an expert wants to find some of the interesting patterns in the given complex neutrosophic data sets to solve the particular problem. To resolve this issue, the current paper proposes a method for step by step demonstration to investigate the complex neutrosophic concepts and their graphical structure visualization based on their Lower Neighbors. One of the suitable examples of the proposed method is also given for precise measurement of uncertainty exists in Air Quality Index (AQI) and its pattern at given phase of time
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m polar fuzzy graph representation of concept lattice
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Prem Kumar SinghAbstract:Abstract Recently, the calculus of fuzzy concept lattice is studied beyond the three-way fuzzy space ([0,1] 3 ) for precise representation of uncertainty and vagueness in the attributes. However, to dovetail the uncertainty in case of voxel, multi-index or multi-polar information the properties of lattice theory need to be explored in component wise m -polar fuzzy space ( [ 0 , 1 ] m ). In this case, another problem arises while finding some of the hidden or interested pattern from the given m -polar fuzzy context for the Knowledge Processing tasks. To conquer this problem, current paper generalizes the mathematical background of concept lattice with m -polar fuzzy sets and its graphical properties. To elicit this objective, two methods are introduced for providing a unified framework based on discovered m -polar formal fuzzy concepts and their projection.
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fuzzy concept lattice reduction using shannon entropy and huffman coding
Journal of Applied Non-Classical Logics, 2015Co-Authors: Prem Kumar Singh, Abdullah GaniAbstract:In the last decade, formal concept analysis (FCA) in a fuzzy setting has received more attention for Knowledge Processing tasks in various fields. The hierarchical order visualisation of generated formal concepts is a major concern for the practical application of FCA. In this process, a major issue is the huge number of formal concepts generated from ‘a large context’, and another problem is their ‘storage’ complexity. To deal with these issues a method is proposed in this paper based on Shannon entropy and Huffman coding. The proposed method is illustrated using crisply generated concepts such that the changes between obtained concepts can be measured using Levenshtein distance. The analysis derived from the proposed method is illustrated with an example for FCA in a fuzzy setting.
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bipolar fuzzy graph representation of concept lattice
Information Sciences, 2014Co-Authors: Prem Kumar Singh, Ch Aswani KumarAbstract:Formal Concept Analysis (FCA) is a mathematical framework for Knowledge Processing tasks. FCA has been successfully incorporated into fuzzy setting and its extension (interval-valued fuzzy set) for handling vagueness and impreciseness in data. However, the analysis in such settings is restricted to unipolar space. Recently, some applications of bipolar information are shown in bipolar fuzzy graph, lattice theory as well as in FCA. The adequate analysis of bipolar information using FCA requires incorporation of bipolar fuzzy set and an appropriate lattice structure. For this purpose, we propose an algorithm for generating the bipolar fuzzy formal concepts, a method for ( α , β ) -cut of bipolar fuzzy formal context and its implications with illustrative examples.
