The Experts below are selected from a list of 151035 Experts worldwide ranked by ideXlab platform
Alessandro Saffiotti - One of the best experts on this subject based on the ideXlab platform.
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Inferring robot goals from violations of Semantic Knowledge
Robotics and Autonomous Systems, 2013Co-Authors: Cipriano Galindo, Alessandro SaffiottiAbstract:A growing body of literature shows that endowing a mobile robot with Semantic Knowledge and with the ability to reason from this Knowledge can greatly increase its capabilities. In this paper, we present a novel use of Semantic Knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has Semantic Knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a Semantic map, a Knowledge representation and reasoning system, a task planner, and standard perception and navigation routines.
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ECMR - Robots that Change Their World: Inferring Goals from Semantic Knowledge.
2011Co-Authors: Cipriano Galindo, Javier Gonzalez, Juan-antonio Fernández-madrigal, Alessandro SaffiottiAbstract:A growing body of literature shows that endowing a mobile robot with Semantic Knowledge, and with the ability to reason from this Knowledge, can greatly increase its capabilities. In this paper, we explore a novel use of Semantic Knowledge: we encode information about how things should be, or norms, to allow the robot to infer deviations from these norms and to generate goals to correct these deviations. For instance, if a robot has Semantic Knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a Semantic map, a Knowledge representation and reasoning system, a task planner, as well as standard perception and navigation routines.
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Editorial: Using Semantic Knowledge in robotics
Robotics and Autonomous Systems, 2008Co-Authors: Joachim Hertzberg, Alessandro SaffiottiAbstract:There is a growing tendency to introduce high-level Semantic Knowledge into robotic systems and beyond. This tendency is visible in different forms within several areas of robotics. Recent work in mapping and localization tries to extract Semantically meaningful structures from sensor data during map building, or to use Semantic Knowledge in the map building process, or both. A similar trend characterizes the cognitive vision approach to scene understanding. Recent efforts in human–robot interaction try to endow the robot with some understanding of the human meaning of words, gestures and expressions. Ontological Knowledge is increasingly being used in distributed systems in order to allow automatic re-configuration in the areas of flexible automation and of ubiquitous robotics. Ontological Knowledge was also used recently to improve the inter-operability of robotic components developed for different systems.While these trends have many questions and issues in common, work on each one of them is often pursued in isolation within a specific area, without being aware of the related achievements in other areas. The aim of this special issue is to collect in a single place a set of advanced, high-quality papers that tackle the problem of using Semantic Knowledge in robotics in many of its different forms.The submissions to this special issue made it clear that there are many ways in which Semantic Knowledge may play a role in robotics. Interestingly, they also revealed that there are many ways in which the term Semantic Knowledge is being interpreted. Before turning to the technical papers, then, it is worth spending a few words on this matter.
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ICRA - Semantic Knowledge-Based Execution Monitoring for Mobile Robots
Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007Co-Authors: Abdelbaki Bouguerra, Lars Karlsson, Alessandro SaffiottiAbstract:We describe a novel intelligent execution monitoring approach for mobile robots acting in indoor environments such as offices and houses. Traditionally, monitoring execution in mobile robotics amounted to looking for discrepancies between the model-based predicted state of executing an action and the real world state as computed from sensing data. We propose to employ Semantic Knowledge as a source of information to monitor execution. The key idea is to compute implicit expectations, from Semantic domain information, that can be observed at run time by the robot to make sure actions are executed correctly. We present the Semantic Knowledge representation formalism, and how Semantic Knowledge is used in monitoring. We also describe experiments run in an indoor environment using a real mobile robot.
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IROS - Handling uncertainty in Semantic-Knowledge based execution monitoring
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Abdelbaki Bouguerra, Lars Karlsson, Alessandro SaffiottiAbstract:Executing plans by mobile robots, in real world environments, faces the challenging issues of uncertainty and environment dynamics. Thus, execution monitoring is needed to verify that plan actions are executed as expected. Semantic domain-Knowledge has been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when a robot moves into a room asserted to be an office, it would expect to see a desk and a chair. We propose to extend the Semantic Knowledge-based execution monitoring to take uncertainty in actions and sensing into account when verifying the expectations derived from Semantic Knowledge. We consider symbolic probabilistic action models, and show how Semantic Knowledge is used together with a probabilistic sensing model in the monitoring process of such actions. Our approach is illustrated by showing test scenarios run in an indoor environment using a mobile robot.
Peter W Foltz - One of the best experts on this subject based on the ideXlab platform.
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quantitative approaches to Semantic Knowledge representations
Discourse Processes, 1998Co-Authors: Peter W FoltzAbstract:(1998). Quantitative approaches to Semantic Knowledge representations. Discourse Processes: Vol. 25, Quantitative Approaches to Semantic Knowledge Representations, pp. 127-130.
Grzegorz Jacek Nalepa - One of the best experts on this subject based on the ideXlab platform.
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Semantic Knowledge Engineering Approach
Modeling with Rules Using Semantic Knowledge Engineering, 2018Co-Authors: Grzegorz Jacek NalepaAbstract:In this chapter we introduce the Semantic Knowledge Engineering approach. It is a development approach for Knowledge-based Systems that uses rule-based Knowledge representation. The core of the approach is the formalized rule representation method XTT. The motivation for the approach, along with its distinctive features are given. Then the SKE design process for rule-based systems is presented. SKE was developed to support a heterogeneous architecture of rule-based applications. The approach is well supported by a number of discussed software tools for Knowledge base design, generation of the executable rule format, and execution of the rule-based system. Furthermore, tools for rule analysis are discussed.
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Semantic Knowledge Engineering Approach
Modeling with Rules Using Semantic Knowledge Engineering, 2018Co-Authors: Grzegorz Jacek NalepaAbstract:In this chapter we introduce the Semantic Knowledge Engineering approach. It is a development approach for Knowledge-based Systems that uses rule-based Knowledge representation. The core of the approach is the formalized rule representation method XTT. The motivation for the approach, along with its distinctive features are given. Then the SKE design process for rule-based systems is presented. SKE was developed to support a heterogeneous architecture of rule-based applications. The approach is well supported by a number of discussed software tools for Knowledge base design, generation of the executable rule format, and execution of the rule-based system. Furthermore, tools for rule analysis are discussed.
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Semantic Knowledge Engineering - Main Concepts
2011Co-Authors: Grzegorz Jacek NalepaAbstract:The Semantic Knowledge Engineering approach aims at providing new design and analysis methods for rule-based in- telligent systems. It uses the XTTKnowledge representation for building modularized rule bases that form decision networks. The representation is formalized, thus allowing for the anayslys of the designed system with respect to its qualitative properties. The visual design is supported by practical tools.
Vijayan Sugumaran - One of the best experts on this subject based on the ideXlab platform.
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Using Semantic Knowledge to improve web query processing
Lecture Notes in Computer Science, 2006Co-Authors: Jordi Conesa, Veda C. Storey, Vijayan SugumaranAbstract:Although search engines are very useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Prior research has attempted to use different types of Knowledge to improve web querying processing. This research presents a methodology for employing a specific body of Knowledge, ResearchCyc, which provides Semantic Knowledge about different application domains. Semantic Knowledge from ResearchCyc, as well as linguistic Knowledge from WordNet, is employed. An analysis of different queries from different application domains using the Semantic and linguistic Knowledge illustrates how more relevant results can be obtained.
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NLDB - Using Semantic Knowledge to improve web query processing
Natural Language Processing and Information Systems, 2006Co-Authors: Jordi Conesa, Veda C. Storey, Vijayan SugumaranAbstract:Although search engines are very useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Prior research has attempted to use different types of Knowledge to improve web querying processing. This research presents a methodology for employing a specific body of Knowledge, ResearchCyc, which provides Semantic Knowledge about different application domains. Semantic Knowledge from ResearchCyc, as well as linguistic Knowledge from WordNet, is employed. An analysis of different queries from different application domains using the Semantic and linguistic Knowledge illustrates how more relevant results can be obtained.
Jianguo Xiong - One of the best experts on this subject based on the ideXlab platform.
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IALP - Syntactic-Semantic Knowledge Representation Framework for Korean Verbs and Its Working Mechanisms
2018 International Conference on Asian Language Processing (IALP), 2018Co-Authors: Yude Bi, Jianguo XiongAbstract:The key to Knowledge base construction is Knowledge representation, in which the representation of syntactic-Semantic Knowledge plays a critical role. Adopting a syntax-based approach to Semantic description and based on the principles of integrated description, a syntactic-Semantic hierarchical framework for Korean verbs was proposed and a Knowledge base was constructed. Besides, the principles for the representation of syntactic-Semantic information of Korean verbs were discussed in detail, and the mechanisms for deriving a verb's syntactic-Semantic items were explored based on a syntactic-Semantic Knowledge base for Korean verbs.
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Syntactic-Semantic Knowledge Representation Framework for Korean Verbs and Its Working Mechanisms
2018 International Conference on Asian Language Processing (IALP), 2018Co-Authors: Jianguo XiongAbstract:The key to Knowledge base construction is Knowledge representation, in which the representation of syntactic-Semantic Knowledge plays a critical role. Adopting a syntax-based approach to Semantic description and based on the principles of integrated description, a syntactic-Semantic hierarchical framework for Korean verbs was proposed and a Knowledge base was constructed. Besides, the principles for the representation of syntactic-Semantic information of Korean verbs were discussed in detail, and the mechanisms for deriving a verb's syntactic-Semantic items were explored based on a syntactic-Semantic Knowledge base for Korean verbs.
