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Auflistung nach Schlagwort "Knowledge representation"

1 - 10 von 12
  • Zeitschriftenartikel
    15 Years of Semantic Web: An Incomplete Survey
    (KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Glimm, Birte; Stuckenschmidt, Heiner
    It has been 15 years since the first publications proposed the use of ontologies as a basis for defining information semantics on the Web starting what today is known as the Semantic Web Research Community. This work undoubtedly had a significant influence on AI as a field and in particular the knowledge representation and Reasoning Community that quickly identified new challenges and opportunities in using Description Logics in a practical setting. In this survey article, we will try to give an overview of the developments the field has gone through in these 15 years. We will look at three different aspects: the evolution of Semantic Web Language Standards, the evolution of central topics in the Semantic Web Community and the evolution of the research methodology.
  • Zeitschriftenartikel
    Anchoring Symbols to Percepts in the Fluent Calculus
    (KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Fichtner, Matthias
    The knowledge representation of an embodied, intelligent, cognitive agent typically relies on symbols denoting objects of the world on the top level and perceptual, structured data on the bottom level. The process of determining and maintaining the correct connection between a symbolic object identifier and its perceptual image, both referring to the same physical object, is called symbol anchoring.The dissertation presented here suggests a formal and general approach to the symbol anchoring problem, which enhances previous approaches in terms of generality and expressiveness.
  • Zeitschriftenartikel
    Episodic Memories for Safety-Aware Robots
    (KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Bartels, Georg; Beßler, Daniel; Beetz, Michael
    In the factories and distribution centers of the future, humans and robots shall work together in close proximity and even physically interact. This shift to joint human–robot teams raises the question of how to ensure worker safety. In this manuscript, we present a novel episodic memory system for safety-aware robots. Using this system, the robots can answer questions about their actions at the level of safety concepts. We built this system as an extension of the KnowRob framework and its notion of episodic memories. We evaluated the system in a safe physical human–robot interaction (pHRI) experiment, in which a robot had to sort surgical instruments while also ensuring the safety of its human co-workers. Our experimental results show the efficacy of the system to act as a robot’s belief state for online reasoning, as well as its ability to support offline safety analysis through its fast and flexible query interface. To this end, we demonstrate the system’s ability to reconstruct its geometric environment, course of action, and motion parameters from descriptions of safety-relevant events. We also show-case the system’s capability to conduct statistical analysis.
  • Zeitschriftenartikel
    GDL-II
    (KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Thielscher, Michael
    The Game Description Language (GDL) used in the past AAAI competitions allows to tell a system the rules of arbitrary finite games that are characterised by perfect information, but does not extend to games in which players have asymmetric information, e.g. about their own hand of cards, or which involve elements of chance like the roll of dice. Accordingly, contemporary general game-playing systems are not designed to play games such as Backgammon, Poker or Diplomacy. GDL-II (for: GDL with Incomplete/Imperfect Information) is a recent extension of the original description language that makes general game playing truly general, because it allows to describe just any finite game with arbitrary forms of randomness as well as imperfect/incomplete information. This brings along the challenge to build the next generation of truly general game-playing systems that are able to understand any game description given in GDL-II and to learn to master these types of games, too.
  • Zeitschriftenartikel
    Image Schema Combinations and Complex Events
    (KI - Künstliche Intelligenz: Vol. 33, No. 3, 2019) Hedblom, Maria M.; Kutz, Oliver; Peñaloza, Rafael; Guizzardi, Giancarlo
    Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for conceptualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL , and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of ‘cracking an egg.’
  • Zeitschriftenartikel
    Knowledge Based Perceptual Anchoring
    (KI - Künstliche Intelligenz: Vol. 27, No. 2, 2013) Daoutis, Marios
    Perceptual anchoring is the process of creating and maintaining a connection between the sensor data corresponding to a physical object and its symbolic description. It is a subset of the symbol grounding problem, introduced by Harnad (Phys. D, Nonlinear Phenom. 42(1–3):335–346, 1990) and investigated over the past years in several disciplines including robotics. This PhD dissertation focuses on a method for grounding sensor data of physical objects to the corresponding semantic descriptions, in the context of cognitive robots where the challenge is to establish the connection between percepts and concepts referring to objects, their relations and properties. We examine how knowledge representation can be used together with an anchoring framework, so as to complement the meaning of percepts while supporting better linguistic interaction with the use of the corresponding concepts. The proposed method addresses the need to represent and process both perceptual and semantic knowledge, often expressed in different abstraction levels, while originating from different modalities. We then focus on the integration of anchoring with a large scale knowledge base system and with perceptual routines. This integration is applied in a number of studies, where in the context of a smart home, several evaluations spanning from spatial and commonsense reasoning to linguistic interaction and concept acquisition.
  • Zeitschriftenartikel
    Knowledge-Based General Game Playing
    (KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Haufe, Sebastian; Michulke, Daniel; Schiffel, Stephan; Thielscher, Michael
    Although we humans cannot compete with computers at simple brute-force search, this is often more than compensated for by our ability to discover structures in new games and to quickly learn how to perform highly selective, informed search. To attain the same level of intelligence, general game playing systems must be able to figure out, without human assistance, what a new game is really about. This makes General Game Playing in ideal testbed for human-level AI, because ultimate success can only be achieved if computers match our ability to master new games by acquiring and exploiting new knowledge. This article introduces five knowledge-based methods for General Game Playing. Each of these techniques contributes to the ongoing success of our FLUXPLAYER (Schiffel and Thielscher in Proceedings of the National Conference on Artificial Intelligence, pp. 1191–1196, 2007), which was among the top four players at each of the past AAAI competitions and in particular was crowned World Champion in 2006.
  • Zeitschriftenartikel
    Querying Rich Ontologies by Exploiting the Structure of Data
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Bajraktari, Labinot
    Ontology-based data access (OBDA) has emerged as a paradigm for accessing heterogeneous and incomplete data sources. A fundamental reasoning service in OBDA, the ontology mediated query (OMQ) answering has received much attention from the research community. However, there exists a disparity in research carried for OMQ algorithms for lightweight DLs which have found their way into practical implementations, and algorithms for expressive DLs for which the work has had mainly theoretical oriented goals. In the dissertation, a technique that leverages the structural properties of data to help alleviate the problems that typically arise when answering the queries in expressive settings is developed. In this paper, a brief summary of the technique along with the different algorithms developed for OMQ for expressive DLs is given.
  • Zeitschriftenartikel
    Reasoning about Imprecise Beliefs in Multi-Agent Systems with PDT Logic
    (KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Martiny, Karsten; Möller, Ralf
    We present Probabilistic Doxastic Temporal (PDT) Logic, a formalism to represent and reason about probabilistic beliefs and their finite temporal evolution in multi-agent systems. This formalism enables the quantification of agents’ beliefs through probability intervals and incorporates an explicit notion of time. In this work, we give an overview of recent contributions on PDT Logic. After describing the syntax and semantics of this formalism, we show that two alternative representation forms are available to model problems in PDT Logic. Furthermore, we outline how abductive reasoning can be performed in PDT Logic and how this formalism can be extended to infinite time frames.
  • Zeitschriftenartikel
    Responsible Intelligent Systems
    (KI - Künstliche Intelligenz: Vol. 28, No. 3, 2014) Broersen, Jan
    The 2013 ERC-consolidator project “Responsible Intelligent Systems” proposes to develop a formal framework for automating responsibility, liability and risk checking for intelligent systems. The goal is to answer three central questions, corresponding to three sub-projects of the proposal: (1) What are suitable formal logical representation formalisms for knowledge of agentive responsibility in action, interaction and joint action? (2) How can we formally reason about the evaluation of grades of responsibility and risks relative to normative systems? (3) How can we perform computational checks of responsibilities in complex intelligent systems interacting with human agents? To answer the first two questions, we will design logical specification languages for collective responsibilities and for probability-based graded responsibilities, relative to normative systems. To answer the third question, we will design suitable translations to related logical formalisms, for which optimised model checkers and theorem provers exist. All three answers will contribute to the central goal of the project as a whole: designing the blueprints for a formal responsibility checking system. To reach that goal the project will combine insights from three disciplines: philosophy, legal theory and computer science.