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Auflistung nach Autor:in "Ragni, Marco"

1 - 10 von 13
  • Zeitschriftenartikel
    Cognitive Complexity and Analogies in Transfer Learning
    (KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Ragni, Marco; Strube, Gerhard
    The ability to learn often requires transferring relational knowledge from one domain to another. It is difficult for humans and computers to identify the respective source domain from which relational characteristics can be applied to the target domain. An additional source of human reasoning difficulty is the complexity of the transformation function. In this article we investigate two domains in which the identification of relational patterns and of a transformation function are necessary: number series and geometrical analogy problems. Characteristics of the human processes are presented and existing cognitive models are discussed.
  • Zeitschriftenartikel
    Cognitive Reasoning: A Personal View
    (KI - Künstliche Intelligenz: Vol. 33, No. 3, 2019) Furbach, Ulrich; Hölldobler, Steffen; Ragni, Marco; Schon, Claudia; Stolzenburg, Frieder
    The adjective cognitive especially in conjunction with the word computing seems to be a trendy buzzword in the artificial intelligence community and beyond nowadays. However, the term is often used without explicit definition. Therefore we start with a brief review of the notion and define what we mean by cognitive reasoning . It shall refer to modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge involving among others the representation of knowledge where all processes from the acquisition and update of knowledge to the derivation of conclusions must be implementable and executable on appropriate hardware. We briefly introduce relevant approaches and methods from cognitive modeling, commonsense reasoning, and subsymbolic approaches. Furthermore, challenges and important research questions are stated, e.g., developing a computational model that can compete with a (human) reasoner on problems that require common sense.
  • Zeitschriftenartikel
    Cognitive Space and Spatial Cognition: The SFB/TR 8 Spatial Cognition
    (KI - Künstliche Intelligenz: Vol. 30, No. 1, 2016) Ragni, Marco; Barkowsky, Thomas; Nebel, Bernhard; Freksa, Christian
    Space and time are two of the most fundamental categories any human, animal, or other cognitive agent such as an autonomous robot has to deal with. They need to perceive their environments, make sense of their perceptions, and make interactions as embodied entities with other agents and their environment. The theoretical foundations and practical implications have been investigated from a cognitive perspective (i.e., from an information processing point of view) within the Sonderforschungsbereich/Transregio SFB/TR 8 Spatial Cognition (http://www.sfbtr8.spatial-cognition.de) over the past 12 years jointly by the Universities of Bremen and Freiburg. The research covered fundamental questions: what are the specific requirements of reasoning about space and time, for acting in space, and for any form of interaction including communication in spatio-temporal domains? It has been a success story in all research lines from foundational research to applications of spatial cognition in robotics, interaction and communication. The SFB/TR 8 actually shaped a new research field by extending a previous subfield of cognitive science with its own interdisciplinary techniques.
  • Zeitschriftenartikel
    Cooperative Human Artificial Intelligence
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Ragni, Marco
  • Zeitschriftenartikel
    Editorial
    (KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Ragni, Marco
  • Zeitschriftenartikel
    Expertise depends on reasoning through alternative scenarios
    (KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Dohn, Nina Bonderup; Ragni, Marco
  • Zeitschriftenartikel
    Higher-Level Cognition and Computation: A Survey
    (KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Stolzenburg, Frieder
    Higher-level cognition is one of the constituents of our human mental abilities and subsumes reasoning, planning, language understanding and processing, and problem solving. A deeper understanding can lead to core insights to human cognition and to improve cognitive systems. There is, however, so far no unique characterization of the processes of human cognition. This survey introduces different approaches from cognitive architectures, artificial neural networks, and Bayesian modeling from a modeling perspective to vibrant fields such as connecting neurobiological processes with computational processes of reasoning, frameworks of rationality, and non-monotonic logics and common-sense reasoning. The survey ends with a set of five core challenges and open questions relevant for future research.
  • Zeitschriftenartikel
    Special Issue on Higher-Level Cognition and Computation
    (KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Stolzenburg, Frieder
  • Zeitschriftenartikel
    Survey: Artificial Intelligence, Computational Thinking and Learning
    (KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Dohn, Nina Bonderup; Kafai, Yasmin; Mørch, Anders; Ragni, Marco
    Learning is central to both artificial intelligence and human intelligence, the former focused on understanding how machines learn, the latter concerned with how humans learn. With the growing relevance of computational thinking, these two efforts have become more closely connected. This survey examines these connections and points to the need for educating the general public to understand the challenges which the increasing integration of AI in human lives pose. We describe three different framings of computational thinking: cognitive, situated, and critical. Each framing offers valuable, but different insights into what computational thinking can and should be. The differences between the three framings also concern the views of learning that they embody. We combine the three framings into one framework which emphasizes that (1) computational thinking activities involve engagement with algorithmic processes, and (2) the mere use of a digital artifact for an activity is not sufficient to count as computational thinking. We further present a set of approaches to learning computational thinking. We argue for the significance of computational thinking as regards artificial intelligence on three counts: (i) Human developers use computational thinking to create and develop artificial intelligence systems, (ii) understanding how humans learn can enrich artificial intelligence systems, and (iii) such enriched systems will be explainable. We conclude with an introduction of the articles included in the Special Issue, focusing on how they call upon and develop the themes of this survey.
  • Zeitschriftenartikel
    The Pleasure will be Always on Our Side
    (KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Becker-Asano, Christian