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Auflistung Künstliche Intelligenz 29(3) - August 2015 nach Titel
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- ZeitschriftenartikelCan Machine Intelligence be Measured in the Same Way as Human intelligence?(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Besold, Tarek; Hernández-Orallo, José; Schmid, UteIn recent years the number of research projects on computer programs solving human intelligence problems in artificial intelligence (AI), artificial general intelligence, as well as in Cognitive Modelling, has significantly grown. One reason could be the interest of such problems as benchmarks for AI algorithms. Another, more fundamental, motivation behind this area of research might be the (implicit) assumption that a computer program that successfully can solve human intelligence problems has human-level intelligence and vice versa. This paper analyses this assumption.
- ZeitschriftenartikelExtending and Completing Probabilistic Knowledge and Beliefs Without Bias(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Beierle, Christoph; Kern-Isberner, Gabriele; Finthammer, Marc; Potyka, NicoCombining logic with probability theory provides a solid ground for the representation of and the reasoning with uncertain knowledge. Given a set of probabilistic conditionals like “If A then B with probability x”, a crucial question is how to extend this explicit knowledge, thereby avoiding any unnecessary bias. The connection between such probabilistic reasoning and commonsense reasoning has been elaborated especially by Jeff Paris, advocating the principle of Maximum Entropy (MaxEnt). In this paper, we address the general concepts and ideas underlying MaxEnt and leading to it, illustrate the use of MaxEnt by reporting on an example application from the medical domain, and give a brief survey on recent approaches to extending the MaxEnt principle to first-order logic.
- ZeitschriftenartikelHigher-Level Cognition and Computation: A Survey(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Stolzenburg, FriederHigher-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.
- ZeitschriftenartikelMarkus Knauff: Space to Reason—A Spatial Theory of Human Thought(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Hamami, Yacin
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015)
- ZeitschriftenartikelOn the Consistency of Approximate Multi-agent Probability Theory(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Madsen, Mathias WintherBayesian models have proven to accurately predict many aspects of human cognition, but they generally lack the resources to describe higher-order reasoning about other people’s knowledge. Recently, a number of suggestions have thus been made as to how these social aspects of cognition might be codified in computational reasoning systems. This paper examines one particularly ambitious attempt by Andreas Stuhlmüller and Noah Goodman, which was implemented in the stochastic programming language Church. This paper translates their proposal into a more conventional probabilistic language, comparing it to an alternative system which models subjective probabilities as random variables. Having spelled out their ideas in these more familiar and intuitive terms, I argue that the approximate reasoning methods used in their system have certain statistical consistency problems.
- ZeitschriftenartikelQualitative and Semi-Quantitative Inductive Reasoning with Conditionals(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Eichhorn, Christian; Kern-Isberner, GabrieleConditionals like “birds fly—if bird then fly” are crucial for commonsense reasoning. In this technical project report we show that conditional logics provide a powerful formal framework that helps understanding if-then sentences in a way that is much closer to human reasoning than classical logic and allows for high-quality reasoning methods. We describe methods that inductively generate models from conditional knowledge bases. For this, we use both qualitative (like preferential models) and semi-quantitative (like Spohn’s ranking functions) semantics. We show similarities and differences between the resulting inference relations with respect to formal properties. We further report on two graphical methods on top of the ranking approaches which allow to decompose the models into smaller, more feasible components and allow for local inferences.
- ZeitschriftenartikelSpecial Issue on Higher-Level Cognition and Computation(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Stolzenburg, Frieder
- ZeitschriftenartikelThe Pleasure will be Always on Our Side(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Ragni, Marco; Becker-Asano, Christian
- ZeitschriftenartikelThe RatioLog Project: Rational Extensions of Logical Reasoning(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Furbach, Ulrich; Schon, Claudia; Stolzenburg, Frieder; Weis, Karl-Heinz; Wirth, Claus-PeterHigher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia “Wikipedia”. In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase—because the knowledge may be incomplete and inconsistent—we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.