Auflistung Künstliche Intelligenz 35(3-4) - Oktober 2021 nach Erscheinungsdatum
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- ZeitschriftenartikelCorrection to: Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Sioutis, Michael
- ZeitschriftenartikelIt’s the Meaning That Counts: The State of the Art in NLP and Semantics(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Hershcovich, Daniel; Donatelli, LuciaSemantics , the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. Nevertheless, how semantics is understood in NLP ranges from traditional, formal linguistic definitions based on logic and the principle of compositionality to more applied notions based on grounding meaning in real-world objects and real-time interaction. “Semantic” methods may additionally strive for meaningful representation of language that integrates broader aspects of human cognition and embodied experience, calling into question how adequate a representation of meaning based on linguistic signal alone is for current research agendas. We review the state of computational semantics in NLP and investigate how different lines of inquiry reflect distinct understandings of semantics and prioritize different layers of linguistic meaning. In conclusion, we identify several important goals of the field and describe how current research addresses them.
- ZeitschriftenartikelConsciousness: Just Another Technique?(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Barthelmeß, Ulrike; Furbach, UlrichThis note is intended as a contribution to the discussion whether artificial systems can have consciousness. Based on the notion of Tononi’s Information Integration Theory we will argue, that AI systems that have to reason with large knowledge bases need such techniques in order to handle them efficiently. We furthermore discuss mind-wandering and creativity on this basis.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021)
- ZeitschriftenartikelThe AI Methods, Capabilities and Criticality Grid(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Schmid, Thomas; Hildesheim, Wolfgang; Holoyad, Taras; Schumacher, KingaMany artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI- $$\hbox {MC}^2$$ MC 2 ) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI- $$\hbox {MC}^2$$ MC 2 Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.
- ZeitschriftenartikelDesigning a Uniform Meaning Representation for Natural Language Processing(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Van Gysel, Jens E. L.; Vigus, Meagan; Chun, Jayeol; Lai, Kenneth; Moeller, Sarah; Yao, Jiarui; O’Gorman, Tim; Cowell, Andrew; Croft, William; Huang, Chu-Ren; Hajič, Jan; Martin, James H.; Oepen, Stephan; Palmer, Martha; Pustejovsky, James; Vallejos, Rosa; Xue, NianwenIn this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries.
- ZeitschriftenartikelThe PANDA Framework for Hierarchical Planning(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Höller, Daniel; Behnke, Gregor; Bercher, Pascal; Biundo, SusanneDuring the last years, much progress has been made in hierarchical planning towards domain-independent systems that come with sophisticated techniques to solve planning problems instead of relying on advice in the input model. Several of these novel methods have been integrated into the PANDA framework , which is a software system to reason about hierarchical planning tasks. Besides solvers for planning problems based on plan space search, progression search, and translation to propositional logic, it also includes techniques for related problems like plan repair, plan and goal recognition, or plan verification. These various techniques share a common infrastructure, like e.g. a standard input language or components for grounding and reachability analysis. This article gives an overview over the PANDA framework, introduces the basic techniques from a high level perspective, and surveys the literature describing the diverse components in detail.
- ZeitschriftenartikelStance Detection Benchmark: How Robust is Your Stance Detection?(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Schiller, Benjamin; Daxenberger, Johannes; Gurevych, IrynaStance detection (StD) aims to detect an author’s stance towards a certain topic and has become a key component in applications like fake news detection, claim validation, or argument search. However, while stance is easily detected by humans, machine learning (ML) models are clearly falling short of this task. Given the major differences in dataset sizes and framing of StD (e.g. number of classes and inputs), ML models trained on a single dataset usually generalize poorly to other domains. Hence, we introduce a StD benchmark that allows to compare ML models against a wide variety of heterogeneous StD datasets to evaluate them for generalizability and robustness. Moreover, the framework is designed for easy integration of new datasets and probing methods for robustness. Amongst several baseline models, we define a model that learns from all ten StD datasets of various domains in a multi-dataset learning (MDL) setting and present new state-of-the-art results on five of the datasets. Yet, the models still perform well below human capabilities and even simple perturbations of the original test samples (adversarial attacks) severely hurt the performance of MDL models. Deeper investigation suggests overfitting on dataset biases as the main reason for the decreased robustness. Our analysis emphasizes the need of focus on robustness and de-biasing strategies in multi-task learning approaches. To foster research on this important topic, we release the dataset splits, code, and fine-tuned weights.
- ZeitschriftenartikelClimbing the Hill of Computational Semantics(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Hershcovich, Daniel; Donatelli, Lucia
- ZeitschriftenartikelArtificial Intelligence: Mind, Computer and the Dance of the Wu Li Masters(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Siekmann, JörgIn these days of exuberant fantasies about the future development of artificial intelligence—mostly written by people who have never in their lives developed an AI program—the GFFT (Society for the Promotion of Technology Transfer) has also unleashed a competition on future AI scenarios to honour Wolfgang Bibel. Because I was allowed to give the laudatory speech for Wolfgang, I was also asked to contribute something to the pen. And because, despite everything else, it is not reprehensible to think about the future, I could not refrain from doing so. Here is my somewhat expanded contribution.