Dr. Angela SchweringThe paper Learning from Inconsistencies in an Integrated Cognitive Architecture said
Whereas symbol-based systems, like deductive reasoning devices, knowledge bases, planning systems, or tools for solving constraint satisfaction problems, presuppose (more or less) the consistency of data and the consistency of results of internal computations, this is far from being plausible in real world applications, in particular, if we take natural agents into account. Furthermore in complex cognitive systems, that often contain a large number of different modules, inconsistencies can jeopardize the integrity of the whole system.
This paper addresses the problem of resolving inconsistencies in hybrid cognitively inspired systems on both levels, in single processing modules and in the overall system. We propose the hybrid architecture I-Cog as a flexible tool, that is explicitly designed to reorganize knowledge constantly and use occurring inconsistencies as a nonclassical learning mechanism.
Angela Schwering, Ph.D. was coauthor of this paper and is
modeling of predictive analogies by heuristic driven theory
projection at the
Institute of Cognitive Science
University of Osnabrück.
Although there is quite a long tradition of research in analogies, there aren’t many formal and algorithmic theories of analogical reasoning yet. Her project tries to close this gap by providing a mathematical or rather formal base for analogies, analogical reasoning, and related phenomena (like analogical learning, metaphors).
As a method, heuristic driven theory projection will be used, an approach that algorithmically produces a generalized theory for given source and target domains. The focus of research is located at the formal properties of theory projection and the development of a formal semantics for analogies. The practical aims are the modeling of predictive analogies and analogical learning. The essential contributions to current research that can be expected from this project are formalisms for the representation of analogies as well as proposals for a denotational semantics of analogies which allow a new perspective on this area. Furthermore, new and efficient algorithms for analogical reasoning will be developed, which allow us to test the theory on practical applications. Last but not least, her project will contribute to the conceptual analysis of analogies.
Angela coauthored Analogical Reasoning: A core of Cognition, Human-Level Intelligence, Analogy as Integrating Framework for Human-Level Reasoning, The Influence of Scale, Context and Spatial Preposition in Linguistic Topology. International Conference on Spatial Cognition, Restricted Higher-Order Anti-Unification for Analogy Making, I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities, Integrating Analogical and Inductive Learning at different Levels of Generalization, and Modeling Human-Level Intelligence by Integrated Cognition in a Hybrid Architecture. Read the full list of her publications!
Angela earned her Bachelors with the thesis Enterprise Application Integration – Grundlagen, Methoden und Techniken in 2001, her Masters with the thesis Repráentations- und Abfragemechanismen für geographische Informationen im Web in 2003, both from the Institute of Information Systems, Universitát Müster, Germany. She earned her Ph.D. with the thesis Semantic Similarity Measurement including Spatial Relations for Semantic Information Retrieval of Geo-Spatial Data in 2006 at the Institute of Geoinformatics, Universitát Müster, Germany.
Her research interests are Semantic Similarity Measurement, Analogies and Analogical Reasoning, Cognitive Modeling, Semantic Interoperability, Artificial General Intelligence, Information Management and Retrieval, Spatial Relations, Spatial Reasoning, and Spatial Cognition.