Cognitive Science, Artificial Intelligence, and Complex Training

Author(s):  
W. Feurzeig
2021 ◽  
Vol 5 (5) ◽  
pp. 23
Author(s):  
Robert Rowe

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.


2015 ◽  
pp. 5-22 ◽  
Author(s):  
Gabriella Pravettoni ◽  
Raffaella Folgieri ◽  
Claudio Lucchiari

2011 ◽  
pp. 66-89 ◽  
Author(s):  
Joanna J. Bryson

Many architectures of mind assume some form of modularity, but what is meant by the term ‘module’? This chapter creates a framework for understanding current modularity research in three subdisciplines of cognitive science: psychology, artificial intelligence (AI), and neuroscience. This framework starts from the distinction between horizontal modules that support all expressed behaviors vs. vertical modules that support individual domain-specific capacities. The framework is used to discuss innateness, automaticity, compositionality, representations, massive modularity, behavior-based and multi-agent AI systems, and correspondence to physiological neurosystems. There is also a brief discussion of the relevance of modularity to conscious experience.


Author(s):  
Thomas J. Marlowe

Classical (Aristotelean or Boolean) logics provide a solid foundation for mathematical reasoning, but are limited in expressivity and necessarily incomplete. Effective understanding of logic in the modern world entails for the instructor and advanced students an understanding of the wider context. This chapter surveys standard extensions used in mathematical reasoning, artificial intelligence and cognitive science, and natural language reasoning and understanding, as well as inherent limitations on reasoning and computing. Initial technical extensions include equality of terms, integer arithmetic and quantification over sets and relations. To deal with natural reasoning, the chapter explores temporal and modal logics, fuzzy logic and probabilistic models, and relevance logic. Finally, the chapter considers limitations to logic and knowledge, via an overview of the fundamental results of Turing, Gödel, and others, and their connection to the state of mathematics, computing and science in the modern world.


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