Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition.
Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition.
Antonio Lieto is a researcher in Artificial Intelligence at the Department of Computer Science of the University of Turin, Italy, and a research associate at the ICAR-CNR in Palermo, Italy. He is the current Vice-President of the Italian Association of Cognitive Science (2017-2022) and an ACM Distinguished Speaker on the topics of cognitively inspired AI and artificial models of cognition.
Inhaltsangabe
1 Cognitive science and artificial intelligence: death and rebirth of a collaboration When Cognitive Science was AI From the general problem-solver to the society of mind: cognitivist insights from the early AI era Heuristics and AI eras Modelling paradigms and AI eras: cognitivist and emergentist perspectives Death and rebirth of a collaboration 2 Cognitive and machine-oriented approaches to intelligence in artificial systems Nature- vs. machine-inspired approaches to artificial systems Functionalist vs. structuralist design approaches Levels of analysis of computational systems The space of cognitive systems Functional and structural neural systems Functional and structural symbolic systems 3 Principles of the cognitive design approach Classical, bounded, and bounded-rational models of cognition Resource-rationality models Kinds of explanations Levels of plausibility and the minimal cognitive grid (MCG) 4 Examples of cognitively inspired systems and application of the Minimal Cognitive Grid Modern AI systems: cognitive computing? Cognitive architectures SOAR ACT-R Two problems for the knowledge level in cognitive architectures Knowledge size and knowledge heterogeneity in SOAR and ACT-R DUAL PECCS 5 Evaluating the performances of artificial systems "Thinking" machines and Turing Test(s) The Chinese Room The Newell test for a theory of cognition The Winograd Schema Challenge DARPA challenges, RoboCup, and RoboCup@Home Comparison 6 The next steps The road travelled The way forward Towards a standard model of mind/common model of cognition Community
1 Cognitive science and artificial intelligence: death and rebirth of a collaboration When Cognitive Science was AI From the general problem-solver to the society of mind: cognitivist insights from the early AI era Heuristics and AI eras Modelling paradigms and AI eras: cognitivist and emergentist perspectives Death and rebirth of a collaboration 2 Cognitive and machine-oriented approaches to intelligence in artificial systems Nature- vs. machine-inspired approaches to artificial systems Functionalist vs. structuralist design approaches Levels of analysis of computational systems The space of cognitive systems Functional and structural neural systems Functional and structural symbolic systems 3 Principles of the cognitive design approach Classical, bounded, and bounded-rational models of cognition Resource-rationality models Kinds of explanations Levels of plausibility and the minimal cognitive grid (MCG) 4 Examples of cognitively inspired systems and application of the Minimal Cognitive Grid Modern AI systems: cognitive computing? Cognitive architectures SOAR ACT-R Two problems for the knowledge level in cognitive architectures Knowledge size and knowledge heterogeneity in SOAR and ACT-R DUAL PECCS 5 Evaluating the performances of artificial systems "Thinking" machines and Turing Test(s) The Chinese Room The Newell test for a theory of cognition The Winograd Schema Challenge DARPA challenges, RoboCup, and RoboCup@Home Comparison 6 The next steps The road travelled The way forward Towards a standard model of mind/common model of cognition Community
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