Cognitive Design for Artificial Minds

<p><em>Cognitive Design for Artificial Minds</em> 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. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science.</p><p>Beginning with an overview of the historical, methodological, and technical issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realize biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biological and cognitive accuracy of artificial systems in order to project and predict their explanatory power with respect to the natural systems taken as a source of inspiration.</p><p>Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science.</p> <p><strong>1 Cognitive science and artificial intelligence: death and rebirth of a collaboration</strong></p><p>When Cognitive Science was AI </p><p>From the general problem-solver to the society of mind: cognitivist insights from the early AI era </p><p>Heuristics and AI eras </p><p>Modelling paradigms and AI eras: cognitivist and emergentist perspectives </p><p>Death and rebirth of a collaboration </p><p>2 Cognitive and machine-oriented approaches to intelligence in artificial systems </p><p>Nature- vs. machine-inspired approaches to artificial systems </p><p>Functionalist vs. structuralist design approaches </p><p>Levels of analysis of computational systems </p><p>The space of cognitive systems </p><p>Functional and structural neural systems </p><p>Functional and structural symbolic systems </p><p>3 Principles of the cognitive design approach </p><p>Classical, bounded, and bounded-rational models of cognition </p><p>Resource-rationality models </p><p>Kinds of explanations </p><p>Levels of plausibility and the minimal cognitive grid (MCG) </p><p>4 Examples of cognitively inspired systems and application of the Minimal Cognitive Grid </p><p>Modern AI systems: cognitive computing? </p><p>Cognitive architectures </p><p>SOAR </p><p>ACT-R </p><p>Two problems for the knowledge level in cognitive architectures </p><p>Knowledge size and knowledge heterogeneity in SOAR and ACT-R </p><p>DUAL PECCS </p><p>5 Evaluating the performances of artificial systems </p><p>"Thinking" machines and Turing Test(s) </p><p>The Chinese Room </p><p>The Newell test for a theory of cognition </p><p>The Winograd Schema Challenge </p><p>DARPA challenges, RoboCup, and RoboCup@Home </p><p>Comparison </p><p>6 The next steps </p><p>The road travelled </p><p>The way forward </p><p>Towards a standard model of mind/common model of cognition </p><p>Community </p>