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Mind design is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Unlike traditional empirical psychology, it is more oriented toward the how than the what. An experiment in mind design is more likely to be an attempt to build something and make it work--as in artificial intelligence--than to observe or analyze what already exists. Mind design is psychology by reverse engineering.When Mind Design was first published in 1981, it became a classic in the then-nascent fields of cognitive science and AI. This second edition retains four landmark essays from the first, adding to them one earlier milestone (Turing's Computing Machinery and Intelligence) and eleven more recent articles about connectionism, dynamical systems, and symbolic versus nonsymbolic models. The contributors are divided about evenly between philosophers and scientists. Yet all are philosophical in that they address fundamental issues and concepts; and all are scientific in that they are technically sophisticated and concerned with concrete empirical research.ContributorsRodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn, William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle, Herbert A. Simon, Paul Smolensky, Stephen Stich, A.M. Turing, Timothy van Gelder

Tabla de contenidos provista por Syndetics

• 1 What Is Mind Design?
• 1 Perspectives and things
• 1.1 The Turing test
• 1.2 Intentionality
• 1.3 Original intentionality
• 2 Computers
• 2.1 Formal systems
• 2.2 Automatic formal systems
• 2.3 Computers and intelligence
• 3 GOFAI
• 3.1 Interpreted formal systems
• 3.2 Intelligence by explicit reasoning
• 4 New-fangled Al
• 4.1 Connectionist networks
• 4.2 Embodied and embedded Al
• 5 What's missing from mind design?
• Notes
• 2 Computing Machinery and Intelligence
• 1 The imitation game
• 2 Critique of the new problem
• 3 The machines concerned in the game
• 4 Digital computers
• 5 Universality of digital computers
• 6 Contrary views on the main question
• 7 Learning machines
• Notes
• 3 True Believers: The Intentional Strategy and Why It Works
• 1 The intentional strategy and how it works
• 2 True believers as intentional systems
• 3 Why does the intentional strategy work?
• Notes
• 4 Computer Science as Empirical Inquiry: Symbols and Search
• 1 Symbols and physical symbol systems
• 1.1 Laws of qualitative structure
• 1.2 Physical symbol systems
• 1.3 Development of the symbol-system hypothesis
• 1.4 The evidence
• 1.5 Conclusion
• 2 Heuristic search
• 2.1 Problem Solving
• 2.2 Search in problem solving
• 2.3 Intelligence without much search
• 2.4 Conclusion
• 5 A Framework for Representing Knowledge
• 1 Frames
• 1.3 Artificial intelligence and human problem solving
• 1.11 Default assignment
• 2 Language, understanding, and scenarios
• 2.1 Words, sentences, and meanings
• 2.6 Scenarios
• 2.7 Scenarios and "questions"
• 2.8 Questions, systems, and cases
• 3 Learning, memory, and paradigms
• 3.1 Requests to memory
• 3.3 Excuses
• 3.5 Clusters, classes, and a geographic analogy
• 3.6 Analogies and alternative descriptions
• 3.8 Frames and paradigms
• Appendix: criticism of the logistic approach
• 6 From Micro-Worlds to Knowledge Representation: AI at an Impasse
• 1 The early seventies: micro-worlds
• 1.1 SHRDLU: understanding natural language
• 1.2 "Scene parsing" and computer vision
• 1.3 Learning new concepts or categories
• 2 The later seventies: knowledge representation
• 2.1 Frames and knowledge representation
• 2.2 Scripts and primitive actions
• 2.3 KRL: a knowledge-representation language
• 3 Conclusion
• Notes
• 7 Minds, Brains, and Programs
• Notes
• 8 The Architecture of Mind: A Connectionist Approach
• 1 Why brain-style computation?
• 1.1 The connectionist framework
• 1.2 Computational features of connectionist models
• 2 The state of the art
• 2.1 Some architecture
• 2.2 The scaling problem
• 2.3 The generalization problem
• 9 Connectionist Modeling: Neural Computation / Mental Connections
• 1 Levels of analysis: neural and mental structures
• 1.1 The symbolic paradigm
• 1.2 The subsymbolic paradigm
• 1.3 Semantic interpretation
• 2 The subsymbolic level
• 2.1 Subsymbolic computation
• 2.2 Subsymbolic inference and the statistical connection
• 3 Higher-level descriptions
• 3.1 The best-fit principle
• 3.2 Productions, sequential processing, and logical inference
• 3.3 The dynamics of activation patterns
• 3.4 Schemata
• 4 Conclusion
• 10 On the Nature of Theories: A Neurocomputational Perspective
• 1 The classical view of theories
• 2 Problems and alternative approaches
• 3 Elementary brainlike networks
• 4 Representation and learning in brainlike networks
• 5 Some functional properties of brainlike networks
• 6 How faithfully do these networks depict the brain?
• 7 Computational neuroscience: the naturalization of epistemology
• 8 Concluding remarks
• 11 Connectionism and Cognition
• Notes
• 12 Connectionism and Cognitive Architecture: A Critical Analysis
• 1 Introduction
• 1.1 Levels of explanation
• 2 The nature of the dispute
• 2.1 Complex mental representations
• 2.1.4 Representations as "distributed" over microfeatures
• 2.2 Structure-sensitive operations
• 2.2.1 Learning
• 2.2.2 Reasoning
• 3 The need for symbol systems: productivity, systematicity, and inferential coherence
• 3.1 Productivity of thought
• 3.2 Systematicity of cognitive representation
• 3.4 The systematicity of inference
• 3.5 Summary
• 4 The allure of connectionism
• 4.1 Replies: why the usual reasons given for preferring a connectionist architecture are invalid
• 4.1.1 Parallel computation and the issue of speed
• 4.1.2 Resistance to noise and physical damage (and the argument for distributed representation)
• 4.1.3 "Soft" constraints, continuous magnitudes, and stochastic mechanisms
• 4.1.4 Explicitness of rules
• 4.1.5 On "brain-style" modeling
• 4.2 Concluding comments: connectionism as a theory of implementation
• 5 Conclusion
• Notes
• 13 Connectionism, Eliminativism, and the Future of Folk Psychology
• 1 Introduction
• 2 Eliminativism and folk psychology
• 3 Propositional attitudes and common-sense psychology
• 4 A family of connectionist hypotheses
• 5 A connectionist model of memory
• 6 Objections and replies
• 7 Conclusion
• Notes
• 14 The Presence of a Symbol
• 1 A slippery LOT
• 2 The pocket Fodor
• 3 On being more explicit
• 4 Connectionism and explicit representation
• 5 Code-fixation: its symptoms and cure
• 6 All the world's a processor
• 7 Conclusions: from code to process
• Notes
• 15 Intelligence without Representation
• 1 Introduction
• 2 The evolution of intelligence
• A story
• 3 Abstraction as a dangerous weapon A continuing story
• 4 Incremental intelligence
• 4.1 Decomposition by function
• 4.2 Decomposition by activity
• 5 Who has the representations?
• 5.1 No representation versus no central representation
• 6 The methodology in practice
• 6.1 Methodological maxims
• 6.2 An instantiation of the methodology: Allen
• 6.3 A second example: Herbert
• 7 What this is not
• 7.1 It isn't connectionism
• 7.2 It isn't neural networks
• 7.3 It isn't production rules
• 7.4 It isn't a blackboard
• 7.5 It isn't German philosophy
• 8 Key ideas
• 8.1 Situatedness
• 8.2 Embodiment
• 8.3 Intelligence
• 8.4 Emergence
• 9 Limits to growth
• 16 Dynamics and Cognition
• 1 The governing problem
• 2 Two kinds of governor
• 3 Conceptual frameworks
• 4 Morals
• 5 Three kinds of system
• 6 Three conceptions of cognition
• 7 An example of dynamical research
• 8 Is the dynamical conception viable?
• Notes
• Acknowledgments
• Bibliography