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Minsky, M. Papert A framework for representing knowledge. Winston Ed. New York: Mc Graw-Hill. Newell, A. Simon Computer science as empirical inquiry: symbols and search. Communications of the Assoc. Triple-loop learning as foundation for profound change, individual cultivation, and radical innovation. Construction processes beyond scientific and rational knowledge. Designing and enabling interfaces for collaborative knowledge creation and innovation. Computers and Human Behavior , The section discussions and exams will emphasize these critical skills, in part by asking students to design and critique experiments related to the course material.
The general goal of this course is to provide advanced students in cognitive science and computer science with the skills to develop computational models of human cognition. Computational modeling is one of the central methods in cognitive science research, and can help to provide insight into how people solve the challenging problems posed by everyday life, as well as how to bring computers closer to human performance for some of these problems. The course will explore three ways in which researchers have attempted to formalize cognition -- symbolic approaches, neural networks, and probability and statistics -- considering the strengths and weaknesses of each.
At the conclusion of this course, students should have the ability to solve problems in the three different approaches to formalizing cognition covered in the course, through a series of six problem sets which involve some programming in Matlab. Students are be able to collaborate when solving problems, but each student must write his or her own code, write the document containing his or her answers independently, and list the people he or she worked with on his or her problem set.
There is also a take-home final exam. The goal of this course is to understand and apply computational methods to the core problems of artificial intelligence: reasoning, decision making, learning, and perception.
[PDF] Minds Brains and Computers: Perspectives in Cognitive Science and Artificial Intelligence
CS provides an introduction to the full range of topics studied in artificial intelligence, with emphasis on the core aspects of intelligent systems: problem solving, reasoning, decision making, learning, and perception, including the mathematical foundations of these activities. Topics include search, planning, logical modeling and inference, probabilistic modeling and inference, utility-based decision making, statistical learning, natural language processing, vision, and robotics.
The course supports the cognitive science curriculum by 1 teaching foundational modeling techniques, 2 examining algorithms which produce complex behavior associated with cognition in humans, and 3 giving students experience in implementing such techniques and algorithms in engineered systems. The learning goals for Ling include familiarity with the goals and key concepts of the scientific study of language and speech, including the.
The overall objective of this course is to understand and critically evaluate classical and contemporary issues in the philosophy of mind, including the mind-body problem, mental causation, freedom of the will, the nature of intentionality, the nature of consciousness, the structure of human action and the relation of cognition and volition. As I teach the course, I regard it as, in a sense, giving the philosophical foundations of cognitive science.
To this end, I examine various alternative conceptions of the nature of cognitive science including Strong Artificial Intelligence, cognitivism, connectionism and my own view, which I call Biological Naturalism. Students are expected to learn to think for themselves about these issues.