Introduction and Overview

The Big Questions

• What is computation, really?

• What role does computation play in cognitive science?

• What are symbolic models, connectionist models, and dynamical systems models of cognition, and how do they differ from one another?

• Are there any limitations to what can be computed, in principle?

• What might these limits imply about human or machine intelligence?

• What is embodied cognitive science?

Our Approach

• The best way to understand computation is to do lots of it. (Added benefit: it's great fun!)

• We need a precise, unambiguous notation for describing computations.

• Many notations are readily available, each with their pros and cons.

• We will use the programming language Scheme as our principal notation. (Added benefit: we can watch our computations in action!)

• Other notations we will use: Turing machines, C, possibly Java

• We will study embodied cognition by building and programming robots

• Many supplemental readings from AI and cognitive science
  • Turing: Computing Machinery and Intelligence
  • Newell and Simon: Computer Science as Empirical Inquiry
  • PDP Group: The Appeal of Parallel Distributed Processing
  • Brooks: Intelligence Without Representation
  • Searle: Minds, Brains, and Programs
  • and more...

• Main goal of course: gain ability to understand and implement computational models of intelligence

Why Scheme?

• Dialect of LISP, the lingua franca of artificial intelligence research for over forty years.

• Easy to learn syntax.

• Incredibly powerful and flexible.

• Naturally recursive style of programming

Introduction to Scheme

• History: lambda calculus (Church, 1930s), LISP (McCarthy, 1959), Scheme (Sussman and Steele, 1975)

• Atoms, lists, and S-expressions

• Symbols and values

• Quotation

• Functions and data

• car cdr cons atom? null? eq? list

Reading for Thursday: LS Chapters 1-3, GEB handout

Work through LS question-by-question, with paper and pencil