University of Wisconsin - MadisonCS 540 Lecture NotesC. R. Dyer

Introduction (Chapter 1)

What is AI?

Goals of AI

Some Application Areas of AI

Some AI "Grand Challenge" Problems

A Framework for Building AI Systems

  • Perception
    Intelligent biological systems are physically embodied in the world and experience the world through their sensors (senses). For an autonomous vehicle, input might be images from a camera and range information from a rangefinder. For a medical diagnosis system, perception is the set of symptoms and test results that have been obtained and input to the system manually. Includes areas of vision, speech processing, natural language processing, and signal processing (e.g., market data and acoustic data).
  • Reasoning
    Inference, decision-making, classification from what is sensed and what the internal "model" is of the world. Might be a neural network, logical deduction system, Hidden Markov Model induction, heuristic searching a problem space, Bayes Network inference, genetic algorithms, etc. Includes areas of knowledge representation, problem solving, decision theory, planning, game theory, machine learning, uncertainty reasoning, etc.
  • Action
    Biological systems interact within their environment by actuation, speech, etc. All behavior is centered around actions in the world. Examples include controlling the steering of a Mars rover or autonomous vehicle, or suggesting tests and making diagnoses for a medical diagnosis system. Includes areas of robot actuation, natural language generation, and speech synthesis.

    Some Fundamental Issues for Most AI Problems

    Design Methodology and Goals

    Alternatively, methodologies can be defined by choosing (1) the goals of the computational model, and (2) the basis for evaluating performance of the system:


    Think like humans
    "cognitive science"
    Ex. GPS


    Think rationally
    => formalize inference process
    "laws of thought"


    Act like humans
    Ex. ELIZA
    Turing Test


    Act rationally
    "satisficing" methods

    Most of AI work falls into Boxes 2 and 4. These don't rely on tests that correspond to human performance.

    Symbols versus Signals

    Most of AI built on an information processing model called a "Physical-Symbol System" (PSS) (Newell and Simon). Symbols usually correspond to objects in the environment. Symbols are physical patterns that can occur as components of an expression or symbol structure. A PSS is a collection of symbol structures plus processes that operate (i.e., create, modify, reproduce) expressions to produce other expressions. Hence, a PSS produces over time an evolving collection of symbol structures.

    => AI is the enterprise of constructing physical-symbol system that can reliably pass the Turing Test [or whatever your performance test is].

    Physical-Symbol System Hypothesis (Newell and Simon, 1976): A physical-symbol system has the necessary and sufficient means for general intelligent action.

    ==> Intelligence is a functional property and is completely independent of any physical embodiment.

    ==> Develop structural/functional theory of intelligence, i.e., what are the mechanisms, physical or formal structures, which form the basis of intelligent behavior.

    An alternative, less-symbolic paradigm: Neural Networks

    Copyright © 1996-2003 by Charles R. Dyer. All rights reserved.