Computational Intelligence

Online Slides

September 2, 2002

These are slides from Computational Intelligence, A Logical Approach, Oxford University Press, 1998. Copyright ©David Poole, Alan Mackworth, Randy Goebel and Oxford University Press, 1999-2002. You may prefer the pdf interface for which these slides were designed (you can read these using the free acrobat reader.

Chapter 1, Lecture 1

Computational Intelligence
A Logical Approach

David Poole
Alan Mackworth
Randy Goebel

Oxford University Press

Lecture Overview

What is Computational Intelligence?

The study of the design of intelligent agents.

An agent is something that acts in an environment.

An intelligent agent is an agent that acts intelligently:

Artificial or Computational Intelligence?

Central hypotheses of CI

Symbol-system hypothesis: Church-Turing thesis:

Agents acting in an environment

Example agent: robot

Example agent: teacher

Example agent: medical doctor

Example agent: user interface


Example representations: machine language, C, Java, Prolog, natural language

What do we want in a representation?

We want a representation to be

Representation and Reasoning System

Problem => representation=> computation

A representation and reasoning system (RRS) consists of Example RRSs: We want something between these extremes.

Chapter 1, Lecture 2

Three Specific Application Domains

Domain for Delivery Robot

Autonomous Delivery Robot

Example inputs:

What does the Delivery Robot need to do?

Determine where Craig's office is. Where coffee is...

Find a path between locations.

Plan how to carry out multiple tasks.

Make default assumptions about where Craig is.

Make tradeoffs under uncertainty: should it go near the stairs?

Learn from experience.

Sense the world, avoid obstacles, pickup and put down coffee.

Domain for Diagnostic Assistant

Diagnostic Assistant

Example inputs:

Subtasks for the diagnostic assistant

Derive the effects of faults and interventions.

Search through the space of possible fault complexes.

Explain its reasoning to the human who is using it.

Derive possible causes for symptoms; rule out other causes.

Plan courses of tests and treatments to address the problems.

Reason about the uncertainties/ambiguities given symptoms.

Trade off alternate courses of action.

Learn about what symptoms are associated with the faults, the effects of treatments, and the accuracy of tests.


Infobot interacts with an information environment:

Infobot inputs

Example subtasks for the Infobot

Derive information that is only implicit in a knowledge base.

Interact in natural language.

Find good representations of knowledge.

Explain how an answer was derived and why some information was unavailable.

Make conclusions about the lack of knowledge or conflicting knowledge.

Make default inferences about where to find information.

Make tradeoffs between information quality and cost.

Learn the preferences of users.

Common Tasks of the Domains

Our approach to teaching CI

©David Poole, Alan Mackworth, Randy Goebel and Oxford University Press, 1998-2002