Chapter 1:
Computational Intelligence and
Knowledge
 What is Computational Intelligence (CI)? (1.1)
 Agents in the world (1.2)
 Representation & Reasoning (1.3)
 Examples of Applications Domains (1.4)
 Our Approach to Teaching CI
D. Poole, A. Mackworth, and R. Goebel, Computational Intelligence: A Logical Approach,
Oxford University Press, January 1998
What is Computational Intelligence? (1.1)
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 CI = The study of the design of intelligent agents
 An agent is something that acts in an environment
(dogs, airplanes, humans…)
 An intelligent agent is an agent that acts intelligently:
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–
–
–
Dr. Djamel Bouchaffra
its actions are appropriate for its goals and circumstances
it is flexible to changing environments and goals (it adapts)
it learns from experience
it makes appropriate choices given perceptual limitations and
finite computation
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
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What is Computational Intelligence? (1.1) (cont.)
 Artificial or Computational Intelligence?
– The field is often called Artificial Intelligence (confusing term:
artificial pearl is a fake pearl !).
– Scientific goal: to understand the principles that make
intelligent behavior possible, in natural or artificial systems.
– Engineering goal: to specify methods (programs) for the
design of useful, intelligent artifacts.
– Analogy between studying flying machines and thinking
machines (bats, birds vs. airplanes). In order to produce the
functionality of intelligent behavior, it is not necessary to
reproduce the structural connections of the human body
(Icarus goal).
Functional Equivalence  Structural Equivalence
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
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 Computational Intelligence researchers are
interested in testing general hypotheses about
the nature of intelligence.
 They do so by building machines that are
intelligent and which does not necessarily
mimic humans or group of agents.
 Can Computers really think ? [Turing] versus
Can airplanes really fly ?
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
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What is Computational Intelligence? (1.1) (cont.)
 Central hypotheses of CI
– Symbol-system hypothesis:

Reasoning is symbol manipulation.
– Church–Turing thesis:

Any symbol manipulation can be carried out on a Turing
machine
Turing Machine = idealization of a digital computer with an
unbounded amount of memory.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Agents in the world (1.2)
Dr. Djamel Bouchaffra
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CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Representation and Reasoning (1.3)
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 To use these inputs an agent needs a
representation of them.
 Knowledge
 Most common sense tasks rely on a lot of
knowledge.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Representation and Reasoning (1.3) (cont.)
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 Representation and Reasoning Systems
– Problem  representation  computation

A representation and reasoning system (RRS) consists of
– Language to communicate with the computer.
– A way to assign meaning to the symbols.
– Procedures to compute answers or solve problems.

Example of RRS’s:
– Programming languages: Fortran, C++,…
– Natural Language
**** We want something between these extremes.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications using RRS (1.4)
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 The Autonomous delivery robot roams around
an office environment and delivers coffee,
parcels,…
 The Diagnostic assistant helps a human
troubleshoot problems and suggests repairs or
treatments. e.g., electrical problems, medical
diagnosis.
 The Infobot searches for information on a
computer system or network.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
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Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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 Autonomous Delivery Robot
– Example inputs:
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
Prior knowledge: knowledge about its capabilities, objects
it may encounter, info about its environment such as maps.

Past experience: which actions are useful and when, what
objects are there, how its actions affect its position.

Goals: what it needs to deliver and when, tradeoffs
between acting quickly and acting safely.

Observations: about its environment from cameras, sonar,
sound, laser range finders, or keyboards.
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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– What does the Delivery Robot need to do?
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
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.
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
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CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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 Diagnostic Assistant
– Example inputs:

Prior knowledge: how switches and lights work, how malfunctions
manifest themselves, what information tests provide, the side
effects of repairs.

Past experience: the effects of repairs or treatments, the
prevalence of faults or diseases.

Goals: fixing the device and tradeoffs between fixing or replacing
different components, patient live longer (medical diagnosis).

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Observations: symptoms of a device or a patient.
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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– Subtasks for the diagnostic assistant
Derive the effects of faults and interventions.
 Search through the space of possible fault or diseases.
 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.

Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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 Infobot
– Infobot interacts with an information environment:
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
It takes in high-level, perhaps informal, queries.

It finds relevant information.

It presents the information in a meaningful way.
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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– Infobot inputs
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
Prior knowledge: the meaning of words, the types of
information sources, and how to access information.

Past experience: where information can be obtained, the
relative speed of various servers, and information about the
preferences of the user.

Goals: the information it needs to find out; tradeoffs
between the volume and quality of information and the
expense involved.

Observations: what information is at the current sites; what
links are available; the load on various connections.
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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– Example subtasks for the Infobot
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
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 lack of knowledge, and determine
conflicting knowledge.

Use default reasoning about where to obtain different information.

Make tradeoffs between cheap but unreliable information sources
and more expensive but more comprehensive [cost vs. quality]

Learn about what knowledge is available where, and what
information the user is interested in [user preferences].
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Applications (1.4) (cont.)
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 Common Tasks of the Three Domains
– 1. Modeling the environment: The robot needs to be able to model the physical
environment, its own capabilities, and the mechanics of delivering parcels.
For the diagnostic assistant, it should know how treatments work, or for the
infobot, it should know how the information can be obtained.
– 2. Evidential reasoning or perception: Given some observations (such as
symptoms) about the world, determine what is really in the world (disease)
(system identification, or diagnosis).
– 3. Action: Given a model of the world and a goal, determine what should be done
in term of actions (robot roves, treatments and tests).
– 4. Learning from past experiences: Learn about the specific case and the
population of cases (how the robot sensors actually work?, know how well
particular diseases responds to particular treatments? the particular patient
background.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge
Our approach to teaching CI
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 Our goal is to study these four tasks.
 We build the tools needed from the bottom up.
 We start with some restrictive simplifying
assumptions and lift them as we get more
sophisticated representations and more
powerful reasoning strategies.
 The theory and practice are built from solid
foundations.
Dr. Djamel Bouchaffra
CS 426 Artificial Intelligence, Ch.1: Computational Intelligence & Knowledge