CSM6120
Introduction to Intelligent Systems
Knowledge representation
rkj@aber.ac.uk
What is knowledge?
Representation

AI agents deal with knowledge (data)




Facts (believe & observe knowledge)
Procedures (“how to” knowledge)
Meaning (relate & define knowledge)
Right representation is crucial



Early realisation in AI
Wrong choice can lead to project failure
Active research area
Choosing a representation

For certain problem solving techniques




Examples




‘Best’ representation already known
Often a requirement of the technique
Or a requirement of the programming language (e.g. Prolog)
First order theorem proving… first order logic
Inductive logic programming… logic programs
Neural networks learning… neural networks
Some general representation schemes

Suitable for many different (and new) AI applications
Knowledge representation

Representation of



Declarative knowledge (what, objects, structure)
Procedural knowledge (how, actions, performance)
Representation formalisms

Declarative knowledge


Frames, Semantic Networks, Inheritance Hierarchies, Schemata,...
Procedural knowledge

Algorithms, Procedures, Plans, Rules,...
What is a Logic?

A language with concrete rules




Many ways to translate between languages



No ambiguity in representation (may be other errors!)
Allows unambiguous communication and processing
Very unlike natural languages e.g. English
A statement can be represented in different logics
And perhaps differently in same logic
Not to be confused with logical reasoning

Logics are languages, reasoning is a process (may use logic)
What is a Logic?

Knowledge can be represented using first-order predicate
logic

More on this later in the module
Non-logical representations

Logic representations have restrictions and can be hard to
work with


Many AI researchers searched for better representations
Non-logical






Semantic networks
Conceptual graphs
Frames
Scripts
Production rules
...
What we’ve ignored

Objects in the world tend to be related to each other




The state of the world can change over time




Classes, superclasses & subclasses
Part / whole hierarchies
Properties are inherited across relationships
Explicit representation of time
Frame problem
Non-monotonic reasoning
We must reason without complete knowledge
Closed world assumption

Not all knowledge is “black & white” (later modules on
CSM…)


Uncertainty
Statistics, fuzzy logic
Classes

“I want to buy a basketball”



Objects organized into a hierarchy by class



I want to buy BB27341 (NO)
I want to buy an object that is a member of the basketball class (YES)
BB27341  basketballs
basketballs  balls
Facts (objects) & rules (classes)





All balls are round
All basketballs are <size> in diameter
BB27341 is red, white and blue
BB27341 is a basketball
(Therefore BB27341 is round and <size> in diameter)
Inheritance

If a property is true of a class, it is true of all subclasses of
that class

If a property is true of a class, it is true of all objects that
are members of that class

(If a property is true of a class, it is true of all objects that
are members of subclasses of that class)

There are exceptions (to be dealt with later…)
Part/whole inheritance

A cow has 4 legs


The cow is in the field


All of the cow’s parts are brown
The cow is happy


All of the cow’s parts are also in the field
The cow is (entirely) brown


Each leg is part of the cow
All of the cow’s parts are happy (?)
Note: some properties are inherited by parts, others are not.
This is generally made explicit by rules, such as

part-of(x,y) and location(y,z) -> location(x,z)
Defaults and exceptions

Exception for a single object


Set a property of the object to the (exception) value
Default for a class


Set a property of the class to the (default) value
If there are multiple default values, the one “closest” to the object wins
Example

All birds can fly
Birds with broken wings are birds, but cannot fly
Penguins are birds, but they cannot fly
Magical penguins are penguins that can fly

Who can fly?







Tweety is a bird
Peter is a penguin
Penelope is a magical penguin
Note that beliefs can be changed as new information comes in
that changes the classification of an object
Semantic networks

Semantic networks are essentially a generalization of
inheritance hierarchies


Each node is an object or class
Each link is a relationship



is-a (the usual subclass or element relationship)
has-part or part-of
Any other relationship that makes sense in context

Note: semantic networks pre-dated OOP

Inheritance: follow one member-of link, as many subclass or
other links as necessary
Graphical representation

Graphs easy to store in a computer

To be of any use must impose a formalism


Jason is 15, Bryan is 40, Arthur is 70, Jim is 74
How old is Julia?
Semantic networks

Because the syntax is the same, we can guess that Julia’s
age is similar to Bryan’s
Semantic networks

Knowledge represented as a network or graph
has-part
Animal
subclass
head
subclass
Reptile
Mammal
subclass
lives-in
Africa
Elephant
size
large
instance
Nellie
apples
likes
Semantic networks

By traversing network we can find:



But: meaning of semantic networks not always well defined




That Nellie has a head (by inheritance)
That certain concepts related in certain ways (e.g., apples and elephants)
Are all Elephants large, or just typical elephants?
Do all Elephants live in the “same” Africa?
Do all animals have the same head?
For machine processing these things must be defined
Frames

Devised by Marvin Minsky, 1974

Incorporates certain valuable human thinking
characteristics:


Expectations, assumptions, stereotypes. Exceptions. Fuzzy
boundaries between classes
The essence of this form of knowledge representation is
typicality, with exceptions, rather than definition
Frames

Frames allow more convenient “packaging” of facts about
an object
mammal:
subclass: animal
elephant:
subclass: mammal
size: large
has-part: trunk
Nellie:
instance: elephant
likes: apples

We use the terms “slots” and “slot values”
Frames

Frames often allowed you to say which things were just
typical of a class, and which were definitional, so couldn’t
be overridden
Elephant:
subclass: mammal
has-part: trunk
* colour: grey
* size: large

Frames also allow multiple inheritance (Nellie is an
Elephant and is a circus animal)
Frame representations

Semantic networks where nodes have structure



When agent faces a new situation




Frame with a number of slots (age, height, ...)
Each slot stores specific item of information
Slots can be filled in (value may be another frame)
Filling in may trigger actions
May trigger retrieval of other frames
Inheritance of properties between frames

Very similar to objects in OOP
Example: Frame representation
Flexibility in frames

Slots in a frame can contain







Information for choosing a frame in a situation
Relationships between this and other frames
Procedures to carry out after various slots filled
Default information to use where input is missing
Blank slots: left blank unless required for a task
Other frames, which gives a hierarchy
Can also be expressed in first order logic
How frames are organised

In the higher levels of the frame hierarchy, typical knowledge
about the class is stored

The value in a slot may be a range or a condition

In the lower levels, the value in a slot may be a specific value,
to overwrite the value which would otherwise be inherited
from a higher frame

Note that a frames system may allow multiple inheritance but,
if it does so, it must make provision for cases when inherited
values conflict...
Multiple inheritance and views

Multiple inheritance


Possibly conflicting information ( = ambiguity)



Sub-concept inherits descriptions from several superconcepts
Skeptical reasoners: “don’t know” (no conclusion)
Credulous reasoners: “whatever” (several conclusions)
Views


Description of concept from different viewpoints
Inheritance of multiple, complementing descriptions

e.g. view computer as machine or as equipment
Multiple inheritance - views
Multiple inheritance - ambiguity
subclass
Person
pacifist
non-pacifist
Republican
Quaker
instance
subclass
instance
Nixon
Other varieties of structured object

Knowledge representation researchers - particularly Roger
Schank and his associates - devised some interesting variations
on the theme of structured objects

In particular, they invented the idea of scripts (1973)

A script is a description of a class of events in terms of contexts,
participants, and sub-events
Scripts

Rather similar to frames: uses inheritance and slots; describes
stereotypical knowledge



(i.e. if the system isn't told some detail of what's going on, it assumes
the "default" information is true)
but concerned with events
These are somewhat out of the mainstream of expert systems
work

More a development of natural-language-processing research
Scripts

Why represent knowledge in this way?

Because real-world events follow stereotyped patterns


Human beings use previous experience to understand verbal accounts;
computers can use scripts instead
Because people, when relating events, leave large amounts of
assumed detail out of their accounts

People don't find it easy to converse with a system that can't fill in
missing conversational detail
Non-monotonic logic

Once true (or false) does not mean always true (or false)

As information arrives, truth values can change (Penelope is a
bird, penguin, magic penguin)

Implementations

Circumscription



Bird(x) and not abnormal(x) -> flies(x)
We can assume not abnormal(x) unless we know abnormal(x)
Default logic

“x is true given x does not conflict with anything we already know”
Truth maintenance systems

These systems allow truth values to be changed during
reasoning (belief revision)

When we retract a fact, we must also retract any other fact
that was derived from it





Penelope is a bird
(can fly)
Penelope is a penguin
(cannot fly)
Penelope is magical
(can fly)
Retract (Penelope is magical) (cannot fly)
Retract (Penelope is a penguin)
(can fly)
Types of TMS

Justification-based TMS



For each fact, track its justification (facts and rules from which it was
derived)
When a fact is retracted, retract all facts that have justifications leading
back to that fact, unless they have independent justifications
Assumption-based TMS



Represent all possible states simultaneously
For each fact, use list of assumptions that make that fact true; each world
state is a set of assumptions
When a fact is retracted, change state sets
Rule-based systems
Knowledge Base
Database
Rule: IF-THEN
Fact
Inference Engine
Explanation Facilities
User Interface
User
Structured objects: final comments

At best, structured objects provide data & control
structures which are more flexible than those in
conventional languages, and so more suited to simulating
human reasoning

However, if we just want an AI system to act rationally
(rather than think/act humanly), then logic might be
better...
Resources

A good introduction to the area:

http://www.cse.buffalo.edu/~shapiro/Papers/kr.pdf