The Road to Intelligence Is Paved with a Million Million Expert Systems

Christine Alvarado6 November 2002

A Dangerous Idea?

You need knowledge to be intelligent

OK, So Why Is this Dangerous?

What is intelligence?Many ideas: passing the Turing Test,

displaying common sense, etc.Perception

What is knowledge?Specific facts about the worlde.g. Humans have two legs, breakfast is

served in the morning, etc.

A More Dangerous Idea

We should explicitly represent specific knowledge within perceptual computer systems

Questions I will address:What does this mean?Why should we do this?How can we do this?

Data-Driven vs. Concept-Driven Perception

Conceptual Information

Sensory Input

The Importance of Context

The Importance of Context

The Importance of Context

The Importance of Context

Another Example

Another Example

2) 1 + 3 = 4

1) 1 + 2 = 2

3) 5 + 6 = 10

Another Example

Where’s the Knowledge?

Conceptual KnowledgeCows, sheep and pigs are

all barnyard animalsStructural Knowledge

Cows’ eyes are above their noses

Cows have spotsCows have long noses

Not included in most recognizers

Only implicitly included in most recognizers

We should represent this knowledge explicitly!

Feature-Based Models

Feature Extraction

Structural Knowledge hidden within network Which part of the

network represents “cows have legs”

Conceptual Knowledge absent

Why Make Knowledge Explicit?

To include conceptual knowledge in recognition

To understand why the computer system is making a mistake

To allow humans to construct recognizers

Including Conceptual Knowledge

Feature-based approach cannot easily handle conceptual knowledgeA picture of a cow encodes

structural knowledgeBut how do you

incorporate “cows and sheep are barnyard animals”?

Contextual Knowledge is Essential

Contextual knowledge determines the interpretation for this shape:

Once We Have Concepts, We Can Generalize

Face

eyes

nose

mouth

Eyes

Nose

Mouth

More Human Understanding

The system can explain the reasons for its beliefs

I think the shape is an arrow even though it only sort of

looks like one because this is a finite state machine.

Should We Get Rid of Feature-Based Recognizers?

Of course not!All these concepts have to bottom out

somewhereFeature-based recognizers very useful for

low level recognitionWe should integrate information from

feature-based recognition with explicitly represented knowledge

OK, This Sounds Good, But How?

Dynamically Constructed Object Oriented Bayesian NetworksCreate a fragment of a Bayesian Network

for each specific piece of knowledgeDynamically link them together as input

arrives

Representing Knowledge

(Define Shape And-Gate (components (Line L1) (Line L2) (Line L3) (Semi-Circle S)) (constraints (parallel L1 L2) (same-horiz-pos L1 L2) (same-length L1 L2) (connected S.p1 L3.p1) (connected S.p2 L3.p2) (meets L1.p2 L3) (meets L2.p2 L3)))

L1:L2:L3:S:

C1:C2:C3:C4:C5:C6:C7:

And-Gate

L1 L2 L3 S C1 C2 C7…

Description Network Fragment

Hierarchical Representation

To encode: “Often, an inverter precedes an and-gate”

(Define Shape-Composition Inv-Before-And

(components (And-Gate ag) (Inverter inv)) (constraints (connects inv ag) (precedes inv ag)))

AG:INV:

Inv-Before-And

AG INV C1 C2

And-Gate FragmentC1:C2:

Knowledge Acquisition

Start small: specific domainsSketches are a good place to begin

Simpler than vision2D with a temporal component

We can put some of the knowledge in ourselves

Conclusion

Explicit representation of knowledge……both structural and conceptual

knowledge……is a powerful way to build intelligent

systems.