Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial...

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Introduction to AI Introduction to AI & & AI Principles (Semester 1) AI Principles (Semester 1) WEEK 4 WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK
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Page 1: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Introduction to AI Introduction to AI &&AI Principles (Semester 1)AI Principles (Semester 1)

WEEK 4WEEK 4

John BarndenProfessor of Artificial Intelligence

School of Computer ScienceUniversity of Birmingham, UK

Page 2: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Review:Further Difficulties with Language, 1

NL expressions can be syntactically ambiguous.

She hit the man with the telescope. 

Does the prepositional phrase with the telescope “attach” to hit or to the man?

I saw that gasoline can explode. 

Is can a noun or a verb here? Is that a complementizer or a demonstrative determiner?

Page 3: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Review:Further Difficulties with Language, 2

Lexical ambiguity: Words often have a range of distinct meanings, possibly without varying the part of speech (noun, verb, or whatever). 

ball, newspaper, bank, mole, sloth, ... 

The different meanings may or may not be related to each other.

When they are: POLYSEMY.  

When they’re not: HOMONYMY.

Page 4: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 3

Words can be vague in their meaning. 

several, recently, thousands, tall, air, book [as noun], chair, think, work [as verb or noun], ...

Page 5: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 4

Some words, such as pronouns and demonstrative determiners, are intrinsically contextual in their reference or other effects. 

I, she, everyone, every, that [pronoun or determiner], the, then, today, here, and so forth.

Page 6: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 5

Pronouns are anaphoric. But there are types of anaphor other than by pronouns, and they can get very implicit:

When John got home, he found he’d lost his key.

Probably the key to his front door. He may have many other keys.

John dropped the teapot. The handle broke.

What handle? None explicitly mentioned. 

John went to clear the windscreen, but the de-icer can was empty. 

Clearing a windscreen doesn’t have to involve a de-icer can at all – the phrase the de-icer is TELLING you that some de-icer in the clearing as well as then REFERRING to it. 

Page 7: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 6

What look like definite references may actually be indefinite or general, or may fail to refer to anything at all, or may be incorrect. 

Susan’s brother is helping her.

This is OK even if Susan has several brothers, at least in suitable contexts.

The dog is an intelligent mammal.

This would often/normally be a statement about dogs in general.

The square root of minus one doesn’t exist.

The man in the corner actually turned out to be a woman.

Page 8: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 7

“Quantification” is usually restricted even when there is no explicit restriction. 

When Tom came into the room, everyone laughed.

Probably means everyone in the room, with the further restriction of probably excluding Tom.

Similar points would have applied if the sentence had said five people laughed.

Did someone drop a pen?

Taken literally, the answer must be YES whatever the situation immediately at hand!

Page 9: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 8

Ellipsis: omitting bits of an utterance out because of parallelism with some other bit. 

Veronica donated a cake and Tom a bottle of wine. 

Suzie chose chocolate ice-cream and Tim strawberry. 

Suzie chose chocolate ice-cream. And Tim? Strawberry.

Page 10: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 9 Metonymy: referring to something indirectly, by referring to

something related to it.  Other passports this way. Peter likes Bach. Probably means he likes the music of Bach.

Plato is on the top shelf. John boiled the kettle. I’m parked in the corner. Probably means my car is!

That car wanted to overtake the lorry. The ham sandwich is ready for his coffee. England won. That area of San Francisco has decided to rebuild. John’s on the left of the picture.

Page 11: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 10 Metaphor: talking about something as if it were something else,

typically of a qualitatively different type. 

The department is a dictatorship.

Ralph is a pig. 

We’re galloping towards Easter. 

Easter’s rushing towards us. 

In the depths of her mind she knew he was right. 

Joy ran through him. 

The variable jumps from 1 to 100. 

The program dumps a lot of rubbish on the screen. 

Managerialism is creeping into academia. 

This list will run over the end of the slide if I don’t hold my horses!

Page 12: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Further Difficulties with Language, 11 Speech as opposed to text introduces extra problems,

notably:

Ambiguity and variability of the acoustic signal as regards what basic sounds are present.

Word boundaries – where?

Extra info such as intonation.

Page 13: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Language as Representation Scheme We use language to represent the world in our communications …

… an perhaps even within our minds, to some extent …

So why shouldn’t an AI system use human language as a way of internally representing things?

The above problems get in the way, so people have developed artificial representation schemes (logic, semantic networks, frames, …) …

… but—CAUTION STUDENTS—those problems are also symptoms of the amazing flexibility and economy of human language.

No artificial scheme yet developed has comparable power.

Page 14: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

WHY IS EVERYDAY AI CHALLENGING?

(continued)

Page 15: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Non-Language Examples

Interpreting highly ambiguous shapes, e.g. in hand drawings. Seeing what’s in this room.

Moving around and performing actions.

Planning actions.

Making a cup of tea/coffee/cocoa.

Page 16: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Seeing What’s in (e.g.) this Room

Difficulty of seeing where/what even the basic surfaces and edges are: very noisy, corrupted signal.

Problem made more complex by shadows, reflections, transparency, distortion, texture, uneven lighting.

Ambiguity of component shapes—is it a wheel? Is it a head? Is it an orange? No … it’s …

Occlusion of objects.

Seeing objects from different angles.

Jointed objects (e.g., people!) being in different configurations.

Soft and/or irregular objects such as clothes, crumpled paper.

Page 17: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Vision Needs in AI

Seeing what objects are around, where they are, how they’re moving, etc. … to some degree …

That degree being dependent on the system’s purposes.

Seeing what the “affordances” (basically, possibilities of manipulation) of objects are, even if the objects can’t be specifically identified.

Some special things:

Recognizing faces.

Recognizing facial expressions, hand gestures, body movements.

Reading text, mathematical formulas, diagrams, etc.

Page 18: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Moving Around and Performing Actions

Remembering a “mental map” of some sort and knowing where oneself is in such a map. Keeping track of movements. Recognizing landmarks.

Creating such a map.

Moving arms, etc. to reach objects efficiently and safely.

Grasping (etc.) objects safely.

Page 19: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Planning Actions: Examples

Planning is discussed in Callan ch. 9 (and 10).

planning the sequence of steps needed to buy presents for people

planning how to get to a particular place

planning the steps needed to build something

planning the steps needed to convince somebody of something

planning moves in a game (whether chess, a shoot-em-up, football, …)

Page 20: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Planning Actions: Some Needs

Envisaging the effect of a series of actions

Remembering different series and their effects, so as to investigate alternatives properly

Taking account of time constraints, effort constraints, etc.

Taking account of interactions between parts of the problem (preconditions, conflicts)

Recovering from unexpected problems and benefits when executing a plan: (partial) re-planning,

incl. because of unexpected changes in the world independent of one’s own actions

Allowing for unknown things (e.g., unknown action effects).

Page 21: Introduction to AI & AI Principles (Semester 1) WEEK 4 John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham,

Making a Hot Drink

Your suggestions please!