Artificial IntelligenceCHA2555

Lee McCluskey

Email lee@hud.ac.uk

CW3/10

Resources on:http://scom.hud.ac.uk/scomtlm/cha2555/

CHA2555 - changed

Change from last year.The module specification has been updated : last year you had 2 perspectives (Term 1 Symbolic AI, Term 2: Subsymbolic with 2 different lecturers) This year we are integrating the course with 1 lecturer (me)

Effectively this will mean less emphasis on Neural Networks ..

OverviewResources: http://scom.hud.ac.uk/scomtlm/cha2555/

The course contains a combination of theory and practical in the area of (mostly symbolic) artificial intelligence

“My brain is a learning neural network” (Terminator 2)

No, its more likely to be symbolic AI …. ;-)

Overview First Term

Practical Prolog – an AI programming language Automated Planning Programs Games Programs

Theory Knowledge Representation, Logic, Search,

Heuristics, Automated Reasoning Planning Algorithms and Representation 2 person games algorithms

Overview Second Term

Tentative Knowledge Engineering Machine Learning Language Understanding

With applications such as Games, Semantic Web and UAVs …

Assessment

Practical Coursework given out Term 1, hand in Term 2 - 40% of assessment

Exam is 3 hours, and 60% of assessment

You have to do 4 Questions out of 6

c.1 out of 2 for semester 1

c.3 out of 4 for semester 2

Artificial Intelligence – its about three aspects1. Intelligent abilities2. Applications embedding intelligent

abilities3. Techniques for

implementing 1. in 2.

In this course we will study 3.

Artificial Intelligence – Intelligent Abilities

Sensing eg Seeing, hearing, recognising Understanding eg language understanding Communicating eg language generation Having beliefs, desires, intentions Reasoning and Problem Solving Planning and Acting to achieve goals Learning

Example Application Areas image processing

Bar code

ANPR

Bar Code

– Example Application Areas Chatbots, Language Translators..

Bar code

E.g.GOOGLE TRANSLATE

My son has grown another foot =>

Mon fils a grandi un autre pied.

Example Application Areas UAVs

Bar codeMars Rover ->

Mission Control…

Move from X to Y

Pickup Rock

Perform Experiment

etc

..., 38: (SHOW-SADNESS-OVER-FAMILY SHYLOCK SHYLOCK-RESIDENCE), ...40: (END-OF-PLAY SHYLOCK)

..., 29: (ASK-FOR-JUSTICE SHYLOCK DUKE COURTROOM)30: (SPEAK-OF-JUSTICE SHYLOCK ANTONIO DUKE COURTROOM)31: (SPEAK-OF-PERSECUTION SHYLOCK ANTONIO COURTROOM)32: (RECEIVE-MERCY-REQUEST SHYLOCK ANTONIO COURTROOM)33: (SHOW-MERCY SHYLOCK ANTONIO COURTROOM)34: (RECEIVE-VERDICT-MERCY SHYLOCK ANTONIO COURTROOM)

....19: (SHOW-DESPAIR-AT-ELOPEMENT SHYLOCK SHYLOCK-RESIDENCE)

.....4: (RECEIVE-LOAN-REQUEST SHYLOCK BASSANIO VENICE-RIALTO)5: (MAKE-BUSINESS-DECISION SHYLOCK BASSANIO VENICE-RIALTO)6: (RESPOND-TO-LOAN-REQUEST SHYLOCK BASSANIO VENICE-RIALTO)7: (RECEIVE-DINNER-INVITATION SHYLOCK BASSANIO VENICE-RIALTO)8: (REFUSE-DINNER-INVITATION SHYLOCK BASSANIO VENICE-RIALTO)9: (RECEIVE-LOAN-REQUEST SHYLOCK ANTONIO VENICE-RIALTO)10: (EXPRESS-ANGER-AT-PERSECUTION SHYLOCK ANTONIO VENICE-RIALTO)11: (ASK-ABOUT-LENDING-WITH-INTEREST SHYLOCK ANTONIO VENICE-RIALTO)12: (RESPOND-TO-LOAN-REQUEST SHYLOCK ANTONIO VENICE-RIALTO)13: (LEND-MONEY-AS-FAVOUR SHYLOCK ANTONIO VENICE-RIALTO)

Example Application Areas Narrative Generation

Goal: (end-of-play)

C1: (shown-despair-at-elopement shylock)

Initial state: (at shylock venice-rialto), ...

C2: (sealed-bond-over-loan shylock antonio)

C3: (received-verdict-of-court shylock)

Goal: (end-of-play)

C3: (received-verdict-of-court shylock)

C1: (shown-despair-at-elopement shylock)

Initial state: (at shylock venice-rialto), ...

C2: (sealed-bond-over-loan shylock antonio)

[extract from a presentation by Dr Julie Porteous, Univ ofTeeside ]

19/04/23 University of Huddersfield

Example Application Areas Robotics

Still huge challenges, but “low level” autonomous behaviour is now becoming well established (example – NASA’s latest robonauts)

19/04/23 University of HuddersfieldPicture from www.carbonated.tv

Robotic Football ;-)

TechniquesArtificial Neural Networks

A network of “simple” processing units that can be trained to simulate complex processing eg recognition

INPUTNODES

OUPUTNODES

Hidden Layers

A FEED-FORWARD ANN

Each link has an adjustable weightEach node takes inputs and produces an output

TechniquesArtificial Neural Networks ..

are really “sub-symbolic” techniques – like evolutionary computing (genetic algorithms) or swarm intelligence (connectionist approaches..)

Their main advantage is their “robustness” or lack of brittleness and their potential to scale-up.

ANNs are techniques within the area of Soft Computing which is primarily aimed at solving complex problems with techniques that allow for uncertainty, imprecision, approximation ..

Techniques inSymbolic AI...

In essence … Use Symbols to represent objects in the world; Use Logic to represent assertions about objects; Use automated inference to simulate reasoning

with assertions; Use heuristics to overcome complexity problems

Fundamental Assumption of Symbolic AI No 1:

To simulate intelligent behaviour you need

Special Logics – Modal, Temporal etc

First Order Logic – relations, properties, V, &, =>, not, variables, quantifiers, terms

Description Logic – classes, membership, properties, disjunction

Objects – state, inheritance, aggregation, polymorphism

Sets, maps, relations, RDBs

pointers, arrays, records

Numbers, characters

Bits, bytes

HIGH LEVEL

LOW LEVEL

Machine Oriented

VERY HIGH LEVEL DATA STRUCTURES EXPLICITLY REPRESENTING KNOWLEDGE

Fundamental Assumption of Symbolic AI No 2: To simulate intelligent behaviour you need

These algorithms are often “SEARCH” - based and “HEURISTIC”

ALGORITHMS THAT REASON WITH (REPRESENTATIONS OF) KNOWLEDGE

Symbolic AI Platforms

To investigate symbolic AI we need a HIGH LEVEL PLATFORM to do so.

We choose the programming language PROLOG to do so:

It has very high level data structures It is “easy” to implement reasoning / search

algorithms

Practical this week – self – study: introduction to Prolog

Prolog is a very high level, logical, declarative language useful for experimenting and prototyping AI algorithms.

Prolog programs are lists of Rules and Facts.

Practical: Work through the file “notes” as directed on the website

http://scom.hud.ac.uk/scomtlm/cha2555/

Summary

The course is (mainly) about Symbolic approaches to AI

Fundamental to symbolic AI is the use of High level logic-based data structures Algorithms which reason with logic-based data In symbolic AI, symbols represent entities in the

outside world We will use Prolog as a Platform for Symbolic AI