Artificial Intelligence CHA2555 Lee McCluskey Email [email protected]@hud.ac.uk CW3/10 Resources on:
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Transcript of Artificial Intelligence CHA2555 Lee McCluskey Email [email protected]@hud.ac.uk CW3/10 Resources on:
Artificial IntelligenceCHA2555
Lee McCluskey
Email [email protected]
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 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