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Artificial Intelligence CHA2555

Lee McCluskey Email lee@hud.ac.uk CW3/10 Resources on: http://scom.hud.ac.uk/scomtlm/cha2555/. Artificial Intelligence CHA2555. Change from last year.

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Artificial Intelligence CHA2555

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  1. Lee McCluskey Email lee@hud.ac.uk CW3/10 Resources on: http://scom.hud.ac.uk/scomtlm/cha2555/ Artificial IntelligenceCHA2555

  2. 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 .. CHA2555 - changed

  3. Overview • Resources: • 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 …. ;-)

  4. 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

  5. Overview Second Term • Tentative • Knowledge Engineering • Machine Learning • Language Understanding • With applications such as Games, Semantic Web and UAVs …

  6. 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

  7. Artificial Intelligence – its about three aspects • 1. Intelligent abilities • 2. Applications embedding intelligent abilities • 3. Techniques for • implementing 1. in 2. • In this course we will study 3.

  8. 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

  9. Example Application Areas image processing Bar Code Bar code ANPR

  10. – 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.

  11. Example Application Areas UAVs Mars Rover -> Bar code Mission Control… Move from X to Y Pickup Rock Perform Experiment etc

  12. Example Application Areas Narrative Generation Initial state: (at shylock venice-rialto), ... Initial state: (at shylock venice-rialto), ... ..... 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) C2: (sealed-bond-over-loan shylock antonio) C2: (sealed-bond-over-loan shylock antonio) .... 19: (SHOW-DESPAIR-AT-ELOPEMENT SHYLOCK SHYLOCK-RESIDENCE) C1: (shown-despair-at-elopement shylock) C1: (shown-despair-at-elopement 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) C3: (received-verdict-of-court shylock) C3: (received-verdict-of-court shylock) ..., 38: (SHOW-SADNESS-OVER-FAMILY SHYLOCK SHYLOCK-RESIDENCE), ... 40: (END-OF-PLAY SHYLOCK) [extract from a presentation by Dr Julie Porteous, Univ of Teeside ] Goal: (end-of-play) Goal: (end-of-play) University of Huddersfield

  13. 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) Robotic Football ;-) Picture from www.carbonated.tv

  14. TechniquesArtificial Neural Networks • A network of “simple” processing units that can be trained to simulate complex processing eg recognition Each link has an adjustable weight Each node takes inputs and produces an output Hidden Layers INPUT NODES OUPUT NODES A FEED-FORWARD ANN

  15. 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 ..

  16. 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

  17. 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 VERY HIGH LEVEL DATA STRUCTURES EXPLICITLY REPRESENTING KNOWLEDGE HIGH LEVEL Machine Oriented LOW LEVEL

  18. Fundamental Assumption of Symbolic AI No 2: ALGORITHMS THAT REASON WITH (REPRESENTATIONS OF) KNOWLEDGE • To simulate intelligent behaviour you need These algorithms are often • “SEARCH” - based and • “HEURISTIC”

  19. 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

  20. 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/

  21. 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

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