1. Artificial Intelligence CHA2555 Lee McCluskey Email lee@hud.ac.uklee@hud.ac.uk CW3/10 Resources on: http://scom.hud.ac.uk/scomtlm/cha2555/

2. 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..

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 ANPR Bar Code

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 Bar code Mars Rover -> Mission Control… Move from X to Y Pickup Rock Perform Experiment etc

12. ..., 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 of Teeside ] 13/07/2015University of Huddersfield

13. Example Application Areas Robotics Still huge challenges, but “low level” autonomous behaviour is now becoming well established (example – NASA’s latest robonauts) 13/07/2015University of Huddersfield Picture from www.carbonated.tv Robotic Football ;-)

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

15. Techniques Artificial 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 in Symbolic 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 HIGH LEVEL LOW LEVEL Machine Oriented VERY HIGH LEVEL DATA STRUCTURES EXPLICITLY REPRESENTING KNOWLEDGE

18. 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

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