1. Artificial Intelligence Simon Lynch This session what is the subject about? course structure an example AI system

2. intelligence (?) learning problem solving vision language production speech generation expertise knowledge speech recognition language understanding social interaction negotiation, collaboration & deceit

3. the subject area criteria activities requiring intelligence (primate, ant...) common tools & techniques approach grounded in cross disciplinary science advanced techniques for code/system construction styles symbolic – “open box”(expert systems, planning, etc) non-symbolic – “black-box”(neural nets, some evolution)

4. this course tools, techniques & applications 90% symbolic, 10% non-symbolic agent based lab sessions experimentation some programming

5. types of agency small scaleeg: Netlogo autonomous but limited communication genericeg: Jade, Boris (.NET?) communicative & distributed BDIeg: 2APL, Goal, Jason intentional (have plans & goals)

6. a brief example a multi-modal dialog system parallel I/O some networked components user dialog implemented as MAS

7. a multimodal dialog system

8. Natural Language Processing what it does what is involved why is it difficult

9. sentence  structured rep n s of meaning "how old is my help3.doc file?" Lisp: (query (file-detail 'date "C:/help3.doc")) "the large cat chased the rat" Logic: (  1 s 1 large(s 1 )  feline(s 1 ))  (  1 s 2 rodent(s 2 ))  chased(s 1, s 2 ) "the young boy ate a bad apple" CD Graph...see next page...

10. CD graph "the young boy ate a bad apple"

11. what is involved symbolic computation ie: symbols manipulated by symbol processors search & inference knowledge representation techniques

12. example sentences the old man the boats my car drinks petrol I saw the Eiffel Tower flying to Paris he opened the door with the key he opened the door with the squeaking hinge the boy kicked the ball under the tree the boy kicked the wall under the tree put the bottles in the box on the shelf by the door