ì  Artificial  Intelligence  

Magy  Seif  El-‐Nasr  

Instructor:        Magy  Seif  El-‐Nasr              Associate  Professor              Northeastern  University              445A  Ryder  Hall                Email:  magy@northeastern.edu  

 Teaching  Assistant:    David  Batelu    

           Masters  Student                College  of  Computer  and  InformaGon  Sciences                Email:  batelu.d@husky.neu.edu  

 

Outline  of  the  class  

ì  IntroducGon  to  the  Course  

ì  IntroducGon  to  the  teaching  staff  

ì  Going  over  Syllabus  

 

ì  Introductions  

Me!  

Magy  Seif  El-‐Nasr  

ì  Born  in  Cairo  Egypt  

 

ì Wanted  to  be  a  Surgeon    (parents  are  both  medical    professors)  

Magy  Seif  El-‐Nasr  

ì  Took  a  Computer    Graphics  class  in    High  School  

 

ì  That  was  1990  

 

ì  Took  my  first  class  in    CS  in  high  school  

Magy  Seif  El-‐Nasr  

ì  I  declared  CS  as  my  major  at  American  University  in  Cairo  (1991)  

 

ì  Directed  in  Theatre    while  studying  

Magy  Seif  El-‐Nasr  

ì  Master’s  in  CS  at  Texas  A&M  

ì  Studied  under  Lo[i  Zadeh’s  Student  John  Yen  

Magy  Seif  El-‐Nasr  

ì  But  studied  psychology  of  emoGons  

Build  believable    characters  

Magy  Seif  El-‐Nasr  

ì  Master’s  in  CS  at  Texas  A&M  

ì  But  studied  psychology  of  emoGons  

Build  believable    characters  

Magy  Seif  El-‐Nasr  ì  So  I  went  to  do  my  PhD  at  Northwestern  with  

Andrew  Ortony  and  Ian  Horswill  

ì  To  do  this:  

Build  believable  characters  

Magy  Seif  El-‐Nasr  

ì  Natural  Language  ì  Decision  Making    

Magy  Seif  El-‐Nasr  

ì  AI  at  Northwestern  ì  Logic  ì  Problem  Solvers    

Magy  Seif  El-‐Nasr  

ì  AI  at  Northwestern  ì  RoboGcs  

Magy  Seif  El-‐Nasr  ì  But  I  was  interested  in  how  to  create  innovaGve  

experiences  that  engage  so  many  people  of  different  ages,  and  cultures  

Magy  Seif  El-‐Nasr  

ì  PhD  in  CS  at  Northwestern  

ì  Study  theatre  and  film  

innova=ve    experience  engaging  a  market  

Magy  Seif  El-‐Nasr  

ì  For  Games:  everything  mabers  

Level  Design  Concept   Texturing,  lighGng,  Framing  

Feedback  visual/audio/hapGc   Mechanics/how  to  lead  player  

Magy  Seif  El-‐Nasr  ì  For  Games:  everything  mabers  

Sound  Physics  

Combat  System   Leveling  up  and  progression  

Current  Projects  

ì  Believable  Characters    and  InteracGve  NarraGve  

Current  Projects  

ì  Game  User  Experience  ì  Games  User  Research  Methodology  ì  GUR  SIG  Steering  Commibee  ì  Game  AnalyGcs:  Data  Mining    

Current  Projects  

ì  InnovaGve  Game  Design  (e.g.,  adapGve  gaming,  visualizaGon  to  sGmulate  moGvaGon)  

ì  INTRODUCTIONS  QuesGonnaire  #  1  

ì  Syllabus  Take  a  look  

AI  

ì  Concepts  ì  Agents  ì  Search  Algorithms  ì  Logic  and  Inference  ì  Knowledge  RepresentaGon  ì  Planning  ì  Decision-‐making  ì  Machine  Learning  and  Data  Mining  ì  Fuzzy  Logic  

ì  Programming  ì  Python  ì  Data-‐driven  programming  

 

Teaching  Method  

ì  Website  hbp://www.northeastern.edu/magy/courses/AI/AI.html  

ì  Schedule:    hbp://www.northeastern.edu/magy/courses/AI/schedule.html  

ì  Communica=on  and  resources:  Piazza  hbps://piazza.com/northeastern/spring2013/cs41005100/home  

Teaching  Method  

ì  Learning  by  doing  

ì  All  concepts  follow  with  assignments  

ì  Lots  of  Gme  to  apply  concepts  

ì  Focus  on  both:    ì  criGcal  thinking,  algorithmic  thinking,  working  

collaboraGvely,  programmer  challenges      ì  Algorithms  and  details  

Books  

ì  Required:  ì  Stuart  Russell  and  Peter  Norvig.  Ar#ficial  Intelligence:  A  

Modern  Approach,  3rd  EdiGon.  PrenGce  Hall,  2009.  

ì  Recommended:  ì  Ken  Forbus  and  Johan  de  Kleer.  Building  Problem  Solvers.  A  

Bradford  Book,  1993.  

Conduct  

ì  No  Cell  phones  or  texts  

ì  No  facebook,  twiber,  etc.  unless  it  is  part  of  project  work  

ì  Late:  more  than  7  minute  late  2%  off  

ì  AHendance:  -‐5%  for  each  class  missed  with  no  viable  excuse  

ì  Use  of  others’  code  or  assets  need  to  be  credited  

Format  

ì  Read  chapters  assigned  before  class  

ì  First  30  minutes  of  class:  discussion  of  the  topic  of  the  chapter  

ì  Second  30  minutes:  concept  introducGon  

ì  Third  30  minutes:  applicaGon  of  the  concept  and  class  assignment.  

ì  Recommended  books  should  be  used  as  supplements  for  the  work  you  will  do  on  the  project  and  assignments  

Evaluation  

Assignments:  35%    ì  Individual  Assignment  0  (5%)  ì  Individual  Assignments  1  and  2  (10%  each)  ì  Pair  Programming  Assignment  (10%)    

Quiz:  20%  

Project:  30%  ì  Group  (4-‐5)  ì  5  parts:  pitch  (0%),  plan  (0%),  iteraGons  3  (10%  each),  final  

(10%)  ì  Each  iteraGon  is  graded  –  prototype  is  graded  ì  Management  (5%)  ì  Documents  submibed  allow  you  to  go  to  the  next  stage  

Schedule    

Homework  for  Friday  

ì  Read  Chapter  1,  for  a  quiz  on  the  topic  on  Friday.  

ì  Sign  up  on  piazza:  hbps://piazza.com/northeastern/spring2013/cs41005100  

ì  Put  in  a  post  on  piazza  a  response  to  the  introducGons  post