Artificial Intelligence

							Artificial Intelligence

             AI in 1999: IJCAI 99

Ian Gent
ipg@cs.st-and.ac.uk
   Artificial Intelligence

                  AI in 1999

Part I :    Practical 1: Imitation Game
Part II:    AI in 1999: IJCAI 99
Part III:   Case based reasoning
Practical 1: The Turing Test

 Write a program to play the imitation game

 Some practical stuff:
    This is practical 1 of 2.
    Each will carry equal weight, I.e. 10% of total credit
    You may use any implementation language you wish
    Deadline(s) are negotiable
       to be decided this week


                                                              3
Practical 1: The Turing Test

 Write a program to play the imitation game
 Aim:
   to give practical experience in implementing an AI system
    for the most famous AI problem
 Objectives:
   after completing the practical, you should have:
      implemented a dialogue system for conversation on a topic of you
       choice
      gained an appreciation of some of the basic techniques necessary
      realised some of the possibilities and limitations of dialogue
       systems
                                                                      4
Some techniques you might use

 Pattern matching:
    my boyfriend made me … -> your boyfriend made you …
       I/me/my … -> you/you/your …
 Keyword identification & response
    my mother said …. -> tell me more about your family
 Deliberate errors
    34957 + 70764  105621
    mistypings
 Non sequiturs
    “Life is like a tin of sardines. You’re always looking for the
     key”                                                          5
Some pointers

 How to pass the Turing test by cheating
   Jason Hutchens, available on Course web pages
 Weizenbaum’s original paper on Eliza
   Comms ACM 1968




                                                    6
Your task

   Choose a domain of discourse, e.g. Harry Potter
   Implement a system to converse on this subject
   Submit your program code, report, two dialogues
   Program code
     in any language you wish
     I need an executable version to converse with
         e.g. via Web interface, PC/Mac executable, Unix
          executable on a machine I can access
         consult me beforehand if in doubt

                                                            7
Your task

 Report
   A summary of the main techniques used and how they
    work in your system
   a critical appreciation the main strengths and weaknesses
    of your system
 (at least) Two Dialogues
   at least one dialogue with yourself
      to allow you to show off your system at its best
   at least one dialogue with another automated system
      e.g. Eliza on the web, a colleague’s system
                                                            8
What I am looking for
 A functioning program
   using appropriate technique(s) for playing the imitation
    game
   need not have thousands of canned phrases
   need not be world standard
   should illustrate understanding of how to write programs to
    play the imitation game
 A report summarising what you have done
   should be a minor part of the work for the practical
   no set word limit but probably just a few pages
 Some illustrative dialogues
   illustrating techniques and points in your report         9
IJCAI 99

 IJCAI 99 in Stockholm, Sweden, August 1999
   associated events such as workshops tutorial #
 IJCAI = International Joint Conference on AI
   leading AI conference
   every two years, odd years
       started in 1969
   other main conferences are AAAI, ECAI
       American Association for AI, five out of six years (really)
       European Conference on AI, even years

                                                                 10
Topics at IJCAI 99, Volume 1

   Automated Reasoning (32 papers)
   Case Based Reasoning (6)
   Papers responding to IJCAI-97 challenges (10)
   Cognitive Modelling (8)
   Constraint Satisfaction (12, should’ve been 13)
   Distributed AI (12)
   Computer Game Playing (4)
   Knowledge Based Applications (9)

                                                      11
Topics at IJCAI 99, Volume 2

   Machine Learning (29 papers)
   Natural Language Processing (11)
   Planning and Scheduling (13)
   Qualitative Reasoning and Diagnosis (12)
   Robotics and Perception (7)
   Search (8)
   Software Agents (3)
   Temporal Reasoning (3)
   Uncertainty and Probabilistic Reasoning (16)
                                                   12
IJCAI 99

 Every published paper passes peer review process
    usually three experts review paper
    programme committee selects best papers from these
 A co-operative effort …
      37 members of the programme committee
      400 reviewers
      195 papers published
      only 26% of total submissions
      such a high standard that my submission was rejected!
 The state of the art of AI research in winter 98/99
                                                               13
Two Best Papers

 Two papers were selected by the P.C. as best
   IJCAI best paper awards always a bit of a lottery
 “A distributed case-based reasoning application for
  engineering sales support”
   Ian Watson, Dan Gardingen
 “Learning in Natural Language”
   Dan Roth
 I will talk about Watson & Gardingen’s paper
   much more readable than Roth’s
   illustrates Case based reasoning, another area of AI
                                                           14
Distributed case based …

 Ian Watson,
    AI-CBR, University of Salford
 Dan Gardingen,
    Western Air Ltd, Fremantle, Australia
 “A distributed case-based reasoning application for
  engineering sales support”
    Proceedings of IJCAI-99, pages 600-605
 A $32,000 project over 6 months to trial system
 Eventually fielded, $127,000 in Pentium notebooks
 Company estimates system made it $476,000 in 1st year
                                                          15
Distributed case based …

 Sales engineers distributed around Australia
 Quoting for Air conditioning/Heating systems
 Each quotation may be complicated
    sales engineers not qualified to quote
    fax details to central company
    wait for central engineers to supply quotation
 Company previously used database of past installations
    hard for sales staff to find similar quotes
 How could Case based reasoning system help?

                                                           16
Case based reasoning

 a problem solving strategy using existing cases
   to automate ‘knowledge reuse’
   assume previous cases have been correctly dealt with
   cases might have been addressed by humans
 associate with a case a set of feature-value pairs
   together form a unique index for the case
   possibly weight features with importance score
 use existing case database to help with new cases
   calculate index of new case
   find some number of the ‘closest’ cases
   use these to help treat new case                       17
Cases for HVAC
 HVAC = heating, ventilation, air conditioning
 Each case contains 60 fields for retrieval
   plus further fields describing installation
   plus links to ftp area for download
 Aim is to find some ‘nearest neighbour’ cases
 From these, sales staff can look at a small number
  of similar cases, and adapt quotes
 Quotes confirmed at central site
   In trial, expertise of central engineers never used
       just for checking quotes that the sales staff proposed
 One benefit is saving in central experts time                  18
Finding similar cases
 Finding the similar cases is not rocket science
 Remember, aim is to find a few similar cases
   can be used by field staff as basis for new quote
   want a manageable number (e.g. 20)
 Main technique is to relax values of features
   e.g. “item Athol_B23” becomes “T31_fan_coil”
      where Athol_B23 is one specific type of T31_fan_coil
      allows retrieval of installations using other types
   e.g. “temperature = 65 F” becomes “60F < T < 65F”
 Knowledge engineering used to find relaxations
   e.g. use of domain experts to advise on suitable relaxations
                                                              19
Distributed reasoning...

 System was distributed using Java & XML
 Server uses relaxation to produce reasonable
  number of items, e.g. a few hundred
 Pushed to client side applet via XML
   runs simple nearest neighbour algorithm to find closest set
   Simply minimise similarity measure

      i f(Ti,Si) wi
      where summation over features i
         • f(Ti,Si) difference measure on feature i between cases S, T
         • wi is weight of feature i
   obtain full details of closest set by ftp                            20
How did this win the lottery?

 Not exactly rocket science
   I’ve almost presented all the technical details already
   Web, Java, and HTML in paper can’t have hurt it!
 Shows a real world application
   saved a company some real money
 Shows maturity of an AI technique
   here, case based reasoning
   fielded good application in 6 months for only $32,000



                                                              21