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Artificial Intelligence. AI in 1999: IJCAI 99. Ian Gent [email protected] 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.

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artificial intelligence

Artificial Intelligence

AI in 1999: IJCAI 99

Ian Gent

[email protected]

artificial intelligence1

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
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
practical 1 the turing test1
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
some techniques you might use
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”
some pointers
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
your task
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
your task1
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
what i am looking for
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
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
topics at ijcai 99 volume 1
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)
topics at ijcai 99 volume 2
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)
ijcai 991
  • 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
two best papers
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
distributed case based
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
distributed case based1
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?
case based reasoning
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
cases for hvac
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
finding similar cases
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
distributed reasoning
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
how did this win the lottery
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