Artificial intelligence
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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

  • 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

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


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


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