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Intelligence Artificial Intelligence Ian Gent AI in 1999: IJCAI 99.

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Presentation on theme: "Intelligence Artificial Intelligence Ian Gent AI in 1999: IJCAI 99."— Presentation transcript:

1 Intelligence Artificial Intelligence Ian Gent AI in 1999: IJCAI 99

2 Intelligence Artificial Intelligence Part I :Practical 1: Imitation Game Part II: AI in 1999: IJCAI 99 Part III: Case based reasoning AI in 1999

3 3 Practical 1: The Turing Test zWrite a program to play the imitation game zSome practical stuff: yThis is practical 1 of 2. yEach will carry equal weight, I.e. 10% of total credit yYou may use any implementation language you wish yDeadline(s) are negotiable xto be decided this week

4 4 Practical 1: The Turing Test zWrite a program to play the imitation game zAim: yto give practical experience in implementing an AI system for the most famous AI problem zObjectives: yafter completing the practical, you should have: ximplemented a dialogue system for conversation on a topic of you choice xgained an appreciation of some of the basic techniques necessary xrealised some of the possibilities and limitations of dialogue systems

5 5 Some techniques you might use zPattern matching: ymy boyfriend made me … -> your boyfriend made you … xI/me/my … -> you/you/your … zKeyword identification & response ymy mother said …. -> tell me more about your family zDeliberate errors y34957 + 70764  105621 ymistypings zNon sequiturs y“ Life is like a tin of sardines. You’re always looking for the key”

6 6 Some pointers zHow to pass the Turing test by cheating yJason Hutchens, available on Course web pages zWeizenbaum’s original paper on Eliza yComms ACM 1968

7 7 Your task zChoose a domain of discourse, e.g. Harry Potter zImplement a system to converse on this subject zSubmit your program code, report, two dialogues zProgram code yin any language you wish yI need an executable version to converse with xe.g. via Web interface, PC/Mac executable, Unix executable on a machine I can access xconsult me beforehand if in doubt

8 8 Your task zReport yA summary of the main techniques used and how they work in your system ya critical appreciation the main strengths and weaknesses of your system z(at least) Two Dialogues yat least one dialogue with yourself xto allow you to show off your system at its best yat least one dialogue with another automated system xe.g. Eliza on the web, a colleague’s system

9 9 What I am looking for zA functioning program yusing appropriate technique(s) for playing the imitation game yneed not have thousands of canned phrases yneed not be world standard yshould illustrate understanding of how to write programs to play the imitation game zA report summarising what you have done yshould be a minor part of the work for the practical yno set word limit but probably just a few pages zSome illustrative dialogues yillustrating techniques and points in your report

10 10 IJCAI 99 zIJCAI 99 in Stockholm, Sweden, August 1999 yassociated events such as workshops tutorial # zIJCAI = International Joint Conference on AI yleading AI conference yevery two years, odd years xstarted in 1969 yother main conferences are AAAI, ECAI xAmerican Association for AI, five out of six years (really) xEuropean Conference on AI, even years

11 11 Topics at IJCAI 99, Volume 1 zAutomated Reasoning (32 papers) zCase Based Reasoning (6) zPapers responding to IJCAI-97 challenges (10) zCognitive Modelling (8) zConstraint Satisfaction (12, should’ve been 13) zDistributed AI (12) zComputer Game Playing (4) zKnowledge Based Applications (9)

12 12 Topics at IJCAI 99, Volume 2 zMachine Learning (29 papers) zNatural Language Processing (11) zPlanning and Scheduling (13) zQualitative Reasoning and Diagnosis (12) zRobotics and Perception (7) zSearch (8) zSoftware Agents (3) zTemporal Reasoning (3) zUncertainty and Probabilistic Reasoning (16)

13 13 IJCAI 99 zEvery published paper passes peer review process yusually three experts review paper yprogramme committee selects best papers from these zA co-operative effort … y37 members of the programme committee y400 reviewers y195 papers published yonly 26% of total submissions ysuch a high standard that my submission was rejected! zThe state of the art of AI research in winter 98/99

14 14 Two Best Papers zTwo papers were selected by the P.C. as best yIJCAI best paper awards always a bit of a lottery z“A distributed case-based reasoning application for engineering sales support” yIan Watson, Dan Gardingen z“Learning in Natural Language” yDan Roth zI will talk about Watson & Gardingen’s paper ymuch more readable than Roth’s yillustrates Case based reasoning, another area of AI

15 15 Distributed case based … zIan Watson, yAI-CBR, University of Salford zDan Gardingen, yWestern Air Ltd, Fremantle, Australia z“A distributed case-based reasoning application for engineering sales support” yProceedings of IJCAI-99, pages 600-605 zA $32,000 project over 6 months to trial system zEventually fielded, $127,000 in Pentium notebooks zCompany estimates system made it $476,000 in 1st year

16 16 Distributed case based … zSales engineers distributed around Australia zQuoting for Air conditioning/Heating systems zEach quotation may be complicated ysales engineers not qualified to quote yfax details to central company ywait for central engineers to supply quotation zCompany previously used database of past installations yhard for sales staff to find similar quotes zHow could Case based reasoning system help?

17 17 Case based reasoning za problem solving strategy using existing cases yto automate ‘knowledge reuse’ yassume previous cases have been correctly dealt with ycases might have been addressed by humans zassociate with a case a set of feature-value pairs ytogether form a unique index for the case ypossibly weight features with importance score zuse existing case database to help with new cases ycalculate index of new case yfind some number of the ‘closest’ cases yuse these to help treat new case

18 18 Cases for HVAC zHVAC = heating, ventilation, air conditioning zEach case contains 60 fields for retrieval yplus further fields describing installation yplus links to ftp area for download zAim is to find some ‘nearest neighbour’ cases zFrom these, sales staff can look at a small number of similar cases, and adapt quotes zQuotes confirmed at central site yIn trial, expertise of central engineers never used xjust for checking quotes that the sales staff proposed zOne benefit is saving in central experts time

19 19 Finding similar cases zFinding the similar cases is not rocket science zRemember, aim is to find a few similar cases ycan be used by field staff as basis for new quote ywant a manageable number (e.g. 20) zMain technique is to relax values of features ye.g. “item Athol_B23” becomes “T31_fan_coil” xwhere Athol_B23 is one specific type of T31_fan_coil xallows retrieval of installations using other types ye.g. “temperature = 65 F” becomes “60F < T < 65F” zKnowledge engineering used to find relaxations ye.g. use of domain experts to advise on suitable relaxations

20 20 Distributed reasoning... zSystem was distributed using Java & XML zServer uses relaxation to produce reasonable number of items, e.g. a few hundred zPushed to client side applet via XML yruns simple nearest neighbour algorithm to find closest set ySimply minimise similarity measure x  i f(T i,S i ) w i xwhere summation over features i f(T i,S i ) difference measure on feature i between cases S, T w i is weight of feature i yobtain full details of closest set by ftp

21 21 How did this win the lottery? zNot exactly rocket science yI’ve almost presented all the technical details already yWeb, Java, and HTML in paper can’t have hurt it! zShows a real world application ysaved a company some real money zShows maturity of an AI technique yhere, case based reasoning yfielded good application in 6 months for only $32,000

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