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

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 • 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 • 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 • 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 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 • 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 • 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 • 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 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 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 … • 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 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 • 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 • 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 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... • 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? • 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

Artificial Intelligence