Artificial IntelligenceIntelligence

Ian Gentipg@cs.st-and.ac.uk

AI in 1999: IJCAI 99

Artificial IntelligenceIntelligence

Part I : Practical 1: Imitation GamePart II: AI in 1999: IJCAI 99Part III: Case based reasoning

AI in 1999

3

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

4

Practical 1: The Turing Test

Write a program to play the imitation gameAim:

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 necessaryrealised some of the possibilities and limitations of dialogue

systems

5

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”

6

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

7

Your task

Choose a domain of discourse, e.g. Harry Potter Implement a system to converse on this subjectSubmit your program code, report, two dialoguesProgram 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

8

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

9

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

10

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

11

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)

12

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)

13

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

14

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

15

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

16

Distributed case based …

Sales engineers distributed around AustraliaQuoting for Air conditioning/Heating systemsEach 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?

17

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

18

Cases for HVAC

HVAC = heating, ventilation, air conditioningEach 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’ casesFrom these, sales staff can look at a small number of

similar cases, and adapt quotesQuotes confirmed at central site

In trial, expertise of central engineers never usedjust for checking quotes that the sales staff proposed

One benefit is saving in central experts time

19

Finding similar cases

Finding the similar cases is not rocket scienceRemember, 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_coilallows 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

20

Distributed reasoning...

System was distributed using Java & XMLServer uses relaxation to produce reasonable number of

items, e.g. a few hundredPushed 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

21

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