Personal information agentsfor travel e-commerce

Boi Faltings

(http://liawww.epfl.ch/)

Traditional travel distribution

UAL

Galileo

Swissair Avis Hilton EasyJetCRS

GDS+ Sabre,Amadeus,Worldspan

Travel agents

Traditional travel e-commerce

UAL

Galileo

Trip.com

Swissair Avis Hilton EasyJetCRS

GDS+ Sabre,Amadeus,Worldspan

Edifact

Web server

Travel e-commerce with agents

UAL

Agent.com

Swissair Avis Hilton EasyJetCRS

Internet

Travel portal

Personal agent

Why personal agents?

• Customize information systems

• Create persistent interaction

• Negotiate on user’s behalf

• Be proactive

• Load distribution/low latency interaction

Requirements

• Customize:– represent choices and preferences– filter data according to individual combinations

• Persistence: – memorize partially solved tasks

• Load distribution:– lightweight

Individual preferences

• Trip from Geneva(GVA) to San Francisco (SFO):

1. Leave after 8 am2. Arrive by 2 pm3. Preferred airline: Swissair4. Price < $5005. No transfer at London Heathrow

– Everyone has their own criteria!

Different people, different tradeoffs:

Departure time

Arrival time

Airline Transfer Cost

1. 6:45 12:15 Lufthansa FRA $485 consciencious

2. 14:15 19:20 Swissair ZRH $720 bureaucrat

3. 11:05 15:30 Air France PAR $380 budget

4. 8:50 13:50 British Air LHR $660 busy

Finding a solution:

• People can’t state their preferences

• Interaction = conversation:– Agent proposes solutions– User states/updates preferences

• Different tradeoffs: no unique optimal solution

Memorizing partially solved tasks

• State of problem solving = combination of preferences

• Same preferences => same solutions

• Advantages:– Complete problem-solving in stages– Optimal rebooking – High-quality data about customer

Lightweight

Preference

Flightschedule

Fare

PreferencePreferencePreferencePreference

Flightschedule

Flightschedule

Flightschedule

Flightschedule

Flightschedule

FareFareFareFareFare

Uniformand

simple mechanism

Optimalanswers

Constraint Programming

• Planning = search for the best flight combinations

• User preferences = constraints

• Partially solved task = set of constraints

• A form of declarative programming

• ...and also very lightweight (Java Constraint Library at http://liawww.epfl.ch/JCL/)

Constraint Satisfaction Problems

• Variables x1,x2,...,xn = choices• Domains D1,D2,...,Dn = possible values• Relations R1 = (xi,xj), R2 = (xk,xl,xm),...,Rm

• Constraints C1 = ((v1,v2),(v3,v4),...),C2,...,Cm

All are fixed for a certain problemFind:

Solutions x1 = vi, v2= vj,...,xn=vk such that all constraints are satisfied

Solving a CSP

• Backtrack search: assign values to variables in sequence, always satisfying all constraints

• Powerful pruning and variable ordering methods

• Parallel methods: assign random values to all variables and make local changes until all constraints are satisfied.

Valued CSP

• Each constraint carries a weight that models its importance

• Solution = value combination with lowest violation cost

• Solving process = online optimization (branch-and-bound search)

Most tractable problems are CSP

CSP is general enough to formulate most interesting NP-hard problems:

• Resource allocation: a coloring problem

• Scheduling

• Planning (assumption: finite world state and operators)

• Diagnosis

Constraint programming for agent intelligence

• Software: does the same thing

• Agent: does the right thing

• Adaptation to context requires abduction (online optimization)

Logical inference modes…

• Deduction: bird(tweety) => flies (tweety)

• Abduction: flies(tweety) => bird (tweety)

• Most inference in information systems is abduction

• Abduction requires closed-world assumption (CWA) to be sound: Birds are the only things that fly

Implementing abduction

• CWA => transform into deduction (procedural programs)

• Programmer makes CWA => inflexible

• Constraint satisfaction makes CWA at runtime => software that easily adapts

• Drawback: NP-hard

Dealing with NP-hardness

0

20

40

60

80

100

120

0.1 0.5 1 1.5

tightness

com

puta

tion

tim

e

A1A2A3

Tightness = 1 must be avoided!

User interaction

• Java applet/application with graphical displays

• Conversation: – user states preferences/constraints– agent displays solutions

1.Drop-down menus allow fine tuning of

preferences

2. Preferences are stored and can be reused at any time

What Iconomic Systems built:

Agent.comTravel portal(mediator)

Personal agent

UAL

Galileo

Swissair Avis Hilton EasyJetCRS

GDS

The applet: All includedTotal Applet Size: 440KB

42%

40%

16%2%

Search engine/GUIWorld mapAirline/Airport codesFlight data

All you need to download from the internet for travel planning is 10kb!

Flights/availability

Fares withconditions/rules

Hotels Availability/prices

Car availability/prices

Java Applet

Search engine

HTML browser

WAP browserJava Servlet

Search engineServ

er Itinerary and Profile Server

Bookings

CRS/GDS/… Web server Customer

ISY Travel Modules

isy-travel: Architecture