1. Personal information agents for travel e-commerce Boi Faltings (http://liawww.epfl.ch/)

2. Traditional travel distribution UAL Galileo SwissairAvisHiltonEasyJet CRS GDS + Sabre, Amadeus, Worldspan Travel agents

3. Traditional travel e-commerce UAL Galileo Trip.com SwissairAvisHiltonEasyJet CRS GDS + Sabre, Amadeus, Worldspan Edifact Web server

4. Travel e-commerce with agents UAL Agent.com SwissairAvisHiltonEasyJet CRS Internet Travel portal Personal agent

5. Why personal agents? Customize information systems Create persistent interaction Negotiate on user’s behalf Be proactive Load distribution/low latency interaction

6. Requirements Customize: –represent choices and preferences –filter data according to individual combinations Persistence: –memorize partially solved tasks Load distribution: –lightweight

7. Individual preferences Trip from Geneva(GVA) to San Francisco (SFO): 1.Leave after 8 am 2.Arrive by 2 pm 3.Preferred airline: Swissair 4.Price < $500 5.No transfer at London Heathrow –Everyone has their own criteria!

8. Different people, different tradeoffs: Departur e time Arrival time AirlineTransferCost 1.6:4512:15 Lufthansa FRA$485conscie ncious 2.14:1519:20 Swissair ZRH$720bureau crat 3.11:0515:30 Air France PAR$380budget 4.8:5013:50 British Air LHR$660busy

9. Finding a solution: People can’t state their preferences Interaction = conversation: –Agent proposes solutions –User states/updates preferences Different tradeoffs: no unique optimal solution

10. 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

11. Lightweight Preference Flight schedule Fare Preference Flight schedule Flight schedule Flight schedule Flight schedule Flight schedule Fare Uniform and simple mechanism Optimal answers

12. 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/)

13. Constraint Satisfaction Problems Variables x 1,x 2,...,x n = choices Domains D 1,D 2,...,D n = possible values Relations R 1 = (x i,x j ), R 2 = (x k,x l,x m ),...,R m Constraints C 1 = ((v 1,v 2 ),(v 3,v 4 ),...),C 2,...,C m All are fixed for a certain problem Find: Solutions x 1 = v i, v 2 = v j,...,x n =v k such that all constraints are satisfied

14. 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.

15. 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)

16. 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

17. Constraint programming for agent intelligence Software: does the same thing Agent: does the right thing Adaptation to context requires abduction (online optimization)

18. 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

19. 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

20. Dealing with NP-hardness Tightness = 1 must be avoided!

21. User interaction Java applet/application with graphical displays Conversation: –user states preferences/constraints –agent displays solutions

22. 1.Drop-down menus allow fine tuning of preferences 2. Preferences are stored and can be reused at any time

23. What Iconomic Systems built: Agent.com Travel portal (mediator) Personal agent UAL Galileo SwissairAvisHiltonEasyJet CRS GDS

24. The applet: All included All you need to download from the internet for travel planning is 10kb!

25. Flights/availability Fares with conditions/rules Hotels Availability/prices Car availability/prices Java Applet Search engine HTML browser WAP browser Java Servlet Search engine Server Itinerary and Profile Server Bookings CRS/GDS/… Web serverCustomer ISY Travel Modules isy-travel: Architecture