IIT — e-Business (Fredericton) Bruce Spencer Research Overview July 10, 2002

Business Rules & Web Services Web Services are software components existing on server sites across the Internet Available for use by clients/browsers –remote procedure call Searchable registries of Web Services provide –yellow page information (who provides what service) –green page information (how the service can be used) Examples: –stock quotes, price quotes, weather reports –More complex: tax calculations, applications for immigration visa Buy-in from IBM, Sun, Microsoft, HP, BEA, etc.

Web services and policy Policy dictates who receives what service –Example: price quote depends on shoppers’ demographics and shopping history –Applicant for immigration subject to eligibility rules “IF THEN” Rules for expressing policy –If shopper has spend $500 in past year here and item for quote is in luxury category then discount is 10% Rule ensure policy is clear, maintainable, explainable Web services that make judgments should be based on clear rules –Reasons for judgments can be explained

1. Explanation of Web Services When a web service judgment is questioned, client can request –A justification of the decision (proof) –A subdialog to gather more information with a chance to offer other alternatives (explanation and clarification) Example –Client: Why not a 10% discount? –Web Service: Have you spent more than $500 here recently? Here is your list which totals only $200. –Client: Add my wife’s purchases to my list. –Web Service: OK. Now I can give you a 10% discount.

Prototype for Explanations Built in Java Server uses j-DREW (Deduction Reasoning Engine for the Web) for making inferences –RuleML (form of XML) for expressing rules Client uses Graphical User Inferface for displaying and interacting with proofs –User can Expand sub-proofs Question any conclusion Open subdialog Dynamically add new information Master’s thesis project for Keping Jia Planned paper in distributed declarative debugging literature

2. Rules for Privacy Policy Web sites collect information about users APPEL from W3C is a rule-based language for expressing a user’s privacy policy –What personal information can web sites take? Who can they share it with? What can they use it for? Discussed on “The National” June 20, 2002, interview with Tim Berners-Lee

Detecting APPEL rule conflicts Master’s thesis project for Fang Wang –Privacy rules can contradict each other –A user’s private information can be protected by a rule but, unknown to the user, that rule can be overridden by another which allows the information out. –Need a tool to detect contradictory rules –Uses 6-connective logic, non-Horn reasoning –Publications planned for logic conference and security conference

3. E-Procurement Planning Purchases of bundles of items –each item has many options From whom to buy How much (could be probabilistically predicted) Limited time offers –Combinatorially many ways to procure bundles Users have different criteria, including price range, quality, reputation, etc. –Use multiattribute utility User may be risk averse or risk seeking –Utility of spending money

Problem Setting: large volume of items, combinations, predicted prices, quotes available at different times, rescinded offers Need purchase a bundle –To maximize utility both of items and of spent money, –To exploit the benefits of deferring choices until many quotes are open (expected utility of future choice set) PhD Thesis for Scott Buffett Planned two journal publications, including IJEC

3. j-DREW Highly configurable reasoning engine for clausal first order logic Written in Java Programmers with basic data structure skills are able to program new search strategies Abstractions of Goal, Choice and BackTrack These abstractions hide –Complex matching –Variable value propagation and retraction Small footprint, easy to integrate as a subsystem Many reasoning tasks handled by systems built from j-DREW Developed by Bruce Spencer Planned publication in implementations of logic

People Bruce Spencer – RO and Adjunct Prof UNB CS Sandy Liu –new RO, recent Masters from Acadia in E-Commerce and Web Services Justin Hickey –CS at NRC Ali Ghorbani –Assoc Prof, UNB CS Harold Boley –Senior researcher, DFKI, Germany Scott Buffett –Guest worker and UNB PhD student Fang Wang and Keping Jia –Guest Workers and UNB Masters students

Highlights Sept 2001 –Bruce Spencer joins NRC as Research Officer Nov-Dec 2001 –Harold Boley from DFKI (German Research Center for Artificial Intelligence) arrives as Visiting Researcher Dec 2001 –Said Tabet (Nisus Inc, MA) invited as Colloquium Speaker Jan 2002 –Scott Buffett joins (Guest Worker and PhD Student in procurement planning systems) Feb 2002 –Business Rules and Web Services Focus begins –Dagstuhl Seminar on Rule Markup Techniques –Invited talk at DFKI Apr 2002 –Edited Special Issue of Computational Intelligence on Agent Technologies for E-Commerce (11 papers, 200 pages) resubmissions from June 2001 workshop –Keping Jia and Fang Wang join (Guest workers and Masters Students) May 2002 –AI 2002 Conference in Calgary Co-edited proceedings, coordinated program and invited speakers Business Agents and the Semantic Web Workshop (Tabet, Boley, Ghorbani and Spencer) June 2002 –E-learning Project begins with Mosaic Information Technologies July 2002 –Sandy Liu joins from Acadia (Research Officer)