1. Xiaoying Sharon Gao Computer Science Victoria University of Wellington Intelligent Agents COMP 423

2. Menu Course Organisation. Intelligent Agents, Information agents, Text Mining, Web Intelligence

3. The Course Objectives: Understand the basic problems and principles in information retrieval, document clustering and text representation, etc. Understand the main approaches and algorithms Practise critical reading, critical thinking, communication skills Develop skills for further research

4. Topics may cover Information agents Information retrieval Document clustering Text representation, Distance Measure Personalized search Information extraction Query expansion Web page ranking Opinion mining Recommender systems

5. Lectures Lecturer: Xiaoying Sharon Gao CO 339 Ph: 463 5978 Email: xgao@ecs.vuw.ac.nz Lectures: Wednesday, Friday, 11:00-11:50, 83 Fairlie Tce, 201 Friday 12-12:50, 83 Fairlie Tce, 201 for some weeks, to be confirmed

6. Course Materials Web page http://ecs.victoria.ac.nz/Courses/COMP423_2016T1/ Read course outline

7. Assessment Write paper summaries and join discussions: 5% Give a presentation: 5% Project: Write proposal: 5% Demonstration: 5% Write project report: 15% Exam 2hr 65% Mandatory requirement: Write summaries for at least 80% of the assigned papers, exam grade D or higher

8. What is an agent? An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. [Artificial Intelligence: a modern approach, Russell and Norvig 1995]

9. Agent description Description of agents in terms of Percepts: what the agents perceives from its envioronment Actions: what the agent can do in its environment Goals: what should the agent try to achieve Environment: what the agent acts and perceives within

10. Information agents Precepts: User’s query Documents User’s feedback User’s actions Actions: Follow links, retrieve documents Query search engines Expand query, etc Goals: find the exact information, organise the information Environment: the internet

11. Why information agents Google is amazing, but… Information overload https://searchenginewatch.com Search engine coverage is low Many pages are not indexed Some are not indexable Do not meet your special needs

12. Information agents Information agents perform the role of managing, manipulating or collating information from many distributed sources Early examples in 1990s BargainFinder, Shopbot FAQ Finder FindMe project Ahoy! Letizia Internet fish Examples from Melbourne: CiFi, CASA, SportFinder, Tools from Melbourne : ARIS, JACK

13. Predictions AI on the web: supply and demand agents Supply agents provide information to demand agents. Supply agents effectively configure information for information consumers. Demand agents search for needed information, also called information-gathering agents. Browsing agents and information extraction agents The information food chain

14. Reality Many domain specific search engines in use Many Web services Many mobile Apps But Each with a different interface, a different task, different format, etc Hard to combine, Real smart agent for general purpose does not exist yet Domain knowledge does not scale well Not robust: easy for one site, one time, one task, but to scale is hard Information agents area is changing Many related research such as personalised search, text mining, web Intelligence

15. Web search examples Find me a property that is close to the university and cheap. Find me a job, a car, a dress Find me a New Zealand researcher who is working on web page clustering. Find me some pictures that make people happy

16. Improve web search Personalised search Query understanding, expansion, Web page clustering, classification relevance feedback Web page ranking Information extraction, wrapper induction Opinion mining ……