Knowledge Networks

1. Knowledge Networks Babak Hodjat December, 2003

2. Contents • Network Value (potential): • Usefulness, Utility: satisfaction gained from the consumption of a "package" of goods and services. • Information Networks • Knowledge Networks • Agent-oriented Approach • Examples • Comparison

3. Network Value: Sarnoff’s Law • The value of a network grows with the number of nodes • V(n) ~ n • n limited by: • Cost of access • $ • Perceived value of access • E.g., buying a cell phone for safety • Perceived ease of access • E.g., not paying for WAP because it is hard to use

4. Network Value: Metcalf’s Law • The value of a network where each node can reach every other node grows with the square of the number of nodes: • V(n) ~ n2 • In many cases, for each user a max a nodes are accessible at any given time (e.g., the telephone network): • V(n) ~ an ~ n (Sarnoff’s law) • Assuming no spam!

5. Network Value: Reed’s Law • The value of a Group Forming network grows exponentially to the number of users: • V(n) ~ 2n • Forming new groups of value is difficult for high n • Is the number of valuable groups a function of n? • A new member of a group does not increase the number of groups. • Will a new group always increase the value of existing groups • Finding valuable groups is difficult: • Money and attention resources scale linearly with n

6. Information Network • Example: WWW • Each node contains: • Information • Navigation (classification) • Primitive control (e.g., car horn) • Transactional/Computational (e.g., bank account, calculator) • Users (e.g., Humans, Google)

7. Value in Information Networks • The value of a network grows as a function of the number of nodes for which utility knowledge has been acquired (k). • V(n) ~ k, (Sarnoff) • V(n) ~ k2, (Metcalf) • V(n) ~ 2k, (Reed) • Intuitions: • For large n: k << n • k proportionate to time, processing power, cost ($), and usability. • k is an over simplification of quality (degree of utility knowledge is not binary, but fuzzy)

8. Usability: Acquiring Knowledge • The cost of acquiring knowledge on a network is proportional to the cost of mapping the user’s model to the actual network. • Implication: Network should facilitate transformation of information to knowledge: • Expression of user intent ↔ actual network

9. Knowledge Networks • Each node reacts to usage with the goal to conform to the mapping the user expects. • Nodes represented by Agents • Map user intent to ontology represented by node • To complete mapping, agents: • Interact with users to resolve ambiguities • Collaborate with other nodes • Use context to predict user intent • Present information, or facilitate navigation or transaction in a usable manner

10. Example: AAOSA networks • Adaptive Agent Oriented Software Architecture

11. Problem posing Problem rerouting Rerouting Problem Delivery Feedback • Problem Solving • Relevance Assignment Failure reports • Problem Composition • Failure recovery Problem posing Feedback Problem Solving Logic • Solution Composition • Failure recovery Learning Request / Response Solutions Programming / Enquiry Solution Solution Delivery Solution Receiver Failure reports Service Request Processing History Service request / response State Local Objects

12. Remote VCR Actuator Microphone Home TV Channel VCR Help Programs TV Actuator Control Interface Broadcaster Demand CRM Available programs Automated Help Advertisement System … Human fallback

13. Intermediate XML Output: example: a financial agent network Gimme a chart for IBM.

14. Sample Dialog • Find contacts in San Jose • 5 contacts found • … and Sunnyvale • 7 contacts found • Not San Jose, San Francisco • 9 contacts found • Change Joe’s city to Mountain View • Billing address or shipping address? • Jill moved to France • Country -> France, city -> “” • New contact named Bill • What’s Bill’s billing address?

15. Other Approaches • Natural Language and Linguistic • Search Engines • Statistical Methods

16. Natural Language and Linguistics Sentence Noun Phrase Verb Phrase Det ADJs Noun Verb NP PrepPhrases • Syntax: • Conditionals • Questions • Imperatives • Assertions • Active/passive • … • Semantics: • boy: active, noun, singular, human, male, age < 15 yrs,… • … • Pragmatics: • Baseball: bat (sense: wooden stick), park (sense: playing field), objective: score runs, hit a ball with a bat -> score runs, … “The young boy hit the ball out of the park”

17. Linguistics & Grammar Approaches • Approach: • Model rules for the language, and then fit an application domain into this model. • Strengths: • Attempts to understand what the user is saying in order to provide useful actions and results • Challenges: • Costly: Modeling language is complicated • Brittle to non-syntactically correct inputs • Requires specialized knowledge of linguistics • Doesn’t port across languages, most work not reusable • Black box: Hard to maintain, understand, because of many textual specialized rules with unclear scope and relationship

18. Search Engines • Approach: • Index document keywords, returns weighted importance. No domain or linguistic modeling (except stemming) • Strengths: • returning multiple scored “hits” from unstructured documents • Fast setup for new domain/data set • Challenges: • No understanding of query, just list of matches • Returns many results • Not suitable for answering precise questions, performing transactions, dialog • Snapshot approach means links or information may be outdated

19. Statistical Methods • Require huge sample collection • Long and costly development • Difficult to change or extend application domain • No dynamic customizations • Unpredictable

20. Example Knowledge Network

21. Task: Query accounts by owner and area code Traditional Method:

22. Ad-hoc Query: accounts by owner and area code With AAOSA:

23. Ambiguity and Contextual Follow-ups

24. Ambiguity and Contextual Follow-ups AAOSA prompts the user to resolve ambiguity… Ambiguity: Which John?

25. Ambiguity and Contextual Follow-ups When the user clicks on a link corresponding to a contact, the system uses context to take the user directly to the target page, filling in required fields

26. Thank you

27. Backups

28. The Problem What is the best combination of claims?

29. Claim made by the HOME_ENTERTAINMENT policy Claim made by TV policy Claim made by VCR policy What is a Claim? • Reasons for which an agent claims a role in dealing with a particular input: • Existence of recognized patterns in input or context • A claim may be made by combining other claims

30. Policies • A rule that if triggered will make a claim: (COND_TRANS: ('if' | 'when') & MARKET-INFO & TRADE {action: {execute:'%NEW_LINE%<conditionalTransaction>', MARKET-INFO, TRADE, '%NEW_LINE%</conditionalTransaction>’ }}), (CATCH: (COND_TRANS, COMPANY, MARKET-INFO, TRADE)+ {action: {execute:'%NEW_LINE%<finance>', /P1, COMPANY, MARKET-INFO, TRADE, '%NEW_LINE%</finance>'}})

31. Propagation of Claims • Each policy makes all the claims • Catch policies discover relationships between claims that have not been identified by non-Catch policies. • After all policies have made their claims the best claims are selected. • The selected claims are sent up-chain

32. Which Claim is better? • More Coverage • A Focus set identifies portions of the input that triggered the claim. • Higher Priority • More Ambiguous • Include all who claimed the same focus. • Heavier Connection Weight • More Proximate

33. Merging Claims

34. Ambiguous Claims