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Determining the trustworthiness of unfamiliar electronic contracts

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Expressing contractual agreements electronically potentially allows
agents to automatically perform functions surrounding contract use: establish-
ment, fulfilment, renegotiation etc. For such automation to be used for real busi-
ness concerns, there needs to be a high level of trust in the agent-based system.
While there has been much research on simulating trust between agents, there
are areas where such trust is harder to establish. In particular, contract proposals
may come from parties that an agent has had no prior interaction with and, in
competitive business-to-business environments, little reputation information may
be available. In human practice, trust in a proposed contract is determined in part
from the content of the proposal itself, and the similarity of the content to that of
prior contracts, executed to varying degrees of success. In this paper, we argue
that such analysis is also appropriate in automated systems, and to provide it we
need systems to record salient details of prior contract use and algorithms for as-
sessing proposals on their content. We use provenance technology to provide the
former and detail algorithms for measuring contract success and similarity for the
latter, applying them to an aerospace case study.

Published in: Technology

Determining the trustworthiness of unfamiliar electronic contracts

  1. 1. DETERMINING THE TRUSTWORTHINESS OF UNFAMILIAR ELECTRONIC CONTRACTS <ul><li>Paul Groth , Simon Miles, Sanjay Modgil, Nir Oren, Michael Luck, Yolanda Gil </li></ul><ul><li>[email_address] </li></ul>
  2. 2. http://www.flickr.com/photos/newbirth/2834643961/
  3. 3. Why do you trust a contract?
  4. 4. Reputation
  5. 5. http://www.flickr.com/photos/el_ramon/3804532661/
  6. 6. Content http://www.flickr.com/photos/ogcodes/2095054686/
  7. 7. Content Nice Letterhead
  8. 8. Content Nice Letterhead Official Seal
  9. 9. Content Nice Letterhead Official Seal A particular statement is present
  10. 10. Content Nice Letterhead Official Seal A particular statement is present ≈
  11. 11. Advantages of Content-based Trust <ul><li>Works well in open systems </li></ul><ul><li>Deals with unknown agents </li></ul><ul><ul><li>Make a decision without reputation information </li></ul></ul><ul><li>Deals with new agents </li></ul><ul><ul><li>No need for a system designer to analyse new entrants </li></ul></ul><ul><li>Deals with agents behaving unexpectedly </li></ul>
  12. 12. Automated Content-based Trust for Electronic Contracts <ul><li>System for gaining experience about contracts </li></ul><ul><li>Algorithms for assessment of contract proposals based on prior experience </li></ul>
  13. 13. Contract Modelling Content-based Trust Provenance Trust in Electronic Contracts
  14. 14. Aircraft Operator http://www.flickr.com/photos/25653307@N03/3040033941/
  15. 15. Aircraft Operator Engine Manufacturer http://www.flickr.com/photos/bikeman04/176483676/ http://www.flickr.com/photos/25653307@N03/3040033941/
  16. 16. Aircraft Operator Engine Manufacturer Service Site http://www.flickr.com/photos/thomas-merton/3119398216/ http://www.flickr.com/photos/bikeman04/176483676/ http://www.flickr.com/photos/25653307@N03/3040033941/
  17. 17. Aircraft Operator Engine Manufacturer Service Site Parts suppliers http://www.flickr.com/photos/vetustense/2660727807/ http://www.flickr.com/photos/thomas-merton/3119398216/ http://www.flickr.com/photos/bikeman04/176483676/ http://www.flickr.com/photos/chimothy27/2913647772/ http://www.flickr.com/photos/25653307@N03/3040033941/
  18. 18. Aircraft Operator Engine Manufacturer Service Site Parts suppliers http://www.flickr.com/photos/vetustense/2660727807/ http://www.flickr.com/photos/thomas-merton/3119398216/ http://www.flickr.com/photos/bikeman04/176483676/ http://www.flickr.com/photos/chimothy27/2913647772/ http://www.flickr.com/photos/25653307@N03/3040033941/
  19. 19. Engine Manufacturer Service Site http://www.flickr.com/photos/thomas-merton/3119398216/ http://www.flickr.com/photos/bikeman04/176483676/
  20. 20. Example Contract <ul><li>An obligation on the service site to repair each engine within D days of it arriving for maintenance. </li></ul><ul><li>An obligation on the service site to pay a penalty P to the manufacturer for each repair not completed in D days. </li></ul><ul><li>A set of permissions and prohibitions, one for each of a set of part suppliers, allowing or denying the service site to source parts from that supplier. </li></ul>
  21. 21. Representation of Clauses Clause 1: An obligation on the service site to repair each engine within D days of it arriving for maintenance. Type Obligation Target Service Site Activating Condition Engine E requires repairing at Time T Normative Condition Engine has been repaired or Time T + D has not been reached Expiration Condition Engine E has been repaired or time T + D has been reached
  22. 22. Representation of Contracts <ul><li>References to domain ontologies </li></ul><ul><li>Contractual roles </li></ul><ul><li>Agents  role mappings </li></ul><ul><li>A set of clauses </li></ul><ul><li>XML-based electronic format from the EU-Contract project </li></ul><ul><ul><li>http://www.ist-contract.org/ </li></ul></ul><ul><li>Important to have a common format for electronic contracts </li></ul>
  23. 23. Contracts specify…. <ul><li>what should happen... </li></ul><ul><li>… not what actually happens. </li></ul>
  24. 24. Provenance Infrastructure <ul><li>Source or origin of data </li></ul><ul><li>Use it for historical documentation </li></ul><ul><li>Addresses two issues: </li></ul><ul><ul><li>1. Application adaptation </li></ul></ul><ul><ul><li>2. Query large amounts of historical documentation </li></ul></ul><ul><li>Use the Open Provenance Model for representing such documentation </li></ul>
  25. 25. Using the Documentation ✔ ✗ Compliance? Outcome? http://www.flickr.com/photos/thomas-merton/3119398216/
  26. 26. Using the Documentation ✔ ✗ s(D C p ) = a query over experience http://www.flickr.com/photos/thomas-merton/3119398216/
  27. 27. Example Trust Values <ul><li>(1.0) The repairs are completed successfully using permitted part suppliers. </li></ul><ul><li>(0.75) The repairs are not all completed successfully, but a penalty payment is received. </li></ul><ul><li>(0.25) The repairs are not all completed successfully, and no penalty payment is received. </li></ul><ul><li>(0.0) The repairs are completed but using a prohibited part supplier. </li></ul>
  28. 28. A Space of Prior Experience .75 1.0 0.0 .25 1.0 .25
  29. 29. Given a new proposal…. ???
  30. 30. … use experience to predicate trust .75 1.0 0.0 .25 1.0 .25 ???
  31. 31. K-nearest neighbor <ul><li>Simple machine learning algorithm </li></ul><ul><li>Perform classification or regression </li></ul><ul><ul><li>Categories of trust </li></ul></ul><ul><ul><li>Trust value </li></ul></ul><ul><li>K-nearest instances vote on the value of the contract </li></ul><ul><li>We use distance weighted voting </li></ul>
  32. 32. Each instance votes, weighted by distance .75 1.0 0.0 .25 1.0 .25 ???
  33. 33. Our implementation <ul><li>Distance = similarity between contract proposal and contracts </li></ul><ul><li>Similarity is maximum similarity between all clauses in a contract </li></ul><ul><ul><li>Clauses are compared based on their parts </li></ul></ul><ul><li>Domain specific comparison functions can be used </li></ul><ul><ul><li>e.g. if two periods are within 5 calendar days then they are equivalent </li></ul></ul>
  34. 34. Benefits of KNN <ul><li>No need for a training phase </li></ul><ul><li>Easy to include more features in the similarity calculation </li></ul><ul><li>Easy to switch between categories and values </li></ul><ul><li>Good jumping off point for using other machine learning algorithms </li></ul>
  35. 35. Results <ul><li>Simulated running aerospace example </li></ul><ul><li>Compute success for prior contracts </li></ul><ul><li>Generate test contract proposals </li></ul><ul><ul><li>Randomly generate a contract proposal </li></ul></ul><ul><ul><li>Run proposal 100 times getting an average success score </li></ul></ul><ul><li>Compare to avg. to predicted success score as more prior contracts are added </li></ul>
  36. 36. Results (2)
  37. 37. Conclusion <ul><li>Content-based approach </li></ul><ul><ul><li>Particularly suited to open environments </li></ul></ul><ul><li>Combines: </li></ul><ul><ul><li>Contract data model </li></ul></ul><ul><ul><li>Provenance data model </li></ul></ul><ul><li>Successful in computing trust values </li></ul><ul><li>Can be combined with other metrics </li></ul>
  38. 38. Questions? <ul><li>Contact: pgroth@few.vu.nl </li></ul><ul><li>Follow: http://twitter.com/pgroth </li></ul><ul><li>Read: http://www.pgroth.com </li></ul>

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