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A qualitative reputation system for multiagent systems with protocol-based communication

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A qualitative reputation system for multiagent systems with protocol-based communication

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A qualitative reputation system for multiagent systems with protocol-based communication

  1. 1. A qualitative reputation system for multiagent systems with protocol- based communication Emilio Serrano1, Michael Rovatos2 and Juan A. Botía1 Contact: emilioserra@um.es, michael.rovatsos@ed.ac.uk, juanbot@um.es 1. University of Murcia, Spain / 2. University of Edinburgh, U.K. Presented in AAMAS 2012, Eleventh International Conference on Autonomous Agents and Multiagent Systems 1
  2. 2. Content  Motivation  Qualitative context mining approach  Example of context model  Reputation system ◦ Basic measures ◦ Individual and collective reputation ◦ Social reputation  Case study  Conclusion and future works 2
  3. 3. Motivation  Reputation in MASs particularly challenging ◦ It may greatly enhance performance  Existing literature ◦ Mostly focus on a purely quantitative trust ◦ No assessment of the qualitative properties  i.e. the content and sequence of messages exchanged and physical actions observed.  This ignores the interaction mechanisms ◦ Semantically rich and can be used to extract qualitative properties 3
  4. 4. Motivation II  Novel reputation system ◦ Based on a qualitative context mining approach  Building models from previous interaction data to evaluated agents' behaviour  Context models ◦ Agents may query the model according to its needs  specific protocols, paths within these protocols, constraint arguments, etc. 4
  5. 5. Qualitative context mining approach Context: Negotiation protocol example: Messages: Performative(sender,receiver, content) Constraints: Nameevaluator(parameters) Context + data mining = context model 5
  6. 6. Qualitative context mining approach II  How exactly training data is constructed? ◦ Dealing with different agents  An agent only can assure its own context  Most cautious strategy  Most trusting: entire path information ◦ Dealing with different paths  Set of variables contained in these may differ  Create a different data sets  Merge data across different paths  Samples can be “stuffed‘” with “unknown”.  Path group label: success ◦ Dealing with loops  Variables used in the loop can have several constants  N “copies” of each variable  First/last ground term 6
  7. 7. Example of context model 7
  8. 8. Reputation system  An evaluating agent a tries to assess the reputation of the target agent b using a CM provided by a modelling agent (or witness) m. ◦ m is not necessarily a  Querying the modelling agent m ◦ Three models  a obtains the entire CM from m  based on a's definitions of success and failure  m answers particular queries of a  a receives the interaction data from m and builds the CM herself. ◦ Uniform treatment, two steps  Providing path classification  Instance querying 8
  9. 9. Reputation system II  Example of path classification  (A CM is built) ◦ relating S/F to qualitative properties  Example of query 9
  10. 10. Basic measures  Reputation measure  Reliability measure 10
  11. 11. Individual and collective reputation  Personal experience  Group experience 11
  12. 12. Social reputation  How much are the witnesses trusted? 12
  13. 13. Case study  Car selling domain ◦ A requests B offers for T ◦ 50 customer agents and 10 sellers ◦ 6 preference profiles Pi for customer agents regarding T, and 3 for sellers 13
  14. 14. Case study II  Trust use to select a good seller for some terms 1. Each customer computes SR with 2. Any seller with positive SR is chosen 3. If no seller is chosen, terms i are updated according to the customer’s preferences (go back to 1). 4. If no seller with positive reputation can be identified after several attempts the seller is chosen randomly.  Compared to: using only personal experience, restricted qualitative, random, and quantitative 14
  15. 15. Case study III 15  Average number of successful negotiations over number of total negotiations across all customers (100 experiments); ◦ error bars show standard deviation
  16. 16. Case study IV 16
  17. 17. Case studyV  Dynamic seller behaviour 17
  18. 18. Conclusion and future work  A novel qualitative approach to reputation systems based on mining “deep models” of protocol-based agent interactions. ◦ More complex, fine-grained, and contextualised queries ◦ Reputation queries can be constructed automatically by agents ◦ Higher prediction accuracy than quantitative methods  If the behaviour depends on the semantics ◦ Good response to unexpected changes ◦ Different levels of privacy toward a reputation-querying  Future works ◦ More elaborate data mining techniques ◦ Real-world examples (variety of interaction protocols) ◦ Explore issues of trust in witnesses ◦ Comparison with well known trust approaches 18
  19. 19. Thanks for your attention Emilio Serrano1, Michael Rovatos2 and Juan A. Botía1 Contact: emilioserra@um.es, michael.rovatsos@ed.ac.uk, juanbot@um.es 1. University of Murcia, Spain / 2. University of Edinburgh, U.K. 19

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