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An Approach for the Qualitative Analysis of Open Agent Conversations

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An Approach for the Qualitative Analysis of Open Agent Conversations

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An Approach for the Qualitative Analysis of Open Agent Conversations

  1. 1. An Approach for the Qualitative Analysis of Open Agent Conversations 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 ITMAS 2012,Third InternationalWorkshop on Infrastructures andTools for Multiagent Systems 1
  2. 2. Content  Introduction  Protocols, context, and context models  Preparing the training data set  ProtocolMiner  Case study  Conclusion and future work 2
  3. 3. Introduction  Interaction in MASs is essential  Analysis tools ◦ Analysis of agents' mental states  Implementation available ◦ Analysis of the interactions among agents  Fixed syntactic elements  Quantitative analysis (QuanA) ◦ What a QuanA can offer?  E.g FIPA contract-net 3
  4. 4. Introduction II  Qualitative Analysis (QualA) ◦ Generalizing individual cases to build a general theory  Context models. ◦ Correlating the status of logical constraints to perceived agent behaviour  Contribution: definition, construction, and use (+tool) ◦ Utility  to make predictions about future behaviour  to infer the definitions other agents  to analyse the reliability and trustworthiness 4
  5. 5. Protocols, context, and context models Context: Negotiation protocol example: Messages: Performative(sender,receiver, content) Constraints: Nameevaluator(parameters) Context + data mining = context model 5
  6. 6. Preparing the training data set  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. ProtocolMiner  Plugin for the OpenKnowledge platform  Comprehensive functionality for QalA ◦ For human designer or agents ◦ Automates the construction of context models ◦ Definition of protocols by LCC  Lightweight Coordination Calculus  E.g. participant role in FIPA Request  other interaction platforms may be used… 7
  8. 8. ProtocolMiner II 8
  9. 9. Protocol Miner III  Context model of a single constraint, acceptable  Weka algorithms integrated 9
  10. 10. Case study  Car selling domain  A requests B offers forT and P  10 customer agents with different mental states 10
  11. 11. Case study II  Protocol outcome prediction ◦ From the point of view of one seller ◦ Average model accuracy across 100 repeated experiments ◦ Evaluated using cross-validation ◦ Three open source data mining techniques 11
  12. 12. Case study III 12
  13. 13. Case study IV  Agents predicting interactions ◦ Agents build and use context models to choose a good seller. ◦ Compared to a random strategy and a quantitative strategy: 13
  14. 14. Case studyV 14
  15. 15. Conclusion and future work  Novel mechanism to exploit qualitative information ◦ logical constraint as semantic annotations ◦ derive knowledge of the internal workings ◦ ProtocolMiner implements the approach  Future works: ◦ more real-world examples ◦ more advanced machine learning methods 15
  16. 16. 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. 16

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