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

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
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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
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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
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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.
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5. Qualitative context mining approach
Context:
Negotiation protocol example:
Messages:
Performative(sender,receiver, content)
Constraints:
Nameevaluator(parameters)
Context + data mining = context model 5

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
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7. Example of context model
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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
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9. Reputation system II
 Example of path classification
 (A CM is built)
◦ relating S/F to qualitative properties
 Example of query
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10. Basic measures
 Reputation measure
 Reliability measure
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11. Individual and collective reputation
 Personal experience
 Group experience
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12. Social reputation
 How much are the witnesses trusted?
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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
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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
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15. Case study III
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 Average number of successful negotiations over number of total
negotiations across all customers (100 experiments);
◦ error bars show standard deviation

16. Case study IV
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17. Case studyV
 Dynamic seller behaviour
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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
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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.
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