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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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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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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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
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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