An Approach for the Qualitative Analysis of Open Agent Conversations

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
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2. Content
 Introduction
 Protocols, context, and context models
 Preparing the training data set
 ProtocolMiner
 Case study
 Conclusion and future work
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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
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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
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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. 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
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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…
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8. ProtocolMiner II
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9. Protocol Miner III
 Context model of a single constraint,
acceptable
 Weka algorithms integrated
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10. Case study
 Car selling domain
 A requests B offers forT and P
 10 customer agents with different mental
states
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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
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12. Case study III
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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:
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14. Case studyV
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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
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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.
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