Fundamentals of Argumentation Theory Curs 3 (Standpoints and Argumentation)
Multiagent learning and argumentation (Br 2008)
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Transcript of Multiagent learning and argumentation (Br 2008)
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Argumentation & Learning in Multiagent Social Choice
Enric Plaza (IIIA-CSIC)
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Outline
•CBR vs. Learning vs. Agents
•Introduction to Social Choice in MAS
•Justified Predictions in MAS
•Arguments and Counterexamples
•Argumentation Process
•Experimental Evaluation
•Conclusions & Future Work
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Learning, RL, and Agents
Why “always” Reinforcement Learning in MAS?It’s Online Learning!
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CBR is Online Learning
Argumentation
CommitteeInput
DeliberationAggregation
Output
Committee: (1)A group of people officially delegated (elected or appointed) to perform a function, such as investigating, considering, reporting, or acting on a matter.Team: A number of persons associated in some joint action. A group organized to work together.Coalition: A combination or alliance, esp. a temporary one between persons, factions, states, etc. An alliance for combined action, especially a temporary alliance of political parties.
Ensemble Effect
Committee of agents
Deliberation + Voting
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Argumentation in Multi-Agent
Learning•An argumentation framework for
learning agents
•Justified Predictions as arguments
•Individual policies for agents to
•generate arguments and
•generate counterarguments
•select counterexamples
Justified PredictionJustification: A symbolic description with the
information relevant to determine a specific prediction
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Justified Prediction
•A justified prediction is a tuple
•where agent J.A considers J.S to be the correct solution for problem J.P
•and this is justified by a symbolic description J.D such that
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Examination (1)
•Agent A1 sends a problem to agent A2
•Agent A2 sends a justification with a symbolic description D of why P1 has solution S2 according to its experience
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Justification example
The predicted solution is hadromerida because the smooth form of the megascleres of the spiculate skeleton of the sponge is of type tylostyle, the spikulate skeleton of the sponge has not uniform length, and there are no gemmules in the external features of the sponge.
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Examination (2)
Set of cases subsumed by
justification J.D
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Examination (2)
Set of cases subsumed by
justification J.D
Counterexamples form theRefutation set
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Counterargument generation
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•Justified Prediction: An argument α endorsing a individual prediction
•Counterargument: An argument β offered in opposition to an argument α
•Counterexample: A case c contradicting an argument α
Argument types
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Case-based Confidence
Preference on Arguments
Confidence on an
argument based on cases
Preference on Arguments(2)
Joint Confidence
Preferred
Relations between arguments
ConsistentIncomparable Counterargument
Relations between cases and justified
predictions
Endorsing case Irrelevant case Counterexample
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Argument Generation
•Generation of a Justified Prediction
•LID generates a description α.D subsuming P
•Generation of a Counterargument
• if no Counterargument can be generated then:
•Selection of a Counterexample
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Counterargument Generation
• Counterarguments are generated based on the specificity criterion
• LID generates a description β.D subsuming P and subsumed by α.D
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Selection of a Counterexample
• Select a case c subsumed by α.D and endorsing a different solution class.
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AMAL protocol
Justified prediction asserted in the next round
Assertions of n agents at round t
Agent states a counterargument ß
Set of contradicting arguments for agent Ai at round t (those predicting adifferent solution)
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Argument Generation
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Confidence-weighted Voting
•Each argument in Ht is a vote for an alternative
•Each vote is weighted by the joint confidence measure of that argument
•Weighted voting: alternative with higher aggregated confidence wins
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Experiments•Designed to validate 2 hypotheses
•average of 5 10-fold cross validation runs
•(H1) that argumentation is a useful framework for joint deliberation and can improve over other typical methods such as voting; and
•(H2) that learning from communication improves the individual performance of a learning agent participating in an argumentation process.
#Agents
Acc
ura
cy
Sponges Data set
Accuracy after Deliberation
Accuracy after Deliberation
#Agents
Acc
ura
cy
Soybean Data set
Individual Learning from
Communication
%cases
Acc
ura
cy
Sponges Data set
Individual Learning from
Communication
%cases
Acc
ura
cy
Soybean Data set
Case Base Size#
case
s
23.4%20%
31.58%20.02%
#ca
ses
23.4%20%
31.58%20.02%
Learning from a few good cases while
arguing
%cases
Acc
ura
cy
Soybean Data set
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Conclusions•An argumentation framework for
learning agents
•a case-based preference relation over arguments,
• by computing a confidence estimation of arguments
•a case-based policy to generate counter-arguments and select counterexamples
•an argumentation-based approach for learning from communication
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Future Work•Framework for agents using induction• better policies for generating CA• collaborative search in generalization space
• Deliberative Agreement
• for social choice and collective judgment models
• aggregation procedures of sets of interconnected judgments
• deliberation over sets of interconnected judgments
AMAL vs Centralized
#Agents
Acc
ura
cy
Sponges Data set
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