an approach to the analysis and design of multiagent...
TRANSCRIPT
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An Approach to the Analysis and Design ofMultiagent Systems based on Interaction
Frames
Michael Rovatsos, Gerhard Weiß, Marco Wolf
Department of InformaticsTechnical University of Munich
AAMAS’2002, Bologna, July 15-19, 2002 – p.1/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.2/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.3/41
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MotivationOpen systems on the Internet
virtual enterprises
supply chain management
electronic marketplaces
ubiquitous information access
New research issuesSomething amiss?
AAMAS’2002, Bologna, July 15-19, 2002 – p.4/41
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MotivationOpen systems on the Internet
virtual enterprises
supply chain management
electronic marketplaces
ubiquitous information access
New research issues
Something amiss?
AAMAS’2002, Bologna, July 15-19, 2002 – p.4/41
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MotivationOpen systems on the Internet
virtual enterprises
supply chain management
electronic marketplaces
ubiquitous information access
New research issuesSomething amiss?
AAMAS’2002, Bologna, July 15-19, 2002 – p.4/41
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MotivationThree claims:
Socio-empirically rational agents thatrecord interaction processes and
employ this experience strategicallyare needed.
Architectures to design and analyse suchagents are missing.InFFrA is a possible solution!
AAMAS’2002, Bologna, July 15-19, 2002 – p.5/41
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MotivationThree claims:
Socio-empirically rational agents thatrecord interaction processes and
employ this experience strategicallyare needed.Architectures to design and analyse suchagents are missing.
InFFrA is a possible solution!
AAMAS’2002, Bologna, July 15-19, 2002 – p.5/41
� � � ���� ��� ��� ���
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MotivationThree claims:
Socio-empirically rational agents thatrecord interaction processes and
employ this experience strategicallyare needed.Architectures to design and analyse suchagents are missing.InFFrA is a possible solution!
AAMAS’2002, Bologna, July 15-19, 2002 – p.5/41
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MotivationIntroduction to InFFrA
Application to Multiagent Learning SystemDiscussion & Outlook
AAMAS’2002, Bologna, July 15-19, 2002 – p.6/41
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MotivationIntroduction to InFFrAApplication to Multiagent Learning System
Discussion & Outlook
AAMAS’2002, Bologna, July 15-19, 2002 – p.6/41
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MotivationIntroduction to InFFrAApplication to Multiagent Learning SystemDiscussion & Outlook
AAMAS’2002, Bologna, July 15-19, 2002 – p.6/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.7/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.8/41
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Interaction Frames & Framing
(Interaction) Frames capture the regularitiesof interaction processes
roles & relationships,
courses of interaction (trajectories),
contexts & conditions,
beliefs.
Local vs. shared knowledgecommon vs. private attributes
Interactions are not subject to direct agentcontrol!
AAMAS’2002, Bologna, July 15-19, 2002 – p.9/41
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Interaction Frames & Framing
(Interaction) Frames capture the regularitiesof interaction processes
roles & relationships,
courses of interaction (trajectories),
contexts & conditions,
beliefs.
Local vs. shared knowledgecommon vs. private attributes
Interactions are not subject to direct agentcontrol!
AAMAS’2002, Bologna, July 15-19, 2002 – p.9/41
� � � ���� ��� ��� ���
� � ��
Interaction Frames & Framing
(Interaction) Frames capture the regularitiesof interaction processes
roles & relationships,
courses of interaction (trajectories),
contexts & conditions,
beliefs.
Local vs. shared knowledgecommon vs. private attributes
Interactions are not subject to direct agentcontrol!
AAMAS’2002, Bologna, July 15-19, 2002 – p.9/41
� � � ���� ��� ��� ���
� � ��
Interaction Frames & Framing
(Interaction) Frames capture the regularitiesof interaction processes
roles & relationships,
courses of interaction (trajectories),
contexts & conditions,
beliefs.
Local vs. shared knowledgecommon vs. private attributes
Interactions are not subject to direct agentcontrol!
AAMAS’2002, Bologna, July 15-19, 2002 – p.9/41
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Interaction Frames & Framing
Framing is social reasoning thatinterprets situations in terms of frames,
adapts frame conceptions,
strategically employs frames to guideinteraction behaviour.
Socio-centric view with individualist “touch”
Frame & Framing concepts grounded inGoffman’s sociological theory
AAMAS’2002, Bologna, July 15-19, 2002 – p.10/41
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Interaction Frames & Framing
Framing is social reasoning thatinterprets situations in terms of frames,
adapts frame conceptions,
strategically employs frames to guideinteraction behaviour.
Socio-centric view with individualist “touch”
Frame & Framing concepts grounded inGoffman’s sociological theory
AAMAS’2002, Bologna, July 15-19, 2002 – p.10/41
� � � ���� ��� ��� ���
� � ��
Interaction Frames & Framing
Framing is social reasoning thatinterprets situations in terms of frames,
adapts frame conceptions,
strategically employs frames to guideinteraction behaviour.
Socio-centric view with individualist “touch”Frame & Framing concepts grounded inGoffman’s sociological theory
AAMAS’2002, Bologna, July 15-19, 2002 – p.10/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.11/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.12/41
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InFFrA – Frames
R3
R1
R1 R2 R3
time
R1
R2
R3
C2
C1
C3
C4
C5C8
C9
C10
preconditions postconditions
conditionsactivation
time
trajectory model
sustainment conditions
C6 C7
deactivationconditions
R2
R3
R4 R5
R1
G2
G1
stat
us
C
B
A
D
E
F G
conceptualbeliefs
causalbeliefs
C
B
A
D
F
E
G
H
R2
A4
A6
A7A2
A1
A5
A3
A8
A7
A9
Frame
context
roles & relationships trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.13/41
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InFFrA – Frames
R3R1
R1 R2 R3
time
A4
A6
A7A2
A1
A5
A3
A8
A7
A9
trajectories
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
CB
ADE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
context
R1 R2 R3
time
Frame
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
AAMAS’2002, Bologna, July 15-19, 2002 – p.14/41
� � � ���� ��� ��� ���
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InFFrA – Frames
R3R1
R1 R2 R3
time
A4
A6
A7A2
A1
A5
A3
A8
A7
A9
trajectories
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
CB
ADE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
context
R1 R2 R3
time
Frame
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
action
actor role
message
AAMAS’2002, Bologna, July 15-19, 2002 – p.15/41
� � � ���� ��� ��� ���
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InFFrA – Frames
R3R1
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
R1 R2 R3
time
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
CB
ADE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
context
R2
R3
R4 R5
R1
G2
G1
stat
usFrame
roles & relationships
AAMAS’2002, Bologna, July 15-19, 2002 – p.16/41
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InFFrA – Frames
R3R1
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
R1 R2 R3
time
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
CB
ADE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
context
R2
R3
R4 R5
R1
G2
G1
stat
usFrame
roles & relationships
group
relationship
role
AAMAS’2002, Bologna, July 15-19, 2002 – p.17/41
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InFFrA – Frames
R3R1
R1
R2
R3
C2
C1
C3
C4
C5C8
C9
C10
preconditions postconditions
conditionsactivation
time
trajectory model
sustainment conditions
C6 C7
deactivationconditions
contextC
BA
DE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1 R2 R3
time
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
R2
R3
R4 R5
R1
G2
G1
stat
usFrame
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
context
roles & relationships
AAMAS’2002, Bologna, July 15-19, 2002 – p.18/41
� � � ���� ��� ��� ���
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InFFrA – Frames
R3R1
R1
R2
R3
C2
C1
C3
C4
C5C8
C9
C10
preconditions postconditions
conditionsactivation
time
trajectory model
sustainment conditions
C6 C7
deactivationconditions
contextC
BA
DE
F G
conceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
beliefs
R1 R2 R3
time
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
R2
R3
R4 R5
R1
G2
G1
stat
usFrame
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
context
roles & relationships condition trajectory
conditionsenactment
conditionsactivation
AAMAS’2002, Bologna, July 15-19, 2002 – p.19/41
� � � ���� ��� ��� ���
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InFFrA – Frames
R3
R1
R3R1
C
B
A
D
E
F G
conceptualbeliefs
causalbeliefs
C
B
A
D
FE
G
H
R2
beliefs
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
R1 R2 R3
time
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
CB
ADE
F G
Frame
context beliefsconceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
AAMAS’2002, Bologna, July 15-19, 2002 – p.20/41
� � � ���� ��� ��� ���
� � ��
InFFrA – Frames
R3
R1
R3R1
C
B
A
D
E
F G
conceptualbeliefs
causalbeliefs
C
B
A
D
FE
G
H
R2
beliefs
R2
R3
R4 R5
R1
G2
G1
stat
us
roles & relationships
R1 R2 R3
time
A4A6
A7A2
A1A5
A3A8
A7
A9
trajectories
R1R
2R3
C2
C1
C3
C4C5 C8
C9
C10
preconditions postconditions
conditionsactivation
timetrajectory model
sustainment conditionsC6C7
deactivationconditions
CB
ADE
F G
Frame
context beliefsconceptualbeliefs
causalbeliefs
CB A
DFE
G
H
R2
ontology
belief net
role
AAMAS’2002, Bologna, July 15-19, 2002 – p.21/41
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InFFrA – Frames
R3
R1
R1 R2 R3
time
R1
R2
R3
C2
C1
C3
C4
C5C8
C9
C10
preconditions postconditions
conditionsactivation
time
trajectory model
sustainment conditions
C6 C7
deactivationconditions
R2
R3
R4 R5
R1
G2
G1
stat
us
C
B
A
D
E
F G
conceptualbeliefs
causalbeliefs
C
B
A
D
F
E
G
H
R2
A4
A6
A7A2
A1
A5
A3
A8
A7
A9
Frame
context
roles & relationships trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.22/41
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Further FeaturesPrivate attributes
Status slots for common attributes
Mappings and assessments
Meta-frame attributesLinks (“alternative”, “variant”, etc.)
History (of frame evolution)
AAMAS’2002, Bologna, July 15-19, 2002 – p.23/41
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Further FeaturesPrivate attributes
Status slots for common attributes
Mappings and assessments
Meta-frame attributesLinks (“alternative”, “variant”, etc.)
History (of frame evolution)
AAMAS’2002, Bologna, July 15-19, 2002 – p.23/41
� � � ���� ��� ��� ���
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Further FeaturesPrivate attributes
Status slots for common attributes
Mappings and assessments
Meta-frame attributes
Links (“alternative”, “variant”, etc.)
History (of frame evolution)
AAMAS’2002, Bologna, July 15-19, 2002 – p.23/41
� � � ���� ��� ��� ���
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Further FeaturesPrivate attributes
Status slots for common attributes
Mappings and assessments
Meta-frame attributesLinks (“alternative”, “variant”, etc.)
History (of frame evolution)
AAMAS’2002, Bologna, July 15-19, 2002 – p.23/41
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InFFrA – FramingData structures:
Perceived frame
Active frame
Difference model
Trial frame
Frame repository
AAMAS’2002, Bologna, July 15-19, 2002 – p.24/41
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InFFrA – FramingInference steps:
Situation interpretation
Matching
Assessment
Framing decision
Adjustment/re-framing
Enactment
Behaviour generation
AAMAS’2002, Bologna, July 15-19, 2002 – p.25/41
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Framing
deviance
F FF
F2
F
deviate comply
situation interpretationmodule
frame matchingmodule
behaviour generation frame enactment
F3 Fn
prescriptivemodel
framing determine frameadequacy
determine framevalidity
determine framedesirability
module
perceptionupdate
currentmodel
descriptive model
normativemodel
generate
framing decision
reasons
modify
create
switch
frame adjustmentmodule
alternativeframes
trial instantiatemodulemodule
derive commitments
framerepository
compliance/
privategoals/values
sub−social level
perception
action
compliance
activated frame difference modelperceived frame
F1. . . . .
assessment
frameupdates
deviancedata
Framing Architecture
F F
roles
contexts
trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.26/41
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Interpretation & Matching
deviance
situation interpretationmodule
frame matchingmodule
perceptionupdate
currentmodel
descriptive model
normativemodel
generate
perception
compliance
activated frame difference modelperceived frame
F F
roles
contexts
trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.27/41
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Assessment: “Comply Case”
deviance
comply
privategoals/values
frame matchingmodule
frame enactment
prescriptivemodel
framing determine frameadequacy
determine framevalidity
determine framedesirability
module
normativemodel
generate
framing decision
module
compliance/
compliance
activated frame difference model
sub−social level
assessment
deviancedata
F
roles
contexts
trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.28/41
� � � ���� ��� ��� ���
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Assessment: “Deviate Case”
deviance
F FF
F2
F
frame matchingmodule
frame enactment
F3 Fn
prescriptivemodel
framing determine frameadequacy
determine framevalidity
determine framedesirability
module
normativemodel
generate
framing decision
reasons
modify
create
switch
frame adjustmentmodule
alternativeframes
trial instantiatemodule
framerepository
compliance/
compliance
activated frame difference model
F1
deviate
. . . . .
assessment
frameupdates
deviancedata
F
roles
contexts
trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.29/41
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Enactment
behaviour generation frame enactment
prescriptivemodel
modulemodule
derive commitments
action
activated frame
F
AAMAS’2002, Bologna, July 15-19, 2002 – p.30/41
� � � ���� ��� ��� ���
� � ��
Framing
deviance
F FF
F2
F
deviate comply
situation interpretationmodule
frame matchingmodule
behaviour generation frame enactment
F3 Fn
prescriptivemodel
framing determine frameadequacy
determine framevalidity
determine framedesirability
module
perceptionupdate
currentmodel
descriptive model
normativemodel
generate
framing decision
reasons
modify
create
switch
frame adjustmentmodule
alternativeframes
trial instantiatemodulemodule
derive commitments
framerepository
compliance/
privategoals/values
sub−social level
perception
action
compliance
activated frame difference modelperceived frame
F1. . . . .
assessment
frameupdates
deviancedata
Framing Architecture
F F
roles
contexts
trajectories
beliefs
AAMAS’2002, Bologna, July 15-19, 2002 – p.31/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.32/41
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OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.33/41
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Example – AdHocAdHoc = Adaptive Heuristic for OpponentClassification
AdHoc agents classify opponentsdynamically in iterated multiagent games
Scenario:randomly moving agents on toroidal grid
fixed number of PD games upon encounter
goal: utility maximisation
AAMAS’2002, Bologna, July 15-19, 2002 – p.34/41
� � � ���� ��� ��� ���
� � ��
Example – AdHocAdHoc = Adaptive Heuristic for OpponentClassificationAdHoc agents classify opponentsdynamically in iterated multiagent games
Scenario:randomly moving agents on toroidal grid
fixed number of PD games upon encounter
goal: utility maximisation
AAMAS’2002, Bologna, July 15-19, 2002 – p.34/41
� � � ���� ��� ��� ���
� � ��
Example – AdHocAdHoc = Adaptive Heuristic for OpponentClassificationAdHoc agents classify opponentsdynamically in iterated multiagent games
Scenario:randomly moving agents on toroidal grid
fixed number of PD games upon encounter
goal: utility maximisation
AAMAS’2002, Bologna, July 15-19, 2002 – p.34/41
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Example – AdHocOpponent class models � consist of:
Deterministic finite automaton describingbehaviour of � [Carmel & Markovitch 96]
Q-table for optimal counter-strategy [Watkins &Dayan 92]
Similarity values � ��� � � �Learning samples for �
Similarity = Ratio of encounters withopponent understood by class model
AAMAS’2002, Bologna, July 15-19, 2002 – p.35/41
� � � ���� ��� ��� ���
� � ��
Example – AdHocOpponent class models � consist of:
Deterministic finite automaton describingbehaviour of � [Carmel & Markovitch 96]
Q-table for optimal counter-strategy [Watkins &Dayan 92]
Similarity values � ��� � � �Learning samples for �
Similarity = Ratio of encounters withopponent understood by class model
AAMAS’2002, Bologna, July 15-19, 2002 – p.35/41
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� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete ones
Adaptive (bounded) number of classesLong-term integration of similar classesConvergence to actual number of opponentclassesProblem: AdHoc vs. AdHoc agentsCan be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete onesAdaptive (bounded) number of classes
Long-term integration of similar classesConvergence to actual number of opponentclassesProblem: AdHoc vs. AdHoc agentsCan be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete onesAdaptive (bounded) number of classesLong-term integration of similar classes
Convergence to actual number of opponentclassesProblem: AdHoc vs. AdHoc agentsCan be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete onesAdaptive (bounded) number of classesLong-term integration of similar classesConvergence to actual number of opponentclasses
Problem: AdHoc vs. AdHoc agentsCan be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete onesAdaptive (bounded) number of classesLong-term integration of similar classesConvergence to actual number of opponentclassesProblem: AdHoc vs. AdHoc agents
Can be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – FeaturesRe-classification, creation of new classes,deletion of obsolete onesAdaptive (bounded) number of classesLong-term integration of similar classesConvergence to actual number of opponentclassesProblem: AdHoc vs. AdHoc agentsCan be solved “heuristically”
AAMAS’2002, Bologna, July 15-19, 2002 – p.36/41
� � � ���� ��� ��� ���
� � ��
Example – Performance
24
26
28
30
32
0 5000 10000 15000 20000 25000 30000 35000
Rew
ard
per 1
00 G
ames
Interactions (IPD Games)
AdHoc AgentOne class per opponent
One class for all opponents
AAMAS’2002, Bologna, July 15-19, 2002 – p.37/41
� � � ���� ��� ��� ���
� � ��
Example – Performance
0
5
10
15
20
25
30
35
0 1000 2000 3000 4000 5000 6000
Num
ber o
f opp
onen
t cla
sses
Encounters
k = 80k = 60k = 30k = 20k = 40k = 10
AAMAS’2002, Bologna, July 15-19, 2002 – p.38/41
� � � ���� ��� ��� ���
� � ��
Example – Performance
24
26
28
30
32
34
36
38
0 2000 4000 6000 8000 10000 12000 14000
Rew
ard
per 1
00 G
ames
Interactions (IPD Games)
Tit For TatRandom Class
Maximum QualityHighest Payoff
AAMAS’2002, Bologna, July 15-19, 2002 – p.39/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent class
Deterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton
Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning
Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values
Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class
History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA AnalysisFraming = Classification procedure
Frame = Opponent classDeterministic finite automaton Trajectory
Q-table for strategy learning Context
Similarity values Difference model
Learning samples for class History
Roles, links and beliefs: trivialPerceived frame = current encounter gamesequence
AAMAS’2002, Bologna, July 15-19, 2002 – p.40/41
� � � ���� ��� ��� ���
� � ��
InFFrA Analysis – Observations
if opponent is known, no re-framing duringencounterelse, matching after each roundframe matching updates all similarity valuesassessment and re-framing only afterencounterno adequacy and desirability test
restricted flexibility
AAMAS’2002, Bologna, July 15-19, 2002 – p.41/41
� � � ���� ��� ��� ���
� � ��
InFFrA Analysis – Observations
if opponent is known, no re-framing duringencounterelse, matching after each roundframe matching updates all similarity valuesassessment and re-framing only afterencounterno adequacy and desirability test
restricted flexibility
AAMAS’2002, Bologna, July 15-19, 2002 – p.41/41
� � � ���� ��� ��� ���
� � ��
InFFrA Analysis – Observations
frame adjustment very complexheart of AdHoc systemtrial instantiation trivialenactment: use Q-tabletrajectory does not restrict ego’s actions!
reason for AdHoc vs. AdHoc problem
AAMAS’2002, Bologna, July 15-19, 2002 – p.42/41
� � � ���� ��� ��� ���
� � ��
InFFrA Analysis – Observations
frame adjustment very complexheart of AdHoc systemtrial instantiation trivialenactment: use Q-tabletrajectory does not restrict ego’s actions!
reason for AdHoc vs. AdHoc problem
AAMAS’2002, Bologna, July 15-19, 2002 – p.42/41
� � � ���� ��� ��� ���
� � ��
OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.43/41
� � � ���� ��� ��� ���
� � ��
OverviewMotivationInteraction Frames & FramingInFFrAExampleConclusions
AAMAS’2002, Bologna, July 15-19, 2002 – p.44/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols?
Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!
Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics.
...another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics.
...another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?
Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning?
Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!
Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
... interaction protocols? Not really!Protocols have pre-specified semantics....another AOSE methodology?Yes, but...
we focus on specific type of reasoning
we assume models to be used by agents
...case-based reasoning? Yes and No!Depends on frame construction &combination.
AAMAS’2002, Bologna, July 15-19, 2002 – p.45/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?
Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!
Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local.
...top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up?
Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!
InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!
...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?
Certainly not!too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly
not!too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
ConclusionAre we talking about...
...individualist or socio-centric approach?Something in-between!Data=social, reasoning=local....top-down or bottom-up? Both!InFFrA is a meta-architecture!...a silver bullet for open systems?Certainly not!
too few InFFrA-compliant systems
heavy cognitive assumptions
AAMAS’2002, Bologna, July 15-19, 2002 – p.46/41
� � � ���� ��� ��� ���
� � ��
OutlookRicher communicative scenarios
Emergence of globally valid frames?Interaction frame calculiOrganisational interaction framesDevelop adaptive InFFrA agents
AAMAS’2002, Bologna, July 15-19, 2002 – p.47/41
� � � ���� ��� ��� ���
� � ��
OutlookRicher communicative scenariosEmergence of globally valid frames?
Interaction frame calculiOrganisational interaction framesDevelop adaptive InFFrA agents
AAMAS’2002, Bologna, July 15-19, 2002 – p.47/41
� � � ���� ��� ��� ���
� � ��
OutlookRicher communicative scenariosEmergence of globally valid frames?Interaction frame calculi
Organisational interaction framesDevelop adaptive InFFrA agents
AAMAS’2002, Bologna, July 15-19, 2002 – p.47/41
� � � ���� ��� ��� ���
� � ��
OutlookRicher communicative scenariosEmergence of globally valid frames?Interaction frame calculiOrganisational interaction frames
Develop adaptive InFFrA agents
AAMAS’2002, Bologna, July 15-19, 2002 – p.47/41
� � � ���� ��� ��� ���
� � ��
OutlookRicher communicative scenariosEmergence of globally valid frames?Interaction frame calculiOrganisational interaction framesDevelop adaptive InFFrA agents
AAMAS’2002, Bologna, July 15-19, 2002 – p.47/41
� � � ���� ��� ��� ���
� � ��
Things to remember!
Open systems
Socio-empirically rational agentsSociological grounding: frames and framing
InFFrA meta-architectureMulti-perspective applicability
AAMAS’2002, Bologna, July 15-19, 2002 – p.48/41
� � � ���� ��� ��� ���
� � ��
Things to remember!
Open systemsSocio-empirically rational agents
Sociological grounding: frames and framing
InFFrA meta-architectureMulti-perspective applicability
AAMAS’2002, Bologna, July 15-19, 2002 – p.48/41
� � � ���� ��� ��� ���
� � ��
Things to remember!
Open systemsSocio-empirically rational agentsSociological grounding: frames and framing
InFFrA meta-architectureMulti-perspective applicability
AAMAS’2002, Bologna, July 15-19, 2002 – p.48/41
� � � ���� ��� ��� ���
� � ��
Things to remember!
Open systemsSocio-empirically rational agentsSociological grounding: frames and framingInFFrA meta-architecture
Multi-perspective applicability
AAMAS’2002, Bologna, July 15-19, 2002 – p.48/41
� � � ���� ��� ��� ���
� � ��
Things to remember!
Open systemsSocio-empirically rational agentsSociological grounding: frames and framingInFFrA meta-architectureMulti-perspective applicability
AAMAS’2002, Bologna, July 15-19, 2002 – p.48/41
� � � ���� ��� ��� ���
� � ��
Thank you for your attention!
AAMAS’2002, Bologna, July 15-19, 2002 – p.49/41