Reputation’s Agent
to ART Testbed Competition
Andrew Diniz da Costa
[email protected]
Roadmap
• Competition
• Strategy
• Important dates
• Future works
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Competition
• Agent Reputation Trust
(ART) Testbed
• Competition with
agents
• AAMAS Conference
• Domain: appraisals for
paintings
• Clients request
appraisals for paintings
from different eras
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Competition
painting *
1
era
LES Agent
era1
1,0
...
era2
0,1
era9
0,5
Agent 2
Agent 1
era1
era2
...
era10
0,7
era9
era10
era1
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era2
...
era9
era10
4
Competition
• It is necessary to complete the knowledge of each agent
• So, transactions with other agents should be executed.
• There are two types of transaction:
– Opinion
– Reputation
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Transactions between agents
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Game
• Each game has 20 sessions
• When a session finishes:
– The true value of the paintings is disclosed.
– It is verified what agent got the best appraisals.
• In the next session each agent has the following
information:
– The true value of the paintings
– The value of each opinion supplied by other agents
– ...
• The winner is the agent that has more money in the end of
the game
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Important Concepts
• Analysis Time
– To analyze a painting of a client
– Painting of an opinion requested
p*=∑i(wi . pi)
∑ i(wi)
• Weights
– Proper evaluations
wi = weight
– Opinions of the competitors
pi = Evaluation
of the opinion
• Generation of an opinion requested by another appraiser
– Information based in the analysis time
– To inform the value
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Strategy
• My evaluation has the higher weight (1,0).
• To spend a good time to analyze my paintings
– Time versus money
– How much bigger the time, next 100% of the my knowledge’s
grade
• It is not enough!
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Strategy
ZeCariocaLES Agent
era1
Reputations
Reputations
Agent 1
Agent 2
era2
...
era9
...
Reputations
Agent n
era10
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Strategy using opinions
• Difference between first and the other sessions
• Complement with opinions of other appraisers
• I don’t ask if my grade is >= 0,7
• If grade < 0,7 I ask always
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Strategy using opinions
• To use opinion like complement
• I determine low weights in relation the Zé Carioca agent
• Weights to the competitors: 0,1 or 0,3
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Strategy - weight
• If estimates >= 0,5 then weight is 0,3
• If estimates < 0,5 then weight is 0,1
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Strategy to send opinions
• To supply opinion
• To spend or not time to supply opinion
• To use the Gaussian formula
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Current Agent: First place – Zé Carioca
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Current Agent: Fourth place – Zé Carioca
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LES team
• Andrew Diniz, Fábio de Azevedo, Sérgio Ciglione
• Improvement of the statisticians
– To adjust the weights
• Can Reputation transaction help us?
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Important dates
• The registration deadline for the 2007 ART Testbed
Competition was April 14, 2007
• The agent submission deadline for registered participants is
May 9, 2007
• Preliminary Phase - May 10-11, 2007
• Final Round games will be conducted May 16-18, 2007
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Future works
• To compare the ZeCariocaLES agent with the other.
• What can we do to improve the agent?
• To analyze in which domains the strategy applied can be
used.
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References
• Agents of the ART-Testbed 2006:
– * Iam (University of Nebraska-Lincoln)
– Neil (Nanyang Technological University - Singapura)
– Frost (Department of Computer Engineering, Bogazici
University – Istanbul na Turquia)
– Sabatini(GIAA, Universidad Carlos III de Madrid )
– Joey (Computer Science and Engineering, University of
Nebraska-Lincoln)
–
•
...
ART Testbed Team. Agent Reputation and Trust Testbed.
http://www.lips.utexas.edu/art-testbed/competition_rules.htm
http://www.lips.utexas.edu/art-testbed/pdf/SpecSummary.pdf
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References
•
Fullam, K., T. Klos, G. Muller, J. Sabater, A. Schlosser, Z. Topol, K. S.
Barber, J. Rosenschein, L. Vercouter, and M. Voss. (2005) "A Specification
of the Agent Reputation and Trust (ART) Testbed: Experimentation and
Competition for Trust in Agent Societies," The Fourth International Joint
Conference on Autonomous Agents and Multiagent Systems (AAMAS2005), Utrecht, July 25-29, pp. 512-518
•
Fullam, K., T. Klos, G. Muller, J. Sabater, Z. Topol, K. S. Barber, J.
Rosenschein, and L. Vercouter. (2005) "A Demonstration of The Agent
Reputation and Trust (ART) Testbed: Experimentation and Competition for
Trust in Agent Societies," The Fourth International Joint Conference on
Autonomous Agents and Multiagent Systems (AAMAS-2005) Demonstration
Track, Utrecht, July 25-29, pp. 151-152.
•
Sen, S., I. Goswami, and S. Airiau. (2006) "Expertise and Trust-Based
Formation of Effective Coalitions: An Evaluation of the ART Testbed," The
Workshop on Trust in Agent Societies at The Fifth International Joint
Conference on Autonomous Agents and Multiagent Systems (AAMAS2006), Hakodate, Japan, May 9, pp. 71-78
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References
•
Stranders, R. (2006) Argumentation Based Decision Making for Trust in
Multi-Agent Systems. Master's Thesis, Delft University of Technology.
•
Fullam, K. and K.S. Barber. (2006) "Learning Trust Strategies in Reputation
Exchange Networks," The Fifth International Joint Conference on
Autonomous Agents and Multiagent Systems (AAMAS-2006), Hakodate,
Japan, May 8-12, pp. 1241-1248.
•
Kafali, O. and P. Yolum. (2006) "Trust Strategies for ART Testbed," The
Workshop on Trust in Agent Societies at The Fifth International Joint
Conference on Autonomous Agents and Multiagent Systems (AAMAS2006), Hakodate, Japan, May 9, pp. 43-49.
•
Fernanda Duran, Viviane Torres da Silva, and Carlos J. P. de Lucena (2006)
“Using Testimonies to Enforce the Behavior of Agents”.
•
José de S. P. Guedes Viviane Torres da Silva, and Carlos J. P. de Lucena
(2006) “A Reputation Model Based on Testimonies”.
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The End!