reputations agent to art testbed competition andrew diniz da costa [email protected]
TRANSCRIPT
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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
Agent 1
era1 era2 era9... era10
Agent 2
era1 era2 era9... era10
LES Agent
era1 era2 era9... era101,0 0,1 0,5 0,7
painting era1*
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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
• Weights
– Proper evaluations
– Opinions of the competitors
• Generation of an opinion requested by another appraiser
– Information based in the analysis time
– To inform the value
p*=∑i(wi . pi) ∑ i(wi)
wi = weight
pi = Evaluation of the opinion
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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
...Reputations
Agent 1
Reputations
Agent 2
Reputations
Agent n
era1 era2 ... era9 era10
era1
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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 (AAMAS-2005), 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 (AAMAS-2006), 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 (AAMAS-2006), 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”.
The End!