1. Reputations Agent to ART Testbed Competition Andrew Diniz da Costa andrew@les.inf.puc-rio.br

2. 2 © LES/PUC-Rio Roadmap Competition Strategy Important dates Future works

3. 3 © LES/PUC-Rio Competition Agent Reputation Trust (ART) Testbed Competition with agents AAMAS Conference Domain: appraisals for paintings Clients request appraisals for paintings from different eras

4. 4 © LES/PUC-Rio Competition Agent 1 era1era2era9...era10 Agent 2 era1era2era9...era10 LES Agent era1era2 era9...era10 1,0 0,1 0,5 0,7 paintingera 1 *

5. 5 © LES/PUC-Rio 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

6. 6 © LES/PUC-Rio Transactions between agents

7. 7 © LES/PUC-Rio 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

8. 8 © LES/PUC-Rio 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

9. 9 © LES/PUC-Rio 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 knowledges grade It is not enough!

10. 10 © LES/PUC-Rio Strategy ZeCariocaLES Agent... Reputations Agent 1 Reputations Agent 2 Reputations Agent n era1 era2...era9era10 era1

11. 11 © LES/PUC-Rio Strategy using opinions Difference between first and the other sessions Complement with opinions of other appraisers I dont ask if my grade is >= 0,7 If grade < 0,7 I ask always

12. 12 © LES/PUC-Rio 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

13. 13 © LES/PUC-Rio Strategy - weight If estimates >= 0,5 then weight is 0,3 If estimates < 0,5 then weight is 0,1

14. 14 © LES/PUC-Rio Strategy to send opinions To supply opinion To spend or not time to supply opinion To use the Gaussian formula

15. 15 © LES/PUC-Rio Current Agent: First place – Zé Carioca

16. 16 © LES/PUC-Rio Current Agent: Fourth place – Zé Carioca

17. 17 © LES/PUC-Rio LES team Andrew Diniz, Fábio de Azevedo, Sérgio Ciglione Improvement of the statisticians –To adjust the weights Can Reputation transaction help us?

18. 18 © LES/PUC-Rio 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

19. 19 © LES/PUC-Rio 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.

20. 20 © LES/PUC-Rio 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

21. 21 © LES/PUC-Rio 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

22. 22 © LES/PUC-Rio 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.

23. The End!