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Agent Reputation Trust (ART) Testbed Andrew Diniz da Costa

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Apresentação em tema: "Agent Reputation Trust (ART) Testbed Andrew Diniz da Costa"— Transcrição da apresentação:

1 Agent Reputation Trust (ART) Testbed Andrew Diniz da Costa

2 © LES/PUC-Rio Introdução Confiança é a segurança, certeza daquele que tem fé na probidade (honradez, integridade de caráter, honestidade) de alguém. Reputação é um conceito atribuído a uma pessoa por parte da sociedade em que vive para medir o grau de confiança. Em sistemas multi-agentes abertos temos sociedades de agentes heterogêneos. Importância da existência de mecanismos para identificar agentes que não se comportam adequadamente.

3 © LES/PUC-Rio Introdução Por quê modelar confiança e reputação ? agentes devem escolher com quem interagir objetivo de capacitar os agentes a fazer a escolha correta. Diversos algoritmos na área de confiança e reputação como compará-los ? quais as características principais ART Testbed competição entre agentes experimentos independentes Introdução

4 © LES/PUC-Rio Visão Geral da Competição ART-Testbed Clientes solicitam avaliações para pinturas de Eras diferentes Agentes avaliadores podem pedir opinião de outros Agentes avaliadores podem comprar reputação de outros avaliadores Objetivo de produzir avaliação mais precisa possível Domínio: Art Appraisal Agentes são avaliadores de pintura com níveis variados de perícias em Eras artísticas diferentes

5 © LES/PUC-Rio Agente Avaliador Agente Competidor 1 era1era2era9...era10 Agente Competidor 2 era1era2era9...era10 Zé Carioca LES era1era2 era9...era10 1,0 0,1 0,5 0,7 pinturaera 1 *

6 © LES/PUC-Rio Transações dos Agentes

7 © LES/PUC-Rio Conceitos importantes Tempo de análise –Analisar uma pintura de um cliente –Pintura de uma opinião requisitada Geração da opinião –Informação baseada no tempo de análise –Informar valor Pesos –Peso das próprias avaliações –Peso das opiniões dos concorrentes Vencedor –Aquele que tiver mais dinheiro no final do jogo. p*=i(wi. pi) i(wi) wi = peso pi = Avaliação da opinião

8 © LES/PUC-Rio Regras Número de sessões entre 100 e 200. Graus de conhecimentos das eras podem sofrer mudanças durante o jogo. Dependendo do jogo pode haver limite de requisições de opiniões e reputações. Dependendo do jogo o agente poderá ou não usar seus conhecimentos em cada era. Avaliações geradas a partir das opiniões solicitadas.

9 © LES/PUC-Rio Agente Zé Carioca LES Agente avaliador com inteligência. Realizar boas avaliações das pinturas solicitadas por clientes. Boas estratégias. Finalista em 2007

10 © LES/PUC-Rio Simulador

11 © LES/PUC-Rio Simulador

12 © LES/PUC-Rio Competição 17 agentes (1 não foi aprovado) de 13 diferentes instituições Duas fases –Preliminar –Final Fase preliminar (Maio 10-11) –8 agentes de diferentes instituições –15 agentes da própria competição (5 ruins, 5 neutros, 5 honestos) –100 sessões Fase final (Maio 16-17) –Apenas os 5 melhores agentes da fase preliminar –15 agentes da própria competição (5 ruins, 5 neutros, 5 honestos) –200 sessões

13 © LES/PUC-Rio Fase Preliminar

14 © LES/PUC-Rio Fase Final 5) Pontifícia Universidade Católica do Rio de Janeiro 4) Agents Research Lab, University of Girona 3) Department of Computer Engineering, Bogazici University 2) Department of Math & Computer Science, The University of Tulsa 1) Electronics & Computer Science, University of Southampton

15 © LES/PUC-Rio Considerações finais Possíveis trabalhos futuros: –Melhorar os agentes criados e que competiram em 2007 e –Criar novos agentes. Grupo trabalhando com reputação –2 professores –5 alunos de mestrado ART-Testbed 2009 nos aguarda.

16 © LES/PUC-Rio A Hybrid Diagnostic-Recommendation Approach for Multi-Agent Systems Andrew Diniz da Costa

17 © LES/PUC-Rio Motivation Governance Framework Multi-agent systems are societies with autonomous and heterogeneous agents, which can work together to achieve similar or different goals. The reason for some agent not to achieve some goal. Buyer desires to buy some product from some seller. –If the goal was not achieved then which was the reason? –What to do?

18 © LES/PUC-Rio Motivation Reputation concept related with diagnoses and recommendation Ubiquitous Computing Systems provide several situations that need of diagnoses and recommendations

19 © LES/PUC-Rio Difficulties of Diagnosing and Providing Alternative Executions We analyzed a set of points that deserved our attention during the creation of the new module 1.Deciding how to analyze the execution of the agents 2.Selecting data for diagnosing 3.Determining strategies for diagnoses 4.Determining trustworthy agents 5.Determining strategies for recommendations 6.Representing profiles of agents 7.Different devices (cell phones, laptops, PDA) Limitations of hardware 8.Types of connection Speed of connection (56Kbps, 512Kbps, etc), IP.

20 © LES/PUC-Rio Mediator Agent Requester Agent Recommendation Agent Diagnostic Agent (2) > (2) > (1) Request name of the Diagnosis Agent (5) Provide name of the Diagnosis Agent (3) Send the Recommendation name (4) Send the Requester name General Idea

21 © LES/PUC-Rio General Idea Requester Agent Diagnostic Agent Recommendation Agent (1) Request advices / Supply information, such as, quality of service (2) Provide diagnosis result (3) Provide advices Plan data base

22 © LES/PUC-Rio Solicitador A Solicitador B Mediador A Mediador B Agente Diagnóstico A Tipo de Diagnóstico 1 Agente Diagnóstico B Tipo de Diagnóstico 2 Agente Recomendação A Agente Recomendação B Tipo de Recomendação Requisita Provê Requisita Provê > General Idea

23 © LES/PUC-Rio Architecture Application Mediation Diagnosis Recommendation Artificial Intelligence Toolset DRP-MAS Reputation

24 © LES/PUC-Rio DRP-MAS (Artificial Intelligence Toolset) API Bigus* AI DRPMAS Forward Chaining Backward Chaining Fuzzy Logic Artificial Intelligence Toolset Inference Diagnoses *Bigus, J., Bigus, J., Constructing Intelligent Agents Using Java, 2nd edition.

25 © LES/PUC-Rio Performing DiagnosisI/IV Goal: to perform diagnosis Such analyses are performed based on a set of information provided by the Requester agent (application agent) Information that can be provided: Goal –The goal that was not achieved Plan executed –The plan executed by the agent Resources: –it may be the case that the resource could not be found, could not used, the amount was not sufficient, … Profile –The agents profile

26 © LES/PUC-Rio Performing DiagnosisII/IV Information that can be provided: Quality of service –A degree used to qualify the execution of the plan Partners –The agents with whom the agent has interacted Services requested –Services used by the agents Belief Base –Base of Knowledge Devices –Devices used by the customers. Connection –Type of connection used.

27 © LES/PUC-Rio Performing DiagnosisIII/IV The strategy used to make the diagnoses is a hot-spot (flexible point) However, the framework provides a set of APIs* to help on the diagnosis: –backward chaining, –forward chaining and –reasoning with fuzzy logic The framework provide a default strategy that: –Compares the amount of resource used and the desired one –Analyzes the quality of the execution *Joseph P. Bigus, Jennifer Bigus; Constructing Intelligent Agents Using Java, second edition.

28 © LES/PUC-Rio Performing DiagnosisIV/IV The diagnosis that the default strategy can provide are: –The wrong amount of resources was used –Several problems happened at the same time –It was not possible to identify the problem

29 © LES/PUC-Rio Providing Recommendations The Recommendation agent incorporates the process of advising alternative ways to achieve some goal. It is composed of three steps: (i) to select plans, (ii) to verify the plans need for agents to request information, (iii) to choose good agents Selecting Plan Verifying Selected Plans Choosing agents

30 © LES/PUC-Rio Selecting Plans The strategy used to select plans is a hot-spot (flexible point) –It depends on the diagnosis and on the information provided by the agent Each plan should be associated with a set of information that describes: –resources used during the execution, desired goal, profiles of agents that accept executing the plan, quality of service that determines how the previous execution of the plan was performed, related diagnoses, etc.

31 © LES/PUC-Rio Verifying selected plans and choosing agents If the plan indicates that the agent will need to interact with other agents, it is necessary to choose the must trustful agents The agents are selected based on their reputations –Using a Reputation agent –We are using the reputation system Report 1 implemented in the Governance Framework 2 and the model Fire. The agent profile defined the minimum accepted reputation of its partners At the end, the recommendations are provided 1)Guedes, J., Silva, V., Lucena, C., A Reputation Model Based on testimonies. In: Agent Oriented Information Systems IV: Proc. of the 8th International Bi-Conference Workshop (AOIS 2006 post-proceedings), LNCS (LNAI) 4898, Springer-Verlag, pp ) Silva, V.; Duran, F.; Guedes, J., Lucena, C., Governing Multi-Agent Systems, In Journal of Brazilian Computer Society, special issue on Software Engineering for Multi-Agent Systems, n. 2 vol. 13, pp ) Huynh, T. D., Jennings, N. R. and Shadbolt, N. (2004) FIRE: an integrated trust and reputation model for open multi- agent systems. In: 16th European Conference on Artificial Intelligence, 2004, Valencia, Spain.

32 © LES/PUC-Rio Scenarios used Translation –Portuguese to English Music Market Place –Buy cd from the name of some music. Customer Provider Service Customer

33 © LES/PUC-Rio Technologies and Future Works Two versions of the DRP-MAS –ASF + Report Framework –Jadex + Report Framework and Fire model Future Works –Extend the DRP-MAS Extend the information set Define new strategies of diagnosis and recommendation Ubiquitous Computing –Learning in agents –Complex scenarios –Etc.

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