agent reputation trust (art) testbed andrew diniz da costa [email protected]
TRANSCRIPT
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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.
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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
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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
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Agente Avaliador
Agente Competidor 1
era1 era2 era9... era10
Agente Competidor 2
era1 era2 era9... era10
Zé Carioca LES
era1 era2 era9... era101,0 0,1 0,5 0,7
pintura era1*
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Transações dos Agentes
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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
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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.
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Agente Zé Carioca LES
• Agente avaliador com inteligência.
• Realizar boas avaliações das pinturas solicitadas por clientes.
• Boas estratégias.
• Finalista em 2007
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Simulador
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Simulador
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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
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Fase Preliminar
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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
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Considerações finais
• Possíveis trabalhos futuros:
– Melhorar os agentes criados e que competiram em 2007 e 2008.
– Criar novos agentes.
• Grupo trabalhando com reputação
– 2 professores
– 5 alunos de mestrado
• ART-Testbed 2009 nos aguarda.
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A Hybrid Diagnostic-Recommendation Approach for Multi-Agent Systems
Andrew Diniz da Costa
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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?
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Motivation
• Reputation concept related with diagnoses and recommendation
• Ubiquitous Computing Systems provide several situations that need of diagnoses and recommendations
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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.
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MediatorAgent
RequesterAgent
RecommendationAgent
DiagnosticAgent
(2)<<create>>
(2)<<create>>(1)
Request name of theDiagnosis Agent
(5)Provide name of the
Diagnosis Agent
(3)Send the
Recommendation name
(4)Send the
Requester name
General Idea
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General Idea
RequesterAgent
DiagnosticAgent
RecommendationAgent
(1)
Request advices / Supply inform
ation, such as, quality of service
(2)Provide diagnosis
result
(3)Provide advices
Plan database
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Solicitador A
Solicitador B
Mediador A
Mediador B
AgenteDiagnóstico A
Tipo de Diagnóstico 1
AgenteDiagnóstico B
Tipo de Diagnóstico 2
AgenteRecomendação A
AgenteRecomendação B
Tipo de Recomendação
Requisita
Provê
Requisita
Provê
<<criar>>
<<criar>>
<<criar>><<criar>>
General Idea
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Architecture
Application
Mediation
Diagnosis
Recommendation
Artificial IntelligenceToolset
DRP-MAS
Reputation
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DRP-MAS (Artificial Intelligence Toolset)
API Bigus*
AI DRPMAS
Forward Chaining
Backward Chaining
Fuzzy Logic
Artificial Intelligence Toolset
Inference Diagnoses
*Bigus, J., Bigus, J., 2001. Constructing Intelligent Agents Using Java, 2nd edition.
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Performing Diagnosis I/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 agent’s profile
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Performing Diagnosis II/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.
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Performing Diagnosis III/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.
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Performing Diagnosis IV/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
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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
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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.
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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 Report1 implemented in the Governance Framework2 and the model Fire.
• The agent profile defined the minimum accepted reputation of its partners
• At the end, the recommendations are provided1) Guedes, J., Silva, V., Lucena, C., 2008. 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. 37-52.
2) Silva, V.; Duran, F.; Guedes, J., Lucena, C., 2007. Governing Multi-Agent Systems, In Journal of Brazilian Computer Society, special issue on Software Engineering for Multi-Agent Systems, n. 2 vol. 13, pp. 19-34.
3) 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.
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Scenarios used
• Translation
– Portuguese to English
• Music Market Place
– Buy cd from the name of some music.
Customer
Provider Service
Customer
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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.
Fim!