Agent Reputation Trust ART Testbed Andrew Diniz da

Agent Reputation Trust (ART) Testbed Andrew Diniz da Costa acosta@inf. puc-rio. br

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. © 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 © LES/PUC-Rio

Visão Geral da Competição ART-Testbed Domínio: Art Appraisal • Agentes são avaliadores de pintura com níveis variados de perícias em Eras artísticas diferentes • 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 © LES/PUC-Rio

Agente Avaliador Zé Carioca LES pintura era 1 1, 0 . . . era 2 0, 1 era 9 0, 5 era 2 . . . era 9 1 era 10 0, 7 Agente Competidor 2 Agente Competidor 1 era 1 * era 10 era 1 © LES/PUC-Rio era 2 . . . era 9 era 10 era

Transações dos Agentes © 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 p*=∑i(wi. pi) ∑ i(wi) wi = peso pi = Avaliação da opinião • Pesos – Peso das próprias avaliações – Peso das opiniões dos concorrentes • Vencedor – Aquele que tiver mais dinheiro no final do jogo. © 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. © LES/PUC-Rio

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

Simulador © 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 © LES/PUC-Rio

Fase Preliminar © LES/PUC-Rio

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

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. © LES/PUC-Rio

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

Motivation • Reputation concept related with diagnoses and recommendation • Ubiquitous Computing Systems provide several situations that need of diagnoses and recommendations © 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 (56 Kbps, 512 Kbps, etc), IP. © LES/PUC-Rio

General Idea (2) <<create>> Mediator Agent (1) Request name of the Diagnosis Agent (3) Send the Recommendation name (5) Provide name of the Diagnosis Agent (2 cre ) ate >> << Diagnostic Agent Re Se (4) qu nd es the ter na me Requester Agent Recommendation Agent © LES/PUC-Rio

General Idea es u eq R t s ice v ad u /S ly pp (1) tion, ma for s, ha uc s in qu ty ali of ice erv s Diagnostic Agent (2) Provide diagnosis result (3) Provide advices Recommendation Agent Plan data base Requester Agent © LES/PUC-Rio

General Idea Tipo de Diagnóstico 1 r>> <<cria Requisita Provê Tipo de Diagnóstico 2 > ar> cri Mediador A << Solicitador A Agente Diagnóstico A > riar> <<c Requisita Agente Diagnóstico B Tipo de Recomendação Solicitador B Provê Mediador B <<criar>> Agente Recomendação B Agente Recomendação A © LES/PUC-Rio

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

DRP-MAS (Artificial Intelligence Toolset) Artificial Intelligence Toolset AI DRPMAS Forward Chaining Inference Diagnoses Backward Chaining Fuzzy Logic API Bigus* *Bigus, J. , 2001. Constructing Intelligent Agents Using Java, 2 nd edition. © LES/PUC-Rio

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 © LES/PUC-Rio

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. © LES/PUC-Rio

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. © LES/PUC-Rio

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 © 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 © 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. © 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. , 2008. A Reputation Model Based on testimonies. In: Agent Oriented Information Systems IV: Proc. of the 8 th 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 multiagent systems. In: 16 th European Conference on Artificial Intelligence, 2004, Valencia, Spain. © LES/PUC-Rio

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

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