A Hybrid Diagnostic-Recommendation Approach for Multi-Agent Systems Andrew Diniz da Costa 1 Carlos J. P. de Lucena 1 Viviane T. da Silva 2 1 Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil 2 Universidad Complutense de Madrid, Madrid, Spain {acosta,

Andrew D. Costa © LES/PUC-Rio Motivation Multi-agent systems are societies with autonomous and heterogeneous agents –Achieve common goals or –Different goals Competitions based on agents (TAC / ART-Testbed) Agents are interactive and goal-oriented entities –Agents execute plans in order achieve their goals –Agents interact with other agents while executing their plans After deciding which goal it will try to achieve, the agent selects one of its plans that may help it to achieve the goal However, it may be the case that the agent could not achieve the goal by executing such a plan There are several reasons for an agent fails while trying to achieve a goal

Andrew D. Costa © LES/PUC-Rio Motivation Interesting scenarios are based on ubiquitous computing A client requests a service from a mobile device (i.e. cell phone or PDA) to a set of provider agents of the service. If the service was not provided correctly –It becomes important to understand why such failures occurred –and to seek a solution to the problem by recommending other plans that will attempt to achieve the goal

Andrew D. Costa © LES/PUC-Rio Hybrid Diagnostic-Recommendation System Our approach: a system to help agents on diagnosis the failures and to recommend alternative plans Diagnosis is assumed as the process of determining the reasons that caused the failures while trying to achieve a goal Recommendation is an alternative plan select based on the diagnosis that could be used to try to achieve the same goal

Andrew D. Costa © LES/PUC-Rio Proposal (I/II) Defining strategies that allow performing different diagnoses. Defining strategies that provide recommendations to agents in order to achieve the desired goals. Providing strategies of diagnosis and recommendation that can be used in different domains. Representing new strategies of diagnosis and recommendation.

Andrew D. Costa © LES/PUC-Rio Proposal (II/II) Providing a set of data that can be used in diagnoses and recommendations. Extending the set of data from the characteristics of the domain. Providing different kinds of reputation used to distinguish which agents should be used in interactions.

Andrew D. Costa © LES/PUC-Rio Difficulties of Diagnosing and Providing Alternative Executions 1.Deciding how to analyze the behavior of the agents –To determine an appropriate way to analyze the behavior of the agents. Two possible ways: (i) the execution of each agent would be monitored (privacy would be violated), (ii) each agent detects the failures and is able to provide related information 2.Selecting data for diagnosing –To define the data needed to perform diagnoses on the executions of agents –A list with such data was defined 3.Determining strategies to diagnoses –To define strategies that could be used in different domains

Andrew D. Costa © LES/PUC-Rio Difficulties of Diagnosing and Providing Alternative Executions 4.Determining trustworthy agents –The information received by an agent can influence on the achievement of its goals –Partners can cause the failures. How can I trust my partners? 5.Providing recommendations –To define strategies that could cope with the different diagnosis 6.Representing profiles of agents –To consider the agents profiles while providing recommendations

Andrew D. Costa © 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

Andrew D. Costa © 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

Andrew D. Costa © LES/PUC-Rio Requester A Requester B Mediator A Mediator B Diagnosis Agent A Diagnosis Type 1 Diagnosis Agent B Diagnosis Type 2 Recommendation Agent A Recommendation Agent B Recommendation Types request provide request provide > General Idea

Andrew D. Costa © LES/PUC-Rio The DRP-MAS object-oriented framework Mediation Diagnosis Recommendation Artificial Intelligence Toolkit DRP-MAS Reputation

Andrew D. Costa © LES/PUC-Rio Mediation Module Goal: Define Mediator agent that is the responsible for creating an exclusive Diagnostic and Recommendation agent to the Requester. Different mediators can be defined. Avoid Requesters wait for a long time. Valid approach when the system supports the amount of agents. It is possible to combine different strategies of diagnosis and recommendation.

Andrew D. Costa © LES/PUC-Rio Information Set 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 be used, the amount was not sufficient, … Profile –The agent’s profile Quality of service –A degree used to qualify the execution of the plan

Andrew D. Costa © LES/PUC-Rio Information Set Partners –The agents with whom the agent has interacted Services used –Services requested while the plan was executed. Belief Base –Knowldge base used by the requester agent.

Andrew D. Costa © LES/PUC-Rio DiagnosisModule Goal: to perform diagnosis Such analyses are performed based on a set of information provided by the Requester agent (application agent) Strategy of diagnosis is a hot spot (flexible point) Diagnosis can be classified as: –Main Diagnosis: met from the information set provided by the Requester agent. –Inferred Diagnosis: met from inferences. Data that were not provided by the Requester agent. DRP-MAS framework helps in the inference process from the artificial intelligence model.

Andrew D. Costa © LES/PUC-Rio Artificial Intelligence Module (I/III) Goal: Provide algorithms in order to create strategies of diagnosis and recommendation. Joseph P. Bigus, Jennifer Bigus; Constructing Intelligent Agents Using Java, second edition.

Andrew D. Costa © LES/PUC-Rio Artificial Intelligence Module (II/III) Algorithms: –backward chaining –forward chaining –fuzzy logic Inferred service from the forward chaining. –Verify which variables on the rule base were used in some execution. –The variables, which were not used, are now used. It considers like the data had been provided by the Requester.

Andrew D. Costa © LES/PUC-Rio Artificial Intelligence Module (III/III) Met diagnosis from data provided by the Requester agent. Inferred Diagnoses from data did not provided by the Requester agent

Andrew D. Costa © 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) selecting plans, –(ii) verifying the plans need for agents to request information, –(iii) choosing good agents

Andrew D. Costa © LES/PUC-Rio Selecting Plans (I/II) Goal: Choose alternative plans in order to achieve the desired goal of the Requester agent. The strategy used to select plans is a hot-spot (flexible point) Plan base used 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, etc.

Andrew D. Costa © LES/PUC-Rio Selecting Plans (II/II) DRP-MAS provides two services that help on the recommendation process: –Selecting plans that are related with provided data –Selecting plans that are not related with a set of data When no plan is met, a message is sent to the Requester and the process is finished. When some plan is met, the second step of the process is executed

Andrew D. Costa © LES/PUC-Rio Verifying Selected Plans Goal: It verifies if the selected plans need of agents. When no plan needs to request services, a message is provided to the Requester agent with the recommendations If the plan indicates that the agent will need to interact with other agents, it is necessary to choose trustful agents In order to choose trustful agents, the recommendation requests to the Reputation agent a set of candidate agents –Necessary services –Requester profile

Andrew D. Costa © LES/PUC-Rio Choosing Agents Goal: It selects agents that will be recommended from the candidate agents provided by the Reputation agent. When a plan does not have agents to recommend, then the plan is not considered. When the plan has some agent, it is recommended. Strategy of recommendation is a hot-spot of the framework, because different reputation models and profiles can be used. At the end the plans are recommended to the Requester agent.

Andrew D. Costa © LES/PUC-Rio Reputation Module Goal: Represent the reputation concept of agents. Provide two models: centralized and decentralized. Centralized model is based on Report 1 system created in the Governance Framework 2. Decentralized model based on Fire model 3. 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. and Shadbolt, N., 2004, FIRE: an integrated trust and reputation model for open multi-agent systems. In Proceedings of the 16th European Conference on Artificial Intelligence, 2004, Valencia, Spain.

Andrew D. Costa © LES/PUC-Rio Reputation Module Decentralized base Centralized base

Andrew D. Costa © LES/PUC-Rio Reputation Module Centralized model –Global reputations –It is possible to define different strategies –Some default strategies are provided Decentralized model –Interaction trust, Witness reputation, Certified reputation –Offer standard calculation proposed in the Fire model –Change calculations –Define other decentralized reputations

Andrew D. Costa © LES/PUC-Rio Providing Support to Ubiquitous Computing DRP-MAS framework relates two new concepts. Device used: Different characteristics of the available devices: (i) type of device, (ii) model, (iii) language that the data must be provided by the agent. Connections: Characteristics of connections, i.e., (i) speed, (ii) tecnology (ex: wireless, LAN, WAN, etc.) and (iii) IP address.

Andrew D. Costa © LES/PUC-Rio Desenvolvimento DRP-MAS Jadex 1 + Report system (centralized model) + Fire (decentralized model) ASF 2 + Report system (centralized and decentralized model) –Easy to adapt with the approach proposed. 1) Poukahr, A. and Braubach, L., 2007c, Jade Tutorial, Distributed System Group University of Hamburg, Germany, Release Acceded at: 2) Costa, Andrew D., Lucena, Carlos J. P., Silva, Viviane T., Azevedo, Sérgio C., Soares, Fábio A., 2008, Computing Reputation in the Art Context: Agent Design to Handle Negotiation Challenges, Trust in Agent Societies workshop, The Seventh International Conference on Autonomous Agents and Multiagent System (AAMAS’08), Estoril, Portugal.

Andrew D. Costa © LES/PUC-Rio Scenarios of Use Four scenarios. Two based on the intelligent home domain 1. Two scenarios based on ubiquitous computing. 1) Horling, B., Lesser, V., Vincent, R., Bazzan, A. Xuan, P., Diagnosis as an Integral Part of Multi-Agent Adaptability, DARPA Information Survivability Conference and Exposition, DISCEX’00, Proceedings, Volume 2, pp

Andrew D. Costa © LES/PUC-Rio Scenarios – Intelligent Home Intelligent home domain is composed for agents that control different appliances. Two scenarios: –Washing dishes –Making 20 cups of strong coffee

Andrew D. Costa © LES/PUC-Rio Washing Dishes Dishwasher Water Heater Request hot water Forward Chaining Reputation DRP-MAS Water Heater Request hot water Request Recommendations Provide Recommendations Agents

Andrew D. Costa © LES/PUC-Rio Making 20 cups of strong coffee Coffee Maker Forward Chaining Reputation DRP-MAS Tester Request Recommendations Provide Recommendations Agents Send coffee Send result

Andrew D. Costa © LES/PUC-Rio Web Agent Team 1 Agent Team 3 Agent Team 2 Expert people on the world Expert people on the world Rio de Janeiro Brazil Waterloo Canada Madrid Spain London England requesting information requesting information requesting information requesting information requesting information Expert person Expert person Mobile Process Service

Andrew D. Costa © LES/PUC-Rio Scenarios – Ubiquitous Computing Two scenarios –Translation (Portuguese to English) –Music Market Place Using mobile devices: cell phones (Jade Leap 1 ) and computers. 1) Caire, G., 2003, LEAP User Guide, Copyright (C) TILAB, LEAP3.1, December.

Andrew D. Costa © LES/PUC-Rio Scenario - Translation Customer Translator Request Update Belief Base Expert Request Translation Forward Chaining Customer Request Recommendation Request Translation Reputation DRP-MAS Agents

Andrew D. Costa © LES/PUC-Rio Scenario– Music Market Place Buyer Seller Request CD from the music Forward Chaining Reputation DRP-MAS Seller (Cheap) Request CD Poor / Rich Buyer Expert Request CD Rich Buyer Request Recommendations Provide Recommendations Agents

Andrew D. Costa © LES/PUC-Rio Final Considerations – Main Contributions Identifying challenges in order to propose a generic solution to define diagnoses and recommendations. Defining a set of information in order to identify diagnoses and recommendations. Such data can be used in different domains. Defining strategies of diagnosis and recommendation that can be used in different domains. Proposing an approach that allows creating different strategies of diagnosis and recommendations from a generic structure. Integrating the reputation, diagnosis and recommendation concepts.

Andrew D. Costa © LES/PUC-Rio Final Considerations – Trabalhos Futuros Adaptation of agents. Provide a better support to ubiquitous computing. Problems of performance.

Andrew D. Costa © LES/PUC-Rio Final Considerations - Papers Third Workshop on Software Engineering for Agent-oriented Systems (SEAS 2007) 3 th International Conference on Software and Data Technologies (ICSOFT 2008) Workshop Trust in Agent Societies: AAMAS’08 Fourth Workshop on Software Engineering for Agent-oriented Systems (SEAS 2008) – 2 papers ACM Transactions on Computer Systems (ACM TOCS) – Journal (submitted) Trust, Reputation, Evidence and Other Collaboration Know-how – 5 th ACM SAC TRECK Track (submitted) Springer book in LNCS/LNAI (submitted)

A Hybrid Diagnostic-Recommendation System for Agent Execution in Multi-Agent Systems Andrew Diniz da Costa 1 Carlos J. P. de Lucena 1 Viviane T. da Silva 2 Thanks !! {acosta,