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

A Hybrid Diagnostic-Recommendation Approach for Multi-Agent Systems

Andrew Diniz da Costa1

Carlos J. P. de Lucena1

Viviane T. da Silva2

1Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil2Universidad Complutense de Madrid, Madrid, Spain

{acosta, lucena}@inf.puc-rio.br, [email protected]

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


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


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


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.


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.


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


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


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


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


Requester A

Requester B

Mediator A

Mediator B

Diagnosis Agent A

Diagnosis Type 1

DiagnosisAgent B

Diagnosis Type 2

RecommendationAgent A

RecommendationAgent B

Recommendation Types

request

provide

request

provide

<<create>>

<<creater>>

<<create>>

<<creater>>

General Idea


The DRP-MAS object-oriented framework

Mediation

Diagnosis

Recommendation

Artificial IntelligenceToolkit

DRP-MAS

Reputation


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.


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


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.


Diagnosis Module

• 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.


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.


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.


Artificial Intelligence Module (III/III)

Met diagnosis from data provided by the Requester agent.

Inferred Diagnoses from data did not providedby the Requester agent


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


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.


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


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


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.


Reputation Module

• Goal: Represent the reputation concept of agents.

• Provide two models: centralized and decentralized.

• Centralized model is based on Report1 system created in the Governance Framework2.

• Decentralized model based on Fire model3.

1) 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. 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.


Reputation Module

Decentralized base

Decentralized base

Decentralized base

Decentralized base

Centralized base


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


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.


Desenvolvimento DRP-MAS

• Jadex1 + Report system (centralized model) + Fire (decentralized model)

• ASF2 + 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 0.96. Acceded at: http://vsis-www.informatik.uni-hamburg.de/projects/jadex 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.


Scenarios of Use

• Four scenarios.

• Two based on the intelligent home domain1.

• Two scenarios based on ubiquitous computing.

1) Horling, B., Lesser, V., Vincent, R., Bazzan, A. Xuan, P., 2000. Diagnosis as an Integral Part of Multi-Agent Adaptability , DARPA Information Survivability Conference and Exposition, DISCEX’00, Proceedings, Volume 2, pp. 211-219.


Scenarios – Intelligent Home

• Intelligent home domain is composed for agents that control different appliances.

• Two scenarios:

– Washing dishes

– Making 20 cups of strong coffee


Washing Dishes

Dishwasher

Water Heater

Request hot water

Forward Chaining

Reputation

DRP-MAS

Water Heater

Request hot water

Request Recommendations

Provide Recommendations

Agents


Making 20 cups of strong coffee

Coffee Maker

Forward Chaining

Reputation

DRP-MAS

Tester


Provide Recommendations

Agents

Send coffee

Send result


Web

Agent Team 1

Agent Team 3

Agent Team 2

Expert people on the world

Expert people on the world

Rio de JaneiroBrazil

WaterlooCanada

MadridSpain

LondonEngland

requesting information


requesting informationrequesting information



Expert person

Expert personExpert person

Expert person

Mobile Process Service


Scenarios – Ubiquitous Computing

• Two scenarios

– Translation (Portuguese to English)

– Music Market Place

• Using mobile devices: cell phones (Jade Leap1) and computers.

1) Caire, G., 2003, LEAP User Guide, Copyright (C) TILAB, LEAP3.1, December.


Scenario - Translation

Customer

Translator

TranslatorRequest Update

Belief Base

Expert

Request Translation

Forward Chaining

Customer

Request Recommendation

Request Translation

Request Translation

Reputation

DRP-MAS

Agents


Scenario– Music Market Place

BuyerSeller

Request CD from the music

Forward Chaining

Reputation

DRP-MAS

Seller(Cheap)

Request CD

Poor / Rich Buyer

Expert

Request CD

Rich Buyer


ProvideRecommendations

Agents


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.


Final Considerations – Trabalhos Futuros

• Adaptation of agents.

• Provide a better support to ubiquitous computing.

• Problems of performance.


Final Considerations - Papers

• Third Workshop on Software Engineering for Agent-oriented Systems (SEAS 2007)

• 3th 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 – 5th 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 Costa1

Carlos J. P. de Lucena1

Viviane T. da Silva2

Thanks !!

{acosta, lucena}@inf.puc-rio.br, [email protected],

Documents

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