A Hybrid Diagnostic-Recommendation System for Agent Execution in Multi-Agent Systems Master Student:...

A Hybrid Diagnostic-Recommendation System for Agent Execution

in Multi-Agent Systems

Master Student: Andrew Diniz da CostaAdvisor: Carlos J. P. de Lucena

[email protected]@inf.puc-rio.br

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Roadmap

• Motivation

• Related Works

• Difficulties of Diagnosing and Providing Recommendations

• Proposal

• Case Study: Intelligent Home

• Conclusions

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Motivation

• Open multi-agent systems are societies with autonomous and heterogeneous agents, which can work together to achieve similar or different goals.

• The agents have behaviors which determine the steps to achieve the goals.

• In order to cope with the heterogeneity, autonomy and diversity of interests among the different agents, governance (or law enforcement) systems have been defined.

• Governance systems enforce that agents must respect the set of norms that describe actions that agents are prohibited, permitted or obligated to do

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Motivation

• The Governance Framework was created.

• It is based on testimonies provided by witnesses about facts or events that they know are related with norms violated.

• The architecture of the framework defines three modules:

– Judgment

• It is responsible for receiving the testimonies and for providing a verdict to the punishment module

– Reputation

• It is responsible for calculating the reputation of the agents and provides them to the judgment module and to other application agents

– Punishment

• It is the responsible to determine the penalties to the agents which have violated the norms of the environment

1- Fernanda Duran, Viviane Torres da Silva, and Carlos J. P. de Lucena (2006), Using Testimonies to Enforce the Behavior of Agents.2- Guedes, José de Souza Pinto; Silva, Viviane Torres; Lucena, Carlos José Pereira de: A Reputation Model Based on Testimonies, AOIS2006@CAiSE workshop, Luxembourg, Grand-Duchy of Luxembourg, June 6, 2006.

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Motivation

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Motivation

• The reason for some agent not to achieve some goal, not necessarily can be some information provided by another agent.

• In software systems in general and in particular, in open multi-agent system, understanding the reason for the occurrence of failures is a very important goal.

• We decided to propose a new hybrid diagnostic-recommendation system of agents’ called Diagnosing and Recommending Plans in Multi-Agent Systems (DRP-MAS).

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Motivation

• Diagnosis is assumed as the process of determining the reason why agents do not achieve their goals

• Recommendations are provided on how to achieve the desired goals that agents have failed to achieve.

• A set of proposals in the literature suggest different ways for agents to perform diagnostic executions

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Related Work - Application of Multi-Agent in Control and Fault Diagnosis Systems

• A monitor (Monitoring Agent) for each component in the system.

• It is the responsible for collecting information about each component.

• When obtained, the data is provided to agents.

• Agents work together to provide the final diagnosis without conflicts.

Tie-Jun Li, Yu-Qing Peng, Hai-Wen Zhao, Kai Li; Application of Multi-Agent in Control and Fault Diagnosis Systems, China, 2004

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Related Work - Diagnosis as an Integral Part of Multi-Agent Adaptability

• This work proposes to examine how an independent domain of diagnosis can behave in multi-agent systems

• An initial step to perform some diagnosis is to indicate the correct hoped behavior from an agent.

• To compare the hoped result on an execution with the result obtained.

• To represent this idea, a decomposition’s language of goals/tasks called TAEMS was used.

• It allows modeling the goals, the possible sub-goals, methods which can be executed to achieve them, besides the resources used.

Bryan Horling, Victor Lesser, Régis Vincent, Ana Bazzan, Ping Xuan; Diagnosis as an Integral Part of Multi-Agent Adaptability, 1999

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Réis Vicent, Bryan Horling; Experiences in Simulating Multi-Agent Systems Using TAEMS, University of Massachusetts, 2000

• The work used as case study was the intelligent home.– This environment has appliances, such as, dishwasher, water heater,

coffee maker, etc., which are controlled by an individual autonomous agent.

• A scenario used by the environment involves a dishwasher and a water heater that exist in a house.

• Dishwasher requests hot water from the water heater – The dishwasher sometimes can not have sufficient resources to perform

its task.

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• Lacking diagnosis or monitoring, the dishwasher could:– perform the same execution repeatedly doing a poor job in washing the

dishes,

– or even not to washing anything due to insufficient amounts of hot water.

• Using diagnosis, the dishwasher could determine through internal sensors or user feedback that a required resource is missing, and then that the resource was not being coordinated over.

• Diagnoses are related with other diagnoses.

• In other words, a diagnosis met previously can help to find other diagnoses.

• A relation between the diagnoses has also been proposed

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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 behavior of the agents– The execution of each agent would be monitored (privacy

would be violated)

– Each agent analyzes its own execution

2. Diagnosing agents– A big challenge was to define which data was necessary to

perform diagnoses on executions of agents.

– A list with such data was defined.

3. Determining strategies to diagnoses – The challenge was to define services or strategies which could

be used by the different domains

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Difficulties of Diagnosing and Providing Alternative Executions

4. Determining trustworthy agents

– The information received by an agent can determine whether it will achieve its goal.

– An agent is guilty or innocent, or good or bad with respect to some request.

5. Providing alternative plans

– A challenge was to decide when a plan would be appropriate to be executed, and when it should be provided.

6. Representing profiles of agents

– We believe that agents can have profiles, which define important characteristics, such as, the minimum reputation of the agents to negotiate.

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General Idea

MediatorAgent

RequesterAgent

MasterAgent

DiagnosticAgent

(2)<<create>>

(2)<<create>>(1)

Request name of theDiagnosis Agent

(3)Provide name of the

Diagnosis Agent

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General Idea

RequesterAgent

DiagnosticAgent

MasterAgent

(1)

Request advices / Supply inform

ation, such as, quality of service

(2)Provide diagnosis

result

(3)Provide advices

Plan base

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Architecture

Application

Mediation Diagnosis

RecommendationArtificial Intelligence

Toolset

DRP-MAS

Reputation

Governance Framework

Judgment Reputation Punishment

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Performing Diagnosis

• The diagnosis is performed by the Diagnostic agent offered by the proposed framework

• Such analyses are performed based on a set of information provided by the Requester agent

• This set can be used in

– Different diagnosing processes

– Recommendation processes

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Performing Diagnosis – Information Set

• Resources*

– In some situations the reason for an execution not to be successfully performed can be the absence of some resource, or maybe the insufficient amount of resources used to perform something.

• Norm violated

– If a goal was not achieved, the reason could be a violated norm. Related data: (i) the agent responsible for the failure, (ii) degree of violation

• Quality of execution

– To define a degree (as defined by TAEMS**) to an execution performed by an agent can be useful to diagnoses.

• Goal

– Goal not achieved by the Requester agent.

**Réis Vicent, Bryan Horling; Experiences in Simulating Multi-Agent Systems Using TAEMS, University of Massachusetts, 2000

*Bryan Horling, Victor Lesser, Régis Vincent, Ana Bazzan, Ping Xuan; Diagnosis as an Integral Part of Multi-Agent Adaptability, 1999

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Performing Diagnosis – Information Set

• Plan executed – Plan executed by the requester agent that not achieved the

desired goal

• Agents negotiated – The diagnosis can indicate some agent as guilty of some

execution.

• Roles of the agents negotiated– They can be important to update reputations, and to advice

other agents which play the same roles.

• Profile – Profile of the requester agent

• Other problems – Other problems perceived by the requester agent, and not

mapped into none above-mentioned data

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Performing Diagnosis

• Strategy of making the diagnoses was defined as a hot spot (flexible point) in the module.

• Default strategy. To verify: – Resource used (to compare with the desired – plan base)– Norm violated

• It damaged the execution (degree of violation), responsible agents for happing the failure

– Quality of execution, etc

• Plans in the plan base can be related with a set of data (ex: goal, resource, norms, etc).

• The fixed part defined was the process of communication between the agents.

• The module is integrated with an API*:– backward chaining, – forward chaining and – reasoning with fuzzy logic

*Joseph P. Bigus, Jennifer Bigus; Constructing Intelligent Agents Using Java, second edition.

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Providing Advices

• The Master 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 action Selecting Plans is the responsible for choosing alternative plans to achieve the desired goal.

• A lot of different strategies can be defined to perform some selection (Strategy pattern).

• Methods are provided to offer support to the strategies created

– To search plans related with an information set

– Method which supplies all the plans which achieve the same goal (except the plan executed by the requester agent), etc.

• Default strategy

– To select plans from the plan base that achieve the same desired goal excluding the plan used by the Requester agent

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Verifying Plans

• After the selection of the alternative plans, it is verified whether some plan needs information provided by other agents.

• Each plan has a list with the roles of the agents which need to perform some communication.

• If the lists of all the plans are empty,

– Without communication with other agents.

• If the list of some plan is not empty

– Request the reputation of the candidate agents.

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Choosing agents

• After the Master agent receives the messages provided by the reputation module of the Governance Framework, this action is executed.

• Profile of the Requester agent can be used.

• Different kinds of recommendations can be provided:

– Alternative plan, which does not need agents and resources to be executed.

– Plan with a list of possible agents to request information.

– Plan with a list of agents and with a list of resources, which can be used.

– Plan with a plan and a list of resources.

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Case Study: Intelligence Home

• This environment, appliances, such as the dishwasher, water heater, coffee maker, etc., are controlled by an individual autonomous agent.

• An interesting aspect of this application are the possible conditions which allow the creation of scenarios involving cooperative interactions, different kinds of conflicts and constrained resources.

• Two cases were chosen:– The first case is about a dishwasher, which tries to achieve its

goal of washing the dishes.

– The second case is about a coffee maker, which has the goal of making 20 cups of strong coffee.

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• Agents

– Coffee maker

– Tester of coffee

• Goal

– To make 20 cups of strong coffee

Coffee Maker Tester

Provides coffee

Bad coffee

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MediatorAgent

Coffee Maker MasterAgent

DiagnosticAgent

(2)<<create>>

(2)<<create>>(1)

Request name of theDiagnosis Agent

(3)Provide name of the

Diagnosis Agent

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Coffee ‘Maker

DiagnosticAgent

MasterAgent

(1)

Request advices / Supply inform

ation, such as, quality of service

(2)Provide diagnosis:

problem_amount_powder_and_cups

(3)Provide advices

Quality of the plan=0 Amount of water=600 (mL)

Amount of powder=20 (grams)Goal: to make 20 cups of

strong coffee…

Rule Base – Forward Chaining

Problem_Strong_Coffee _20_Cups: IF conclusion_coffee= weak_coffee AND conclusion_water = Coffee_Incorrect_Water AND quality_service <10 THENproblem= problem_amount_powder_and_cups

Weak_Coffee: IF amount_powder <30 THENconclusion_coffee= weak_coffee

Strong_Coffee: IF amount_powder >29 THENconclusion_coffee= strong_coffee

Coffee_Correct_Water: IF amount_water=1000 THENConclusion_water= Coffee_Correct_Water

Coffee_Incorrect_Water: IF amount_water!=1000 THENConclusion_water= Coffee_Incorrect_Water

problem_amount_powder_and_cups

It verifies the amount of necessary resources to make 20 cups of strong

coffee

It searches other plans with the same goal

With the selected plans, the correct quantity of powder and water to make the

coffee is informed in the plans.

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Conclusions and Future Works

• Paper: A Hybrid Diagnostic-Recommendation System for Agent Execution in Multi-Agent Systems

• To apply the approach in a case study more complex.

• DRP-MAS to mobile

• Scenario using Mobile Process Service concept

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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

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Conclusions And Future Works

• Applying diagnoses

– If some agent did not complete its execution

– If some device did not receive the desired service

• Possible problems: space of memory, connection, etc

• Applying reputation to provide advices of execution

– In agents / experts

• provide bad information

• have problems with its execution

• delay a long time to provide some information

– Services / Products

• Prices

• Quality

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References

• Nicholas R. Jennings, Michael Wooldridge (1999), Agent- Oriented Software Engineering; Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World : Multi-Agent System Engineering (MAAMAW-99), Vol. 1647, Springer-Verlag: Heidelberg, Germany, oo. 1-7.

• Michael Wooldridge, Paolo Ciancarini, P. (2000) Agent-Oriented Software Engineering: The State of the Art, in First Int. Workshop on Agent-Oriented Software Engineering, Vol. 1957, Springer-Verlag, Berlin, pp. 1-28

• López, F.: Social Powers and Norms: Impact on Agent Behaviour. PhD thesis. University of Southampton. UK (2003).

• Boella, G.; van der Torre, L.: Regulative and Constitutive Norms in Normative Multi-Agent Systems. In Proceeding of 9th International Conference on the Principles of Knowledge Representation and Reasoning. California (2004).

• Singh, M.: An Ontology for Commitments in Multiagent Systems: Toward a Unification of Normative Concepts. Artificial Intelligence and Law v. 7 (1) (1999) 97-113.

• Tie-Jun Li, Yu-Qing Peng, Hai-Wen Zhao, Kai Li; Application of Multi-Agent in Control and Fault Diagnosis Systems, China, 2004 IEEE

• Bryan Horling, Victor Lesser, Régis Vincent, Ana Bazzan, Ping Xuan; Diagnosis as an Integral Part of Multi-Agent Adaptability, 1999

• Nico Roos, Annette tem Teije, André Bos, Cees Witteveen; An Analysis of Multi-Agent Diagnosis, AAMAS’02, 2002

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References

• Fernanda Duran, Viviane Torres da Silva, and Carlos J. P. de Lucena (2006), Using Testimonies to Enforce the Behavior of Agents.(referência incompleta)

• Guedes, José de Souza Pinto; Silva, Viviane Torres; Lucena, Carlos José Pereira de: A Reputation Model Based on Testimonies, AOIS2006@CAiSE workshop, Luxembourg, Grand-Duchy of Luxembourg, June 6, 2006.

• Patel, J., Teacy, W., Jennings, N., Luck, M., Chalmers, S., Oren, N., Norman, T., Preece, A., Gray, P., Shercliff, G., Stockreisser, P., Shao, J., Gray, W., Fiddian, N., Thompson, S.: Monitoring, Policing and Trust for Grid-Based Virtual Organisations. In Proc. of the UK e-Science All Hands Meeting 2005 UK (2005).

• Victor Lesser, Bryan Horling, and et al. The TAEMS whitepaper / envolving specification. http://dis.cs.umass.edu/research/taems/white/. Last access in November, 2007.

• Réis Vicent, Bryan Horling; Experiences in Simulating Multi-Agent Systems Using TAEMS, 2000 IEEE.

• Tom Wagner, Valerie Guralnik, John Phelps; TAEMS Agents: Enabling Dynamic Distributed Supply Chain Management.

• Silva, V., Cortês, M., Lucena, C. J. P.: An Object-Oriented Framework for Implementing Agent Societies, MCC32/04. Technical Report, Computer Science Department, PUC-Rio. Rio de Janeiro, BR (2004).

• Andrew D. Costa, Carlos J. P. Lucena, Viviane T. Silva: Remodelando e Estendendo o Agent Society Framework. Techical Report, Computer Science Department, PUC-Rio. Rio de Janeiro, BR (2006).

• The Foundations of Intelligent Physical Agents; Official web site; http://www.fipa.org/; February, 2008.

• Gamma, E.; Helm, R.; Johnson, R.; Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software.

• Joseph P. Bigus, Jennifer Bigus; Constructing Intelligent Agents Using Java, second edition.

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