The deployment of the proposed architecture is a crucial step for the enabling of the configuration methodology, while supporting some of decisions taken in the architecture design. In a multi suppliers environment with an infinite number of modules it is not feasible to have all of the Equipment Module Agents running on the same computer. The problem is the computational strain to reach solutions would rise exponentially based on the number of available modules. Therefore it is proposed that the deployment of the Equipment Module Agent be done in the suppliers servers, allowing them control over the agents, and more importantly distribute the computational load across different computers. The equipment supplier will have the motivation to have this since it potentially can bring new business for them, while for the system integrator (representing the customer) it is advantageous since solutions will be provided quicker due to the distribution of the computer load.
Figure 5.16 provides a deployment overview highlighting the communications
across different computers.
Figure 5.16 - Agent Architecture Deployment Overview
Customer Site
MAS Expert Knowledge
Equipment Supplier 1 Simulation Resources Simulation Resources Equipment Supplier n 3 1 2 4 5 5 6 3 2 1 4 7 Simulation Resources 1 2 Simulation Resources 3 Requirements Agent Equipment Module Agent MAS Expert Agent
124 The other important aspect of this distributed deployment is that the Performance Simulation Agent can also be deployed in other machines to distribute to computer processing load, facilitating quicker solutions.
The final consideration of the deployment of the proposed agent architecture is placement of the MAS Expert Agent in a separated machine that is updated by configuration experts and where the libraries proposed in Chapter 4 would also be placed. The information on this machine could be in other machines in order to take advantage of the distributed computing paradigm. Nevertheless, it is crucial that all updates made to the MAS Expert Agent and the library change at the same time across different machines to ensure the proper operation of the configuration methodology.
5.5 Chapter Summary
In this chapter a multi agent model for the self-configuration of MAS was proposed. The chapter contains detailed agent descriptions, their roles and behaviours that enable the self-configuration methodology. It also provides a detailed description of the agent model, as well as the necessary interaction to ensure the execution of the self-configuration methodology.
The agent architecture provides an original representation of MAS that is able to reflect its concepts. Furthermore, the proposed agent architecture caters for the evolution of expert knowledge over time, by providing the means to introduce new knowledge without a need for changing the configuration methodology. Finally the proposed agent architecture provides a simulation level that provides early simulation results for potential configuration solutions. Furthermore these results are use in the configuration methodology towards achieving better results.
125
6
Local Behaviour Models
for Distributed Self-
Configuration Methodology
Figure 6.1 - Overview of Enabling Aspects for Emergence of Configuration in Agent Architecture System Configuration Equipment Configuration Base Table Glue Dispenser Transport Apply Glue Feed Pick Up
Place Cure Transport Manipulator B
Conveyor Feeder
Assembly Process Configuration System Configuration Protocols Be liefs Eq uipm ent Mo du le Ag ent M AS Exp ert Ag ent Perfo rma nce Simu latio n Agen t St ru cture s C o llab ora tion s Algo rithms Req uire ment s Agen t Self- Configuration
126
6.1 Introduction
In this chapter we will cover the specific methods of the multi agent architecture that will enable self-configuration of modular assembly systems. The chapter will break down the agent specific methods as well as provide the method for assessing the validity of the configuration results.
The proposed configuration methods were developed based on the described model of Chapter 4 and the characteristics of the agent environment in Chapter 5. This is important to highlight because the entire input information and agent environment definitions for the proposed configuration methods are already defined in these chapters.
One important note for this chapter is the distinction between configuration and reconfiguration of the module assembly systems. For the purposes of this work reconfiguration is defined as a configuration with some extra constraints. The constraints in the event of a reconfiguration process are the description of the existing system, including the ability to force the use of certain equipment modules. This ability in conjunction with the introduction of equipment module agents with quite advantageous characteristics, such as near zero cost, provides the basis for the reconfiguration using the same methodology as for the configuration process. These constraints are as defined in Chapter 4.
The development of methods for an agent environment requires a clear communication structure. This structure entails the definition of available protocols, which enable agents to trigger other agents using predefined collaboration rules that are understood and followed by both. Despite the existence of protocols for multi agent systems, these tend to be domain and solution specific (Kraus [98]). Therefore, protocols for this multi agent system need to be described in this chapter.
In order to develop the methods for the configuration of modular assembly systems using a multi agent environment, the configuration process steps should be clear as defined in Chapter 5.
The configuration of modular assembly systems will be driven by an established set of capability requirements. This is the first stage of the configuration process, which
127 consists of the individual equipment module agents matching their own capabilities to the ones required. Once this is done a cluster of interested equipment modules is created (Oliveira [60]).
These Equipment Module Agents will then need to establish preliminary collaborations with other equipment module agents, in order to establish potential configurations. This stage will require an assessment by each individual agent. The method for this assessment will be presented throughout this chapter.
The equipment module agents will be able to participate in a number of different potential solutions. This raises an issue of participation on multiple solution clusters. If a solution is not possible the agent will expand its search parameters for other agents until no more equipment module agents can be found. This highlights the iterative nature of the method. There are two outcomes for this stage, either no solution is found, or a series of potential configuration solutions are found.
The next stage of the configuration process is the assessment of the solutions by the configuration expert agent. The assessment consists of the configuration expert agent checking its internal knowledge for existing configuration patterns and relaying missing elements to the established collaborations. This stage might have required the repetition of the prior stages, if missing elements are identified.
The formulation of the next assessment requires the creation of the simulation agents. These will perform specific methods to validate the potential configurations. Once the results are achieved, these are relayed back to the equipment modules for final assessment.
The equipment module agents perform the final assessments of the potential configuration solutions and decide on which one they foresee to be the best one. This choice involves also the pulling out of other potential configuration solutions, which in turn will lead these collaborations to find other potential equipment modules.
The final stage is the final assessment of the requirements agent for the selection of the top three configurations for system integrator decision.
128 It is important to underline that the proposed configuration methodology is based on the emergence that distributed systems can obtain (Kennedy and Eberhart [132]). The principle is that simple rules distributed across multiple agents while enabling them to interact will result in this emergent complex solution. In addition, the fact that the domain of modular assembly system raises issues of future scalability of the different systems highlights the need for a distributed system than can be enhanced with more equipment modules and new concepts.
The description of the emergent complexity of the methodology requires firstly the development of the distributed blocks, in this case the agents. The agent environment has been described in the previous chapter, however the specific decision making methods have not been presented yet. Therefore, this chapter will start by covering the specific communication requirements of the agent environment, and this will be followed by the detailed methods for each agent and finally the emergent method of distributed self-configuration of modular systems.