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Architectural design of a distributed application with autonomic quality requirements

Architectural design of a distributed application with autonomic quality requirements
Architectural design of a distributed application with autonomic quality requirements

Architectural Design of a Distributed Application with Autonomic Quality Requirements

Danny Weyns,Kurt Schelfthout and Tom Holvoet AgentWise,DistriNet,Department of Computer Science,K.U.Leuven Celestijnenlaan200A,B-3001Leuven,Belgium {Danny.Weyns,Kurt Schelfthout,Tom.Holvoet}@cs.kuleuven.ac.be

Abstract

Architectural design plays a key role in mainstream soft-ware engineering.The software architecture is the backbone of the designed solution,it meets the functional requirements of the system and aims to satisfy the essential quality require-ments.An autonomic system is essentially characterized by quality requirements that specify that the system should be able to adapt(manage,con?gure,heal)itself under varying circum-stances and situations.These quality requirements call for an architecture-centric software engineering approach.

In this paper,we discuss and illustrate the architectural de-sign of a complex real-world distributed application with auto-nomic quality requirements.In particular,we present an archi-tecture with autonomous entities(agents)for managing ware-house logistics.The autonomic requirements are met by aban-doning any form of central control,by ensuring that the col-lective behaviour of the agents can cope with environmental change,and by providing the agents with an adaptive architec-ture.We illustrate how the subsequent architectural decisions are guided by a reference architecture for situated multi-agent systems on the one hand,and by functional and quality require-ments of the application on the other hand.

1Introduction

Software architecture is generally acknowledged as a crucial part of the design of a software system.During architectural design,the architect“shapes”the structures of the software sys-tem.The software architecture is the backbone of the designed solution,it meets the functional requirements of the system and aims to satisfy the essential quality requirements.

The software architecture of a systems comprises different structures,re?ected in different architectural views[1].Most common is the module view that provides modules as containers for holding responsibilities and data?ow relationships among the modules.Other views are the concurrency view that focuses on dynamic aspects of the system such as parallelism and syn-chronization,or the deployment view that describes the alloca-tion of the available processors in the system.

Software systems are built to perform particular function-ality,in the?rst place.However,various stakeholders inter-ested in the software system(project managers,developers,end users,customers,maintainers,etc.)will have additional re-quirements regarding the quality of the software system such as performance,scalability,adaptability,maintainability,etc.To-day,software engineers generally recognize that quality require-ments of a system are primarily achieved through its software architecture.Many architectural styles[6]have been developed to realize particular quality requirements.A couple of well-known styles are client-server,pipe-and-?lter,layers,or black-board.Reference architectures[1]go one step further in reuse of best practices in architectural design.A reference architecture de?nes an abstract architecture that serves as a blueprint to de-velop software architectures for a family of applications that are characterized by speci?c functional and quality requirements. Autonomic systems and quality requirements.Autonomic computing is recognized as a viable solution to challenge the increasing complexity of managing software systems.To be au-tonomic,a system must be able to adapt itself under varying circumstances.Examples of autonomic capabilities are self-management,self-con?guration or self-healing.Since auto-nomic systems are essentially characterized by quality require-ments that specify that the system should be able adapt itself in changing situations,the design and development of autonomic systems call for an architecture-centric software engineering ap-proach.

In our research,we study the engineering of complex de-centralized applications with autonomic quality requirements. Example domains are self-managing networks or decentralized control of logistic machines in a warehouse.Flexibility,adapt-ability and openness are important quality properties for systems to be able to adapt autonomously.We put forward situated mul-tiagent systems(situated MASs)as an approach to build such complex applications.We have developed a reference architec-ture for situated MASs to guide the development of applications with autonomic quality requirements.In this paper,we discuss and illustrate the architectural design of a complex real-world application with autonomic properties.We show how the sub-sequent architectural decisions are guided by the reference ar-chitecture for situated MASs on the one hand,and by functional and quality requirements of the application on the other hand. Overview of the paper.The remainder of this paper is struc-tured as follows.Section2elaborates on situated MASs and the reference architecture we have developed for situated MASs.In section3we discuss the architectural design of a transportation system for managing warehouse logistics,and illustrate how we

have applied the reference architecture to the design of this real-word application.Finally,we draw conclusions in section4.

2A Reference Architecture for Situated MASs

In this section we brie?y introduce situated MASs and moti-vate the use of situated MASs for the design of autonomic ap-plications.Next,we give a high-level overview of the reference architecture we have developed for situated MASs.

2.1Situated MASs

A situated MAS consists of a set of autonomous entities (agents)that are located in an environment.The agents can per-ceive and manipulate the environment in their neighborhood.A situated agent selects actions based on its current position,the state of the perceived environment and limited internal state.Sit-uated agents cooperate to solve a complex problem in a decen-tralized way.Interaction is at the core of problem solving in a situated MAS,rather than capabilities of individual agents.

In situated MASs,agents and the environment are?rst-order abstractions[10].Situated agents exploit the environ-ment to share information and coordinate their actions.A digital pheromone,for example,is a dynamic structure in the environ-ment that aggregates with additional pheromone that is dropped, diffuses in space and evaporates over time.Agents can use pheromones to dynamically form pheromone paths to locations of interest.Another example is a gradient?eld that propagates through the environment and changes in strength the further it is propagated.Agents can use a gradient?eld as a guiding bea-con.Situated MASs have been applied with success in practical applications over a broad range of domains.Some examples are manufacturing control[3],supply chains systems[4],and net-work management[2].

Cooperating agents situated in an environment is a natu-ral concept to manage complexity in a decentralized manner. Agents encapsulate their own behavior and are able to adapt to changes in their environment.Well known bene?ts of situated MAS are ef?ciency,robustness and?exibility[14].These fun-damental properties make situated MASs a suitable approach for building self-managing applications.

2.2Reference architecture in a nutshell

In the last three years,we have been developing a reference architecture for situated MASs.This reference architecture em-bodies the knowledge and experiences we have acquired dur-ing our research.The reference architecture generalizes and extracts common functions and structures from various exper-imental applications we have studied,including a simple peer-to-peer?le sharing system1,a simulation of an automatic guided vehicle transportation system2,and several basic robot applica-tions.Currently,the reference architecture is applied in an AGV transportation system.We elaborate on this complex real-world application in section3.

1https://www.doczj.com/doc/977024151.html,/andy/www/site mai/main.php

2http://www.cs.kuleuven.ac.be/~distrinet/taskforces/agentwise/agvsimulator/

The goal of the reference architecture is to offer support to software engineers for the architectural design of complex de-centralized applications with autonomic properties.Fig.1lists the characteristics of the autonomic systems we consider,the corresponding target quality properties of the reference archi-tecture,and the basic principles we apply to realize these qual-ity properties.Flexibility,adaptability and openness are target

Figure1.System characteristics,quality proper-ties and principles

quality properties for the reference architecture to cope with dy-namism and change.Flexibility enables a system to cope with different environmental situations,adaptability refers to a sys-tem’s capability to adapt its behavior over time with changing circumstances,and openness enables a system to cope with ex-pansion(new agents that join the system)and reduction(agents that leave the system).The main software engineering principles we apply to realize these quality properties are decentralized control,collective behavior and adaptive behavior.Manageabil-ity and unambiguity are target quality properties of the reference architecture to cope with complexity.The main software engi-neering principles we apply to realize these quality properties are modularity,louse coupling and separation of concerns;and we provide a formal speci?cation of the reference architecture. We now give a brief overview of the main modules of the reference architecture.Fig.2depicts a high-level module view of the reference architecture for situated MAS.The architecture integrates three primary abstractions:agents,ongoing activities and the environment.We successively look at the architecture of each abstraction.

Agents.The agent architecture models different concerns of the agent(perception,decision making and communication)as separate modules.The P erception module maps the local state of the environment onto a percept for the agent.We devel-oped a model for active perception that enables an agent to di-rect its perception at the most relevant aspects in the environ-ment according to its current task[13].The Consumption module“consumes”consumptions for the agent.A consump-tion is an effect of the reaction of the environment for a par-ticular agent.When an agent consumes a consumption,the consumed effect can be absorbed(e.g.the agent acquires en-ergy),the agent may simply hold an element(e.g.an object it has picked up)or the consumption may affect the agent’s state (e.g.the arm of a robot is wrenched through an external force). The CurrentKnowledge module integrates the percepts and

Figure2.High-level module view of the reference architecture

consumptions to update the current knowledge of the agent.The Decision module is responsible for action selection[7].We de-veloped the decision module as a free-?ow architecture.Free-?ow architectures allow?exible and adaptive action selection [8].Since existing free-?ow architectures lack explicit support for social behavior,we introduced the concepts of a role and a situated commitment.A role covers a logical functionality of the agent,while a situated commitment allows an agent to ad-just its behavior towards the role in its commitment.An agent can commit to itself,e.g.when it has to ful?ll a vital task.How-ever,in a collaboration agents commit relatively to one another via communication.Roles and situated commitments are build-ing blocks for collective behavior.The operator selected by the decision module is passed to the ActionExecution mod-ule that invokes an in?uence in the environment.An in?uence is an attempt to modify the course of events in the world.The Communication module takes care for the communicative in-teractions.We developed a communication module that pro-cesses incoming messages and produces outgoing messages ac-cording to well-de?ned communication protocols[12].Com-munication enables agents to exchange information,and set up collaborations re?ected in mutual situated commitments. Ongoing activities.Next to agents,we introduced the concept of an ongoing activity to model other processes in the system. An ongoing activity produces in?uences according to the state of the environment.Examples of ongoing activities are a mov-ing object or an evaporating pheromone.Ongoing activities en-able indirect coordination between agents,and as such form a basis for collective behavior.[9]discusses ongoing activities in detail.

Environment.As for agents,the architecture of the envi-ronment models different concerns(communication,percep-tion generation and action handling)as separate modules.The MessageDelivering module of the environment handles mes-sage transport.The P erceptGenerator module generates rep-resentations of the local state of the environment for the agents [13].The Collector module collects the in?uences produced by agents and ongoing activities and passes them to the Reactor module.This latter calculates,according to a set of domain spe-ci?c laws,the reaction,i.e.state changes in the environment and consumptions for the agents.For a thorough discussion on in-?uences and reactions we refer to[9].

3Architectural Design of an Automatic Guided Vehicle Transportation System

In this section we illustrate the architectural design of an au-tomatic guided vehicle(AGV)transportation system.This ap-plication is investigated in an ongoing research project in close cooperation with Egemin,a manufacturer of automated ware-house systems3.First we introduce the application and brie?y discuss the existing centralized approach.Next we discuss new autonomic quality requirements for the application and illus-trate how we have applied the reference architecture for situ-ated MASs to design a new decentralized solution to meet these quality requirements.

3.1AGV transportation system

An AGV transportation system uses unmanned vehicles (AGVs)to transport loads through a warehouse.An AGV uses a battery as its energy source.Typical applications are repackag-ing and distributing incoming goods to various branches,or dis-tributing manufactured products to storage locations.An AGV can move through a warehouse,following a physical path on the factory?oor,typically marked by magnets or cables that are ?xed in the?oor.

Functionalities.The main functionality the AGV transporta-tion system should perform is handling transports,i.e.moving loads from one place to another.Transports are generated by client systems.Client systems are typically business manage-ment programs,but can also be particular machines,employees or service operators.A transport is composed out of multiple jobs:a job is a simple task that can be assigned to an AGV.

A transport typically starts with a pick job,followed by a se-ries of move jobs and ends with a drop job.In order to execute transports,the main functionalities the system has to perform are:(1)transport assignment:transports have to be assigned to AGVs that can execute them;(2)routing:AGVs must route ef-?ciently through the layout of the warehouse when executing their transports;the best route for the AGVs in general is dy-namic,and depends on the current conditions in the system;(3) collision avoidance:obviously,AGVs may not collide.AGVs can not cross the same intersection at the same moment,how-ever,safety measures are also necessary when AGVs pass each other on closely located paths.And?nally,(4)deadlock preven-tion:the system must ensure that at least one of the necessary conditions for deadlock can never hold.

3https://www.doczj.com/doc/977024151.html,/home.html

When an AGV is idle it can park at a neighboring park loca-tion;however,when the AGV runs out of energy,it has to charge its battery at one of the charging stations.

3.2Traditional Solution

Traditionally,vehicles are controlled by one central server, using wireless communication.This server has global knowl-edge of the system.It plans routes for AGVs according to in-coming transports and instructs AGVs to perform the jobs.The server continuously polls the AGVs about their status.The low-level control of the AGVs in terms of sensors and actuators (staying on track on a segment,turning,and determining the current position,etc.),is handled by the AGV control software called E’nsor4.To this end,the layout of the factory is divided into logical elements:segments and nodes.A logical segment typically corresponds to a physical part of a path of three to?ve meters.E’nsor can be instructed to drive the AGV over a given segment;to drive the AGV over a given segment and pick up –or drop–a load at the end of it;to drive the AGV over a given segment to a battery charging station and start charging;and?-nally to drive the AGV over a given segment and park at the end of it.

New quality requirements.The evolution of the market put forward new requirements for AGV transportation systems.

A?rst observation is that the scale of requested systems in-creases.In the traditional approach,each additional AGV in the system increases the load on the central server.The server has to contact each AGV at regular time intervals to poll its status and give it new jobs.Experiences of deployed systems show that the time between two contacts with the server becomes a critical factor for systems with twenty-?ve or more AGVs.The problem of scalability calls for an new architectural approach. Second,customers request for?exibility of the transporta-tion systems,AGVs should adapt their behavior with chang-ing circumstances.In the traditional approach,the assignment of transports,the routing of AGVs and the control of traf?c is all planned by the central server.Planning is based on prede-?ned rules.Although this planning is ef?cient,it lacks?exibil-ity.Customers have various requests with respect to adaptabil-ity,we discuss brie?y a number of desired properties.AGVs should be able to exploit opportunities,e.g.,when an AGV is assigned a transport and moves toward the load,is should be possible for this AGV to switch tasks on its way if a more inter-esting transport pops up.AGVs should also be able to anticipate possible dif?culties,e.g.,when the battery level of an AGV de-creases,the AGV should anticipate this and prefer a zone near to a charge station.Another desired property is that AGVs should be able to cope with exceptional situations,e.g.,when a segment is blocked,the AGVs should avoid that segment.

In summary,scalability,?exibility and adaptability are high-ranking quality requirements for today AGV transportation sys-tems.These new quality requirements urge for a new architec-ture.

4E’nsor R is an acronym for Egemin Navigation System On Robot.3.3A decentralized approach with situated MAS

To cope with the quality requirements of scalability,?exibil-ity and adaptability,a solution based on situated MASs has been built.Vehicles then become autonomous agents which make decisions based on their current knowledge,and who coordi-nate with other agents to ensure the system as a whole processes transports in time.

In this section we follow a trace in the architectural design of the situated MAS.We focus mainly on the module view of the architecture.In each step we re?ne one particular module of the architecture and motivate the main architectural decisions. STEP1:System decomposition as a situated MAS.At the top-level,the AGV transportation system is modelled as a situ-ated MAS.

Motivation.Since decision making in a situated MAS is de-centralized(the agents decide for themselves,locally),the MAS solution scales well with increasing numbers of agents(AGVs). Furthermore,situated agents are able to react?exibly to dif-ferent situations and adapt their behavior to changing circum-stances.These properties make situated MAS a valuable candi-date to cope with the target quality requirements.On the other hand,decentralization of control introduces additional complex-ity.In the MAS approach there is no repository of global knowl-edge available to resolve con?icts,AGVs have to coordinate among themselves.To avoid moving the bottleneck from the server to the communication network,communication must be localized as much as possible.The challenge in this project was (and is)to guarantee existing functionality,while realizing the listed advantages by using a MAS based approach.

Fig.3depicts a high-level model view of the architecture of the MAS solution.The situated MAS consists of an environ-

Figure3.Module view of the system architecture

ment and two kinds of agents,transport agents and AGV agents.

A transport agent represents a transport in the system,it is re-sponsible to determine the priority of the transport,to assign the transport to an AGV and to ensure that the transport is com-pleted correctly.The priority of a transport depends on the kind of transport,the pending time since its creation,and the nature of other transports in the system.Therefore,transport agents in-teract with other related transport agents to determine the correct

priority over time.AGV agents are responsible for executing the assigned transports.The environment provides communication infrastructure that enables agents to exchange information and to coordinate their behavior.The physical infrastructure,i.e., the AGVs equipped with sensors and actuators,the?oor infras-tructure to guide AGVs through the factory,and the loads that AGVs have to transport are also part of the environment.In the next step,we look at the architecture of the environment,in the following steps we elaborate on the architecture of the AGV agents.

STEP2:Environment architecture.We?rst zoom in on the architecture of the environment that is set up as a layered archi-tecture.

Motivation.To cope with the complexity of the environment, we have selected layers as architectural style for the https://www.doczj.com/doc/977024151.html,yers separate functionality,and support reuse.

Fig.4depicts the model view of the architecture of the environ-ment.AGV agents and transport agents are situated in a virtual

Figure4.Architecture of the environment

environment that is located at the top of the layered architec-ture.The virtual environment uses the middleware layer,that is composed of a message transfer system that enables agents to communicate with each other,the ObjectPlaces middleware[5] and E’nsor.The bottom layer of the environment consists of the infrastructure for communication and the physical infrastructure of the AGV transportation system.We now motivate and clarify the use of a virtual environment.The goal of the ObjectPlaces middleware is discussed in the next paragraph.For a detailed study of the virtual environment,we refer to[11].

Since the physical environment of a factory is very con-strained,it restricts how agents can use their environment.We introduced a virtual environment for the agents to live in.This virtual environment offers a medium that agents can use to ex-change information and coordinate their behavior.The use of the virtual environment is illustrated in Fig.5.For clarity,we have simpli?ed the explanation,for details see[11].First we look how AGV agents coordinate through the virtual

environ-

Figure5.A virtual environment for AGV agents ment to avoid collisions.AGV agents mark the path they are going to drive in their environment using hulls.The hull of an AGV is the physical area the AGV occupies.A series of hulls then describes the physical area an AGV occupies along a cer-tain path,see Fig.5.If the area is not marked by other hulls (the AGV’s own hulls do not intersect with others),the AGV can move along and actually drive over the reserved path.Af-terwards,the AGV removes the markings in the virtual environ-ment.Another use of the virtual environment are road signs. At each node in the layout,a sign in the virtual environment represents the cost to a given destination for each outgoing seg-ment,see Fig.5.The cost per segment is based on the average time it takes for an AGV to drive over the segment.The agent perceives the signs in their environment,and uses them to deter-mine which segment it will take next.Transport agents use the virtual environment to?nd AGV agents to assign the transports, and to follow the progress of the assigned transports.To assign the transport,the transport agent negotiates with AGV agents of idle AGVs near to the location of the load.Once the transport is assigned,the awarded AGV handles the transport.

Besides a medium for coordination,the virtual environment also serves as a suitable abstraction that shields the AGV agents from low-level issues,such as the physical control of the AGV. As part of the middleware,we fully reused the E’nsor software that deals with the low-level control of the AGVs.As such,the AGV agents control the movement and actions on a fairly high level.

Deployment view of the environment.We now explain how the virtual environment is deployment in the system,and what the role is of the ObjectPlaces middleware.

Fig.6depicts the deployment view of the AGV transportation system.A deployment view shows how the software is assigned to hardware processing and communication elements.Transport agents are located at transport bases.AGV agents are located in AGVs that are situated on the factory?oor.Each AGV and each transport base is equipped with a https://www.doczj.com/doc/977024151.html,munica-tion infrastructure provides a wired network that connects client systems and transport bases,and a wireless network that enables mobile AGVs to communicate with each other and with trans-port agents.To avoid overload of this network,AGVs are only able to communicate with other AGVs in their neighborhood. Since the only physical infrastructure available to the AGVs is a wireless network to communicate,the virtual environment

Figure 6.Deployment view of the architecture is necessarily distributed.In effect,each agent in the system maintains a local virtual environment ,which is a local manifes-tation of the virtual environment.Synchronization of the state of the local virtual environment with neighboring neighboring agents is supported by the ObjectPlaces middleware.The local virtual environment uses the middleware by sharing objects in a tuplespace-like container,called an objectplace .Every AGV has one objectplace locally available.Objects in objectplaces on remote AGVs can be gathered using a view .A view spec-i?es (1)which AGVs’objectplaces need to be included in the view (e.g.the objectplaces of all AGVs within a speci?c range),and (2)what objects need to be included in the view (e.g.hull objects).Fig.7depicts the architecture of the software that is de-ployed on each AGV .The AGV agent is

shown in the top layer

Figure 7.Software architecture deployed on AGVs of the model.The two other layers correspond to the two top layers of the architecture of the environment,see Fig.4.

Figure 8.The AGV agent architecture

STEP 3:AGV agent architecture,a data repository pattern.Now,we zoom in on the AGV agent.The AGV agent architec-ture corresponds to the agent architecture of the reference archi-tecture and is basically modelled as a data repository pattern.Motivation.The data repository pattern supports separation of concerns and loose coupling.

Fig.8depicts the module view of the AGV agent.Different con-cerns of the agent behavior,i.e.,perception,communication,and decision making,are modelled as a separate modules of the ar-chitecture.The current knowledge module serves as data repos-itory for the different modules.

STEP 4:Decision making,a combination of blackboard and sequential processing.We now zoom in on the decision mak-ing module.The decision making module is set up as a hybrid architecture that combines a blackboard pattern with sequential processing.

Motivation.This architecture combines complex interpretation of data with decision making at subsequent levels of abstraction.

The architecture of the decision making module is depicted in Fig.9.The current knowledge module serves as blackboard data structure,while the action controller coordinates the selection of an appropriate operator.After job selection,the action selection module selects an action at a fairly high level (move,pick,park etc.).The action generation module transforms this action into a concrete preliminary operator (e.g.,move(segment x)).The collision avoidance module is responsible to lock the trajectory associated with the selected operator.As soon as the trajectory is locked,the collision avoidance module passes the con?rmed operator to the action execution module.If during the subse-quent phases the selected operator can not be executed (e.g.,an obstacle is detected on the selected trajectory),feedback is sent to the action controller that will inform the appropriate module to revise its decision.This feedback loop enables ?exible deci-sion making.

STEP 5:Action selection:a free-?ow architecture.To con-clude,we zoom in on the action selection module that is mod-elled as a free-?ow architecture.

Figure 9.The Decision module

Motivation.Free-?ow architectures allow to model ?exible ac-tion selection.

In [7],we have described a design process for free-?ow archi-tectures.As a ?rst step in the design,the role model is de?ned.The role model describes the behavior of the agent in terms of roles and their interdependencies.Fig.10depicts the role dia-gram of the AGV’s.A role diagram consists of a hierarchy of

Figure 10.The role diagram of the AGV

roles of which some are related through situated commitments.The ?rst role of the AGV is the Working role that consists of two sub-roles,Moving and Manipulating .The Manipulating role is further split up in two sub-roles,Picking and Dropping .Dur-ing the execution of a typical transport,the AGV starts moving toward a load.Once the AGV reaches the load it will pick the load and subsequently moves toward the drop location where the AGV will drop the load.

Besides the Working role,the AGV has the Charging role and the Park role.The AGV executes the Park role when it is idle.In this role the AGV simply moves to the nearest parking place.The Charging role ensures that the AGV keeps its battery loaded.When the energy level crosses a critical value,the AGV concludes its current job and moves towards the nearest charging station.

The rounded rectangle that connect roles represent situated commitments.When a situated commitment is activated the be-havior of the agent tends to prefer the goal role of the commit-ment (i.e.,the role to which the arrow points)over the source roles.In Fig.10,the Charge commitment ensures that the AGV maintains its energy level.Since energy is vital for the AGV to function,all roles (except the Charging role of course)are connected as source roles to the Charge commitment.Charge is a commitment of the agent relative to itself.The Work com-mitment is activated when the AGV starts executing a transport.This commitment ensures that the AGV keeps its focus on the transport once it has committed to start working.Working is an example of a commitment in a collaboration,relative to the corresponding transport agent.

The role diagram serves as a basis to structure the free-?ow architecture.For a thorough discussion of the detailed design of free-?ow architectures,we refer to [7].Here we zoom in on a part of the architecture and illustrate the ?exible selection of actions with a free-?ow architecture.A free-?ow architecture consists of a tree with nodes that collect activity from internal or external stimuli (derived from the agent’s current knowledge)and feed it down in the tree.At the lowest level,operator nodes receive the activity.The operator node that collects the most activity is selected for execution.A role is de?ned as a subtree that covers a logical functionality of the agent.Fig.11depicts the sub-trees for the parking and charging roles.

Suppose that the agent is idle (no commitment is activated)and moves toward the nearest parking place.The activity of the root node of the Parking role is fed down in the Parking tree.The stimulus !nearPark will inject additional activity in the to park node.This activity is fed to the moveP and skipP nodes.The activity of the moveP node is multiplied with the inverse values of the multi-dimensional stimulus costToChargeStation and fed to the corresponding operator nodes for moving (Move ,TurnRound ,TurnLeft ,and TurnRight ).The activity of the M en-try of the costToChargeStation stimulus corresponds to the cost for the AGV to move forward toward the nearest park location.Similarly,the T ,L and R entries corresponds to the cost for the AGV to turn round,turn left and turn right and move toward the nearest park location for that particular direction.The AGV cal-culates these cost values by combining static traf?c costs for the applicable paths with dynamic traf?c information it perceives from the virtual environment.The cost is set to a default value if a particular direction is not applicable.Analogously,the Charg-ing role will fed the operator nodes for the AGV to move.The initial value of activity in the root node of the Charging role is only a fraction of the activity of the Parking role,incremented with the activity of the energy need stimulus.Thus,the effect of the activity of the Charging role in the operator nodes for moving will be proportional to the need for energy.The goal of combining the activities of the moving operators of both roles becomes clear when an AGV reaches an intersection to ?nd a

Figure11.Free-?ow architecture for Charging and Parking roles

parking place.Based on only the activity of the parking role, the AGV will select the cheapest direction.However,if there is a certain need for the AGV to charge its battery,the AGV may select another direction that is closer to a charge station,improv-ing the location of the AGV for future charging.This example illustrates how a free-?ow tree allows?exible action selection by taking different preferences into consideration simultaneously.

4Conclusion

In this paper,we argued that autonomic systems are essen-tially characterized by quality requirements that specify that the system should be able to adapt itself under varying circum-stances and situations.We identi?ed dynamism and change,and complexity,as important characteristics of autonomic systems. Target quality properties for an architecture to cope with dy-namism and change are?exibility,adaptability and openness. Target quality properties to cope with complexity are manage-ability and unambiguity.

We have put forward situated MASs as an approach to build decentralized applications with autonomic quality require-ments.In this paper,we discussed the architectural design of a self-managing AGV transportation system.In this applica-tion,AGVs should be able to exploit opportunities,anticipate possible dif?culties,and cope with exceptional situations.Pri-mary quality requirements to enable such self-managing behav-ior are?exibility and adaptability.We illustrated how the ar-chitectural decisions are guided by a reference architecture for situated MASs on the one hand,and architectural styles that aim to satisfy the system’s requirements on the other hand.

So far,we have validated the solution in an industrial test setup with two physical AGVs that execute prede?ned batches of jobs.The next challenges are order assignment and deadlock avoidance,and subsequently the validation of the integral solu-tion in an advanced setup.References

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教育界的科技革命 如果让生活在年的人来到我们这个时代,他会辨认出我们当前课堂里发生的许多事情——那盛行的讲座、对操练的强调、从基础读本到每周的拼写测试在内的教学材料和教学活动。可能除了教堂以外,很少有机构像主管下一代正规教育的学校那样缺乏变化了。 让我们把上述一贯性与校园外孩子们的经历作一番比较吧。在现代社会,孩子们有机会接触广泛的媒体,而在早些年代这些媒体简直就是奇迹。来自过去的参观者一眼就能辨认出现在的课堂,但很难适应现今一个岁孩子的校外世界。 学校——如果不是一般意义上的教育界——天生是保守的机构。我会在很大程度上为这种保守的趋势辩护。但变化在我们的世界中是如此迅速而明确,学校不可能维持现状或仅仅做一些表面的改善而生存下去。的确,如果学校不迅速、彻底地变革,就有可能被其他较灵活的机构取代。 计算机的变革力 当今时代最重要的科技事件要数计算机的崛起。计算机已渗透到我们生活的诸多方面,从交通、电讯到娱乐等等。许多学校当然不能漠视这种趋势,于是也配备了计算机和网络。在某种程度上,这些科技辅助设施已被吸纳到校园生活中,尽管他们往往只是用一种更方便、更有效的模式教授旧课程。 然而,未来将以计算机为基础组织教学。计算机将在一定程度上允许针对个人的授课,这种授课形式以往只向有钱人提供。所有的学生都会得到符合自身需要的、适合自己学习方法和进度的课程设置,以及对先前所学材料、课程的成绩记录。 毫不夸张地说,计算机科技可将世界上所有的信息置于人们的指尖。这既是幸事又是灾难。我们再也无须花费很长时间查找某个出处或某个人——现在,信息的传递是瞬时的。不久,我们甚至无须键入指令,只需大声提出问题,计算机就会打印或说出答案,这样,人们就可实现即时的"文化脱盲"。 美中不足的是,因特网没有质量控制手段;"任何人都可以拨弄"。信息和虚假信息往往混杂在一起,现在还没有将网上十分普遍的被歪曲的事实和一派胡言与真实含义区分开来的可靠手段。要识别出真的、美的、好的信息,并挑出其中那些值得知晓的, 这对人们构成巨大的挑战。 对此也许有人会说,这个世界一直充斥着错误的信息。的确如此,但以前教育当局至少能选择他们中意的课本。而今天的形势则是每个人都拥有瞬时可得的数以百万计的信息源,这种情况是史无前例的。 教育的客户化 与以往的趋势不同,从授权机构获取证书可能会变得不再重要。每个人都能在模拟的环境中自学并展示个人才能。如果一个人能像早些时候那样"读法律",然后通过计算机模拟的实践考试展现自己的全部法律技能,为什么还要花万美元去上法学院呢?用类似的方法学开飞机或学做外科手术不同样可行吗? 在过去,大部分教育基本是职业性的:目的是确保个人在其年富力强的整个成人阶段能可靠地从事某项工作。现在,这种设想有了缺陷。很少有人会一生只从事一种职业;许多人都会频繁地从一个职位、公司或经济部门跳到另一个。 在经济中,这些新的、迅速变换的角色的激增使教育变得大为复杂。大部分老成持重的教师和家长对帮助青年一代应对这个会经常变换工作的世界缺乏经验。由于没有先例,青少年们只有自己为快速变化的"事业之路"和生活状况作准备。

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