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Expert Systems with Applications 31 (2006) 728–733

Expert Systemswith Applications

SYMBAD—Similarity based agents for design

Daniel Pinho *, Adriana Vivacqua, Sergio Palma, Jano de Souza

COPPE/UFRJ, Department of Computer Science, Graduate School of Engineering, Federal University of Rio de Janeiro,

Rio de Janeiro, RJ, Brazil

Abstract

This paper presents the SYMBAD (similarity based agents for design) system, exploring multi-agent aspects in an architecture com-pany, capturing, cataloging, and communicating information produced by the team members. The main task managed by the designers isto build stands to present the image of a company, project its presence in the market and emphasize the corporate identity to all pros-pects. From conceptual design to the construction of a final product, a stand project passes through many hands, each one adding bitsand pieces until it is completed. Reuse of materials and ideas is less feasible as design complexity increases. The processes and problems instand projects are quite common and can be easily found in other design situations. We present an agent framework to improve processawareness in an architecture company. The agents instrument the process to produce global awareness, to facilitate reuse and optimizethe process as a whole. In this paper we present the agent architecture, as well as each agent’s general functioning and reasoning rules.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Collaborative design; Agent systems; Case based reasoning

1. Introduction

The SYMBAD (similarity based agents for design) sys-tem is a solution for managing architectural designs andintends to be a configurable and extensible infrastructureintegrating the work among designers and constructors ofevent stands. This project, established in Rio de Janeiro,is a platform for the generation and execution of integratedmodels of stands, enhancing communication and knowl-edge management in a sustainable development environ-ment, through the use of multi-agent technology, whichmaintains the awareness needed for the fulfillment of thedesign tasks.

In a case study of an architecture company, we identifiedsome problem areas that could be addressed and that arepresent in other segments and companies. The main prob-lem in this type of company is that there are disjoint workgroups, and, even though work done by one group (design)

0957-4174/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.eswa.2006.01.040

* Corresponding author.E-mail addresses: dpinho@centroin.com.br (D. Pinho), avivacqua@

cos.ufrj.br (A. Vivacqua), palma@cos.ufrj.br (S. Palma), jano@cos.ufrj.br(J. de Souza).

defines the work that will be done by the other (physicalproject); there is little communication between them. Thereis no feedback from the second group as to what could beimproved or what has generated problems for them. Thislack of awareness of the project as a whole often generatesmaterials waste, delays and similar project difficulties.

We have devised an agent-based system to provide aseamless way of integrating the different teams involvedand promoting information exchange and awareness ofthe process as a whole. Agents work with available infor-mation about the users’ tasks and their current work andprovide information on potential problems of the currentdesign. The intent is to cause as little impact as possibleon the way designers work, but to promote changes in theirway of designing. Ideally, designers would learn about theconsequences of their design choices and about the poten-tial problems they may cause in the later stages of the pro-ject, and would design in a more informed way. We will beimplementing this system in our case study company andverifying if the new knowledge brings about changes inthe designs produced and the designers’ way of thinking.

We begin by presenting some background work and thengo on to describe our case study, H. Camargo Promotional

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Architecture and Landscaping, examining its processes andinformation flow. We then go on to describe our approachand the communication agents we are implementing.We wrap up with a brief discussion and conclusions.

2. Related work

This research was preceded by an extensive literaturereview and by a theoretical analysis. The purpose of the the-oretical analysis was to understand the traditional designsystems, their problems and means to improve their effi-ciency. In this section, we present some related research thathas inspired and guided ours, in particular, agent systemsand awareness systems. Computer supported design systems(CSCW) have been the object of much research in the past:ranging from expert and case based reasoning systems todistributed agent approaches, many alternatives have beenproposed. A good review of agent based engineering systemscan be found in Shen, Norrie, and Barthes (2001).

2.1. Multi-agent systems

Russel and Norvig define intelligent agents as entitiesthat perceive its environment through sensors and act uponit (Russell & Norvig, 1995). agent-oriented techniques arebeing increasingly applied to a range of telecommunication,commercial, and industrial applications, as developers anddesigners realize its potential (Fish, Kraut, & Chalfonte,1990). Agents are well suited to the construction of complex,peer-to-peer systems because they are lightweight and per-mit parallelization and easy reconfiguration of the system.

It is currently believed that multi-agent systems (MAS)are a better way to model and support distributed open-ended systems and environments. A MAS is a loosely-cou-pled network of problem solvers (agents) that work togetherto solve a given problem (Isaacs, Tang, & Morris, 1996). Acomprehensive review of agent systems applied to coopera-tive work can be found in Gresse von Wangenheim (1999).

2.2. Awareness systems

Awareness has received a lot of attention amongresearchers in the past few years, as they start to realizethe importance of being aware of collaborators and theenvironment while working (Aldunate, Nussbaum, & Gon-zalez, 2002). Initial awareness work focused on video andaudio support for cooperation as, for instance in Fishet al. (1990) or Isaacs et al. (1996), but other tools andmethods have appeared since.

The most basic form of awareness is the one currentlyprovided by messenger systems (such as Yahoo or MSNMessenger, AOL Instant Messenger, etc.). These systemshave been widely accepted and adopted.

More interestingly, some proposals involve motivation,incentives and support for cooperation, such as describedin Pinheiro, Lima, and Borges (2002). They propose aframework to provide past event awareness, where users

are informed of past occurrences, results and work historyof each other (which includes evolution of shared data,members’ actions and decisions, etc.), so as to better collab-orate in the present.

Closer to our ideas, Hoffman and Hermann propose aprospect awareness system that allows individuals to envi-sion the potential benefits of collaboration, in an attemptto motivate collaboration (Hoffman & Herrmann, 2001).Our system provides potential problem information, inan effort to generate better and more cost-effective designs,avoiding problems in future steps.

2.3. Case-based reasoning (CBR)

Decision rules, neural networks, Bayesian networks andCBR are examples of decision techniques using predictionstrategies based on indicators (Gresse von Wangenheim,1999). Due to the nature of this work, CBR is the mostpromising and hence investigated technique; the otheralgorithms are left as topics for future research. CBR is amethod to solve new problems by adapting solutions thatwere used to solve past problems (Gresse von Wangen-heim, 1999). With CBR, the system searches for past casesthat are analogous to the current case; the solutions of themost similar past cases are then used to create a solutionfor the current case. The outcome of this prediction tech-nique is a list of cases with its similarity indicator allowingthe user choice among all alternatives.

CBR systems are used to calculate the shorter distancebetween previous cases and the current case and thenretrieve them to determine the best fit for the current case.The closer a past case is to the current situation, the moreimportant that case should be in determining the outcomefor the current request. The four most frequently used dis-tance measurements in CBR are (Gresse von Wangenheim,1999): unweighted Euclidean distance (ued), weightedEuclidean distance (wed), maximum measure (mms) andmean squared difference (msd).

3. Case study: architecture company

H. Camargo Promotional Architecture and Landscap-ing has been a leader in its segment since 1971. It specializesin developing custom-made architectural projects for fairand exhibit stands. It has a permanent team of 120employees and is located in a large pavilion, (with spacefor exhibition, administration, workshops and stocks).Communication problems have started to arise, as in anylarge company, generating difficulties during design activi-ties and implementation phase.

The company is divided in four main departments: Thefirst one Sales, finds potential clients and their needs. Thereis a design department, which creates proposals for thesepotential clients, establishing the overall designs and someof the materials to be used. A project department solves allproblems once a proposal has been accepted, furtherdetailing the project, defining the physical specification:

Fig. 1. Sample design and implementation created by designers atH. Camargo.

Fig. 2. Envisioned information workflow.

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measurements, quantity of materials and how these are tobe put together. Finally there is an Execution department,which given the physical specification, builds the actualstand and whatever components may be necessary.

Project proposals need to be created quickly, be originaland innovative. Designs are not charged for, and the com-pany will only get paid if the project is accepted (and built).It is important to note that communication flows almostexclusively in one direction: from the Design Department,a design (a 3D Studio drawing) is handed on to the ProjectDepartment and then to Execution. Given that these lasttwo have no say whatsoever during the design phase, often-times problems are generated.

The construction of a stand, from conception to themoment it is mounted at a fair, involves a series of pro-cesses and materials: after approval, a project has to bedetailed (further specified) so that it can be mounted inthe originally designed way. This specification leads tothe use of in-stock materials, and it also creates transfor-mation processes to reuse materials (wood, aluminumand impressions). Some of these transformations are cut-ting, painting, silking and assembling. In the end, all thepieces have to be arranged in trucks and taken to the fair,where it has to be mounted exactly as initially designed.

Currently, each team uses computers to perform theirpart of the process, and hands down files with specifica-tions to the next one. A knowledge base with all the designscreated (executed or not) by the company is under con-struction, and will be used to furnish information to ouragents.

In the current model there is a total lack of communica-tion between the teams that design and the teams that buildthe stand. In many cases this lack of communication andglobal awareness on the part of the architects generatesserious quality problems and makes it hard to reuse ofthe existing materials in stock.

The great majority of problems are generated when thedesigner develops a project that demands materials that arenot available in stock. In this case, extra costs will beincurred, to purchase materials so that the project is prop-erly executed. In many cases problems occur because thestand is designed without any concern for the way in whichit will be constructed. This is an even worse problem,because the project cannot be built in the way it wasdesigned, causing serious quality problems and issues withclients (see Figs. 1 and 2).

Given these issues, we can see that the biggest cause ofproblems is the lack of awareness and consciousness inrelation to other phases of the process. A good designershould be conscientious of all the project phases. Lack ofinformation causes many problems.

4. Project vision

We have envisioned an agent-based system to informdesigners of potential problems during the conceptualdesign phase. Agents extract information from each

designer’s current design and verify the feasibility of thisdesign given previous designs, materials in stock andshapes being utilized.

Our main goal is to provide designers with informationon the possible consequences of their current work (forinstance, if a certain type of material is out of stock, thereis a chance the ordering process will cause a delay in con-struction). We expect that, given this information, design-ers will make different decisions, which will benefit thecompany as a whole.

Our agent system is divided in three main layers as seenin Fig. 3. The data acquisition layer is composed by threedatabases: project base, xml base and knowledge base.The project base stores all the required information aboutthe stands. The CBR function will work on this database,extracting information about previous designs. The xmlbase store information from the current design and allthe separate parts built before. The knowledge base willkeep all the communication information and questions thatarise during the project. This database will also keep infor-mation on stands while they are being built until the timethey are mounted in the fair.

Fig. 3. Platform architecture.

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Information related to the project will be delivered tothe designers as the design is developed. Agents have accessto stock, process and shape information problems. Thisinformation will be used to assess the feasibility and iden-tify possible problems with a project as it is designed.

Four agents compose the data processing layer: filtering,processing, distribution and project management. Theseagents will transform the information stored in the data-base layer into knowledge. The information is requestedby the client layer is processed by the agents. Most of theinformation is constructed using detailed metadata thatallows the correct understanding of the stored data. Theseconcepts will be explained in more detail in the nextsections.

The agent system is being implemented using IBM’sAglets library. The Aglets library supplies a standard tem-plate for agent creation. As mentioned before, there will bethree types of agents: filtering, processing and distributionagents.

The client access layer is divided in four main windows:case base search, 3D project assistant, communication linkand project management. This layer will be the interfacebetween the clients and the agents. The agents adoptawareness concepts to facilitate the work of the clientsand to return only the most appropriate answers. Similarproblems must be solved with solutions learned from pastexperience, so the agents must understand the semanticbehind the objects manipulated by the workers to satisfythem accordingly.

The semantic comprehension of the problem is builtupon a strong ontology used by the CBR component,which helps the agents in their decisions. Similarity isunderstood based on the proximity in the Ontology tree.Most of the Ontology is built automatically by past cases,but a manual verification and aids must exist to cope withdifficulties concerned to natural complexity of this task.

In the figure below we present a sample of a standOntology used with the CBR. The stand is split into some

criteria, including client, event, year, stand area, stand cost,floor number and furniture. The furniture field is a bit morecomplex because it is divided into other criteria. From thisontology we can classify each stand according to manyaspects and then retrieve them by similarity using CBR.The complete hierarchy can be obtained by demand tothe research group. As can be seen, the Ontology is builtwith the Treebolic Application, which implements a Hyper-bolic tree vision.

After the designs have been fed to the system, users canuse the CBR facilities to find similar projects and designobjects. It is not necessary to have an entire similar projectto it be found by the agents. Any part that is identified as auseful piece can appear to the user as an advice or a clue tothe current design. On the next figure we show a case basesearch example. We can see in more detail the casebasesearch window with the criteria chosen and the possiblesolutions found. We can see that the most similar standfound was the one built for Siemens – Telexpo – 2003.One great thing that we can see from these figures is thatthe third one found was the CVRD – Marmore & Granito– 2002. Although the attribute company name chosen bythe client as being Siemens, the agents returned as a resulta stand of CVRD (CVRD is a company’s name) due thesimilarity of other project characteristics (see Fig. 4).

On the stand details tab the commercial departmententers all the information about each stand to be built. Thisinformation is based on the briefing sent by the client. Thedata is inserted in a web page in the same way, where theyare searched. In a near future it is expected that this infor-mation can help user to understand better the work processthrough a data mining analysis.

The filtering agents that are in charge of presenting sim-ilar projects will present all this information and willextract some of the necessary information from the abovedata and some from the 3D Studio drawing that is beingworked by the designers. The information extracted fromthe 3D Studio will be stored on the xml base and an exam-ple of this data can be seen in the next table.

From this file all the necessary information can beextracted. The file contains all the objects represented asa mesh. A mesh has a name, the vertex numbers and theface numbers. Each vertex has coordinates x, y, z and thenormal vectors. Each face is formed by a triangle and indi-cates which vertex from the list is being used. In this fash-ion it is easier for the processing agents to determine thearea and volume of each object and it’s still possible toreconstruct the object separately from the others.

Processing agents evaluate items that exist in stock andshapes that are under construction. They work with the fil-tering agents to determine, in real time, if an object orshape can be used in that project, given the expected dateof completion. We are using shape analysis algorithms tocompare with other cases and assess the viability to con-struct each shape. This agent is also responsible for deter-mining costs of materials used and generating a list ofmaterials that will need to be purchased (see Fig. 5).

Fig. 4. Stand sample Ontology using hyperbolic tree.

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On the figure below we can see the 3D project assistantwindow showing the material list extracted from the cur-rent design. This list shows the quantity, material and costin use and also calculates the total cost. This is really inter-esting information for the designer, since it enables him orher to evaluate if this cost is in accordance with the brief-ing. We also have another very important tab called Outof Stock. This tab becomes red when any material is outof stock. As soon as the designer receives this kind of infor-mation, it becomes easy for him to take action. Once againhe can evaluate if he will change that material or make aquick negotiation to buy it.

Fig. 5. Case base search and 3D project assistant example.

This window also shows the similar projects, based onthe items in the current design. Differently from the case-base search, in this case, the CBR analyzes the materialsinvolved and not the briefing.

Finally on the stand suggesting tab the agent searchesfor similar materials that exist in stock, comparing theshapes and bringing up the lowest prices.

The initial list of materials will also help the physicalspecification teams, as one of their tasks is to generate acomplete list of materials, with sizes and quantities. Thisteam also determines which parts from other projects canbe reused and what transformations should be made onprevious designs.

As soon as the project is approved the project accompa-niment agents start to follow it. These agents work on theinformation received from the processing agents, followingthe workflow from the beginning of the stand constructionto its boarding on the truck for delivery.

Initially, a list is generated, containing all the materialsthat need to be bought and shapes that need to be built.For each shape, there is a registry of when constructionwas started, when it was finished and a text field for enter-ing difficulties encountered during construction. The pro-duction teams insert all these data. After the purchase ofeach material and the construction of each shape, theseitems are removed from the list, so that the team stays upto date on the items that still need to be constructed. Acountdown on the days until shipping is always visible sothe production team is aware of the schedule. Finallyanother list is created to verify which materials havealready been loaded in the trucks. This list is necessarybecause a very common problem that occurs is that certainparts are forgotten, which causes a great deal of troubleand cost increase.

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Distribution agents are able to create a direct communi-cation link between the design and stock team so that fas-ter, joint analysis can be performed. The agent alsoprovides a communication link between designers and theexecution department, to clear doubts and create an expe-rience base. This communication is asynchronous, as thequestions inserted in an issue base are answered duringthe course of the project. These issues will have an answerdate limit and a priority order. They are sent by email (sim-ple and fast method of communication) to individualsaccording to the type of issue. All the answers are storedfor future consultations of similar issues.

5. Conclusions

With filtering, processing and distribution agents weexpect to change the way in which designers work: by pro-viding them with data to inform their designs, they will beable to make better design choices, leading to more reuseand fewer errors.

An agent-based system was presented in this paper,showing how CBR and awareness disciplines can help dif-ferent team members construct and integrate stands in adesign environment, promoting information exchange,reuse aids and awareness of the process as a whole. Withthese agents we expect to change the way in which design-ers work: by providing them with actual information inorder to help them to make better design choices, leadingto more reuse and fewer errors.

It is important to note that the agents are not meant torestrict the design and never force the designer into anyspecific solution at any moment. They must provide aware-ness so that the designer can make conscious choices. Thedesigner may still choose to build all-new modules andcomplicated shapes that won’t be reused, but he or she willbe aware of what is being done (it will be a consciouschoice). The database layer keeps information of previous

designs (such as time spent on construction, objects andmaterials, spent on assembly, time spent on physical spec-ification, etc.), it will also establish a design history anddifficulties.

Acknowledgements

This work was partially supported by CAPES andCNPq grants.

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