Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

Ontology Based Design for Similarity Discovery in SYMBAD Project1 1 1 1 2

Daniel Pinho , Adriana S. Vivacqua , Sergio Medeiros , Jano M. de SouzaI9epartment ofComputer Science, Graduate School ofEngineering (COPPE),Institute ofMathematics (DCC-IM), Federal University ofRio de Janeiro,

Rio de Janeiro, RJ, Brazildpinho@centroin. com. br, tavivacqua, palma, jano}@cos. ufrj. br

Abstract

The SYMBAD (Similarity Based Agents for Design)system is a project developed to aid Architectural designteam members to share knowledge used in Stand design.Stands play important role in promoting a Companyimage, projects its presence and emphasizes thecorporate identity in events and Fairs. The processes andproblems in standprojects are quite common and can beeasily found in other design situations. From conceptualdesign to the construction of a final product, a standproject passes through many hands, each one adding bitsand pieces until it is completed. The physical distance,difficulties in communication and cultural aspects makedesigners interact less than necessary, generatingproblems in all phases of the design activities. TheSYMBAD project uses an Ontology basedframework toimprove process awareness in a Design activity. Basedon the knowledge kept from previous projects andSimilarity aspects, agents can identify possibilities forreuse andprovide information to help the design team tobuilt new Stands using past materials and ideas.Through a constructed Ontology and collaborativefeatures agents can produce global awareness, tofacilitate later steps and optimize the process as a whole.In this paper we present some of the functions andarchitecture aspects, as well as some of theimplementations choices madefor this project.

Keywords: Ontology, similarity, agents, architecturaldesign, knowledge sharing.

1 - Introduction

Knowledge Management (KM) has been in our time,one of the most important issues in most organizations.But the interest in KM is not enough to make it happen.KM introduction in most circumstances is not painlessand has to overcome several humans, technical andorganizational aspects. Many efforts have been made andcountless KM projects fail as a result of insufficientknowledge representation policies. One way to deal withthis fact is to start by understanding the informationconsumed by the workers and how they interact with eachother. Building an Ontology structure through

Collaborative and Artificial Intelligence techniques mayhelp a successful implementation ofKM and should allowthe identification of past experiences and when and wherethey can be reused. The problem of this approach is, thatthe existing cases descriptions are usually not wellstructured or not directly applicable to novel situations.An Ontology can help a lot, but in most cases demandsgreat effort from the organization on its construction andmay lead to unpredictable results. The integratedapproach of this paper will cover some problemsconcerning this common situation in many organizationsthrough the introduction of the use of CBR (Case BasedReasoning) and Awareness Technologies.

Organizations are going through metamorphicchanges caused by dramatic technological advances andnew needs. Reorganizations, takeovers, mergers,downsizing and other changes demand new modes ofwork in order to cope with all emerging situations. It hasbecome easier to establish communication, exchangeinformation and be aware of previously hidden processes.For many companies, however, it has been hard to keepup with the new technological demands and to adapt tonew work or organizational formats that may improvetheir performance, without impacting their currentbusiness. Companies struggle to change with as littleimpact as possible, so as not to compromise theirbusinesses.

Although organizations may adopt technology in theirdaily work environment, these facilities haven't beenintegrated in such a way as to produce organizationalchanges and enable improvements. Most companies stilladopt strict organizational models and, in many cases,information flows only in one direction, causing breaks incommunication. In many cases, technology automates theinformation flow as it exists, not introducing any change.

In a case study of an architecture company specializedin Stand designs, we identified some problem areas thatshould be addressed and that are present in othersegments and companies. The main problem in this typeof company is that there are disjoint work groups, and,even though the work done by one group (design) definesthe work that will be done by the other (physical project),there is little communication between them. There is nofeedback 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 stems from the

1-4244-0165-8/06/$20.00 C 2006 IEEE.

Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

company' s organizational structure and difficulty incommunication between teams and often generates waste,delays and related problems.

The SYMBAD (Similarity Based Agents for Design)system is a solution for managing Architectural designsand intends to be a configurable and extensibleinfrastructure integrating the work among designers andconstructors of event Stands. This project, established inRio de Janeiro, is a platform for the generation andexecution of integrated models of Stands, enhancingcommunication and knowledge management in asustainable development environment, through the use ofmulti-Agent technology, which maintains the awarenessneeded for the fulfillment of the design tasks.

An agent-based system is being constructed tointegrate the different teams involved and to promoteinformation exchange and awareness of the process as awhole. These Agents use past and present knowledge tohelp team members to interact and reutilize previous andactual experiences in their daily work. Agents work withavailable information on the users' tasks and their currentwork and provide information about potential problems ofthe current design. As a premise, the tool must cause aslittle impact as possible on the way designers work, butshould promote enhancements in their way of designing.Agents working in the background can provide a seamlessintegration between the different design teams. Thissystem is being implanted in our case study company andwe'll verify its potential in augmenting designs qualityand workers productivity.

We begin by presenting some background work andthen go on to describe a case study of an architecturecompany, H Camargo Promotional Architecture andLandscaping, whose problems we are attempting to solve,and examine its processes and information flow. We thengo on to describe our approach and the communicationagents we are implementing, followed by a scenario toillustrate how the system functions and a brief discussionand conclusions in the last section.

2 - Related Work

In this section we present some related research thathas inspired and guided ours, in particular, agent, CaseBased Reasoning (CBR) and awareness systems.Computer supported design systems have been the objectof much research in the past: ranging from expert andcase based reasoning systems to distributed agentapproaches, many alternatives have been proposed. Agood review of agent based engineering systems can befound in [1].

2.1 - Multi-Agent Systems (MAS)

Agent-oriented techniques are being increasinglyapplied in a range of telecommunication, commercial, andindustrial applications, as developers and designers

realize its potential [3]. Russel and Norvig defineintelligent Agents as entities that perceive its environmentthrough sensors and act upon it [2]. Agents are especiallysuited to the construction of complex, peer-to-peersystems, because they permit parallelization and easyreconfiguration of the system. It is currently believed thatMulti-Agent Systems (MAS) are a better way to modeland support distributed, open-ended systems andenvironments. A MAS is a loosely-coupled network ofproblem solvers (agents) that work together to solve agiven problem [4]. A comprehensive review of agentsystems applied to cooperative work can be found in [5].

2.2 Awareness Systems

The perception of elements in the environment and thecomprehension of their meaning can help the projectionof their status in the near future. This situation can beenhanced by the use of agent-based systems and throughthe correct understanding of these elements. Theawareness process can happen through the observationprocess among the agents, during their communicationprocess or when reasoning happens as decision rules arecaptured and learned by the system. Awareness hasreceived a lot of attention among researchers in the pastfew years, as they start to realize the importance of beingaware of collaborators and the environment whileworking. Initial awareness work focused on video andaudio support for cooperation as, for instance in [6] or [7],but other tools and methods have appeared since.

The most basic form of awareness is the one providedby messenger systems (such as Yahoo or MSNMessenger, AOL Instant Messenger, etc.), which havebecome widely accepted and adopted. A more specializedcollaborative tool, GROOVE introduces the concept of"shared spaces" to increase the scope of personalawareness. Within GROOVE's shared spaces, users canbe aware of what others are doing and on what spaces'objects they are working.

2.3 - Ontology in CBR

The concept of Case-Based Reasoning (CBR)combines the knowledge-based support philosophy with asimulation of human reasoning when past experience isused, i.e. uses explicit, documented experiences to solvenew problems searching for similar situations happened inthe past and reusing the experience gained in thosesituations.

With CBR, one searches for past cases that areanalogous to the current case; the solutions of the mostsimilar past cases are then used to create a solution for thecurrent case. The outcome of this prediction technique is alist of cases with its similarity indicator allowing the userchoice among all alternatives. Decision rules, neuralnetworks, Bayesian networks and CBR are examples ofdecision techniques using prediction strategies based onindicators [8]. However, due to the nature of this work,

Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

CBR is the most promising and hence investigated andthe other algorithms above are left as topics for futureresearch. CBR is a method to solve new problems byadapting solutions that were used to solve past problems.

CBR systems are used to calculate the shorter distancebetween old cases present 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 useddistance measurements in CBR are [8]: unweightedEuclidean distance (ued), weighted Euclidean distance(wed), maximum measure (mms) and mean squareddifference (msd). For quantitative measures the distancecan be easily specified, but when it comes to qualitativeattributes some other techniques must be implemented.For instance, when a purple part is being looked for, thesystem can recommend a blue as similar to the purple. Arelationship between these attributes domains must beconstructed and a good solution is to build an Ontology,which can relate similar terms.

3 - A Design Problem Example

H. Camargo Promotional Architecture andLandscaping is specialized in developing custom-madearchitectural projects for fair and exhibit stands. Thecompany is divided in four main departments: The firstone Sales Department, finds potential clients and theirneeds. There is a Design department, which createsproposals for these potential clients, establishing theoverall designs and some of the materials to be used. AProject department solves all problems once a proposalhas been accepted, further detailing the project, definingthe physical specification: measurements, quantity ofmaterials and how these are to be put together. Finallythere is an Execution department, which given thephysical specification, builds the actual stand andwhatever components may be necessary.

H. Camargo group presents their proposals in twoformats - hard copy printouts and 3D visuals on CDfeaturing fly through animations. Project proposals needto be created quickly, be original and innovative. Designsare not charged for, and the company will only get paid ifthe project is accepted (and built). Viewing the stand fromall angles and in 3 dimensions allows the client tovisualize exactly how the stand will look and work at theexhibition. One of the first difficulties is to sit and talk tothe client, trying to obtain their company goals andobjectives, learn about their business and provide the bestexhibition stand design solution to fit the clients needs.After the project acceptance, a specialized team starts towork on the design and subsequent stages of the standimplementation.

As stated before, SYMBAD project will work on threesignificant awareness dimensions named as Observation,Communication and Reasoning. It is important to notethat communication flows almost exclusively in one

direction: from the Design Department, to the ProjectDepartment and then to Execution group. All groups mustbe aided by tools, which can augment the perception ofkey events happening in their pair workers. A design (a3D Studio drawing) is handed on from one Department toanother. The Project and Implementation teams don'tparticipate in the design phase, and this oftentimesgenerates problems that need to be mended.

At this time, each team uses computers to performtheir part of the process, and hands down files withspecifications to the next one. A knowledge base with allthe designs created (executed or not) by the company isunder construction, and will be used to furnishinformation to our agents.

The lack of global awareness and communicationincreases the possibility of delays in the project, materialswaste, old stands storage, and increase in costs. Naturally,completed projects must be delivered on schedule, whichmay also lead to a need for overtime or hiring extrapersonnel to help with construction.

Fig. 1. Platform Architecture

Most problems are generated when the designerdevelops a project that demands materials that are not instock. In this in case, extra costs will be incurred, topurchase materials so that the project is properlyexecuted. In many cases problems occur because thestand is designed without any concern for the way inwhich it will be constructed. This is a worse problem,because the project cannot be built in the way it wasdesigned, causing issues with clients.

4 An Ontology approach

We have envisioned an agent-based system to informdesigners about similar projects in the knowledge baseand potential problems during the conceptual design

Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

phase, based on previous problems. The agents alsopresent ideas based on the information in the briefing tothe designers, so they can consider previous similar needsand what solutions were given. Agents also extractinformation from each designer's current work and verifythe feasibility of it given previous designs, materials instock and shapes being utilized. Agents can also monitorusers' actions and work progress, to display informationto the user when necessary. Autonomous agents can workin parallel with the user, keeping track of their work andautomatically provide information.

Our agent system is divided in three main layers, asseen in Figure 1. The Case Base layer is composed bythree databases: project base, xml base and knowledgebase. The project base is responsible for storing all therequired information from the stands. The xml base storesinformation from the current design and all the separateparts built before. The knowledge base keeps all thecommunication information and an ontology of some ofthe Stands characteristics. The CBR function will work onthis layer extracting information from the previousdesigns rationale to identify similarities.

Four agents compose the data processing layer:Similarity, Communication, Recommendation and ProjectManagement. These agents will turn the information inthe database layer into knowledge. They will be called bythe client layer and will process on their own. Most of theinformation is constructed with a detailed metadataenvironment, since the metadata allows the correctunderstanding of the stored data.

The client access layer is divided in five maincomponents: Ontology Management, Case Base search,3d Assistant, Communication link, and ProjectManagement. This layer will be responsible for theinterface between the Users and the Agents. The Agentsadopt awareness concepts to facilitate the work of theclients and to return only the most appropriate answers.Similar problems must be solved with solutions learnedfrom past experience, so the agents must understand thesemantic behind the objects manipulated by the workersto satisfy them accordingly.

The Similarity Agent is responsible for building mostsimilar cases in order to help designers with older projectsand parts, which matches current specifications. He is alsoin charge for capturing new best practice cases andrefining existing cases. The Recommendation Agentprovides to upper layers similar cases recommendationsfound by the Similarity Agent. He is always active andtrying to capture opportunities for case reuse. TheCommunication Agent brings collaborative features to theworkers interaction. Its main task is to keep track of therationale capture in order to reuse previous decisions andthe reasons for the choices made by the team members.Finally, the Project Management Agent keeps track of allactivities through a consistent workflow tool. A moredetailed explanation of these agents can be found inSouza [9]

5 The Ontology for Stand parts

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.The main problem in this approach is how the system canhelp the users filling in the Ontology's structureddescriptions. The Ontology construction can be a strongbarrier in the feasibility of the proposed method.

The 01-Modeler [10] is being evaluated to enhance theOntology maintenance, aiding the users with thisdifficulty. This product is a module of the open-sourceontology management infrastructure KAON1 [10}, whichincludes a comprehensive tool suite allowing easyontology creation and management, as well as building ofontology-based applications. The 01-Modeler generates aXML file with the Ontology, which can be imported tothe SYMBAD base. Most of the Ontology is built withthe aid of past cases, but a careful verification must existto cope with difficulties concerned to natural complexityof this task.

Fig. 2. Ontology sample in 01-Modeler

In the Figure 2 we present a sample of the stored StandPart Ontology to be used by the Similarity Agent. TheStand part is defined by some attributes, which certainlyincludes client id, event, year, stand area, stand cost, floornumber and furniture. The furniture attribute is a bit morecomplex because it should be represented by a recursiverelationship. The similarity retrieve function uses itemsincluded in the ontology built based on the parts classifiedby each previous usage. The complete hierarchy can beobtained on demand, from the research group.

The Ontology tree has two main attributes: the nodenumber, and the relative link to enhance hierarchal search.This latter is used to speed up searches for subordinatesand upper level members in the tree structure. The node

Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

number is a pointer to a table where a detailed descriptionof the part is made, the name of its image file, and a list ofsynonyms for the piece.

The Ontology attributes depend on the subject beingdescribed. For the Stand Parts these attributes weredivided into five classes:

* Structure (dates, users, method, description...),* Appearance (color, texture, material, 3D Studio

format ... )* Usage (type, use ...)* Administrative (ID, location, cost, rights ...)* Relation (joint, time relation, substitute ...)The Parts hierarchy can be understood by the following

example: the Ark7112 is an instance of a class namedArk, which belongs to a class named Cases, whichbelongs to a class named Storage, which belongs to amain class Furniture. Following the CBR algorithm theArk8245 has a similarity of 0,8 with Ark7112, since theyhave the same parent Ark, but the Bookcaser2830 wouldhave a similarity index of only 0,6 since they don't sharethe same parent, but they are both Storage parts. The treelevel determines the degree of similarity between twomembers. The XML file below shows the way thishierarchy is exported by the 01-Modeler:

<owl:Class rdf:ID="Parts"><rdfs:label xml:lang="en">Parts</rdfs:label>

</owl:Class><owl:Class rdf:ID="Storage">

<rdfs:label xml:lang="en">Storage</rdfs:label><rdfs:subClassOf rdf:resource="#Parts"/>

</owl:Class><owl:Class rdf:ID="Cases">

<rdfs:label xml:lang="en">Cases</rdfs:label><rdfs:subClassOf rdf:resource="#Storage"/>

</owl:Class><owl:Class rdf:ID="Arks">

<rdfs:label xml:lang="en">Arks</rdfs:label><rdfs:subClassOf rdf:resource="#Cases"/>

</owl:Class><owl:Class rdf:ID="Ark71 12">

<rdfs:label xml:lang="en">Ark7 12</rdfs:label><rdfs:subClassOf rdf:resource="#Arks"/>

</owl:Class>

The initial work of building the first version of the treewas coped by a multidisciplinary group involvingmembers of the three departments. New parts are beingfitted in the Ontology tree in an easier way, since most ofthe work has already been done. Now they aremaintaining the structure for the incoming situations,which is a lighter task then the initial one.Now that some designs have been fed to the system,

users can use the CBR facilities to find similar projectsand design objects. It is not necessary to have a wholeequal project for it to be found by the agents. Any partthat is identified as a useful piece can appear to the user as

an advice or a clue to the current design. On the nextfigure we show a case base search example. Now we cansee in more detail the Casebase Search window with thecriteria chosen and the possible solutions found. At thebottom of the window we can see that the most similaritystand found was the one built to Siemens - Telexpo -

2003. One great thing that we can see from these figuresis that the third one found was the CVRD - Mamore &Granito - 2002. Although the attribute company namechosen by the client as being Siemens, the agents returnedas a result a Stand of CVRD (CVRD is a company'sname) due the similarity of the project items.

Fig. 3. Case Base search example

On the Stand Details tab, the commercial departmententers with all the information of each stand to be built.This information is based on the briefing sent by theclient. The data is inserted in a web page in the same waywhere they are searched. In a near future it is expectedthat this information can help user to understand better thework process through a data mining analysis. The usercan also do a full text search on all attributes of the base,finding words contained in the part description and in anyattribute of the project. This feature is very useful and weare studying the possibility of using search enginetechnology as the base grows up.

The Recommendation agents that are in charge ofpresenting similar projects will present all thisinformation and will extract some of the necessaryinformation from the above data and some from the 3DStudio drawing that is being worked by the designers. Theinformation extracted from the 3D Studio will be storedon the XML base. A pointer to this structure is maintainedin the Ontology instance for this part.

From this database all the necessary information can beextracted. The file contains all the objects as a mesh. A

.

I=mm

Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design

mesh has a name, the vertex numbers and the facenumbers. Each vertex has coordinates x, y, z and thenormal vectors. Each face is formed by a triangle andindicates which vertex from the list is being used. In thisfashion it is easier for the Similarity agents to determinethe area and volume of each object and it's still possibleto reconstruct the object separately from the others.

Similarity agents evaluate items that exist in stock andshapes under construction. They work with theRecommendation agents to determine, in real time, if anobject or shape can be used in that project, given theexpected date of completion. Shape analysis algorithmsare being used to compare with other cases and assess theviability to construct it. This agent is also responsible fordetermining costs of materials used and generating a listof materials that will need to be purchased.

6 Conclusions

In this paper we described our current work ondeveloping a framework which supports different teammembers construct and integrate Stands in a designenvironment by providing recommendations based onCBR techniques and the usage of multi-agentTechnologies, promoting information exchange, reuseaids and awareness of the process as a whole. For thedevelopment of this framework an extensive Ontologywas built from different information sources by usingspecialized tools. With these agents we expect to changethe way in which designers work: by providing them withactual information in order to help them to make betterdesign choices, leading to 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 provideawareness so that the designer can make consciouschoices. The designer may still choose to build all-newmodules and complicated shapes that won't be reused, buthe or she will be aware of what is being done (it will be aconscious choice). The database layer keeps informationof previous designs (such as time spent on construction,objects and materials, spent on assembly, time spent onphysical specification, etc.), it will also establish a designhistory and difficulties.

For the future we intend to enhance the agents to makesimilarity recommendations through the introduction of

fuzzy logic in the qualitative attributes. Moreover, we willcontinue to validate this implementation and frameworkunder stress conditions, which might be realized in thecontext of a variety of projects. For last, we intend tocreate a tool to allow intelligent searches on the rationalecaptured through the communication agents in order tomake these in formations useful for incoming decisions.

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