Thinking With Containers: A Multi-Agent Retrieval Approach for the
Case-Based Semantic Search of Architectural Designs
Viktor Ayzenshtadt
1,2
, Christoph Langenhan
3
, Saqib Bukhari
2
,
Klaus-Dieter Althoff
1,2
, Frank Petzold
3
and Andreas Dengel
2
1
University of Hildesheim, Institute of Computer Science, Samelsonplatz 1, 31141 Hildesheim, Germany
2
German Research Center for Artificial Intelligence (DFKI), Trippstadter Strae 122, 67663 Kaiserslautern, Germany
3
Faculty of Architecture, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
Keywords:
Multi-Agent Systems, Case-Based Agents, Case-Based Retrieval, Design Support.
Abstract:
To provide the retrieval of information, that can be considered useful during the design conceptualization
process, with advantages of distributed artificial knowledge, an approach, that distributes retrieval-related and
knowledge maintaining tasks among autonomously working and case-based self-learning agents and agent
groups, can be used. In this work we present the distributed retrieval system MetisCBR for the architectural
design domain, where agents work in groups (containers) on resolving of user queries built with a semantic
description model Semantic Fingerprint. The main aim of our approach is to carry out a basis for a considerable
retrieval tool for architects, where the combination of case-based reasoning and multi-agent methods helps to
achieve valuable and helpful search results in a comprehensive building design collection.
1 INTRODUCTION
The early conceptualization phase of a building is the
stage of the architectural design development, during
which an architect can increase the efficiency of the
working process by communicating with a computer-
aided helper system. Such a system should be able to
present new ideas and helpful recommendations for
inspiration and guidance in combination with similar
building designs to a currently composed one.
The study of buildings that have a similar con-
text to the design problem at hand, or that are based
on a similar initial premise, is seen as a way of ap-
proaching a design problem and developing a possible
course of action. Prior built projects or architectural
designs serve here as a knowledge base that contains
both examples of spatial constellations as well as so-
lutions for specific architectural situations.
Plans, models, and the documentation of architec-
tural designs and existing buildings and urban envi-
ronments represent an extensive architectural knowl-
edge base which contains implicit design knowledge
that is valuable for the design process. The problem
is, that this knowledge is not available in an explicit
form, without first analyzing and interpreting the in-
formation available.
To handle this architectural knowledge, for the
purpose of providing a retrieval component that ef-
ficiently interprets this data, the helper systems of-
ten use case-based reasoning (CBR) methods as the
underlying retrieval technique. This aims to achieve
high concentration on the quality of results, and to
make the system knowledge-intensive and extensible.
For implementation of such retrieval features,
many approaches were proposed and applied to the
existing systems in the last three decades. The
most significant of them are listed in (Heylighen and
Neuckermans, 2001) and (Richter et al., 2007).
In this work we present MetisCBR, a multi-agent
and case-based retrieval approach for similar archi-
tectural designs. This approach uses inter alia case-
based learning agents as retrieval units in a distributed
container-based application. MetisCBR was imple-
mented as a prototype and integrated into the existing
infrastructure in context of a basic research project
Metis Knowledge-based search and query methods
for the development of semantic information models
(BIM) for use in early design phases, an interdisci-
plinary project of CBR, multi-agent systems (MAS),
and computer-aided architectural design (CAAD),
partially funded by the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG).
This paper is structured as follows: first, the back-
ground and the interdisciplinarity of the research
Ayzenshtadt, V., Langenhan, C., Bukhari, S., Althoff, K-D., Petzold, F. and Dengel, A.
Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs.
DOI: 10.5220/0005683801490156
In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 1, pages 149-156
ISBN: 978-989-758-172-4
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
149
project Metis will be briefly presented. The next sec-
tion contains the overview of related approaches and
studies. Next, a short overview of the system struc-
ture will be presented. The next section character-
izes the proposed agent types and their correspond-
ing tasks in detail. In the next section the prototype
of the approach and its context and validation within
the research project will be presented. The conclusion
summarizes the work and the future project planning.
2 BACKGROUND
Technological developments and globalized working
processes have transformed the building design pro-
cess. However, current digital semantic building
models are no longer able to adequately represent
the increasing complexity of modern architectural
projects. The potential of combining agent-based,
case-based, and graph-based methods with novel se-
mantic models, for example, for energy calculations
or spatial research strategies, is not fully exploited.
To find essential ways to utilize this potential, a basic
research project Metis was initiated. The distributed
CBR retrieval system MetisCBR we present in this
work is one of the integrant parts of the project. The
interdisciplinary nature of the project provides a nec-
essary mixture to overcome the problem of timely
finding similar building designs as source of inspira-
tion to guide the design process of buildings.
CAAD considers computer-related topics regard-
ing buildings and delivers insights into ways an archi-
tect can work/interact with such a system or how she
would evaluate search results.
CBR, as the discipline responsible for working
with intensive knowledge data, is the means for the
actual finding of similar building designs. CBR’s all-
purpose nature and a multitude of different CBR re-
trieval approaches provide efficient tools for the ac-
complishment of the project’s main tasks.
MAS play a role of executing units in the project.
Being a means for distributed decentralized problem
solving, MAS, and the corresponding research area,
provide a necessary collection of techniques for dis-
tributed and concurrent retrieval processes.
To combine the above named fields in order to
work together in a common system, a building in-
formation model (BIM) infrastructure was built for
the project. It integrates every project-related service,
including a building model server, a graph database
with building designs represented as graphs, a content
management system, and software prototypes.
3 RELATED WORK
The system we present in this work is set in context
of the above mentioned research project Metis, and is
related to the CAAD and BIM applications with case-
based reasoning as the underlying retrieval technique.
The project-related work includes (Siebert, 2014),
a research work that contains the initial setting of our
system where a concept of agent-based experience
sharing system is applied to the architectural domain.
A semantic building structure description model
Semantic Fingerpint (Langenhan and Petzold, 2010)
was initiated to provide a structure to idea that build-
ing floor plans can be described by abstracting the
building data at the level of building, storey, and
room, as well as their respective topological relation-
ships. Based on this model, a subgraph matching-
based retrieval algorithm was proposed in (Ahmed
et al., 2014), where an information extraction process
of floor plans is also contained and later used in the
Metis project for the graph database.
Applications for Android and iPad, and a touch-
table interface, were developed for query construction
with modern haptic HCI methods. A web-based user
interface (Bayer et al., 2015) is part of the project as
well. This application uses the (AGraphML) specifi-
cation (Langenhan, 2015) for the query construction.
Finally, a distributed domain model that builds a
CBR base for the retrieval process of MetisCBR was
introduced in (Ayzenshtadt et al., 2015).
CBR-based approaches with architectural empha-
sis can be classified as a subdomain of case-based de-
sign (CBD). In these systems, the retrieval function-
ality is one of the most essential system modules.
For example, in FABEL (Voss, 1997) functional
entities called specialists are used to execute the
retrieval tasks. The specialists work with special
aspect-specific representations of cases and queries.
These representations are produced by applying a
transformation process to the user query or to a cur-
rently comparing case of the case base. Retrieval spe-
cialists determine similar cases by comparing the cor-
responding aspects of cases and queries.
CBArch (Cavieres et al., 2011) is a supporting
framework for the conceptualization phase of archi-
tectural design. With emphasis on the early energy
performance evaluation of buildings with commercial
background, it provides an approach for a completely
CBR-based solution for such cases.
Other systems, such as DYNAMO (Heylighen
et al., 2002), SEED (Flemming, 1994), PRECE-
DENTS (Oxman and Oxman, 1993) or CaseBook
(Inanc, 2000), can also be mentioned as closely re-
lated, as they serve the same purpose as our system.
ICAART 2016 - 8th International Conference on Agents and Artificial Intelligence
150
Web UI
CBR domain model
(myCBR API)
Graph
DB
Semantic
ngerprints
(extracted from sketches
with image processing)
GraphML IO
Semantic
ngerprint
Case
Bases
Coordinator
CBR agent 1
CBR agent 2
CBR agent 3
More CBR agents ...
Room µ
Edge µ
Maintainer agent
GraphDB agent
User query
GraphML agent
Ontological
query
Cases to import
Semantic FP
Ontological
query
Search result
Sets the retrieval strategy based on
case-based and/or rule-based reasoning
and starts a new retrieval container
Floorplan µ
Checks the DB for new
graphs and imports them
into the case base
Sends
Semantic FP
Looks up for the similar
graphs in the DB by
querying it and sends
the proper data back
Consructs an ontological query
from the XML/GraphML of the
Semantic ,ngerprint
Gateway
Processes
Semantic FP
CBR manager
Ontological
query
+
Retrieval strategy
More retrieval containers ...
Retrieval container
Separates the query into
the sequences and
collects result data
Graph DB query
Figure 1: The system overview of MetisCBR.
4 THE SYSTEM OVERVIEW
In this section we provide a brief overview of the pro-
posed approach MetisCBR. The main task of the en-
tire multi-agent system is to process a query in the
AGraphML format that was sent by a user (an archi-
tect) from a web-based query construction interface
(not part of this approach, see also Sections 3 and 6).
As depicted in Figure 1, the system is constructed
in a circular form, where the gateway marks the entry
point into the system, after a user query was sent from
the web interface.
Successfully passing the gateway agent that con-
ducts an initial error analysis, the query reaches the
coordination component that is responsible for the
correct accomplishment of the entire case-based re-
trieval process. Its first action is to send the query to
the parsing agent that transforms it from the architec-
tural GraphML into an ontological representation in
SL content language to provide human-readability as
well as suitability for communication among agents
according to (Caire and Cabanillas, 2002).
Back at the coordination point, the query is analyzed
and sent to a retrieval container that conducts the ac-
tual resolving of this particular query. The coordina-
tor selects the proper retrieval strategy and conducts
the setup of the container, where a retrieval manager
and at least one case-based retrieval agent that oper-
ates with at least one similarity measure µ are manda-
tory units for successful processing of the retrieval
stage in the case base(s) of the CBR domain model.
After finishing the actual retrieval process, the
achieved result data is sent back from the container’s
manager to the coordinator, who forwards it to the
gateway that transforms it into the proper processing
format (JSON) and sends the result data object to the
web interface component for displaying.
Parallel to the user query resolving process, new
cases (building designs) are being imported into the
case base(s) of the underlying CBR domain model.
This is done by another essential component of the
proposed MAS, the maintainer, whose main task is
to extract cases from the BIM infrastructure’s graph
database and to import them into these case base(s).
Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs
151
5 AGENT TYPES AND TASKS
This section presents detailed descriptions of the
agent types that are directly or indirectly involved in
the process of retrieval of similar building designs.
Their tasks will be also described in detail.
5.1 Agent Categories
The categorization of agents into classified groups
helps to achieve a better understanding of the entire
system functionality. For our case-based multi-agent
approach with retrieval as main task, we defined the
following categories of agents: case-based, manag-
ing, connecting and service agents. An agent may be-
long to one or many categories.
Case-based agents are directly involved in the re-
trieval process of similar architectural designs and
possess an internal reasoning and learning component
with rule-based and/or case-based reasoning abilities.
Retrieval executing agents of this category (see Sec-
tion 5.3.2) are able to apply different similarity mea-
sures when searching for designs (or parts of it) in the
case base(s) of the CBR domain model described in
(Ayzenshtadt et al., 2015) (see also Sections 3 and 6).
Managing agents ensure that the entire system (in-
cluding the retrieval process and building designs im-
port into the case base(s)) is working properly, with-
out failures, such as abruptions or sudden bottlenecks.
The agents of this class monitor ongoing internal pro-
cesses, share their statuses/intentions, and forward
queries and results to the corresponding agents.
Connecting agents play the role of connection
points between the MAS and linked external services.
They make sure that the connection and communica-
tion of MetisCBR with these environments (e.g., the
query builder interface or the graph database) is es-
tablished, and query or case streams can be forwarded
to the destinated system components.
Service agents are helper agents that execute tasks
that do not belong directly to retrieval, management,
or connection task domains. The agents of this class
are involved in the accomplishment of intermediate
steps of the retrieval process (e.g., query parsing).
5.2 Coordination
For the efficient coordination of the retrieval process
within our system, a corresponding agent (the coordi-
nator) was implemented. Its role is to manage the en-
tire case-based retrieval stage, after the user query has
been received from the gateway. Therefore, this agent
belongs to both categories of case-based and manag-
ing agents, and has a number of particular tasks.
Retrieval Strategy Selection. The selection of the
proper retrieval strategy is the coordinator’s most es-
sential task. It is based on the combination of the
properties of the query, previous experiences, rules,
and user-specified data from the query.
For example, if the user’s current objective is to
conduct a retrieval without much detailed settings for
the search, the coordinator can derive the objective
from the user-specified data (in form of a specific at-
tribute) and select the associated retrieval strategy to
resolve the query. Otherwise, the common strategy
can be applied. The objective recognition and setting
is an example of applying the rule-based reasoning,
the first one of the reasoning abilities of the coordi-
nator. The second one is the case-based classification
of the query, where selection of the strategy is mainly
the task of the learning component of the coordinator.
Learning is a significant property of an agent that
aims to improve its ability to make decisions based
on previous experiences. Therefore, the coordination
component of our retrieval system, being such an
agent, uses its case-based learning ability as an
essential feature to select the proper procedure for
query resolving. Each query that the coordinator gets
from a user, together with the achieved results, is
registered as a case with the IB2 algorithm (Aha et al.,
1991) that controls the filling of the coordinator’s
internal case base and provides the coordinator with
the learning ability. These cases are the base for the
reasoning process that gets activated if the rule-based
classification misses the explicit specification from
the user side. During the CBR-based classification,
the corresponding floor plan information of the query
(e.g., room count and room types) is used to find the
most similar case in the coordinator’s internal case
base, and apply a strategy that was used in this case.
Retrieval Container Activation. After the strategy
for the retrieval has been selected, the coordinator ac-
tivates a new retrieval container (see Section 5.3). The
setting of the container is defined by the coordinator
it selects the retrieval agents that will execute the
actual retrieval steps and starts the activity of the con-
tainer. In MetisCBR the retrieval strategy is essen-
tial for selection of the agents retrieval agent types
are bound to the strategy, but not relevant for the se-
lection of the strategy. The coordinator is also able
to terminate one or more currently running retrieval
containers. This happens when a container has fin-
ished its retrieval activities, and the user has finished
her retrieval session. In case of unexpected interrup-
tion of the connection between the user interface and
the MAS, the retrieval is continued and the results are
persisted in an intermediate object.
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Forwarding of the Query Modifications. If it is
the user’s wish to modify the current query during
the ongoing retrieval, the coordinator receives the
modified query from the gateway agent and forwards
the changes while notifying the manager of the
corresponding container about these changes.
Sending of the Results to the Gateway. When the
actual retrieval phase is finished, the coordinator
receives the results from the manager of the container
and provides the gateway agent with these results.
Assignment of the Query Parsing Task. The
coordinator assigns the GraphML agent (see Section
5.6) the task of parsing the current query, which it
gets back transformed as an ontological query in SL
content language format.
Receiving the Import Status. From the maintainer
agent the coordinator receives the current status of
the extraction/import process and coordinates the cur-
rently running retrieval tasks with respect to this sta-
tus (see also Section 5.5).
5.2.1 Coordination Techniques
The efficient accomplishment of the above described
tasks requires possession of corresponding coordina-
tion techniques. The coordination component of our
retrieval system is developed with the integration of
combination of some of the coordination scenarios
described in (Nwana et al., 1996).
Organizational Structuring is applied when the
coordinator creates and destroys retrieval containers
and assigns strategies to them. It is the main coordi-
nation technique in MetisCBR.
Contracting (more precisely, a sub-feature of con-
tracting, the sub-distribution of the tasks) is applied
when a manager of a retrieval container assigns parts
of the query to be resolved by corresponding agents.
Centralised Multi-agent Planning is activated when
the maintainer sends an import status to the coordina-
tor (see Section 5.5)
5.3 Retrieval Container
In a retrieval system, where concurrent queries are ex-
pected to be a usual case, a big advantage is to im-
plement a structure that is able to handle each of the
queries separately to achieve more efficient process-
ing. For the actual building design retrieval step of our
approach, we use a container-based structure, where
each retrieval container during its activity can be seen
as a completely autonomous multi-agent sub-system
that is nested inside a parent MAS, and can only be
terminated if its retrieval process and the user session
are both finished.
The working cycle of a retrieval container starts
with receiving of an ontological query from the co-
ordinator and ends after the retrieval of the assigned
query is finished. Between those two steps, a case-
based resolving of the query takes place inside the
container. Thus, every container has three goals to
complete during its working cycle: receiving of the
query, resolving of the query by means of applying
CBR methods, and replying to the coordinator with a
message that contains the achieved results.
Case
base
Coordinator
CBR Agent 1
CBR Agent 2
CBR Agent 3
Room µ
Edge µ
Floorplan µ
CBR Manager
Ontological query
Retrieval results
Retrieval container
Figure 2: Exemplary configuration of a retrieval container.
An exemplary configuration of a single retrieval con-
tainer can be seen in Figure 2. Every container con-
sists of a mandatory CBR manager an agent that
manages resolving of the current query – and at least
one mandatory CBR retrieval agent an entity that
is responsible for the actual search for similar cases.
The following sections provide a detailed description
of both agent types.
5.3.1 CBR Manager
As mentioned above, the CBR manager is a manda-
tory agent inside an active retrieval container. It orga-
nizes the current case-based retrieval of an ontological
query that was received from the coordinator. There-
fore, this agent belongs to the category of managing
agents (see Section 5.1), and was primarily created to
take load off the coordinator, that can in turn concen-
trate on its strategic and system managing tasks.
The tasks of the manager include two primary
classes: communication with the coordinator and
query separation. During the communication with the
coordinator, (i.e., receiving an ontological query and
sending the results back), the manager completes re-
ceiving and replying steps of the container goals. The
separation process cuts the given query into the num-
ber of parts according to the selected retrieval strategy
and forwards these parts to the corresponding agents.
Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs
153
The secondary class of tasks of the manager consists
of helper operations, which are accomplished only if
required by the strategy as well. Those helper op-
erations can include some computational tasks, such
as determination of rooms and room connections that
originate from the same floor plan.
5.3.2 CBR Retrieval Agents
CBR retrieval agents execute the last and most
important part of the retrieval process they apply
CBR retrieval methods for determination of the most
similar cases to the query or its parts in the case
base(s) of the underlying attribute-value based CBR
domain model (described in detail in (Ayzenshtadt
et al., 2015)). To achieve this goal, they use sim-
ilarity functions prepared for each agent, special
query types, and an internal reasoning mechanism
to deal with incoming queries from the manager.
CBR retrieval agents belong to the case-based agents,
where several agent types of this class currently exist.
Floor plan Agent. This agent’s aim is to search
for cases that contain floor plan meta data from
the AGraphML specification and additional related
information such as room count and room types.
Floor plan agent works with following query types:
Floorplan Query every attribute of the meta
data and additional data will be taken into account
for the similarity measurement.
Floorplan ID List Query returns cases, if
their corresponding IDs exist in a defined ID list.
Room Agent. To find similar rooms to a given one
this agent uses every room attribute from the domain
model. Its only query type is currently Room Query
that accomplishes this similarity computation task.
Edge Agent. This retrieval agent takes every edge
attribute into account to compute a similarity measure
between two room connections. The corresponding
query type is the Edge Query.
5.3.3 Learning of CBR Retrieval Agents
CBR retrieval agents are able to learn from previous
queries in the same manner as the coordinator does.
Each of the retrieval agents possesses an internal rea-
soning mechanism, where a special internal CBR do-
main model exists for each retrieval agent’s knowl-
edge base. Special attributes are determined to hold
information about previous queries the agent has re-
solved (e.g., room count from the query and from the
found case(s) for the Room agent).
5.3.4 Container Identification
Retrieval container can be identified via an UUID as
defined in the RFC 4122. The coordinator assigns an
UUID to each container it creates, the same ID is also
temporarily assigned to the query that the container is
working with to be able to forward modifications to
the associated container.
5.4 Gateway
As described above in the overview section, the gate-
way agent is an entry point to the MAS from the user
side, and an interface for forwarding queries and re-
sults in corresponding directions. The gateway agent
belongs to the category of connecting agents (see Sec-
tion 5.1).
Gateway agents of such type are common in sys-
tems with structure similar to ours. For example, in
(Greenwood et al., 2005) a gateway agent is a part of a
component that is used for the OWL-S-based seman-
tic interaction between a web client from an external
web service environment and an agent environment.
The activity area of our gateway agent is divided
into two main sections: accepting of user queries and
forwarding them to the coordinator, and receiving re-
sults from the coordinator and forwarding them to the
web user interface. Both task sections are controlled
by special agent behaviors (see Figure 3).
Receiver
behavior
Sender
behavior
Case-based
retrieval
Web UI
Query
Result
Figure 3: The behaviors structure of the gateway agent.
Sending a formalized and validated user query in
AGraphML format to the coordinator is the main pur-
pose of creating of the Sender behavior. During the
validation of the query the gateway agent traverses
through the data in order to find formal errors, such
as missing meta data or false (or missing) room or
room connection definitions. Empty queries will not
be forwarded to the coordinator. Incomplete queries,
where data is fragmentary or only partly missing, will
be forwarded to the coordinator, however, missing at-
tributes will not be used for the retrieval.
Receiving result data from the coordinator and deliv-
ering it to the web-based user interface is the activity
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154
field of the Receiver behavior of the gateway agent.
Similar to the process of the sender behavior, the re-
sult data (in SL content language) will be validated as
well, following by the transformation into the JSON
format (plain HTML is also possible). It is optional if
results will be returned in sequences or at once.
5.5 Maintainer
Maintainer is an agent that is responsible for the cor-
rect functionality of the extraction and import of the
graph-based designs from the BIM infrastructure’s
graph database into the case base(s) of the underlying
case-based domain model of the system. The main-
tainer belongs to the class of managing agents.
During the three-step case base(s) update process
the maintainer executes tasks defined for each step.
In the first phase it queries the graph database for
new graph representations of building designs. In the
second phase, the maintainer requests the RESTful
service (implemented in the BIM infrastructure) for
each graph of the previous result set and gets back
an AGraphML-formatted graph. In the last step, the
building graph is imported to the case base(s) of the
domain model with a special import function.
For the essential workflow of the system, the
maintainer is also able to set and share an import
status, which currently can be one of the following:
Import Ready, Import Active, Import Paused,
and Import Done.
The coordinator receives the status and may exe-
cute actions according to it. For example, if the sys-
tem performance is critical the coordinator may stop
some retrieval containers or request to stop recently
started extraction/import process to allow for a better
system resource sharing.
5.6 GraphML Agent
As previously mentioned (see Section 5.2), the
GraphML agent is the system entity that is re-
sponsible for parsing of the incoming architectural
GraphML queries into ontological queries in SL con-
tent language format. The ontological queries get
their name from the underlying communication on-
tology that defines the communication vocabulary
among agents. By means of applying an ontology
to the communication, the ontological query becomes
part of the message content as demonstrated in (Caire
and Cabanillas, 2002).
6 SYSTEM EVALUATION
For the above mentioned basic research project Metis,
a working prototype of the proposed retrieval system
MetisCBR was developed to demonstrate its suitabil-
ity for the ”real-world” application. Java language
based frameworks JADE (for the implementation of
the described agent system) and myCBR were used to
develop the prototype. It is also implemented as one
of the sub-systems of the BIM infrastructure.
The prototype works with the BIM infrastructure’s
content management system (that contains graphical
representations of building designs) and from it de-
rived graph database that contains every building de-
sign as graph representation that is then extracted and
imported as specified in Section 5.5. In Section 3
mentioned web-based user interface is planned to be
used as the main graphical architectural GraphML
query builder for the retrieval system. Furthermore,
the proposed system and the software prototype are
part of the master thesis (Ayzenshtadt, 2015).
For the validation and demonstration of the abili-
ties of the approach, an evaluation of the system was
conducted, that is completely described in (Ayzensh-
tadt et al., 2015). The evaluation process included
the querying of the system with an exemplary query
consisting of 5 rooms and 6 connecting edges with
two different similarity measures: with and without
attribute weighting.
The MAS task area of the evaluation should show
that MetisCBR is already able to handle search re-
quests and return results according to the similarity
measures.
Considering the early phase of the system imple-
mentation, the agents were able to properly handle
the user queries. The coordinator was able to recog-
nize a new query, to create a corresponding container,
and to assign the strategy and the query type. The
manager of the container was able to recognize if it
is required to separate the query, and then assigned
the corresponding parts to the CBR retrieval agent(s).
The retrieval agent(s) retrieved the case base with the
similarity measure that was set for the current query
(manually, on this stage of development). The man-
ager collected the results and sent them to the coordi-
nator that then forwarded them to the gateway agent.
A couple of minor technical problems occurred dur-
ing the import process, but could be traced by inspect-
ing the consistency service of the BIM infrastructure.
The results of the evaluation of the CBR task area,
including the information about retrieval strategies,
similarity measures, building design dataset used, av-
erage and highest similarity, and the similarity distri-
bution, are described in (Ayzenshtadt et al., 2015).
Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs
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7 CONCLUSION
In this paper we presented MetisCBR, a distributed
case-based approach for the retrieval of similar build-
ing designs in the corresponding case base(s). The
system consists of different agent types, where the
coordinator controls the retrieval process and ap-
plies rule-based or case-based reasoning to select the
proper retrieval strategy, retrieval containers execute
concurrently the actual case-based retrieval processes,
the gateway agent connects the system with an exter-
nal service (web-based query builder) and the main-
tainer agent manages the import of new cases into the
case base(s) of the underlying CBR domain model.
Service agents contribute to the smooth run of the sys-
tem by executing small intermediate tasks.
The future work includes the full integration of the
graphical user interface to the presented MAS. Index-
based retrieval method that queries the graph database
directly is also planned to be added, a new GraphDB
agent will be integrated to the MAS to accomplish
this alternative retrieval task. This will also allow for
evaluating and hopefully deeply integrating these dif-
ferent kinds of retrieval approaches.
ACKNOWLEDGEMENT
This work for the Metis project was supported by the
DFG (German Research Foundation).
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