=Paper=
{{Paper
|id=Vol-1815/paper24
|storemode=property
|title=Distributed Case-based Support for the Architectural Conceptualization Phase
|pdfUrl=https://ceur-ws.org/Vol-1815/paper24.pdf
|volume=Vol-1815
|authors=Viktor Ayzenshtadt
|dblpUrl=https://dblp.org/rec/conf/iccbr/Ayzenshtadt16
}}
==Distributed Case-based Support for the Architectural Conceptualization Phase==
https://ceur-ws.org/Vol-1815/paper24.pdf
246
Distributed Case-based Support for the
Architectural Conceptualization Phase
Viktor Ayzenshtadt
Competence Center Case-Based Reasoning
German Research Center for Artificial Intelligence
Trippstadter Straße 122, 67663 Kaiserslautern, Germany
University of Hildesheim, Institute of Computer Science
Samelsonplatz 1, 31141 Hildesheim, Germany
ayzensht@uni-hildesheim.de
Abstract For the early phase of conceptualization in the architectural
design a case-based retrieval approach for finding building designs that
have similar semantic and topological structures to a currently created one,
can provide a helpful tool for inspiration and comparison of architect’s
own ideas with the solutions available in a case base of previously created
designs. The approach presented in this research summary is aimed to
provide such a tool that can deal with queries and cases that can be
represented as graphs. Moreover, in the late phases of the research, the
approach should be extended for application beyond architectural design
and provide a generic framework for distributed case-based search of
similar graphs for other suitable domains. Constraints of the search,
explanations, initialization of the case base, and the knowledge about
user behaviour are the important aspects of the concept of the framework.
Keywords: case-based design, case-based retrieval, distributed CBR
1 Introduction
The conceptualization phase of architectural or industrial design is considered
a process of knowledge-intensive reasoning for the purpose of finding ideas
and concepts that are helpful for the solution of the current design task. For
architectural domain, a combination of methods of case-based design (CBD) and
computer-aided architectural design (CAAD) can provide helpful solutions to
support the conceptualization phase. The retrieval of similar designs to a currently
created one is mostly a key feature of such a solution. The basic CBD/CAAD
research project Metis – Knowledge-based search and query methods for the
development of semantic information models for use in early design phases of the
German Research Center for Artificial Intelligence (DFKI) and the KSD Research
Group of the TU Munich aims to determine such solutions by considering building
Copyright © 2016 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2016 Workshops. Atlanta, Georgia,
United States of America.
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design as representation of semantically enhanced graphs. During the project
activities a comprehensive modular retrieval system for the purpose of finding
of similar designs was elaborated. It consists of different retrieval engines and
supporting modules and can be connected to a user interface for constructing of
queries and receiving of results.
2 Focus Phases and Goals of the PhD Research
In the first phase of my PhD thesis research I focused on the tasks of consolida-
tion, extension, and evaluation of the distributed multi-algorithmic case-based
retrieval engine for architectural designs MetisCBR (which became one of the
retrieval engines of the above mentioned modular system), according to the
project’s research goals and requirements. The systems differs from other ap-
proaches for CBR-assisted architectural design in its underlying structure (which
is distributed, i.e., based on a multi-agent system with possibility to accomplish
retrieval processes in parallel and achieve results in reasonable time amount).
Research work, that describes the system more in detail and evaluates its retrieval
performance, has been published during this first phase. This work consists of
the following contributions:
– The description of the mode of operation of the system [2], where the
underlying concepts of the system’s CBR-based retrieval of architectural
designs with (case-based) agents are described. In this work the overview
over the system architecture, the distribution of the retrieval process between
several agent categories (such as case-based, managing, and service agents),
and the retrieval coordination concept is presented. The most essential feature
of the system, the retrieval containers that are responsible for the actual
search for similar architectural designs (or its parts), is described in detail.
– The description of the domain model of the system [1], where the underlying
structure of a case within the case base of the system is presented in detail
including the influential concepts for this structure, the attributes, and
similarity measures. The basic retrieval strategy is presented as well. This
work also includes an evaluation of the model and the strategy with an
exemplary design query.
– The system-wide concept of the ontology-based communication architecture
[4], that describes how the agents of the system communicate using the specific
domain ontology and communication patterns. This work also includes an
overview over the early concept of the explanation module for the system
(see also Section 2.3).
– A comprehensive cross-comparison and evaluation [3] of MetisCBR’s rule- and
case-based retrieval coordination component and a rule-based coordination
service of the KSD Research Group that has access to the exact subgraph
matching methods and direct search in the databases. In this study the
human experts of the architectural design domain evaluate the results of
both systems for different user scenarios using different rating criteria. The
computed similarity values returned by the systems are analyzed as well.
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The next phases of my PhD research will be focused on the conceptualization,
initial implementation, and a stepwise evaluation of a generic distributed case-
based retrieval framework that can be applied to other domains than architecture.
The main requirement for domains to be considered for the retrieval framework
will be that a typical query can be represented as a graph for which similar
(sub-)graphs (cases) should be retrieved. Architectural domain will remain the
showcase domain for demonstration of the abilities of the system, as it differs
from other domains in context of the targeted user group, variable complexity of
retrieval requirements (e.g., search for sub-structures only is often a case), and
case base-related challenges (e.g., the case base of architectural designs of the
Metis project consists of a relatively small number of cases, but they strongly
vary in constructional aspects). Another important aspect of the difference
between architecture and other domains is the case representation of graphs: in
architectural domain, and especially in the Metis project, a graph represents a
single floor plan of a building, whereas for example in the process-oriented CBR
it represents the process steps.
The main research and development activities during the work on this frame-
work will be focused on the following aspects:
– Elaboration of distributed CBR-based retrieval, learning, and explanation
methods that can help to overcome the complexity of subgraph isomorphism.
– Determination of hard and soft constraints under which such a CBR-based
subgraph isomorphism detection can deal with given search requests.
The Figure 1 demonstrates the research process of my PhD work in an overview
of the phases that it consists of. In the following sections the main research goals
of the next phase are described in detail.
Phase 1 Phase 2: Going beyond architecture Phase 3
General mode of Concept of the explanation component Determination of
operation constraints
User behaviour model
Concept of the Evaluation of the
domain model and Improvement, extension and adaptation framework
retrieval strategies of retrieval and retention algorithms for
case-based search of graph structures Working
Concept of the prototype of the
communication Comparison to other frameworks framework
architecture
Concept of the case base initialization
Cross-evaluation for suitable domains
Figure 1. PhD research phases for the distributed case-based retrieval framework.
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2.1 Retrieval and Retention Algorithms
The CBR retrieval algorithms currently available in the system will be extended,
improved, and adapted according to the results of the (cross-)evaluations with
other retrieval approaches of the previously mentioned modular system (with
optional participation of external approaches). For this purpose, a comprehensive
user study is currently planned to be conducted that is based on the hypothesis
that different user scenarios in the architectural domain can have one or more
contexts in common. That is, the study should result in initial sets of user scenarios
and contexts suitable for each algorithm. The properties of the scenarios and
contexts will be seen as constraints under which this retrieval algorithm is the
best possible choice. Such user evaluations of results that the algorithms are
returning will be conducted periodically and play a role of a key step on the way
to narrower exactness of the constraints.
Parallel to the improvement of the retrieval algorithms, a collection of retention
algorithms suitable for needs of the framework will be conceptualized to provide
learning ability for the agents of the system. Some of the agents are already able
to learn cases (that are the previous queries), this will be extended for other
agents in order to provide the features of indexing and fast response with suitable
results on all retrieval levels.
2.2 Model for User Behaviour During Conceptualization Phase
During the next phase of my research I am also planning on creating a patterned
model of user behaviour during conceptualization of an object (in this particular
case: an architect that creates a building design) that can be represented as a
graph. For this purpose the analysis of currently existing models and experience
gained during the project’s activities will be combined to provide an initial
base for the model, that will be then modified and adapted according to user
studies that will be conducted especially for this purpose. An important aspect
of the model and of the studies will be the strict consideration of graph-based
conceptualization objects.
2.3 Explanation Component
An additional context-based component that provides an explanation why a
particular result was included in the result set, or why it was placed on this
position in the set, or why the result set consists of results of a certain type,
will be implemented in the retrieval framework and is currently elaborated as a
particular task in a bachelor thesis. The explanation contexts can refer to retrieval
primitives (such as semantic fingerprints [5] that are used for the architectural
domain) or to other common features of results (such as that the results can
have a common identifier, e.g. the floor plans can belong to the same building,
and can be grouped by this context). The concept of the explanation component
can be adapted by another search engines of the common modular system: they
should support the foundations of this context-based explanation concept, but
provide own methods of detailed composition of explanations.
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2.4 Case Base Structure Initialization for Suitable Domains
A very important task for transition from the architectural domain into the
generic purpose of the framework will be the task of initialization of the case
base for a given domain. To accomplish this task, the methods of information
extraction, file format (e.g., XML) parsing and data linkage will be implemented
to provide an automated method of creation of structure of the case base. It
will be also possible to adapt the structure by a user (e.g., domain expert) to
provide more realistic model for cases to be retrieved. The research question that
I am going to answer with this step is if it is possible for a CBR application to
initialize the graph-based representation of cases fully automatically by itself,
which grade of human influence during the initialization is needed, and how
domain-dependent such an initialization could be.
2.5 Comparison to Other Frameworks
The (distributed) CBR architectures and frameworks like SEASALT [7] or
DRAMA [6] provide approaches for conceptualization of an experience shar-
ing system for the given purpose or domain (e.g., travel medicine or aerospace).
During the further research activities I am going to compare such approaches
with the generic framework of my PhD work. For this purpose, these approaches
will be exemplary applied to the domain of retrieval of similar graph-based cases.
This comparison should result in a suitability evaluation of the examined systems
for this purpose, it will also show which features from the opposite systems can
be adopted by another system.
3 Current Progress
Currently the research and development activities in context of the generic
retrieval framework are in the stage of prototypical integration in the above
mentioned modular system architecture that includes, among other things, the
web-based user interface for query construction, a result augmentation module
for visualization of mapping of components between query and result, and an
analysis module that is able to apply common data analysis techniques to the
results returned by search engines of the system. The improvement and evaluation
of visualization of the results of the retrieval and the corresponding data analysis
are the upcoming tasks of the modular system.
The next steps for the retrieval framework include adjustment of the scoring
approach of results for all retrieval algorithms and the initial implementation of
the explanation component. After that a research and a user study is planned
to be conducted, in order to create the first version of the above mentioned
user behaviour model, that will hopefully provide insights and patterns that can
reconstruct the conceptualization process when an object that can be represented
as graph is going to be conceptualized.
251
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