=Paper=
{{Paper
|id=Vol-2738/paper20
|storemode=property
|title=Student Graduation Projects in the Context of Framework for AI-Based Support of Early Conceptual Phases in Architecture
|pdfUrl=https://ceur-ws.org/Vol-2738/LWDA2020_paper_20.pdf
|volume=Vol-2738
|authors=Viktor Eisenstadt,Klaus-Dieter Althoff,Christoph Langenhan
|dblpUrl=https://dblp.org/rec/conf/lwa/EisenstadtAL20
}}
==Student Graduation Projects in the Context of Framework for AI-Based Support of Early Conceptual Phases in Architecture==
https://ceur-ws.org/Vol-2738/LWDA2020_paper_20.pdf
Student Graduation Projects in the Context of
Framework for AI-Based Support of Early
Conceptual Phases in Architecture ⋆
Viktor Eisenstadt1,2 , Christoph Langenhan3 ,
Klaus-Dieter Althoff1,2 , Andreas Dengel2
1
University of Hildesheim, Institute of Computer Science
Samelsonplatz 1, 31141 Hildesheim, Germany
2
German Research Center for Artificial Intelligence (DFKI)
Trippstadter Strasse 122, 67663 Kaiserslautern, Germany
{viktor.eisenstadt, klaus-dieter.althoff}@dfki.de
3
Chair of Architectural Informatics, Technical University of Munich
Arcisstrasse 21, 80333 Munich, Germany
langenhan@tum.de
Abstract In this paper, current, past, and planned student graduation
projects in the context of MetisCBR, the distributed AI framework for
intelligent support of the early room configuration process in architectural
design, will be presented. During the last years, a number of such projects
were initiated to achieve a master’s or bachelor’s degree. All these projects
have in common that they intend to extend the currently available
functionalities of the framework with new features using the modern AI
techniques and trends, such as explainable AI or generative adversarial
nets, in order to keep up with the recent AI developments. For each
project, a summary of the concept(s), results of the experiments (if any),
and the current status (e.g., defended or ongoing) will be presented. The
main goal of this paper is to reward the student contributions to the
MetisCBR framework by making them visible to the research community.
1 Introduction
MetisCBR1 is a framework for AI-based support of early conceptual phases in
architectural design and was initially created 2015 as a master thesis project
(during the research project Metis2 ) in the form of a retrieval engine for similar
building designs based on established artificial intelligence technologies case-
based reasoning (CBR) and multi-agent systems (MAS). In the next years, the
framework was gradually extended with additional functionalities, and possesses
currently (2020), the following features to support the conceptual creation of
building designs in the form of abstract graph-based room configurations:
⋆
Copyright © 2020 by the paper’s authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
1
http://veisen.de/metiscbr
2
https://www.ar.tum.de/en/ai/research/ksd-research-group/funded-projects/
1. Retrieval – The system looks for similar room configurations for the given
query using attribute-value-based or graph-matching-based search strategies.
The former is used for a broad general search for inspiring designs, the latter
to find exact or almost identical structure of the query within other graphs, for
example to examine how the current structure is used in another context. For
each search process, a group of agents, placed in a container, is responsible.
2. Suggestion – This functionality was developed to recommend the possible
next design steps to the designer based on her previous design steps. A step
is an action, e.g. Add, Delete, or Change type of the room. Using recurrent
neural networks (RNN) and the design steps record (the process chain), the
system suggests the next step using the most similar previous process chains.
3. Adaptation – The framework applies convolutional neural networks (CNN) in
the form of the currently popular GAN structure (generative adversarial nets),
to produce a number of possible evolutions of the current room configuration
to show the designer how it might look in the future merging its feature
matrix with the matrices of the most similar previously saved designs and
letting the system decide which evolution can be considered real.
4. Explanation – Using the methods of explainable AI (XAI) with explanation
patterns Transparency, Justification, and Relevance, the system can enrich
the results of the previous three modules with explanations that contain the
contextual insights into the currently executed process. For example, such
explanations can provide information on search patterns used for retrieval or
which steps of the current session were used to produce a step suggestion.
Quer
User y
Room
interface Graph
conf. Result
s database
query
GraphML
Gateway agent
Results,
explanations,
suggestions, Coordinator + Maintainer
coordination team agent
adaptations
Retrieval container
Adaptation
Connection agent
map data CBR Case
manager bases
ConvNets Retrieval CBR domain
agents model
Adaptation
Explanation Suggester
Ground- Suggestion RNNs +
generation Explanation
truth CB preparation CBs
deliverer Action
Explanation
patterns
Explanation Relations Position Process
creator chain data
Explainer
Figure 1. Overview of the current system architecture of MetisCBR.
Being now part of a PhD thesis, MetisCBR was specifically conceptualized for
use in student graduation projects in order to conceptualize new functionalities
based on the already existing ones and test and implement them if they prove or
seem promising. In the next sections, a selection of the most notable graduation
projects that had influence on the further development of the framework will be
presented. Subsequently, we provide a summary of other relevant projects that
relate to the framework but do not directly extend or evaluate it (e.g., surveys).
2 BDI-Based Explainable AI Component
The first graduation projects that will be described in this paper extend MetisCBR
with an additional explanation module, the BDI-Explainer, that is based on the
established multi-agent systems paradigm Belief, Desire, Intention. This new
explainer was inspired by the research work by Broekens et al. [1] and based on
its complexity it was divided into two separate graduation projects: an already
defended master thesis [4] dedicated to conceptualization and future-proof of the
concept by comprehensive evaluation among the targeted user group of MetisCBR
(architects), and the currently ongoing bachelor thesis that ains at implementation
and quantitative evaluation of this BDI-based explanation module.
The BDI-based explainer (see Figure 2) makes use of different types of
knowledge in the form of Beliefs (architectural knowledge of the system), Desires
(explanation goals), and Intentions (current action to generate an explanation).
The architectural knoweldge is represented by the commonly used architectural
techncial terms and vocabularies in the form of typologies or taxonomnies.
Identically to the other explainers [2], the BDI-Explainer makes use of explanation
patterns described in Section 1. The patterns are used as the current explanation
goals (e.g., justify the suitability of the suggested design step). The explanation
generation component provides the user with the explanation expression.
Explanation
deliverer Beliefs
Explanation
Desires patterns
Explanation
BDI Agent Intentions generation
Figure 2. The planned implementation of the BDI-Explainer (figure adapted from [4]).
The evaluation of the concept of the BDI-Explainer revealed that 75% of the
participated architects find the explanations in architecture modeling software
useful and helpful for understanding of its functionality, however, this does not
stimulate the creativity. Currently, the BDI-Explainer is being implemented using
the multi-agent systems framework JADE and its BDI extension BDI4JADE [5].
3 Construction of Spatial Layouts with Game Theory
In this currently ongoing master thesis, it is planned to apply game theory, the well-
known business negotiation technique, to construct an optimal room configuration
(an early representation of a floor plan) based on predefined optimum criteria and
the negotiation strategy. Game theory is one of the core features of cooperative
multi-agent systems, it provides the relevant agents with a means to achieve an
optimal agreement for distribution of the currently planned tasks among them.
If executed properly as planned, the game-theory based cooperation strategy
usually results in an optimal outcome for each of the collaborating agents.
Game theory was already applied for a multitude of domains, architectural
design is among them. However, for construction of an optimal spatial layout of
a building, to the best of our knoweldge, this technique was not applied before.
The task of the master thesis is to explore the possibilities of game theory for
early phases of architectural design and create a concept for this application. In
general, it is planned to elaborate a number of specific negotiation strategies that
the agents can use to find an optimal configuration for the current design task,
e.g., an apartment for an elderly married couple or a standalone multi-functional
bungalow. Two general agent setups are possible to apply a strategy:
1. Holistic – Every agent is able to propose a solution for the configuration of
rooms available in the layout, other agents might or might not suggest the
improvements and are able to justify their suggestions using utility functions.
2. Distributed – In this setup, each agent is responsible for one room (or room
type) only and has a task of placing this room in the best possible position
in the configuration. For example, the agent responsible for living rooms will
claim the central position for them and easy access from other rooms.
Room
Design Layout Living
Agent 1 Room Type
Design Working
Room Type
Agent 2 Negotiation
Negotiation
Design Solution Kitchen
Solution Room Type
Agent 3
Holistic Strategy Distributed Strategy
Figure 3. Strategies used for game theory-based spatial layout.
In Figure 3, an overview of both types of the agent setups is shown. After
conceptualization, the game-theory-based construction strategies should be proto-
typically implemented using the aforementioned MAS frameworks JADE and/or
BDI4JADE and evaluated by a representative of the architecture domain.
4 Speech UI-Supported Room Configuration Design
Similarly to the BDI-Explainer, this project is also a combination of two grad-
uation projects, where a master thesis contains the research and the detailed
concept with implementation instructions, complemented by a practical bachelor
thesis which will contain the implementation and quantitative evaluation.
In this combined project a human speech-controlled component for early
phases of architectural design should be conceptualized and implemented in
MetisCBR and its web-based user interface (UI) RoomConf Editor3 . Inspired
by the modern natural language generation (NLG) assistant systems, such as
Apple’s Siri and Amazon’s Alexa, the collaboration between the system and the
architect will be enriched by a human dialog-based module that should listen to
the architect’s voice commands and questions via the specific web browser API
(application programming interface), forward them to the backend of the system
for parsing and producing the NLG-based answer that accompanies the achieved
results and reproduce it using a human voice imitation in the UI.
In general, all four main functionalities of MetisCBR described in Section
1 should be covered by the speech-based dialog with the system. All functions
that can be executed with the non-voice interaction methods, such as requesting
the next design action suggestion should be also possible to execute with a voice
command. In specific situations, a real dialog with the system should be possible
as well, for example to ask for improvement if certain conditions in the layout
look doubtful. Another example is providing the step-by-step guidance to the
user during a specific design task. Two different methods should cover both
situations described above: dialog trees and artificial neural networks (ANN). A
gateway selects the most suitable parsing method for the current user expression.
In Figure 4, an overview of the voice-controlled design support module is shown.
User Command
Expression Gateway
OR
Question
ANN OR
Answer NLG
Dialog
User Interface Tree
Figure 4. Overview of the voice-controlled design component.
An example of a dialog tree and instructions for implementation of ANNs
were already described in the aforementioned defended master thesis [7]. In the
bachelor thesis, it is planned to extend them, implement a proof of concept, and
comparatively evaluate using different automatically applied design scenarios.
3
http://veisen.de/metiscbr/roomconf/
5 Surveys and Reviews
Besides the conceptual and practical projects that directly extend the MetisCBR
framework, a number of thematically related approach surveys were assigned as
graduation projects as well. Their goal is to collect the knowledge available in
the research domain and so determine the position of the framework among the
related approaches. The following list describes the most notable reviews:
– MAS in Architecture [6] – This already defended master thesis examined
major monographies, journals, and conference proceedings to identify and
classify the existing and past MAS approaches that aim at supporting the
design phases in architecture. Using a specific self-defined classification, each
approach was assigned into a category and feature-wise compared with the
systems from this category. As a quintessence, the work suggests a meta-model
for MAS systems in Architecture, including the MetisCBR framework.
– Data Augmentation and Augmented Reality in Architecture – In this currently
ongoing masther thesis literature review, systems and approaches that enrich
architectural design with methods that introduce augmented reality and/or
make use of data augmentation for datasets are examined and classified. The
goal is to identify the relevant approaches that might provide a benchmark
for MetisCBR, as it is planned to extend the framework with augmented
reality features, e.g., to map the room layout onto different building contexts.
– Survey of Open Source CAD Systems [3] – In this defended master thesis,
the currently available open source tools for computer-aided design (CAD),
were examined. The goal was to estimate the acceptance of such tools in
comparison to proprietary tools and propose a holistic marketing strategy
that MetisCBR (if it will be decided to open source it completely) and other
approaches can use to find a proper position and user group on the market.
References
1. Broekens, J., Harbers, M., Hindriks, K., Van Den Bosch, K., Jonker, C., Meyer, J.J.:
Do you get it? user-evaluated explainable bdi agents. In: German Conference on
Multiagent System Technologies. pp. 28–39. Springer (2010)
2. Espinoza-Stapelfeld, C., Eisenstadt, V., Althoff, K.D.: Comparative quantitative
evaluation of distributed methods for explanation generation and validation of floor
plan recommendations. In: ICAART-2018. pp. 46–63. Springer (2018)
3. Kromm, E.: "Analysis of Status Quo of Open Source CAD Tools and Criteria for
Improvement of their Market Position". University of Hildesheim (2019)
4. Mikyas, A.: "Concept for Development of an Explanation Component for BDI Agents
to Support the Design Phases in Architecture". University of Hildesheim (2018)
5. Nunes, I.: Improving the design and modularity of bdi agents with capability
relationships. In: Dalpiaz, F., Dix, J., van Riemsdijk, M.B. (eds.) Engineering Multi-
Agent Systems. pp. 58–80. Springer International Publishing, Cham (2014)
6. Trabelsi, G.: "Multi-Agent Systems in Architecture – Classification and Evaluation
of Approaches for Distributed Agent-Based Support of Design Phases". University of
Hildesheim (2020)
7. Younis, B.: "Concept of a Dialog-Based Human-Machine-Interaction for the Domain
of Architecture Based on Modern AI Methods". University of Hildesheim (2020)