=Paper=
{{Paper
|id=Vol-1670/paper-63
|storemode=property
|title=Ontology-based Communication Architecture Within a Distributed Case-Based Retrieval System for Architectural Designs
|pdfUrl=https://ceur-ws.org/Vol-1670/paper-63.pdf
|volume=Vol-1670
|authors=Viktor Ayzenshtadt,Klaus-Dieter Althoff
,Syed Saqib Bukhari,Andreas Dengel,Ada Mikyas
|dblpUrl=https://dblp.org/rec/conf/lwa/AyzenshtadtABDM16
}}
==Ontology-based Communication Architecture Within a Distributed Case-Based Retrieval System for Architectural Designs==
https://ceur-ws.org/Vol-1670/paper-63.pdf
Ontology-based Communication Architecture
Within a Distributed Case-Based Retrieval
System for Architectural Designs
Viktor Ayzenshtadt1,3 , Ada Mikyas1 , Klaus-Dieter Althoff1,3 ,
Saqib Bukhari3 , Andreas Dengel2,3
1
University of Hildesheim, Institute of Computer Science
Samelsonplatz 1, 31141 Hildesheim, Germany
2
Kaiserslautern University
P.O. Box 3049, 67663 Kaiserslautern, Germany
3
German Research Center for Artificial Intelligence
Trippstadter Strasse 122, 67663 Kaiserslautern, Germany
{firstname.lastname}@dfki.de
Abstract The communication and cooperation of agents is one of the
key features of the multi-agent systems theory. In this work we discuss
how the agents can communicate by means of applying a domain-specific
ontology for the purpose of case-based retrieval of similar architectural
designs. The domain ontology and the corresponding communication
patterns are parts of the communication architecture of the distributed
case-based retrieval system MetisCBR. We also present a vision of the
results explanation component that enhances the existing architecture
with own patterns and concepts and is able to recognize the corresponding
contexts in search results returned by the system.
Keywords: case-based design, multi-agent systems, ontology, communication
1 Introduction
In multi-agent systems, the communicative interconnection of agents available in
the system is established by providing a communication module that is able to
transport messages from one agent to another. Modern FIPA-compilant1 multi-
agent frameworks like JADE [5] support the ontology-based communication of
agents [7]. This allows for a convenient way of implementing a communication
and cooperation component that is based on a domain-specific ontology where
concepts and relations can be appropriately selected for the given task.
In this work we present the communication architecture of MetisCBR [3],
the distributed case-based retrieval system for search of architectural designs,
1
The Foundation for Intelligent Physical Agents, http://fipa.org
developed in context of the Metis project (Metis – Knowledge-based search
and query methods for the development of semantic information models for
use in early design phases).2 This interdisciplinary project was initiated by
the DFKI (German Research Center for Artificial Intelligence) and the TUM
(Technical University of Munich) and unites the research areas of computer-
aided architectural design (CAAD), case-based reasoning (CBR), and multi-agent
systems (MAS). The project is funded by the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG).
This paper is structured as follows: first we present the related work in the
area of ontology-based agent communication. In the next section we describe the
current communication architecture of the system that consists of the communi-
cation ontology and the corresponding communication patterns. After that we
present a vision of results explanation module and the corresponding modification
of the communication architecture. Discussion and conclusion close this paper.
2 Related Work
To date much important work has been done in the domain of ontology-based
multi-agent communication. In this section we shortly describe some of the papers
that we consider inspirational and helpful for conceptualization and development
of our communication architecture.
The work of Steels [13] discusses the creation mechanisms of ontologies in
multi-agent systems by using a number of conventions adapted from biology
(self-organisation, selectionism, and co-evolution). These mechanisms are applied
to the agents domain in this paper. Steels’ general conclusion for co-evolution
is especially important for the purposes of our approach: a shared ontology
emerges during the communication and possesses abilities of dynamism and
incompleteness – dynamism allows for the extension of the ontology by new
concepts, incompleteness implies the possibility of communication with different,
yet compatible definitions.
Brena und Ceballos propose in [6] a hybrid approach that combines the
centralization and distribution of ontologies within a multi-agent system. In this
approach, a special ontology agent plays a role of a carrier of the complete ontology
and delivers the needed parts of it to other agents of the system that implement
only basic parts of the ontology and make requests for needed parts when required.
This approach gave us an inspiration to keep the main (meta) information of
the ontology centralized, and to distribute the parts for communication and
explanation into two separate ontology modules (see Section 4).
For the structure of the communication ontology (see Section 3.1), we took
the work of Zhan [14] as source of inspiration. In this work the layered ontology
is applied to the product design and analysis domain, the advantages of such a
structure (extensibility and domain-oriented efficiency) are described in [14] as
well. We adapted the main idea of this approach for the purposes of our domain.
2
Metis – Wissensbasierte Such- und Abfragemethoden für die Erschließung von Infor-
mationen in semantischen Modellen für die Recherche in frühen Entwurfsphasen.
3 MetisCBR Agents Communication
MetisCBR is a case-based search engine for retrieval of architectural building
designs that uses the application of Semantic Fingerprint [9] patterns to the design
instances during the search. The retrieval with fingerprint patterns (fingerprints)
is related to the concept of similarity footprints described in [11], the fingerprints
themselves are structured by means of applying the AGraphML specification [8]
to the designs. The cases (semantically transformed building designs) in the case
bases of MetisCBR are built with the specific domain model described in [2].
Being a distributed system, MetisCBR contains a number of (case-based)
agents that are able to communicate with each other in order to coordinate their
tasks, as well as to cooperate in order to achieve their common goal (find the most
similar cases for a given design query). To establish the communication between
the agents and to standardize the cooperation processes inside the system, a
special communication architecture was developed that governs the normalization
of the communication process. It currently consists of the specific communication
ontology created for the domain of retrieval of architectural building designs, and
the corresponding specific communication patterns that are based on the retrieval
tasks of the system and its agents. In the following sections we present the
structure of the communication ontology and the structure of the basic retrieval
communication pattern.
3.1 The General Structure of the Communication Ontology
The communication ontology is based on the concepts of MetisCBR’s domain
model, but contains some additional features (for example, a number of concepts
that are specific only for some particular agents or agent groups). The communi-
cation ontology is divided in three different layers (see Figure 1), where each of
them is used during the corresponding step of the retrieval process:
– Object Layer – This layer represents the general concepts of the query and
result objects that are being received from or sent to the user as the object
that is created or parsed by the user interface that is connected to MetisCBR.
Thus, this layer is used in the first and the last step of the retrieval.
– Data Layer – In this layer the query and result objects are decomposed
into the data representations according to the CBR domain model [2] of the
retrieval system. The architectural design concepts FLOORPLAN, ROOM
and EDGE from the model will be represented with their corresponding
ontological equivalents (metadata, rooms and edges data) and will be used
during the actual retrieval steps of the complete retrieval process.
– Action Layer – This layer is responsible for representation of categories of
actions that agents of the system are able to execute. For example, to parse
and transform the query object into an ontological representation, resolve
the query using the given retrieval strategy, forward the query (or its parts)
to other agents, or to construct and save a concept instance that will be used
to represent a case for the retention component of the coordination agent.
Object
Object Layer
Query Result
Data Layer Query Result
Rooms Data Edges Data
Metadata Metadata
Action Layer
Action
Figure 1. The current general structure of the communication ontology.
To utilize the ontology in order to communicate with each other, the agents
of the system use communication patterns that are based on the concepts of
the ontology. Communication patterns consist of steps that are named after
the action class that contains the action the agent is requested to execute. The
patterns can contain further sub-patterns. Following components are required for
construction of a communication (sub-)pattern:
– Action Class – The category of the action to be assigned. Strategical system
restrictions specify which actions an agent is free to execute when requested.
– Actor – The local identification address of the agent that is requested to
perform the selected action.
– Purpose – The goal(s) of the action the agent is requested to accomplish, if
it has committed or was assigned to this task.
– Content – An ontological object (for example the rooms data or a list of
result floorplan IDs) that the agent uses as information source to accomplish
its task. Can also contain further objects or references to objects.
3.2 Retrieval Communication Pattern
In this section we demonstrate how the agents communicate with each other
using the communication patterns of the system. We show it by providing a
detailed description of the steps of the general retrieval communication pattern.
The communication flow of this pattern involves almost every agent type available
in the system (except the case base maintainer agent). This pattern is the basic
pattern of the communication and cooperation and uses almost all of the available
action classes and ontology concepts to establish the undisturbed communication
process during the retrieval. Figure 2 shows the graph-based representation of
the structure of the pattern that consists of the following steps:
– XHR – The purpose of this action class is the transmission of the user query
in XML format for later parsing and resolving. The actor (receiver of the
query) in this case is the coordination agent (denoted as Coord. in Figure 2).
Parse Queries
Coord. GraphML
Timeout
e t
Sav eo u
Forward Tim
XHR Sub-
Query Solve CBR
Coord.
Forward Manager
Gateway Coord.
Result
Result
Result
Skip Solve
Fo
rw
ard
Forward CBR Forward CBR
Coord. Manager Retrieval
e
Sav
Sub-
Coord.
Figure 2. The graph-based representation of the retrieval communication pattern. The
node labels denote the agents, the edge labels denote the action classes (steps).
– Parse – The receiver (actor) of the parsing request is the corresponding
parsing agent (GraphML) that transforms the XML-formatted query into
the ontological query in SL language format.
– Forward – This action class is intended to be used for forwarding of the
content (ontological query or result) to other agents. Any agent can be a
sender or receiver of this kind of task.
– Solve – The purpose of this action category is to request an appropriate agent
to resolve the ontological building design query (or its parts) and to find
results in a case base of such designs. A manager agent or a retrieval agent
can be requested to accomplish this task.
– Timeout – The Timeout agent that receives the request containing this action
class is asked to add the current retrieval task to the list of active tasks and
to check periodically if the query is expired (not resolved during the given
amount of time).
– Skip and Save – These two classes are related to each other and are intended
to be used in the case learning sub-pattern (enclosed in the dashed areas in
Figure 2), which is a sub-pattern of the retrieval communication pattern and
is used during the retention step of the Coordinator’s internal CBR cycle,
which is the step that helps to find the most similar previous query to the
current one (this task is delegated to the SubCoordinator). The internal case
base of Coordinator consists of the previous queries and is filled by means
of applying the IB2 algorithm [1], in the way that only unique queries (and
corresponding results) will be saved there. If the current query is identical to
a previous one, the Skip action will be sent to the Coordinator to indicate
this fact. The Save action class will only be used if the current query is not
identical to a previous one. In this case the query will be saved before the
actual retrieval starts, the results after the retrieval has been finished.
4 Vision of Results Explanation Module
Explanations are one of the core elements in user-centered CBR applications.
Foundations, perspectives, and goals of explanations in CBR are described in [10]
and [12]. In this section of the paper we present our vision of the extension of
the MetisCBR system with an explanation module (see Figure 3) that contains
its own explanation ontology. This module is currently being conceptualized in a
bachelor thesis. Our general idea is to combine two separate ontology modules (the
communication ontology and the new explanation ontology) into a system ontology
(where the main meta information about these modules is kept permanently),
but to use their concepts separately for the corresponding communication and
explanation tasks. The explanation ontology will be used for the corresponding
explanation patterns (that will have a structure similar to the communication
patterns described in Section 3.2) and connected to a specific explanation engine
that can use this patterns to work with different contexts (i.e., recognize if some
results have one or more contexts as common criteria) and return an explanation
of the retrieval results (based on these recognized contexts) to the user (an
architect). The contexts can represent different semantic fingerprints or other
criteria (for example, some of the floor plan results can belong to the same
building, i.e., have the common building ID). It should be possible to have the
permanent contexts (saved in the explanation ontology) as well as the temporary
contexts that are specific only for the current search process.
Results can be grouped
Result set by different contexts
Result 4
Contexts
Result 1
Result 3
Result 2
Explanation
Engine
System
Ontology
Explanation
Ontology
Communication
Ontology
Figure 3. The current vision of the results explanation component for MetisCBR.
5 Discussion
The whole potential of the ontology-based agent communication architectures is
not fully explored, but is used often to provide a basis for template- or pattern-
based communication and cooperation among the agents contained in the system.
In our retrieval system the ontology plays a role of the layer-structured relational
vocabulary of objects and corresponding action classes that can be used by
appropriate agents to request an action that needs to be executed for the current
retrieval task.
In the evaluations of MetisCBR conducted to date (for example in [2] and
[4]), and also during the development process, the ontology-based communication
architecture showed a good performance (currently the size of the communication
ontology does not allow for conducting of the performance test for the ontology
only, so that the performance could only be estimated in context of the complete
retrieval process, but no technical ontology-related issues worthy of mention were
detected during the evaluations). The clearest advantage of such an architecture is
the possibility to extend and restructure the underlying structure of concepts and
actions by adding the new ones and/or deleting/editing the currently available
ones. Though extensible, a certain technical limitation of the ontology scope exists
as well, characterized by non-extensibility of the number of actions available for
each of the agents at the runtime of the system.
6 Conclusion and Future Work
In this paper we presented the current communication architecture of MetisCBR,
a distributed case-based retrieval system for search of semantically represented
architectural designs. In this architecture the communication ontology plays a key
role by providing a communication and cooperation basis for the agents of the
system. We showed that the communication relies on the special communication
patterns and provided and explained in detail an example of such a pattern
(the basic retrieval communication pattern). We also provided our idea of how
the concept of such a communication architecture can be used and adapted by
an explanation module that is able to detect certain contexts in the result sets
returned by the retrieval system, and how we can combine the ontologies of both
communication and explanation.
In our future work on our case-based retrieval system we will concentrate on
finalizing of the conceptualization of the above named explanation component
and include it as a permanent part of the retrieval system and the corresponding
retrieval process. Elaboration and extension of the available contexts, in order
to improve the context recognition, will also be part of our future work in this
area. The further development of other parts of the retrieval system, for example,
implementation of new retrieval methods, will also be continued.
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