=Paper=
{{Paper
|id=Vol-2846/paper12
|storemode=property
|title=Decision Support combining Machine Learning, Knowledge Representation and Case-Based Reasoning
|pdfUrl=https://ceur-ws.org/Vol-2846/paper12.pdf
|volume=Vol-2846
|authors=Carlo Mehli,Knut Hinkelmann,Stephan Jüngling
|dblpUrl=https://dblp.org/rec/conf/aaaiss/MehliHJ21
}}
==Decision Support combining Machine Learning, Knowledge Representation and Case-Based Reasoning==
https://ceur-ws.org/Vol-2846/paper12.pdf
Decision Support combining Machine Learning, Knowledge
Representation and Case-Based Reasoning
Carlo Mehlia, Knut Hinkelmanna,b and Stephan Jünglinga
a
FHNW University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600 Olten,
Switzerland
b
University of Pretoria, Department of Informatics, Pretoria, South Africa
Abstract
Knowledge and knowledge work are essential for the success of companies nowadays.
Decisions are based on knowledge and better knowledge leads to more informed decisions.
Therefore, the management of knowledge and support of decision making has increasingly
become a source of competitive advantage for organizations. The current research uses a
design science research approach (DSR) with the aim to improve the decision making of a
knowledge intensive process such as the student admission process, which is done manually
until now. In the awareness phase of the DSR process, the case study research method is applied
to analyze the decision making and the knowledge that is needed to derive the decisions. Based
on the analysis of the application scenario, suitable methods to support decision making were
identified. The resulting system design is based on a combination of Case-Based Reasoning
(CBR) and Machine Learning (ML). The proposed system design and prototype has been
validated using triangulation evaluation, to assess the impact of the proposed system on the
application scenario. The evaluation revealed that the additional hints from CBR and ML can
assist the deans of the study program to improve the knowledge management and increase the
quality, transparency and consistency of the decision-making process in the student application
process. Furthermore, the proposed approach fosters the exchange of knowledge among the
different process participants involved and codifies previously tacit knowledge to some extent
and provides relevant externalized knowledge to decision makers at the required moment. The
designed prototype showcases how ML and CBR methodologies can be combined to support
decision making in knowledge intensive processes and finally concludes with potential
recommendations for future research.
Keywords 1
Case-Based Reasoning, Machine Learning, Decision Support, Knowledge Management,
Knowledge Representation, Knowledge-Intensive Process
1. Introduction
Knowledge and knowledge work are essential for the success of companies nowadays. It is
observable that there is a shift from routine work to knowledge work [1]. Since decisions are based on
knowledge and better knowledge leads to more informed decisions, governance of decision making has
increasingly become a source of competitive advantage for organizations [2]. Decision making often
occurs in knowledge-intensive processes, where the knowledge is responsible to determine an output
[3]. This process is also referred to as decision-making process and can be classified as a knowledge-
intensive process. To some extent, decisions are guesses about the future based on information available
1
In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI 2021 Spring
Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) - Stanford University, Palo Alto, California,
USA, March 22-24, 2021.
E-mail: carlo.mehli@students.fhnw.ch, knut.hinkelmann@fhnw.ch, stephan.juengling@fhnw.ch
ORCID: 0000-0002-1746-6945 (K. Hinkelmann), 0000-0002-2969-7257 (S. Jüngling)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
at the time of the decision. As complexity builds up, decision-makers often have to rely on their intuition
and judgement [4]. Therefore, the knowledge of the involved decision-makers is often implicit and not
accessible for others.
The transfer and flow of knowledge between different knowledge-workers is crucial to achieve a
successful process completion [5]. Researchers propose many different approaches such as Ontologies,
Knowledge Management, Artificial Neural Networks, Case Based Reasoning (CBR) etc. to support
various knowledge intensive processes (KIP) and decision makings [3, 5–9]. CBR has been proven to
be useful for decision support in several use cases, in particular also in business processes [10–13]. It
helps people to make knowledge-based decisions by showing appropriate previous cases to be
considered. It augments people’s ability of reasoning, since people are bad at remembering details from
past cases [14]. However, knowledge acquisition for CBR systems is still an issue, since case attributes
and similarity measures have to be defined when designing the system [13]. Furthermore, CBR systems
are not able to deal with large amount of data [14]. Machine Learning (ML) on the other hand is able
to deal with large amount of data and offers an approach to learn from data autonomously. It can identify
implicit regularities based on data of previous cases and build a classification model accordingly [15].
However, the results usually lack of explainability and therefore transparency for the decision maker
[16]. Furthermore, Machine Learning needs large amount of data and does not inherit general domain
knowledge [17].
The objective of this research is to identify the criteria for knowledge support in a concrete
application scenario and develop an appropriate combination of CBR and ML, which helps to use the
knowledge of previous cases based on an explainable set of human interpretable rules, supplemented
by ML which can generate recommendations from a subsymbolic perspective, in order to implicitly
learn the patterns based on the features from all previous cases. After a short literature review that refers
to related work in supporting human decisions in knowledge intensive processes, the particular
application scenario of the student admission process as well as the improvement potential is described
in the chapter 3 and 4. The system design of the prototype shows how the aspects of knowledge
engineering are combined with machine learning, followed by an evaluation, outlook and conclusion.
2. Literature
The importance of knowledge for companies, as well as for decision making, is well described in
the literature [18, 19]. However, since even the definition of knowledge remains research worthy, the
management of knowledge within companies and processes is still a challenge. The shift from routine
work to knowledge work and the increasing importance of knowledge-intensive tasks and processes are
crucial for businesses [8, 19]. Making the right knowledge available and accessible at the right time
usually leads to better decisions [4]. Several dimensions of knowledge have been identified, some of
which cannot easily be explained and shared [20, 21]. Therefore, knowledge needs to be identified first
in order to manage it. However, not all types of knowledge can easily be extracted and managed, which
is a particular challenge for tacit knowledge. For decisions to be consistent and knowledge to be shared
and reused, tacit knowledge needs to be somehow made explicit. While knowledge-based decisions are
usually better than intuition-based decisions (in an organizational environment) and knowledge-based
decision making can be supported and fostered by providing the right methods, it is not clear which
methods and systems are best to support certain kind of decisions [3]. Several knowledge management
strategies and decision support methods are suggested for certain use cases [11, 22–24]. There are many
use cases in literature which use knowledge-based systems such as CBR for decision support [10–13].
Machine Learning is considered in decision support, often in combination with knowledge-based
systems [15, 17, 25]. The different strengths and weaknesses of the approaches can be beneficial when
implemented for certain scenarios. However, it is not clear which combination is suitable for a specific
decision and knowledge need. Therefore, the analysis of a specific knowledge-intensive business
process and knowledge flow with the process-oriented knowledge management methodology can
provide more insights if there is a possibility to integrate intelligent systems and identify patterns in the
decision-making scenario.
3. Application Scenario
In order to get a holistic view on how CBR and ML can be combined to support decisions, the
knowledge-intensive process of student admission for a Master’s program at FHNW University of
Applied Sciences and Arts was analyzed in depth.
Universities are nowadays operating in a complex and competitive environment. Alongside
identifying their uniqueness and building a sustainable strategy, one of the challenges for modern
universities is to admit the right students [26]. The decision about a candidate’s admission is crucial for
the university as well as the student, and the decision is therefore taken with the appropriate caution.
Every candidate’s profile and application documents are analyzed individually and there is no general
solution applicable for all cases. As the current admission process does mostly rely on human experts
applying their judgment and intuition, transparency and consistency of the decision is not always given.
Moreover, the applicants come from various countries with different educational systems, grading
schemes etc., which can lead to extensive research in order to fairly assess and compare the applicants’
education background. The research can take up to several days, increasing the processing time of a
case significantly. Since decisions and research about previous cases are not formally recorded, it is
also possible that the research is done multiple times for similar cases while the decisions can vary, and
the outcomes be different.
However, since the process and the decision about admission of a candidate should be fair for all
candidates, the decision-making aims to be consistent and the outcome for two candidates with similar
professional background and education should be the same. Since the university admission process
heavily depends on knowledge workers performing various interconnected knowledge-intensive
decision-making tasks, the process itself is considered a knowledge intensive process [27].
To get a better awareness of the problem within the application scenario, multiple methods from
case-study research such as interviews, assessment of data, observation of involved parties regarding
decision making and a business decision maturity assessment of the business decision management
were assessed. The case study resulted in the formalization of the admission process and the
identification of decisions and corresponding knowledge used for decision making. The knowledge
sources were further analyzed regarding knowledge coordination, knowledge development and
classified in knowledge types. Finally, issues regarding the knowledge management and business
decision management were identified.
The case study has shown that the knowledge within the admission process is currently not
sufficiently managed. Despite the decision makers having a lot of knowledge, the tacit knowledge is
not captured and therefore bound to the decision-maker. Furthermore, knowledge from previous cases
or investigations is not managed or stored resulting in risk of losing knowledge if people leave or are
not available. However, the interviews with process participants also showed, that they would
appreciate appropriate knowledge about previous cases to support decision making, if no substantial
additional work is required to maintain or enter the data. The assessment also points out, that there is a
lack of tool integration within the process. Several tools and sources are used, and data must be
manually entered several times for different systems. This is not only time consuming and error prone,
but also leads to duplicity of data, making it harder to analyze previous cases. The following issues and
knowledge management methods were outlined by the case study:
• There is currently no defined knowledge management strategy applied in the application process.
• No knowledge base or tool other than spreadsheets is currently used to capture structured
knowledge.
• Decisions are often based on intuition and experience of executors.
• Knowledge exchange is mainly done by socialization on request, leading to knowledge staying tacit
even after transferring the knowledge.
• Most knowledge sources are tacit knowledge which is currently not externalized in a structured
manner.
• There is no modelling standard, performance measure or decision model applied during the
admission process
• There is a risk of losing knowledge throughout the process, as well as in case of changing personal.
Based on the insight gained by the case study, strategies to better use existing knowledge and
therefore support and manage decision making within the student admission process were evaluated
and developed.
4. Methods for improvement
As the assessment of the current admission process revealed, that there is a vast amount of
knowledge available among the decision-makers. However, this knowledge is currently not capitalized
and neither actively managed.
Three elements People - Process - Technology have been identified as the important influencers for
successful knowledge management [28]. As the assessment of the admission process showed, there is
currently no knowledge management process in place and no tool or software implemented to support
knowledge management and decision making. The People component in the admission process seems
not to be the issue, since the decision makers collaborate and share their knowledge. In order to improve
the decision support and implement a knowledge management strategy in the admission process, the
focus should be on the component Technology.
In order to achieve the goals of knowledge management, there are several challenges that must be
faced. The challenges in knowledge management outlined by [29] do also apply for the admission
process. In order to support the decision making and manage the knowledge in the student admission
process, the supporting methods must be able to face the challenges.
The decisions within the admission process require different knowledge. As previously described,
the challenge is not only to acquire the knowledge, but also to provide the right knowledge to the right
person at the right time in an understandable way. Therefore, we assessed what kind of support methods
are suitable for the decisions within the admission process.
Since the decisions require different knowledge and the current knowledge is available in different
types and comes from different sources, the requirements and appropriate methods to support the
decision making differ. The suitability of the method depends on the decision itself. Knowledge based
systems provide consistency in decision making and make expert knowledge available to less
experienced personal [30]. They provide the capabilities required to achieve the previously defined
goals of knowledge management.
How a knowledge-based system is built, and what techniques are used are dependent on the situation
and the AI techniques available [31]. Knowledge-based systems consist of a knowledge base, which
represents the knowledge either as rules or as cases [32]. Therefore, there is in any case a need of a
knowledge base in order to store and reuse the knowledge within the admission process.
The intuition and experiential knowledge of the knowledge workers, which are mainly tacit
knowledge, cannot be easily described. Among the methods that have been proven to be useful for
knowledge acquisition as well as decision support, are CBR and ML. Both having different strengths
and therefore can be applied in different scenarios for different kind of knowledge. Several approaches
and designs of CBR and ML exist for certain use cases. However, the parallel implementation of both
methods is rarely considered.
As outlined in the case study of the admission process, the decision makers currently often use
experiential knowledge to derive their decisions. This indicates that the experience and therefore the
knowledge gained of past cases and decisions, is relevant for the decision making. CBR uses analogical
reasoning methods to learn from previous cases and present similar cases. However, while humans are
prone to not remember all aspects of each previous case, CBR can support human decision making by
presenting the decision maker similar cases. In the admission process, this can be crucial in order to
support consistent decision making. Furthermore, CBR keeps the instances (cases) in its original form
and does not generalize from past cases, making it able to maintain explainability and transparency.
However, in situations where knowledge must be learned inductively or universally valid rules need to
be deducted from data, CBR is not suitable. In such situations, ML is suitable since it is able to learn
from situations and build a generally valid model. It is especially useful in areas where not all situations
can be defined upfront, or tacit knowledge cannot explicitly be explained by the decision maker, but
data of previous decision is available. While CBR finds similar past situations, ML builds a model
based on data in order to apply the model to newly entered data.
The decisions in the student admission process were analyzed with regards to the aforementioned
strengths of the AI methods, in order to identify the best supporting method for each decision.
Furthermore, as described in the case study, the current knowledge is available in different types and
has different forms of coordination. In order to assess the decision support method, it was determined
in what form the required knowledge is available and how the knowledge can be transferred. Hence,
the suitable knowledge management strategy for the decisions within the admission process was
analyzed (see Table 1).
During the analysis of the admission process application scenario it was observed that individuals
tend to apply a certain CBR method implicitly while performing knowledge-work oriented tasks.
Therefore, it is reasonable to conclude that individuals are already familiar with this type of problem-
solving method.
Table 1 Suggested decision support method and knowledge management strategy per decision
Ontologies have been proven useful in combination with CBR and especially in knowledge-
intensive environments. Therefore, with regard to business applications, the use of an ontology in an
ontology-based CBR approach can be regarded as suitable for the application scenario of a student
admission process.
With regards to the identified knowledge management challenges, the following suggestions for the
system design were derived by reviewing literature of proposed system designs [23, 33, 34]:
• Structural ontology-based CBR to address knowledge intensity of the process and bridge the gap
between knowledge acquisition and retrieval
• Viewpoint based implementation to satisfy different knowledge needs per decisions and
participants to address the challenges of knowledge publishing and retrieval
• Artificial Neural Networks to learn a model to support grade conversion
While the suggested decision support methods described for each decision the rationale of what method
is best used, they did not cover architecture and design specific detail questions and practical
applicability in a real-world setting. This will be examined in the next chapter.
5. System Design
Based on the identified support methods and the insights gained in the case study, a system design,
user interface design and a ML prototype were developed.
5.1. Case-Based Reasoning
As suggested by several researchers [12, 33, 35] the ontology-based structural CBR approach is a
successful method in knowledge-intensive environments. Based on the case study of the student
admission scenario and the identified decision support methods, the following high-level objectives for
the ontology-based CBR system design have been derived:
• A single case base should be used for all CBR-supported decisions
• The case content should contain an update functionality in order to capture knowledge of previous
situations and investigations (new cases of university recognition, how to convert grades etc.)
• The case should be able to contain information or at least link to data objects and documents (e.g.
CV, motivation letter)
• The case should be described in a structured way to allow analysis and filtering
In order to build the case-base for CBR, the relevant case attributes, needed to retrieve cases and
base decisions on, were defined. In the scenario of the admission process, a case represents a single
student admission. We applied an ontology-based and viewpoint-based CBR approach as suggested by
Martin et al. [25]. Based on the identified attributes the ontology for the admission process was
developed using OWL. In Figure 1 the graphic representation of the ontology is displayed. The used
ontology language OWL provides a scheme containing different properties, which are used to determine
the similarity of the case.
Figure 1: Admission process ontology illustrated as a graph
For an ontology-based CBR approach, the main task is to compute the similarity of properties to
present similar cases to the user. In the structural ontology-based CBR approach, the instances and
relations representing the case characterizations of the learned cases and the query case are compared.
In Figure 2 the principle of case comparison using case characterization is schematically illustrated.
Figure 2: Schematic illustration of case comparison in structural CBR (adapted from [23])
As mentioned in the previous chapter, a viewpoint-based CBR approach is applied. The applied
viewpoints allow the relevant cases to be retrieved by the same CBR-system from a singular case base,
while considering the individual information need of the decision and decision maker. Hence, for each
decision a viewpoint is applied using different attributes and similarity weights for the retrieval of
similar cases. In the admission process viewpoints are the equivalence of a specific decision within the
admission process, such as the grade conversion, the recognition of the university and the assessment
of the work experience. Depending on the viewpoint, different attributes are of interest and therefore
different cases are found. In Figure 3 the different viewpoints on a case is illustrated.
Figure 3: Viewpoint example on a case (adapted from [23])
Since for each viewpoint different attributes are relevant and have to be compared, the attributes per
viewpoint (decision) were identified. As not all attributes have the same relevance for the retrieval of a
case, the weight per attribute was defined. The weight is defined as 0 being not relevant, 10 being the
most relevant attribute for the similarity check. The identified attributes are presented to the user and
will be the basis of the decision. Therefore, it is also relevant to display attributes to the decision maker,
which are not relevant for case retrieval itself. Table 2 shows the relevant attributes and their weights
similarity check exemplary for one decision.
Table 2 Attributes and weights per viewpoint (decision) and their relevance to similarity calculation
5.2. Machine Learning Prototype
In order to support and partly automate the conversion of foreign grades to Swiss grades, a machine
learning (ML) prototype was implemented. The learning approach considers the data of past grade
conversions done by the process executors and the conversion tables, such as the grading scales of the
Universities of Hannover, Göttingen and Freiburg. All of these conversion tables are currently used for
the conversion of foreign grades. However, since the conversion tables are differing, the tables are only
used as indicators for the conversion. The final decision on how to convert the grade is currently up to
the decision maker and his/her experience. This experience could be capitalized by ML as previous
conversions can be used to train a ML model.
Since deep learning is known to generalize and should therefore be able to build a useful grade
conversion model, the prototype was realized with a simple (deep) artificial neural network (ANN)
[36]. The prototype was developed using freely available resources, such as the R programming
language and the Keras package.
A limitation of the prototype is the number of available data. There exists no dataset containing the
foreign grades and the converted Swiss grades of previous admission cases. Only the converted grades
have been explicitly documented. Therefore, the training set has been simulated by using the data of
previously mentioned conversion tables. The test dataset consists of grades of three countries (Germany,
India and South Africa). In order to assess the conversion, different grades of the said countries were
converted by the prototype, after training was completed.
The results of the ML-converted grades are listed in Table 3 and compared to the conversion tables.
The advantage of ML over conversation tables is, that ML learns the exact conversation key instead of
only providing a range, as shown in row 3. Furthermore, if the conversion models differ it takes into
account the different conversions. Therefore, the result takes into account all conversion tables, which
have currently to be checked manually by the decision maker.
Table 3 Results of ML-converted grades in comparison to conversion tables
The prototype has shown that the grade conversion can be supported by applying machine learning.
However, other ML-architectures could further be analyzed to assess whether the grade conversion can
be done more accurately.
The developed ML and CBR designs are not tangible for process executors, since the design is
rather technical. In order to assess the usability of the proposed system design, to make the proposed
decision support methods easily understandable for all knowledge workers and to show the integration
into the admission process, some examples of a possible user interface were created using mockups.
These mockups make it possible for the decision makers to understand how the system could support
them and how the decision process would change.
6. Evaluation
The evaluation was done by triangulation using decision maturity assessment, qualitative interview
and performance analysis. The Business Decision Maturity Model (BDMM) [37] was conducted to
assess the impact of the designed support system on the admission process and its decision management
and to point out in what areas the proposed methods and system design could improve the decision
maturity. A qualitative evaluation in form of interviews with knowledge-workers were conducted in
order to assess the applicability and the impact of the system in the application scenario. Finally, the
performance of the ML-prototype was tested by comparing the results of the automated grade
conversion to the results derived by the currently used conversion tables.
The results of the different evaluation methods revealed that the proposed system design can support
knowledge management and decision making in the knowledge-intensive decision process of student
admission. The conducted maturity assessment analyses the system from a business decision
management perspective:
• The results of the assessment show that the system can in fact improve the business decision
management of the admission process. This improvement is mainly due to the fact, that by
implementing the system the decisions can be retraced, knowledge is shared, and the decisions are
therefore more consistent and transparent.
• Also, the results of the interviews outline that the system is able to improve the knowledge
management in the admission process and supports decision makers in their decision-making
process. Due to the automated case retrieval, it provides decision makers with the relevant
knowledge at the right moment. The system augments the natural thinking process by finding
similar cases and allows decision makers to document and therefore share their knowledge.
• It was furthermore argued that the system increases confidence in the correctness of preceding
decisions done by other decision makers.
• Finally, also the data driven results of the Machine Learning prototype evaluation point out that the
prototype can be used to support conversion of grades.
The combined results of the different evaluations indicate that the proposed system design addresses
most of the current issues of the admission process. The only issue, which is not addressed by the
system, is the definition of modelling and performance standards. However, the evaluation shows that
the applied methods to address the identified issues are suitable.
When the applied support methods (CBR / ML) are compared to the identified types of knowledge
in the admission process, there can be no clear causality seen between the knowledge type and the
applied supporting method. It becomes clear, however, that ML needs explicit, structured data in order
to learn a valid model. Furthermore, ML can only be applied in cases in which a generalization based
on the training data is desired. In many decisions of the student admission process, a generalization is
however not suitable. The evaluation of the applied methods compared to the knowledge also outlines,
that CBR is especially useful in cases in which experiential knowledge is applied for decision making.
However, the suitable method to support a certain decision still remains use case specific and is
dependent on many factors such as available data, kind of decision, available knowledge, decision
making process etc. Nevertheless, the decision making within a knowledge-intensive decision process
can be improved by combining CBR and ML, can be considered proven by the results of the evaluation.
7. Conclusion and Outlook
The management of knowledge and the choice of most appropriate methods to support the decision-
making process is regarded as a daunting task throughout the literature [1, 14, 38]. This is due to the
fact that knowledge occurs in different forms and can be difficult to describe [5]. Furthermore, the
methodology mix to support a specific knowledge intensive process and its decisions, remains use case
specific [12, 25, 39, 40]. Therefore, there is still a considerable lack of specific decision support design
patterns that fit best for specific knowledge-intensive and complex decision problems. In order to
potentially identify a correlation between decision or knowledge patterns and different supporting
methods and tools, a system design including the combination of CBR and Machine Learning approach
has been proposed and applied to the application scenario of the student admission at our university.
The collected data through maturity assessments and qualitative interviews was used to evaluate the
applicability of the proposed system design for the chosen application scenario. The result of the
evaluation has shown that decision making within a knowledge-intensive decision process for
university student admission can be improved by combining Case-Based Reasoning and Machine
Learning.
The conducted user experiments with the decision makers revealed that the proposed system design
supports most information needs for their decisions. The conducted maturity model also confirmed that
the system design improves various factors of decision making and knowledge management in the
admission process. Namely, the system can contribute to reduce the risk of knowledge loss, increase
the transparency of decision making, enhance knowledge sharing among decision makers and support
consistent decision making. Furthermore, existing tacit knowledge can be made explicit and duplication
of work in the time-intensive research activities can be reduced due to previous admission cases with
the help of CBR.
CBR is especially useful in cases where experiential knowledge is applied and can augment the
natural thinking process of the decision maker. ML on the other hand is suitable to support decisions,
in which training data is available, generalization is desired and no detailed explanation of the derived
result is required. Hence, CBR and ML can complement each other and can be combined in various
ways, which eventually can lead to specific design patterns in knowledge-intensive decisions processes.
However, more data and experience need to be collected. The current prototype has shown promising
results in a real-world setting, how some of the knowledge management issues can be solved and how
the consistency and transparency of the admission process can be improved.
The research has shown that [[29]29][29]a desired shift from intuition-based decision making, to
fact-based decision making as described by [18], can be achieved by providing clearly arranged,
sufficient knowledge to the decision maker. Finally, the research also validated theory [3] that the
coordination and support of single decisions within a business process leads to more business value
creation.
As the scope of this research was limited to the knowledge-intensive decision process in a university
student admission scenario, it could investigate into a feasible combination of CBR and ML for this
specific problem only. However, more research remains to be done in order to work out best practices
in how to combine and better understand the correlations of both approaches. The final goal, to design
reusable decision systems that can serve as AI-related design patterns for knowledge-intensive decision
processes, will need further investigations.
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