INTELLIGENT SOLUTION EVALUATION BASED ON

ALTERNATIVE USER PROFILES

Georgios Bardis

Laboratoire Méthodes et Structures Informatiques – MSI, Faculté des Sciences, Université de Limoges

83, rue d’Isle, 87060 Limoges cedex, France

Technological Education Institute of Athens, Department of Computer Science,

Ag.Spyridonos St., 122 10 Egaleo, GREECE

Georgios Miaoulis

Technological Education Institute of Athens, Department of Computer Science,

Ag.Spyridonos St., 122 10 Egaleo, GREECE

Dimitri Plemenos

Laboratoire Méthodes et Structures Informatiques – MSI, Faculté des Sciences, Université de Limoges

83, rue d’Isle, 87060 Limoges cedex, France

Keywords: Machine Learning, Multicriteria Decision Making, User Modeling

Abstract: The MultiCAD platform is a system that accepts the declarative description of a scene (e.g. a building) as

input and

generates the geometric descriptions that comply with the specific description. Its goal is to

facilitate the transition from the intuitive hierarchical decomposition of the scene to its concrete geometric

representation. The aim of the present work is to provide the existing system with an intelligent module that

will capture, store and apply user preferences in order to eventually automate the task of solution selection.

A combination of two components based on decision support and artificial intelligence methodologies

respectively are currently being implemented. A method is also proposed for the fair and efficient

comparison of the results.

1 INTRODUCTION

The continuously increasing performance of modern

computer hardware has made available software

features that used to be prohibitive in terms of

required time and complexity a few years ago.

System developers are now not only willing but also

able to design and implement powerful

environments, rich in characteristics, capable of

producing vast numbers of results in limited time.

Nevertheless, the diversity of the user basis as well

as the increased complexity and power of software

systems call for intelligent features that will adapt

the environment to each user’s characteristics.

Personalized system behavior with respect to an

individual user’s profile facilitates its use, increases

both user and system efficiency and improves

quality of the results.

Adoption of user preferences for intelligent

stem response has been presented in numerous

efforts in the area of hypermedia and the WWW,

e.g. (Brusilovsky 01), (Chen 02), (Pazzani 97),

(Soltysiak 98). Incorporation of user preferences in

geometric representations is presented in (Essert-

Villard 00), where the user submits a set of constants

together with a sketch from which the system

extracts additional solution restrictions as well as in

(Joan-Arinyo 03) where a genetic algorithm is

periodically aided by the user to produce solutions

closer to the latter’s preferences.

On the other hand, the notion of multicriteria

aluation of building assemblies has also been

Bardis G., Miaoulis G. and Plemenos D. (2005).

INTELLIGENT SOLUTION EVALUATION BASED ON ALTERNATIVE USER PROFILES.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 74-82

DOI: 10.5220/0002523900740082

 SciTePress

Figure 1: A Typical MultiCAD Session

presented in (Nassar 03), based on AHP calculated

weights and a heuristic evaluation algorithm, but

machine learning and user preferences have not been

discussed.

Our work proposes a component for intelligent

solution evaluation according to user’s preferences

in a declarative description environment. The

proposed component combines multicriteria decision

support and machine learning techniques for user

modeling requiring only qualitative feedback on

behalf of the user instead of exact geometric

properties.

2 THE MULTICAD

ENVIRONMENT

The MultiCAD system, presented in detail in

(Miaoulis 02), was introduced as a platform

supporting declarative object modeling (Plemenos

95), thus assisting the transition from the intuitive to

the geometric object representation. The described

system is a complete design environment including

modules for validation of the object description,

storage and maintenance of the solutions produced,

etc. Subsequent works have successfully

implemented most of the described modules as well

as additional ones that have evolved from this initial

design – solution generation using CSP (Bonnefoi

02) or genetic algorithms (Vassilas 02), concept

modeling and ontology (Ravani 04), incorporation of

architectural styles (Makris 03) and collaborative

design (Golfinopoulos 04). Solution evaluation

based on user preferences in this context was first

introduced in (Plemenos 02) proposing a system

based on the representation of each scene by a

dedicated neural network. A new approach towards

a user profile module was presented in (Bardis 04)

describing two methods for user modeling and

solution evaluation: a method based on the

multicriteria nature of the problem and one relying

on a neural network for the representation of each

user’s preferences.

The current work continues towards this

direction by presenting the final design of the

specific module – an approach incorporating

multicriteria decision support and machine learning

techniques – and the current stage of the

implementation. Moreover, a set of criteria

regarding alternative methods performance is

introduced, that will serve as the basis for future

testing and adjustment of the implemented module.

3 THE INTELLIGENT USER

PROFILE MODULE

Figure 1 shows a typical session of the current

implementation of MultiCAD where the declarative

description of a scene appears together with the

INTELLIGENT SOLUTION EVALUATION BASED ON ALTERNATIVE USER PROFILES

3. Solution

Visualisation

5. Attribute Values

Extraction

4. Human

Solution

Evaluation

6. Automatic

Solution

Evaluation

User Profiles

Database

7. Intelligent

User Profile

Update

1. Scene

Description

Delarative

Representation

2. Solution

Generation

Geometric

Representation

0. User

Profile

Initialisation

3. Solution

Visualisation

5. Attribute Values

Extraction

4. Human

Solution

Evaluation

6. Automatic

Solution

Evaluation

User Profiles

Database

7. Intelligent

User Profile

Update

1. Scene

Description

Delarative

Representation

2. Solution

Generation

Geometric

Representation

0. User

Profile

Initialisation

Figure 2: User Profile Module - Block Diagram

visualization of one of the corresponding solutions.

The diagram in Figure 2 concentrates on the

integration of the User Profile Module to the

MultiCAD platform.

This version of the system applies user profile

information only after the geometric representations

of the described objects have been generated, i.e. not

during their generation. The current stage of our

work focuses on the construction of those modules

that are immediately connected to the user profile

component of the system.

Each declarative scene description may lead to a

few thousands geometric representations complying

with this description: the solutions. However, not all

solutions are equally preferred by the user. Our

intention is to eliminate those solutions not

conforming to the user's preferences. Ideally, this

has to happen with minimal or no user intervention.

In particular, optimal user profile incorporation to

the solution visualization process will maximize

solution visualization throughput (SVT) (Bardis 04)

and minimize the user intervention at later stages. In

order to achieve this we have to resolve the

following inter-connected problems:

• Solution representation and evaluation.

• User preferences modeling and representation.

4 SOLUTION REPRESENTATION

The geometric representation of each solution is

translated to a set of attributes. This is a need that

arises mainly from the fact that we have to reduce

the complexity of the representation of each solution

in order to be able to submit it as input to a neural

network. In addition, this approach complies with

the multicriteria decision methodologies (Vincke

92), (Goodwin 98), in particular, their requirement

to request the user’s evaluation through a limited set

of object attributes instead of numerous geometric

properties. We choose to observe a minimal set of

attributes (Fribault 03), (Bardis 04). This set will be

extended or revised in the future, since, for the

moment, we care more for the development of a

prototype covering all stages of the MultiCAD cycle

(Miaoulis 02) instead of capturing all possible

aspects of user preferences with respect to a building

assembly. The attributes we have chosen are based

on geometric characteristics of each solution and,

therefore, can be easily extracted by its geometric

representation.

In particular, the observed attributes for any

solution S

are:

= Number of bedrooms

= Number of bathrooms

= Night-zone area

= Day-zone area

NDS

= Night-zone / Day-zone separation

SWB

= Existence of at least one south-western

bedroom

Therefore, each solution S

is represented by a

vector of values:

= ( BD

, BT

, NA

, DA

, NDS

, SWB

for example

= ( 2, 3, 52.4, 40.8, Partial, No).

ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

USER

DECLARATIVE

DESCRIPTION

DECLARATIVE

DESCRIPTION

SOLUTION

ATTRIBUTE

PROJECT

TYPE

PROJECT

TYPE

SUBMITS

YIELDS

REPRESENTED

BELONGS

DS PROFILE

WEIGHT

DATE

IMPORTANCE

VALUE

GENERATOR

MIDDLE

GRADES

INTUITIVE

AUTOMATIC

ML PROFILE

NEURAL

NETWORK

PARAMETERS

NEURAL

NETWORK

PARAMETERS

USER

DECLARATIVE

DESCRIPTION

DECLARATIVE

DESCRIPTION

SOLUTION

ATTRIBUTE

PROJECT

TYPE

PROJECT

TYPE

SUBMITS

YIELDS

REPRESENTED

BELONGS

DS PROFILE

WEIGHT

DATE

IMPORTANCE

VALUE

GENERATOR

MIDDLE

GRADES

INTUITIVE

AUTOMATIC

ML PROFILE

NEURAL

NETWORK

PARAMETERS

NEURAL

NETWORK

PARAMETERS

Figure 3: User Profile Module - Entity Relationship Model

The exact range of values for each observed

attribute will be affected by the design of the ML

component described in Section 6 as well as the

solution generator used. Nevertheless, it is important

to observe that solution generation of the existing

system is not based on observed attributes. This

implies that the number of generated solutions is not

restricted by the range of values of the observed

attributes. The observed attributes map each

generated solution to a vector of values of restricted

range. Thus, it may be the case that, at the present

stage, two or more different solutions, i.e. of

different geometric representations, are mapped to

the same vector of observed attribute values.

5 USER MODELING

Figure 3 presents the database model that has been

developed in order to store and maintain user profile

information. Notice that only a few representative

properties of entities and relationships appear in the

ER graph. Apart from the entities directly connected

with the user profile module, the database model

also includes entities representing scene descriptions

and geometric representations of the corresponding

solutions, namely the DESCRIPTIONS and

SOLUTIONS entities. Specialized databases have

already been developed as part of other work, taking

place in the context of the MultiCAD platform

(Ravani 03). The database model proposed here is

flexible enough to cooperate with these already

existing structures and yet able to incorporate

alternative implementations in case these become

available in the future.

Figure 2 offers insight regarding the choice of

the specific entities and relationships appearing in

the database model. In particular,

USER. Example fields for the properties for the

specific entity are the (unique) User Name,

Password, First/Last Name, etc.

PROJECT TYPE. A text name plus extra

information connecting the specific project type with

the corresponding description prototypes contained

in alternative databases of the MultiCAD

environment.

DESCRIPTION. This entity represents the

Prolog-like description of a scene. Importance

represents the influence of the results of this session,

i.e. the properties of the approved solutions during

the specific this session, to the overall user profile

for the specific project type.

SOLUTION. Full geometric representation of

each solution.

INTELLIGENT SOLUTION EVALUATION BASED ON ALTERNATIVE USER PROFILES

Figure 4: Decision Support Component - Weight Assignment using AHP

ATTRIBUTE. Attributes used to map solution to

a smaller space that will allow further processing

with respect to user profiles. The fields include

name, type (int, real, scalar – big, medium, small,

max, min, etc.)

NEURAL NETWORK PARAMETERS. The

values of all neural network construction variables.

In addition to the aforementioned entities, a set

of relationships will maintain the information about

the active interconnection of the entities during user

sessions. In particular,

submits. Users submit scene descriptions for

processing. Each scene is connected with a certain

project type.

yields. Each description, using one of the

alternative solution generators that are available,

results in a set of solutions complying with the

description.

is mapped to. Each solution is mapped to a set of

values for the attributes we have chosen to observe.

decision support profile. This relationship

contains all information regarding the initial user

profile as obtained by the Decision Support

component. In particular, for each attribute of a

specific project type, the user has already provided

his/her personal interpretation of its importance for

solution evaluation. This importance is represented

by the corresponding weight. An additional personal

parameter is that of the middle value for any

attribute. The user is requested to suggest the middle

value for all attributes, i.e. the actual value of the

attribute that represents 50% performance of a

solution with respect to the specific attribute.

machine learning profile. This is the

relationship that interconnects the user, in the

context of (any) specific project type, with the

Machine Learning component, i.e. all values needed

to fully describe the neural network used to

represent the specific user’s dynamic profile.

grades. Solutions are evaluated by the user

through visual inspection, thus yielding the intuitive

grade (approved/rejected or a number in case an

alternative grading scheme is used). In addition,

solutions are also evaluated based on the specific

user’s profile for the specific project type. This

information may be regenerated based on the

contents of the database. Nevertheless, solution

evaluation is a crucial and time-consuming task;

hence, once the results are available they are stored

in the database.

6 SOLUTION EVALUATION

Two alternative approaches are used for solution

evaluation based on user preferences forming two

independent components of the user profile module.

Nevertheless, their concurrent operation and results

affect the overall behavior of the system, as it will

become apparent in the following section.

ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

Figure 5: Decision Support Component - Weight Assignment Using SMART

6.1 Decision Support Component

Each user is requested to assign weights representing

the importance of each one of the observed

attributes. Two alternative methods for weight

assignment are available to the user based on the

corresponding stages of SMART (Goodwin 98) and

AHP (Saaty 90) multicriteria decision algorithms

respectively. An interesting discussion regarding

dynamic weight assignment versus fixed weight

values is presented in (Roberts 02).

The weights are then used to produce a score for

each generated solution using a fitness function.

Alternative functions may be used in the future but

we currently use the inner product of the user

weights with the attribute values for each solution

(Bardis 04). These scores are then used in order to

sort the set of solutions as a list of descending user

preference order: from the most to the least

preferable solution. This Decision Support (DS)

component has already been implemented and

currently operates on solutions in the form of

attribute vectors. An example execution of the DS

component for user profile initialization is shown in

Figures 4-5. Notice the inconsistency index in the

AHP method, signifying discrepancies in the user’s

answers, as well as the normalized weights

appearing at the top row of the sample solution set

evaluation of the SMART method. The user may

rely on this component in order to obtain a set of

automatically selected solutions based on the

aforementioned weights. In this case solutions will

be automatically visualized and presented to the user

in descending preference order. However, the user

may choose to manually select the preferred

solutions thus contributing to the training of the

neural network described in the next section.

6.2 Machine Learning Component

The ML component will be based on a neural

network of six inputs – one for each observed

attribute, at least one hidden layer and a single

output, representing the approval/rejection of a

solution by the user. Alternative structures of the

network will be implemented and tested according to

the criteria presented in the next section. This

process may lead to the selection of more than one

structures for systematic use as alternative ML

components, similar to the use of two alternative

methods for attribute weight assignment in the DS

component.

The user, having submitted a scene description,

will evaluate the solutions that are generated and

visualized for the specific scene. This set of

approved/rejected solutions will serve as one of the

training sets for the network(s). In particular, each

example in a training set represents a correct input-

INTELLIGENT SOLUTION EVALUATION BASED ON ALTERNATIVE USER PROFILES

output mapping. In the present context, the input

part, for any given solution, is comprised by the

attribute values representing the specific solution.

The output part simply contains the user’s approval

or rejection for the specific solution.

It is important to notice that the aforementioned

process will be a completely transparent system task:

the user will not have to submit any additional

information with respect to the network(s) training

and therefore, does not have to be aware of it.

Automatic solution evaluation will be available from

the very first session via the Decision Support

component. In general, automatic solution

evaluation will be at the user’s discretion and the

choice of the most appropriate component for this

purpose will be based on the criteria presented in the

next section.

Figure 6: Example Methods Performance

7 PERFORMANCE COMPARISON

Either during the testing period or during the regular

use of the system, at least two alternative methods

will have to be compared with respect to their

performance. In particular, solutions approved by

each method will be compared to the solutions

approved by the user.

7.1 Performance Indices

In order to be able to compare these methods we

must provide the means to measure their

performance. We concentrate on the solution

selection stage that takes place after solution

generation. Therefore, in the following, we will

assume that solution generation has already taken

place and the methods have been applied to the

results. The application of each method to the

solutions yields the corresponding subset of

approved solutions. For simplicity, we mention only

two methods in the following whereas, in practice,

more alternatives – due to alternative weight

assignment, alternative network adjustments, etc. –

may be concurrently evaluated.

In particular, let us define the following sets:

G = The solutions generated based on the

specific description of a scene.

U = The preferred solutions, i.e. solutions in G

that comply with the user preference. These

solutions represent the user preference in the current

context. Formally, U ⊆ G. In the following we may

also refer to the members of U as approved

solutions.

G-U = The discarded solutions, i.e. the

generated solutions that are not preferred by the

user.

= solutions in G approved by Method 1.

Formally, M

⊆ G.

= solutions in G approved by Method 2.

Formally, M

⊆ G.

|S| = the number of members of any set S.

Therefore we may now define the hit rate of each

method as:

}21{ ,

∈

∩

= ,

i.e. the percentage of approved solutions

captured by the specific method.

The ratio of approved vs. total solutions selected

by each method could also be used as measurement

of their performance:

}2,1{,

∈

∩

= i

There are more than one ways to define a miss

rate. We may define it as the percentage of discarded

solutions that are selected by the method, expressed

as:

}2,1{,

∈

−

= i

However, we expect that, only a small number of

the generated solutions will fulfill the user

preference. This is mainly due to time limitations

posed by the requirement for human visual

inspection. On the other hand, |G| greatly depends on

the description and can vary significantly. Therefore,

we need to relate the size of the error for each

method with |U| instead of a quantity including |G|.

Hence, we could alternatively define

}2,1{,

∈

−

= i

MMR

ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

and interpret a lower value as a better performance.

Intuitively, this interpretation implies that a method

should not select many discarded solutions when

only a few preferred solutions exist. For example,

when this rate is more than 1 the method gives more

discarded solutions than the total number of

approved solutions. Instead of 1, another value may

be selected to reflect a specific performance

threshold.

The above are clarified in the example Venn

diagram of Figure 6, representing a general case (i.e.

no intersection is empty, no two sets are equal). For

the sake of simplicity of the picture, the total number

of solutions is rather small, i.e. |G| is only 35

whereas this is generally not the case. Nevertheless,

for the specific example, we have the following

numbers:

|G| = 35, |U| = 10, |M

| = 6, |M

| = 10, |M

∩ U| = 3,

∩ U| = 4, |M

– U| = 3, |M

– U| = 6

Therefore for M

we have:

= 3/10, MR

= 3/25, MMR

= 3/10, PR

= 3/6

and for M

we have:

= 4/10, MR

= 6/25, MMR

= 6/10, PR

= 4/10

Table 1: Example Methods Performance Indices

Metho

Hit Rate

Performance

Ratio

Miss Rate

Modified

Miss

Rate

Method 1 3/10 = 30% 3/6 = 50% 3/25 = 12% 3/10 = 0.3

Method 2 4/10 = 40% 4/10 = 40% 6/25 = 24% 6/10 = 0.6

Extreme

Case 1

1/10 = 10% 1/1 = 100% 0/25 = 0% 0/10 = 0.0

Extreme

Case 2

10/10 = 100% 10/35 = 28.6%

25/25 =

100%

25/10 = 2.5

could be considered a worse (because of the

higher miss rate) or a better (because of the higher

hit rate) method than M

depending on the

interpretation of these numbers. Ideally, hit rate

should be equal to 1, miss rate equal to 0 and

performance ratio equal to 100%. Notice, however,

that a method selecting only one preferred solution

every time it is invoked (Extreme Case 1) would

yield a performance ratio of 100% without

necessarily representing an optimal method as

shown by the low hit rate. On the other hand, simply

selecting all produced solutions (Extreme Case 2)

maximizes the hit rate but yields a low performance

ratio. Extreme Case 1 appears to represent a method

with acceptable performance whereas Extreme Case

2 represents a trivial approach of no practical use.

Therefore, a high Performance Ratio appears to be a

necessary, although not sufficient, indication of an

efficient method and it becomes apparent that we

need a combination of these indices in order to

accurately evaluate the performance of each method.

7.2 Method Integration

The training process of the neural network will have

to continue, with alternative scene descriptions, until

the ML component is considered ready to support

automatic solution evaluation. We may define this

threshold of ML component based on the values of

the performance indices we described above for the

two alternative DS components as well as for the

ML component itself. In the following we will

assume that the user has initialized his/her profile

giving answers to the DS components that

reasonably represent his/her preferences.

In particular, we will be able to rely on the

results given by the network, and therefore adopt

fully automated solution selection, as soon as the

ML component performs consistently better than

both of the DS alternatives. We can state that the

ML component is mature, and therefore ready to

take over automatic solution selection iff:

> PR

DS1

∧ PR

> PR

DS2

∧

> HR

DS1

∧ HR

> HR

DS2

∧

MMR

< MMR

DS1

∧ MMR

< MMR

DS2

for an (adjustable) number of recent descriptions

submitted by the user.

The strictness of this set of conditions may be

relaxed by omitting some of the inequalities. In any

case, it is important to observe that, if the complete

set of conditions is repeatedly true, this implies that

the ML component will be capturing preferences

better than the weight vectors submitted by the user

himself/herself. In such a case, it will also be

interesting to explore the possibility of capturing

additional criteria that the user is not fully aware of,

i.e. sub-conscious criteria. This could become

apparent through the examination of experimental

results and users’ comments regarding system

performance with respect to their preferences.

8 FUTURE WORK

This stage of our work will conclude with the

detailed design and implementation the ML

component. Subsequent performance comparison of

the two components will lead to further refinement

of their properties. Extending the solution evaluation

to grade assignment instead of plain

approval/rejection will also be considered. In that

case performance indices will have to be modified to

also reflect the quality of the selected set of

solutions.

INTELLIGENT SOLUTION EVALUATION BASED ON ALTERNATIVE USER PROFILES

The next major stage of our work will focus on

the enhancement of the user profile model with

information originating from the connection between

the declarative description and the corresponding

approved solutions. Such an association will offer

insight regarding the specific user’s interpretation of

declarative properties and relations. Successful

modeling of user preferences at the declarative as

well as the geometric level will allow incorporation

of user profile information to the process of solution

generation, thus significantly improving system

performance.

ACKNOWLEDGMENTS

This study was co-funded by 75% from the

European Union and 25% from the Greek

Government under the framework of the Education

and Initial Vocational Training Program –

‘Archimedes’.

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SETN 203-214, Thessaloniki, Greece.

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ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS