Generative Art Applied in the Design of Information Access Interfaces
+31 40 2744180, firstname.lastname@example.org
Kees van Overveld
+31 (0)40 2457953, email@example.com
+31 (0)40 2745276, firstname.lastname@example.org
The path of ubiquitous computing and the domain of ambient intelligence are expected to stimulate emergence of new interaction paradigms between humans and machines. There is a need to develop adequate means of natural communication with an intelligent information system. This, presumably, requires a more integrated development of form and function of the interface. The interface design concept proposed in this paper is based on an evolutionary mechanism and it aims at development of an interactive and adaptive animation system. It is proposed that through a coherent process of adaptation of a system's functionality and its appropriate visualization, a personalized and more natural experience of interaction might be achieved.
natural interaction, ambient intelligence, artificial evolution, interactive animation system, personalization, user experience
Ubiquitous computing  brings a new era in which users will be surrounded by many computing devices enabling personalized access to information and services. This path of computing and the domain of ambient intelligence  can provide appropriate technologies for the paradigm of natural interaction between humans and machines. Within a possible application domain of in-home personalized interactive services, humans are seen as a group of people, like a family, a group of friends or a local community, and machine is a system, which is a personalized, adaptive, perceptive, and anticipating network of products and devices. Ambient intelligence technology will presumably diminish the obtrusiveness of interaction with large amounts of computing devices placed everywhere in the environment. The interaction might become more natural and pleasurable if adequate means of communication will be developed.
This paper presents a possible approach to develop some natural means of communication. This approach assumes a possibility of implementation of an evolutionary mechanism in an adaptive and interactive user interface. This is based on understanding of the natural as something born in, lasting, changing and adapting over a period of time. The idea of growing potentially infinite amounts of shapes within artificial life populations is seen as a possible solution to the problem of personalization of interfaces in a multi-user environment where a large variety of user representations needs to be created. The issue of personalization follows current economical trends to migrate from commodities via services to experiences . An experience in this context can be defined as a conglomerate of real and virtual contents that together result in customer satisfaction. Characteristic of experience economy is the high degree of individualization of the offered experiences. In order to be successful, an experience should harmonize with the individual preferences, expectations and wishes of a customer, and ideally it should adjust itself smoothly to varying moods and tempers of users. Additionally, the customer should not be bothered by endless interrogations regarding the current values of all parameters of some computer-based model of his preferences. In this respect, computerized choice assistants to help users selecting experiences are very different from user interfaces supporting more standard tasks. The former will be called an adaptive user interface, in contrast with the latter conventional user interface.
One of the major differences between these two, as introduced in this paper, is the relation to the user. An Adaptive UI should understand the user, while the user is supposed to understand the conventional user interface in order to operate it. It this respect the creation of an Adaptive UI aims at defining a new interface design paradigm, which goes beyond traditional interface paradigms, as distinguished in .
This paper contains an interface design concept that focuses on explaining the role of generative art in the context of adaptive information access interfaces. The concept has not been prototyped yet, and therefore only a limited part of an Adaptive UI is presented. A full implementation should offer to the users the adaptation of all components of the generic architecture described in this paper. Only a part of the whole architecture is elaborated here in detail. This is the visual appearance and in particular the shape component.
The Adaptive UI is based on two hypotheses. One hypothesis refers to the possibility of personalization of an UI through an interactive visualization and the other one addresses personalization of content through a content filtering mechanism.
In Section 2 a poetical attribute as means of delivering personalized and intuitive experiences in interaction with an ambient intelligence system is discussed. Further the user interface concept titled Communicative Profiles is introduced. This shows how artificial life forms might be applied while accessing information.
The reference architecture of an Adaptive UI is called here an Adaptive Choice Assistant. It is a conceptual framework containing all components that are presumably needed for a development of an interactive and adaptive animation system. In Section 3 the architecture is described, and insight into one of the Adaptive UI components is given. The shape component is based on shape grammars and genetic algorithms.
Section 4 concludes this paper with a short discussion of present results and some indications of future work.
2.1 Adaptation of a system to personal users' needs
The first hypothesis underlying the Adaptive UI holds that a visual representation of any form of advice should have some appeal to the user. Then, presumably, it becomes possible to make the advisor more credible, so that it might become easier for users to identify themselves with the advisor. A visual appearance, that is the visual attributes of a shape, are the parameters that can be subject to personalization. Such personalization of a user interface might help to experience more natural interaction.
The second hypothesis holds that for a content filtering procedure, user preference profiles should be taken into account. These profiles could be represented as sets of earlier selected items that were considered enjoyable by the user. An advisor will be taken more seriously, if it had high prediction performance in the past.
Traditionally an intentional representation is based on attribute-value pairs that should capture the ‘essence’ of the user’s taste. In order to verify if a new item should be suggested to the user, it is proposed to assess the ‘distance’ D(N,P) between a new item N and the items P that together form the user’s preference profile. If this distance is sufficiently small, for instance if it is less than the distance D(N,Q) between the new item and items Q that are assumed to be outside the user’s preference profile, the Adaptive Choice Assistant could conclude that it is worthwhile to recommend N to the user; otherwise, a negative advice should be given.
There is a large amount of freedom in the precise version of the offered experience, since the user’s expectations are most often not very detailed. What can be known about the user’s requirements and preferences typically comes in the form of examples rather than intentions. For instance: the favorite movies are ‘High noon’ and ‘Once upon a time in the West’, instead of: ‘for movies, the preferred country=US; the preferred era=1960-1975; preferred style=Western; preferred suspense-level=average’.
The experience in the digital environment can purely consist of information contents, like watching television, browsing the Internet, or reading a virtual book. However, such experience does not have to be reduced to pure information content. The digital experience can be enriched by non-digital once for instance when remotely buying cloths after having visited a virtual fashion show or booking a journey after having visited a virtual travel agency. Both types of scenarios offer experiences for which the Adaptive UI mediates. The experiences are the things that are offered, that is the digital content, as well as feelings that are associated with these things. In both experiences of things and feelings the design and art forms in UI development are essential. In this respect the vision of natural interaction with ambient intelligence goes far beyond commonly used task-actions protocols and it enters the field of aesthetics. The interaction may be described as an individual experience of a non-human intelligence existing partially in physical and virtual spaces as an interactive art form. Such an interaction experience is built up of personal feelings arising from unique situations and mental pictures, which can be compared to the experience of reading and living through a poetry.
Thus, an interface which would be able to deliver certain personalized and intuitive experiences to a user, should hypothetically have a poetical attribute. Such a poetical attribute could give an impression of some human-likeness of the system. This presumably could enable more natural, easy and pleasurable interaction, and in particular information access. By definition of poetry, the attribute of a poetic interface should be an aesthetic form which provokes an emotional response through meaning, sound and rhythm. A balance between aesthetics and meaning in a poetic interface should be achieved, in order to create a comprehensive structure. This balance, that is the unity between form and function, can be achieved through a creation of an adaptive information access interface.
In this context, adaptive, evolutionary, means growing shape and behaviour of an artificial life populations. This fulfils the requirement of aesthetic creation of emotional response – having “sound and rhythm”.
And access, namely access to digital content and services, fulfils the requirement of a message carried within the aesthetic form, that is, having a “meaning”.
2.2 Communicative Profiles scenario
The information and experiences offered by the Adaptive UI could be of a diverse nature, for example, digital TV or web content, but it could be also of a less explicit nature, such as information about clothing, food or journeys. The main characteristic of the digital information as presented in this paper is that it comes in continuous flows and constantly growing quantities that exceed human abilities to process all of it. It is assumed that users of complex systems will wish to use only certain parts of the information stream, namely these parts that fit the individual taste. As long as the individual taste or preference can be expressed by means of a digital description based on personal experiences, there is a chance of filtering only those small portions of information that could be satisfying to the user.
Next to the continuous flow and constantly growing amounts of information, there is the problem of continuously changing taste of users. Even a very consistent and self-conscious personality can experience changes of mood.
Adaptive filtering of information and its adaptive and interactive visualization might be a possible solution to these problems. In order to achieve adaptation, a user representation and a mechanism for user feedback are required. The user representation, as perceived by the system, refers to the description of user's needs. This representation as perceived by the user, refers to a visualization of information that describes user needs. In the interface concept described in this paper, the visual representation of user needs and preferences takes the form of a collection of favorite items, the so-called user preference profile or user profile. The user's feedback is the necessary input by means of which the system can adapt to possible changes in user needs.
It is assumed that it can be beneficial for users that their profile, as maintained by the system, could get a visible and interactive representation. Such a representation in the form of animated shapes that can depict user preferences is communicative, in the sense of being able to communicate about internal system states and changes in information. The main interactive role of Communicative Profiles is to be a direct manipulation tool for accessing content. Such a direct manipulation tool can enable an explicit user feedback. In this way the personalization through adaptation might be achieved.
The idea of growing potentially infinite amounts of shapes in a process of artificial evolution is proposed as a solution to the problem of personalization. In this respect the personalization is understood broader than being only the delivery of filtered digital content, and it includes the appropriate visualization for the adaptive system.
This means that sets of liked or disliked contents are taken as a starting point and the problem of interactive visualization is how to show the changing status of such sets, as well as how to show that different sets belong to different users, assuming that all users would like to identify themselves with their own representation, and would want to recognize to whom visible sets belong.
Below there are definitions of these features of communicative profiles that are essential for the interactive visualization in a poetic access interface. Access means here a digital content access of data like digital TV programs.
User profile is a collection of favourite digital contents. The idea of visualization of a user profile assumes existence of an animated body. The animation, that is behaviours of the body, depends on processes inside of the body, in the so-called “belly”, as well as outside, within the environment. In the c-profiles interface concept the bodies of user profiles exist within a video-stream environment and their bellies contain video-clips. Video-clips can be grouped in sets according to meta-data content descriptions and according to articulated knowledge of the user. The grouping is done for the purpose of enabling database–like operations on sets of favourite programs in a pleasurable and engaging manner.
These sets are not a subject of evolutionary growth.
Fig.1 "Communicative Profiles",
animated representations of personal collections of digital information. A
vision of a multi-user interactive board inhabited by a-life populations.
Fig.1 "Communicative Profiles", animated representations of personal collections of digital information. A vision of a multi-user interactive board inhabited by a-life populations.
Fig. 1 shows a multi-user profile, that is a visualization of two sets of favourite content of two different users, which are displayed on the same screen or on multiple screens at the same time. Social interaction amongst users is enabled this way, and an interaction amongst profiles of different users is possible.
The idea of applying an evolutionary growth of shapes assumes that the bodies of user profiles can adapt to individual users in the process of growth. In this way generated shapes can enable personalization of communication. Visual adaptation of the interface is achieved by introducing a set of default shapes, from which users can select the preferred one. The selected shape indicates the direction of evolution of an individual user profile, as shown in fig. 2.
Fig. 2 A set of default shapes. The user can
select a preferred shape which will indicate the direction of growth of an
embodiment of a user profile.
Fig. 2 A set of default shapes. The user can select a preferred shape which will indicate the direction of growth of an embodiment of a user profile.
The final shape of a profile body depends not only on the style chosen by a user, but it is also determined by behaviours, which depend on environmental circumstances, such as the spatial arrangement of content, which might provoke a quicker growth of some limbs and atrophy of others.
This means that even if each “embryo” will have encoded a possibility of growth of, say, at most five legs, it is not necessarily the case that it will grow all of them, neither what precise shape and size they will be.
The coded features of possible growth – that is “genes” - are the graphemes  resulting from the parameterisation of shapes. The approach to parameterize shapes for growth is described in detail in the next section.
Besides their largely unpredictable growth, profiles will have defined behaviours to communicate in a natural way about their current status. This means that they will be allowed to grow, for example, a hand to be able to wave, if a filtering agent will find a piece of content relevant to user preferences. In this way a human-like emotional communication of system messages can be preserved without explicit imitation of a human body which proved to be controversial in user interfaces .
Explicit feedback is seen as the crucial interaction tool, which can give a feeling of control over an adapting system. In graphical user interfaces explicit feedback can be given through direct manipulation, that is selecting, clicking, dragging and dropping, etc.
Below, in fig. 3 a-h, the interface design concept is presented. It shows a possibility for the interactive application of evolutionary grown shapes.
a. An initial profile
This is a shape depicting a favorite piece of digital content, for example, a digital TV program.
The profile gets its initial shape at a moment when a user notifies the system that (s)he likes what (s)he is currently watching.
b. A growing communicative user profile
The profile grows limbs and a face to be able to grasp, look at, compare and swallow cells depicting contents of high preference.
Limbs are also seen as a necessary means of a gesture based communication with users.
c. A grown-up profile
After a period of time, when a lot of programs have been selected or accepted by a user, the “belly” of the profile grows big.
Selection means voting on currently watched content.
Acceptance means agreement on contents, which is offered by the filtering mechanism.
d. Split/merge cells (1)
A user can split the profile, for example with a button press on the remote control. The profile splits according to the meta-data contents description, for example into groups showing different genres.
The profile shows two genre groups.
These are, for example, two news programs and four movies.
e. Split/merge cells (2)
This is the same profile as above, but split according to a different criterion. These are, for example, two favorite TV channels. Each group has three programs.
f. Divide body
The profile could split its body into many smaller bodies, which then act independently.
Such ability is foreseen for large profiles, when a user would like to have a simplified view, and interact only with one set at a time, for example, only with movies, or only with news, etc.
g. Offering of a filtered content.
As a result of filtering some content which might be relevant is offered to the user, so that s(he) could accept or reject the offer.
A new finding is communicated by the profile, in this case by waving an arm.
h. Deletion of a program
A program, which relevance has decreased, can be deleted. It disappears from the user profile.
The profile gets thinner and communicates this operation on the set of content, for instance by a trembling body.
Fig. 3 a-h. Communicative profiles interface
Fig. 3 a-h. Communicative profiles interface scenario
The idea of applying generative art forms in order to deliver personalized poetic-like experiences while accessing information might be realized through the development of an interactive and adaptive animation system. To achieve such an aim two conditions have to be fulfilled: the animation must adapt visually and it has to enable direct access to information.
Amongst the existing systems there are examples of evolutionary interactive installations which enable growth of graphical forms , but these are not information access interfaces, and as such they do not enable direct manipulation in terms of information access.
Other systems are real-time interactive animations . These enable direct manipulation and information access, but they are not adaptive. The visual forms do not change over time.
The development of the interactive and adaptive animation system is progressing in two phases. The first phase aims at the creation of evolving forms which are interactive at a simple level of selection of generated graphical shapes, as explained below. The second phase aims at the application of an adaptive information access algorithm and is outside the scope of this paper.
3.1 A reference architecture for an Adaptive Choice Assistant
An example of an Adaptive UI, that is an Adaptive Choice Assistant is based on the same hypotheses as the interface design concept described in section 2. The Adaptive Choice Assistant architecture is depicted in fig. 4 below.
The area (1) depicts the components that together form the Adaptive Choice Assistant. The source of information contents is depicted in (2). The purpose (3) of the Adaptive Choice assistant is to help the user in making a selection of the items in (2).
Different Adaptive Choice Assistants might exist per item type, for instance for selecting television programs, the Assistant might be an electronic program guide (EPG) embedded in a set-top box. Given the particular purpose of this Choice Assistant, an Adaptive UI module (4) can be conceived that is characterized by two internal components: the first one is a (user preference) profile, which holds a collection of items P that were enjoyed in the past and possibly also a collection of items Q that were disliked in the past. The second component is a filter that compares the relative distances D(N,P) and D(N,Q), for new incoming items N. Any new incoming item is passed by this filter and the outcome of the relative distances triggers the subsequent behaviors of the interface module, which are generated in (5). Further, the user interface module (4) manages the sets P and Q. Gradual changes in the user’s preference profile could lead to the removal of old items or to replacement by other items. Also these updates give rise to visual behaviors, which might require user feedback.
At a high level, a behavior consists of rather complex motion patterns, each motion pattern associated to one type of database update or one primitive interaction clause. At a lower level, these behaviors are decomposed in terms of primitive motion primitives: waving a tentacle, grasping an icon, jumping up and down to draw attention, and so on. These low-level motions are generated in module (6). Module (7) represents the articulated ‘body’ topology of the moving creature; it represents all kinetic degrees of freedom. However, the components in (7) are not directly visible: they could be seen as skeletons. The actual geometric shape of the various parts that adorns these skeletal elements in many possible ways is determined in module (8), and the visual quality in the form of color schemes, textures and (faked) illumination effects is added in module (9). As in the example of our pre-prototype, the total animated images may contain both the synthesized moving creatures and iconic material that represents the current preference profile. Of course, the motions of the animated creatures should drive the placement of these icons, and module (10) manages this. Notice that it gets its contents from the current state of the preference profile component. The rendered animated advisor-creature and the iconic material that represents the current preference profile is shown on a display device (12) that may or may not form a physical unity with the display device that shows the actual visual contents (13), for example a TV screen, a virtual book or an LCD screen. A separate module, (11), that falls outside the scope of the Adaptive Choice Assistant is responsible for showing the actual visual contents which can be selected from the profile. Further, there could be other means to communicate the selected experience-item to the user: from electronically controlled devices such as kitchen equipment to door-to-door delivery services. All these functions are assumed to be in module (14). Finally, a very important module (15) is placed outside the Adaptive Choice Assistant. This is a mechanism offering a direct selection of contents. Since a useful profile that truly represents user's taste will only grow over time, there have to be means to solve the ‘cold start problem’. The first couple of choices for a new user cannot be made using the filter mechanism outlined above. So the first entries in P will result from direct selection.
A primary mechanism for adaptation is the dynamic update of the sets P (likes) and optionally Q (dislikes). But according to the first hypothesis in sub-section 2.1, the visual appearance of the advisor should also adapt. A general proposed mechanism is the following: each of the modules (5), (6), (8), and (9) can be defined by means of a set of rules that generate a behavior, a motion, a shape and a rendering style. The shape module (8) indicates a possible way of implementing and applying these rules, as explained in sub-section 3.2. Every rule has a weight factor, and rules can by applied at random where the chance that a rule is chosen is determined by its weight factor. The rules should be such that whatever rules are applied, in whatever order, the total behavior is always according to the high-level goals as defined in (3), and the updates to preference profiles and the filtering actions always take place in an appropriate way. Also, whatever the precise shape, defined by the shape-formation rules, it has always to be compatible with the structure as defined in (7), since this structure is necessary to execute the motion patterns that together form the visual representation of the database updates on the sets P and Q.
With these rules, adaptation to the user’s taste can be realized through an evolutionary process . At specified time points, for example once per interaction session, some random selections of rules is performed, and behavior, motion, shape and rendering styles are constructed according to the selected rules. All of this should generate a small number of different creatures which are all identical with respect to the semantics of the activities they perform in order to convey the system messages, e.g. database operations. They are, however, very different with respect to the further details of their behavior, their motions, their shape and their rendering styles. Next the user is asked to select one of these creatures. The weights or fitness of the rules that contributed to the generation of the chosen version are somewhat increased; whereas the weights of the other rules are decreased. A new generation of creatures is then performed, where the probability of using a certain rule is proportional to its fitness. In the next session, the exercise is repeated; where it is made sure that the preferred creature of last time is again among the offered alternatives. In this way, the user is certain that he never gets creatures that are a worse match to his taste than the previous time. In this process, the population of creatures evolves, together with the set of weights of the rules generating those creatures. It is assumed that the set of weights obtained in this way forms a representation for the user’s taste according to behavior details, motions details, shapes and rendering styles. If this can be confirmed, the mechanism will indeed lead to converging weights, and hence, to a creature that is adapted to the user’s taste.
3.2 A rule-based shape definition: applications of generative art
Of all the modules (5), (6), (8), and (9) in the reference architecture of the Adaptive Choice Assistant, through which adaptation can be achieved by means of generation rules, the shape generator is the most straightforward to conceive. This can be done through a combination of a shape grammar and non-linear warping. Shape grammars have been used to describe shapes in a procedural way [10, 11, 12, 13]. A shape grammar, as defined in the context of this paper, consists of a finite set of substitution rules. A substitution rule has three components:
a type name-substitution rule,
a structure-substitution rule,
and a shape-substitution rule.
A substitution rule serves to substitute a shape segment by one or more other shape segments. A shape segment is, in its simplest form, a curve together with a quadrilateral (ABCD) and a type name. A type name-substitution rule has the form
<type name 0> ® <type name 1><type name 2><type name 3> …<type name n>,
for instance: U®VU.
The structure-substitution rule and the shape-substitution rule define what the two curves, in this case, and the quadrilaterals are that together will replace the single shape segment with type name U from the left hand side of the type name-substitution rule. The structure-substitution rule and the shape-substitution rule that accompany the type name-substitution rule U®VU are defined graphically. This replacement rule is shown in fig. 5.
The interpretation of this image is as follows. Whenever this rule is to be applied onto an existing shape segment, this shape segment has to have name-type ‘U’ (this is expressed by the left hand clause, ‘U’ in the type name-substitution rule U®VU). If such an existing shape segment, say u1, has been found, a transformation is constructed that maps the quadrilateral ABCD in the above figure to u1’s quadrilateral. It can be shown that such a mapping is a so-called bi-linear transformation. This geometric mapping is then applied to the two quadrilaterals in the right hand part: the dashed one and the dash-dotted one. The same geometric transformation is also applied onto the two thick curves (the solid curve and the stippled curve). These two curves will replace the original curve that belonged to u1. Further, u1’s quadrilateral is replaced by the two smaller (transformed) quadrilaterals, the dashed one and the dash-dotted one. Finally, the name type ‘U’ for the entire segment u1 is now replaced by a concatenation of two name types, ‘V’ and ‘U’. This means that in a next round, the same rule could again apply to the right most newly created shape segment; however, in order to replace the left most newly created shape segment, we cannot use this rule. If no rule was provided that has the name type ‘V’ in its left hand part, it cannot be transformed at all; in this case it is called a terminal symbol. If one or more rules of the form ‘V® ….’ are provided, these rules could be applied at a later stage, and the shape segment is called a non-terminal symbol.
As mentioned, it has particular advantages to specify the geometric transformation that will be applied by means of the structure-substitution rule in terms of bi-linear transformations, parameterised by quadrilaterals. Indeed, suppose that the shape segment that belongs to u1 has a curve that starts in the point A of u1’s quadrilateral and ends in the points D of u1’s quadrilateral. We observe that the two new curves then have the same property. So if the original curve was connected to the rest of the shape (that is, the rest of the shape connected exactly to the points A and D of u1’s quadrilateral), then this connection is maintained throughout the shape segment substitution. Even stronger: suppose that the shape segment belonging to u1 was tangent to AB and tangent to CD, and that the rest of the shape smoothly connected to u1’s shape segment in A and D (i.e., the tangent of the remaining part of the shape in A was parallel to AB, and the tangent of the remaining part of the shape in D was parallel to CD), then this smoothness is also preserved. In other words, using structure-substitution rules that are parameterised by means of quadrilaterals, and that are implemented by means of the bi-linear mapping that maps the destination quadrilateral to the source quadrilateral, enables us to preserve, if so desired, connectivity (so called C0 continuity) and smoothness (so called C1 continuity).
In this way, subsequent application of rules mimics the development of a complex shape out of a primitive shape. The figures below illustrate a part of the process that leads to the rules that should generate a complex shape. Fig. 6 shows the final shape which a designer wants to obtain. Fig. 7 shows a deconstruction of the final shape into a series of simpler shapes that represent the growth path leading to the final shape. In order to propagate from one version to the next over this growth path the rules are needed. The fig. 8 shows how these rules are derived and cast in the form of substitution rules, including structure substitution in the form of appropriate quadrilaterals.
Fig. 6 The final shape proposed by designer. Fig. 7 Deconstruction of
the final shape into number of layers
Fig. 6 The final shape proposed by designer.
Fig. 7 Deconstruction of the final shape into number of layers
Fig. 8 The replacement rules
for the first growth layer of the final shape.
Fig. 8 The replacement rules for the first growth layer of the final shape.
The Adaptive UI concept introduced in this paper is used as a reference to define an interactive and adaptive animation system. The interface design concept proposes visual adaptation of shapes in accordance with changes in the system. This is a feature that does not exist in conventional user interfaces. In this respect the Adaptive UI idea aims at creating a different relation with users than currently known interaction paradigms. This relation is based on understanding of the user by the system and the new interface paradigm is based on adaptation of the interface to user characteristics with respect to both: the system and the UI. In consequence, personalization is understood here broader than just a content filtering mechanism. The visual adaptation is a reflection of the taste of an individual user. The adaptation is enabled by means of interactive animation that is based on generative art principles. In this respect the Adaptive UI proposes experiences of more natural interaction than is possible when interacting with a conventional UI.
The Adaptive UI is worked out so far as an interface design concept. At the moment the shape component is being developed in a way that will accept potentially any design style. The further work plan aims at definition of the motion component. The motion component will specify interactive behaviors of artificial life forms, so that the explicit user feedback on system functions would be possible.
 Weiser, M. and Brown, J.S. (1995).
''Designing calm technology,''
 Aarts, E. and Harwig, R. "Ambient Intelligence: A new user experience,'' press release CeBIT, 2000
 Pine II, Joseph B., Gilmore, James H. The Experience Economy, Harvard Business School Press Boston, 1999
 Cooper, A. "About Face. The Essentials of User Interface Design", IDG Books Worldwide, 1995
 Ware, C. Information Visualization: Perception for Design, Morgan Kaufmann Academic Press, 2000
 Shneiderman, B. Designing the User Interface: Strategies for Effective Human-Computer Interaction, Addison-Wesley, 1998
 Holland, J.H., Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press, 1975. Revised second edition 1992 from the MIT press
 Lindenmayer, Aristid, Mathematical Models for cellular interactions in development, I & II, Journal of Theoretical Biology, 18, pp. 280-315, 1968
 Prusinkiewicz, P., and A. Lindemayer, and J. Hanan, "Developmental methods of Herbaceous Plants for Computer Imagery Purposes", Proceedings of SIGGRAPH 1988, 467-478
 M.Sonka, V.Hlava,c R.Boyle, "Image Processing, Analysis, and Machine Vision", 1999, Brooks/Cole Publishing Company, Pacific Grove, CA (USA), pp. 315-323
 Christian Jacob: Principia Evolvica, Simulierte Evolution, dpunkt.verlag (publisher), Heidelberg, Germany, 1997
 it can be conceived that the user’s taste is not only represented in terms of items that he likes, but also items that he dislikes.