Arties: Meta-Design as Evolving Colonies of Artistic Agents

R. Neal Elzenga, Michael S. Pontecorvo.

Emergent Design, San Francisco, USA.






Meta-design, the act of designing a system or species of design instead of a design instance, is an important concept in modern design practice and in the generative design paradigm. For meta-design to be a useful tool, the designer must have more formal support for both design species definition/expression and the abstract attributes which the designer is attempting to embody within a design. Arties is an exploration of one possible avenue for supporting meta-design. 


Arties is an artistic system emphasising the co-evolution of colonies of Artificial Life design or artistic agents (called arties) and the environment they inhabit.  Generative design systems have concentrated on biological genetics metaphors where a population of design instances are evolved directly from a set of ‘parent’ designs in a succession of generations. In Arties, the a-life agent which is evolved, produces the design instance as a by-product of interacting with its environment.


Arties utilise an attraction potential curve as their primary dynamic. They sense the relative attraction of entities in their environment, using multiple sensory channels. Arties then associate an attractiveness score to each entity. This attractiveness score is combined with a 'taste' function built into the artie that is sensitised to that observation channel, entity, and distance by a transfer function.  Arties use this attraction to guide decisions and behaviours. A community of arties, with independent evolving attraction criteria can pass collective judgement on each point in an art space. As the Artie moves within this space it modifies the environment in reaction to what it senses.


Arties support for Meta-design  is in  (A) the process of evolving arties, breeding their attraction potential curve parameters using a genetic algorithm and (B) their use of sensory channels to support abstract attributes geometry. Adjustment of these parameters tunes the attraction of the artie along various sensing channels.


The multi-agent co-evolution of Arties is one approach to creating a system for supporting meta-design. Arties is part of an on-going exploration of how to support meta-design in computer augmented design systems.


Our future work with Arties-like systems will be concerned with applications in areas such as modelling adaptive directives in Architecture, Object Structure Design, spatio-temporal behaviours design (for games and simulations), virtual ambient spaces, and representation and computation of abstract design attributes.


1. Introduction


Arties is an experimental multi-agent environment for exploration of emergent behaviour and  meta-design. Arties was originally conceived of as a set of agent tools for creating adaptive game dynamics.  We are also using it as one model for meta-design.

In Arties, the emphasis is on the co-evolution of colonies of A-Life (Artificial Life) design or artistic agents and the environment they inhabit.  Generative design systems for the most part have tended to concentrate on biological models involving genetic metaphors [1][2]. In these systems a population of design instances are evolved from a set of ‘parent’ designs in a succession of generations.  In the Arties system we have sought to define a co-evolutionary means [3] for evolving multiple flavours of agents which generate new design/art instances as a by-product of their ‘life’ activity.

2. Arties


The Arties system consists of a collection of software agents. These agents are called Arties. An artie is an artistic a-life agent for exploring and modifying artistic 'spaces'.  Arties are a-life ‘beings’ that possess the following characteristics:


Situatedness - Arties exist with an environment. The arties micro-world defines a Complex Adaptive System (CAS) [4].  This environment must have the following characteristics:


-         A unique co-ordinate system for locating an artie and each object in the environment relative to the artie's co-ordinates. 

-        A distance metric that represents the environment geometry's direct or shortest path (used for action at-a-distance calculations).  In addition, a mobile artie will be able to traverse the environment along a continuous trajectory.  The environment must support this notion of a trajectory.  Given two co-ordinates in the environment which satisfy the above criteria, points A and B, we have the following:



coordinates (x,y,z,…)



coordinates (x,y,z,…)


|A – B|

distance metric for 'straight' path (in the given geometry) between A and B



distance metric of the path an artie takes to get from A to B


|A – B| / Path(A,B)

deviation metric of the  Path(A,B) form the straight line metric |A - B|.



Table 2.0 Distance properties.



Sensing at a Distance - Each artie has a means of sensing the attraction or repulsion of entities in its environment.  Action at-a-distance is sensed in a 'straight' line metric for the given environment geometry.  Each artie can have an arbitrary number of sensing channels, each tuned to a different sensing metric.  For example, an artie might have the ability to sense red, green, and blue colour values of environmental objects using different sensing channels.  The effect and quality of a given sensing channel can be an arbitrary function of the straight-line distance from the artie to the sensed entity.  For simplicity we can normalise all such sensing actions to the scale of measurement of the sensing channel.  All sensing can then be limited to the range (0,1), with 0 representing no sensing or attraction, and 1 representing full sensing.


Interpreting sensings as Attraction and Repulsion - The result of sensing at-a-distance for each artie is that each sensed entity receives an attractiveness score based on the distance and quality of the observed entity, combined with a 'taste' function built into the artie that is sensitised to that observation channel, entity, and distance by a transfer function.  The artie uses this attraction and repulsion metric to guide its decisions and behaviours.  From the sensing interpretation an artie can choose to re-act to these sensings.  For example, an artie may choose to move towards or away from a sensed item based on a transfer function of attraction or repulsion.  The transfer function can also be used to change the artie or the environment based on a transfer function.  For instance, if an artie senses something it is sufficiently repulsed by, it may emit a colour or gas cloud akin to a skunk in self-defence. Likewise, it may decide to try and clone or co-opt colours it highly likes in its neighbourhood.  This makes an artie a type of rogue filter that operates on a local neighbourhood, but is also able to help evaluate the pertinence of its location or local neighbourhood against the sensing at-a-distance metric.


2.1 Examples of taste functions

Voting, Memory, and Collective Critique are three primary mechanisms of ‘taste’ in Arties.

A community of arties, each with their own evolved (and evolving) attraction criteria can pass collective judgement on the relevance of each point in an art space to the preferences of the artie. This allows each artie to vote on how willing they are to contribute to the changing of a given entity (or pixel) to satisfy their curiosity.  The sum of the votes cast by a community or arties within a space is the collective pressure to change or morph the art space to the preferences of the arties. Furthermore, each Artie has the ability to remember its own collective history of past judgements in a persistent local memory.  This memory is also how the knowledge space of a given Artie can be initialised, shared, and compared with the memories of other Arties.  An Artie’s memory contains the attraction/repulsion reaction of the Artie at a particular geometric co-ordinate at a given time. 



3.0 Programming


Programming the Artie - Arties are programmed by adjusting the distances of repulsion and attraction and the strengths of repulsion and attraction at those distances.  See drawing of attraction/repulsion curve.  If this curve is sufficiently non-linear, it can be shown that the arties will exhibit attraction/repulsion trajectories that fall into one of three guises – a fixed point, in which the attraction and repulsion of the artie is in stasis, a limit cycle, in which the trajectory of the artie stays within a fixed boundary (which may be chaotic), and divergence, in which the path of the artie never approaches a fixed point or stays within a predictable boundary.  The salient features of the attraction/repulsion potential curve become weighted components in a genetic vector representation of the attraction/repulsion within the artie.  These salient features are akin to inflection points and zero crossings of a potential curve.  Adjustment of these parameters tunes the attraction and repulsion of the artie along various sensing channels.  Tied to this tuning must be a cost or fitness function that helps the artie assess whether the current configuration of its potential curve should be changed, and in what direction.  Note that this potential curve can also be explained as the arties’ curiosity.


We anticipate that much Arties programming will be non-algorithmic or self-organising.  Because of the non-linear nature of complex systems such as the Arties universe, it is difficult to program Arties by setting up their transfer functions and parameters to known values with a priori intent as to their behaviour and the artistic meaning of that behaviour.  Programming Arties thus becomes an interplay between interactions that result in emergence from the interaction of Arties in the environment, reinforced by fitness function judgements and inputs by the meta-designer programming the Arties. 



4.0 The Artie Lifecycle



The operation of Arties in their environment is best understood by their lifecycle.  Arties operate according to the following schedule of constraints and rules:


Constraint 1 – Only 1 Artie can occupy a given (x,y) co-ordinate at any time t.


Constraint 2 – The Arties’ universe is an NxN square of (x,y) co-ordinates.


Step 1 – Initialisation.  Locate the Arties at an (x,y) co-ordinate within the universe. 


Step 2 – For each sensing channel i, compute the resultant attraction/repulsion of the Artie to each other (x,y) co-ordinate in its universe according to the following relationships:


Xn,n = f1(Channeli(x,y)) * åxåyR(Distance(x,y)) /Distance(x,y) * x


Yn,n = f1(Channeli(x,y)) * åxåyR(Distance(x,y)) /Distance(x,y) * y


Where the Distance(x,y) function is ( ( n – x)2 + (n – y)2 ) 1/2


Step 3 – Act upon the resultant vector according to the Arties’ design.  At any step an Artie can perform one or more of the following actions:


a.    Move from (Xt,Yt) to (Xt+1, Yt+1) [Subject to constraints of co-occupancy]

b.    Vote or remember to itself its opinion at (Xt+1, Yt+1)

c.    Contribute to the collective vote for category j for its perception of all points (x,y) at time t.

d.    Emit a new colour at (Xt+1,Yt+1), subject to a blending transform B(colour).

e.    Copy or carry a colour subject to a blending transform B(colour) for the pixel at (Xt+1, Yt+1)

f.    Evolve the attraction/repulsion transform R(Distance(X,Y)) using genetic principles.

g.    Exchange attraction/repulsion DNA with another Artie, adjusted by an inheritance transfer function I().


Step 4 – Deplete the Arties’ energy balance according to the cost of performing the operations in Step 3 (a-g).  If the Arties’ energy has annealed below a stability threshold, halt the system and remember the system state as a quiescent snapshot of the system evolution.  Otherwise, return to step 2 and keep the system going.


Note that the annealing relationship should reflect a form of exponential decay.


5.0 Results


Tests to date have concentrated on understanding the sensing channel perception dynamics for the Arties.  Consider the following initial Arties universe, which ironically enough is a picture of an Artie!  This is a 128x128 24-bit per pixel bitmap image. 



Figure 5.1 Artie

The Arties view this image as three sub-images corresponding to red, green, and blue channels respectively as shown below.



Figure 5.2 Red Green Blue Channels



Lets look at what an Artie ‘sees’ in these images along the red, green, and blue channels when located at co-ordinate (0,0), looking toward (63,63).



Here’s the red view


Figure 5.3 Red view


Here’s the green view


   Figure 5.4 Green View


Here’s the blue view


Figure 5.5 Blue View





5.1 Implementation Notes

The Arties system just described is currently simulated using mathematical models using the MathCAD 7 software from MathSoft Corporation.  This simulation environment facilitates rapid prototyping during the exploratory phases of designing the Arties’ universe.  Of particular note is the ease of defining new and complex transfer functions at the many places within the Arties’ dynamics that permit such transitions.  Appendix A contains the MathCAD 7 worksheet used to establish the basic principles of Artie dynamics used in this paper.


5.2 Discussion

The Arties universe is strongly motivated by advances in Artificial Life research.  There is a growing literature relating the fields of dynamical systems, chaos, cellular automata[5], self-organisation[6], and theories of computability.  For an excellent overview of the interplay between these fields see Langton et. Al [7].  Arties diverges from this research primarily in its intent.  Much of the A-Life literature is engaged in understanding the underlying forces and dynamics at play in interacting systems of agents that exhibit biologically-motivated interactions.  At the present stage Arties is more concerned with the generation of rich dynamics than in total characterisation of how or why.  The goal of Arties is to provide a test-bed by which the learned attraction/repulsion of a system of agents can be brought to bear on problems of meta-design.  The ability of Arties to iterate over a problem domain and to evolve their reactions to the tastes of the meta-designer is key.  Arties become true agents of the designer.


Perhaps one of the biggest deviations from classic A-Life research to date is Arties’ reliance on human judgement as a necessary and integral component of the fitness function.  While a system of Arties does exhibit strong self-organisational components, the solidification of such organisation into persistent system characteristics will only happen if the ‘result’ of applying the self-organised parameters is re-inforced by the fitness function.  Since an Arties’ reinforcement includes input from the designer building the meta-design process, Arties acquire ‘tastes’ that reflect meaningful dynamics to those perceiving their output.


6.0 Summary and Next Steps


Arties is one approach to creating a system for supporting meta-design. Arties is part of an on-going exploration of how to support meta-design in computer augmented design systems.


We have concentrated thus far on fields of attraction and repulsion as a primary source of behavioural dynamics, but we can envision other such mechanisms including families of probabilistic Cellular Automata, other survival landscapes, self-organising and adaptive systems phenomena.


Some possibilities for on-going experimentation with Arties will be concerned with applying Arties-like systems in the areas of adaptive directives in Architecture, Object Structure Design, design of spatio-temporal behaviours (for games and simulations),  virtual ambient spaces, and representation and computation of abstract design attributes.





[1] Gero J., Adaptive systems in Design: New Analogies from Genetics andDevelopmental Biology,  Adaptive Computing in Design and Manufacture, Springer, 1998.

[2] Pontecorvo M., Designing the Undesigned: Emergence as a Tool for Design, Generative Arts ’98 proceedings, Milan, 1998.

[3] Sims K., Evolving Virtual Creatures, Siggraph ’94 proceedings, 1994.

[4] Holland J., Hidden Order: How Adaptivity Builds Complexity, Addison-Wesley, 1995.

[5] Toffoli, T., and N. Margolus.  Cellular Automata Machines.  MIT Press, Cambridge, 1987.

[6] Kohonen, T.  Self-Organizing Maps.  Springer-Verlag, Berlin, 1988.

[7] Langton, C. ed., Artifical Life II, Addison-Wesley, Reading, 1992.