Abstract
Simple mechanics generate complex phenomenon
parallel system, interact at local level like CA
example of sensori-motor intelligence
interaction w/ env, learning process, simple perceptions of forms
discusses morphogenetic properties of swarm behavior
p1
presents an example of mapping trajectories in the space of forms onto 3d flocking boids.
1. Simple agents and turtles. Sensori-motor intelligence and
perception
Grey W. Walter built first recorded turtle
eerie impression of purposefulness, independence and spontaneity
p2
Valentino Braitenberg inspired by Walter
very intricate behaviors emerge from the interaction of simple component parts
Braitenberg ‘constructs’ intelligent behavior – “synthetic psychology”
turtle built based on the reflexive behavior of moths and other insects in their attraction to light – known as “positive phototropism”
“automation consisted of two light sensors connected each to threshold devices and these to two electric motors (one in each side of the body). The device is thus made of two completely independent effectors (sensory-motor units). The automation exhibits different behaviors depending on the configuration of its parts.”
p3
Manurana and Varela, conition is contigent on embodiment, consequence of organsim’s specific structure
Enaction – ‘cognition depends upon the kinds of experience that come from having a body with various sensorimotor capacities’
autopoiesis-system description that was said to define and explain the nature of living systems. autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. (wiki)
“There are two types of structural coupling:
1) A System coupling with its Environment.
2) A System Coupling with Another System.’ If the two plastic systems are organisms, the
result of the ontogenic structural coupling is a consensual domain.'”
p4
Swarms
“Swarm-based intelligence relies on the anti-classical-AI idea that a group of agents may be able to perform tasks without explicit representations of the environment and of the other agents and that planning may be replaced by reactivity. (R.Kube and E.Bonabeau)”
“Complex adaptive behaviour is the result of interactions between organisms”
p5
Reynolds boids 1986
“Each agent has direct access to the whole scene’s geometric description, but reacts only to
flock mates within a certain small radius of itself. The basic flocking model consists of three simple steering behaviours:”
p6
“Separation:
Gives an agent the ability to maintain a certain separation distance from others nearby. This
prevents agents from crowding to closely together, allowing them to scan a wider area. To
compute steering for separation, first a search is made to find other individuals within the
specified neighbourhood. For each nearby agent, a repulsive force is computed by subtracting
the positions of our agent and the nearby ones and normalising the resultant vector. These
repulsive forces for each nearby character are summed together to produce the overall steering
force.
Cohesion:
Gives an agent the ability to cohere (approach and form a group) with other nearby agents.
Steering for cohesion can be computed by finding all agents in the local neighbourhood and
computing the “average position” of the nearby agents. The steering force is then applied in
the direction of that “average position”.
Alignment:
Gives an agent the ability to align itself with other nearby characters. Steering for alignment can be computed by finding all agents in the local neighbourhood and averaging together the ‘heading’ vectors of the nearby agents. This steering will tend to turn our agent so it is aligned with its neighbours.”
“Obstacle avoidance:
In addition, the behavioural model includes predictive obstacle avoidance. Obstacle avoidance
allows the agents to fly through simulated environments while dodging static objects. The
behaviour implemented can deal with arbitrary shapes and allows the agents to navigate close
to the obstacle’s surface. The agents test the space ahead of them with probe points. When a
probe point touches an obstacle, it is projected to the nearest point on the surface of the
obstacle and the normal to the surface at that point is determined. Steering is determined by
taking the component of this surface normal, which is perpendicular to the agent’s heading
direction. Communication between agent and obstacle is handled by a generic surface protocol:
the agent asks the obstacle if a given probe point is inside the surface and if so asks for the
nearest point on the surface and the normal at that point. As a result, the steering behaviour
needs no knowledge of the surface’s shape”
p7
“Results:
In this first experiment, as a result of the way the collision detection algorithm worked (slowly rectifying the heading of the agent until it found a collision free trajectory), the individual agents had a tendency to align with the surfaces of the geometric model of the site. This ended in the emergence of the ‘smoothest’ trajectory on the environment, which in the case of the test model of a site where the meanders of a river. The swarm is able to discriminate the edges of a long wide curvy grove, that is, the geometric form of the river, from any other information such as buildings or building groups or infrastructures.”
Social insects such as ants learn through their environment through stigmergy and sematectonic communication.
p8
“Grassé introduced stigmergy (from the Greek stigma: sting, and ergon: work) to explain task
coordination and regulation in the context of nest reconstruction in termites”
coordination and regulation are acieved by chains of environmental stimulants that trigger specific behaviors.
“sematectonic communication [12], when the only relevant interactions between individuals occur through modifications of the environment”
p9
flock model system has same basic properties of a network/ “easy to appreciate the capacity of a sematectonic system in terms of ‘learning’
“many other types of connectionist models, such as autocatalytic chemical reactions, classifier systems, and immune networks”
p10
“In their paper ‘The use of Flocks to drive a Geographic Analysis Machine’, J. Macgill and S.
Openshaw [14] discuss how the emergent behaviour of interaction between flock members
might be used to form an effective search strategy for performing exploratory geographical
analysis.”
p11
“The system shows the same characteristics for cognition explained earlier, that is, the capacity
for remembering and forgetting, which we described when describing evaporation of the
morphogen as essential in the process of learning.
The Algorithm.
Each agent would have now a variable speed, with a common minimum and maximum for all
agents. In case of collision trajectory, the agent will slow down. In the absence of collision, the
agent will steadily speed up until it reaches its maximum. This means that in the event of a
‘conflict’ space, or an area where one agent detects many collisions consecutively, agents will
cluster; since their speed is low, they will have the inertia to remain there, where as faster ‘free’
agents in the neighbourhood will be easily attracted to the area. The information about
collision areas is therefore stored in the speed of the agents. Speeding up will be the equivalent
of forgetting in the system.”
p12
“With this mechanism, the swarm will move around detecting collision areas. If the area doesn’t
have enough weight compared with another, it won’t be able to attract enough agents. The
system will end up discriminating the areas were most collisions occur and which are more
accessible, after a time.”
p13
“The algorithm for this swarm is also a development of the basic Reynolds Boids algorithm,
where each agent has been given a mass variable in order to incorporate the capacity of learning
as well. The acceleration the individual agents experience each iteration depends on this
variable: light weights mean higher speeds, heavy weights slower ones. The cohesion of the
flock is also influenced by the mass: heavy agents will attract others to their neighbourhood
stronger than light ones. Light agents will also have less inertia, where heavy ones will tend to
keep their variables unmodified.
The system needs the slow “evaporation” of the mass variable in order to be adaptative and
therefore to learn.
Some “sympathetic mass transition” has also been implemented, in order to make agents in the
close neighbourhood of a very heavy one become also heavier and slower, and consequently
clustering in that region (In the previous swarm this happened automatically from the
interaction with the environment).
The weight that is assigned to each agent could have its origin in a “fitness function”. The
position of each individual of the swarm would then be mapped onto a “phenotype” and a
fitness value calculated for it. In cases of good fitness a heavy weight would be given to the
individual, to indicate the system that that position is worth keeping and mimicking. Bad
positions would this way be forgotten, since the agents in those areas would have low inertia
and the tendency to move rapidly away from them, towards more successful territories.
Regions with good values will compete with others for the attention of the agents, and if not
successful enough, they will be forgotten.”
p14
“Since there is some kind of ‘conversational’ human/machine relationship between the swarm
and a person interacting with it, the forms work in some way as signs, in the sense that they
are interpreted by the person and meanings attached to them, such as good/bad, spider-like,
spongy, etc. The swarm tends to ‘understand’ and ‘agree’ with the choices made by the
person interacting with it, but it also seems to ‘disagree’ slightly, or at least to not fully
understand the preferences of the user. It is only in this way that the conversation is possible,
and the consensual domain formed. If the machine would agree immediately, that is, if all
agents would converge exactly to the point specified, conversation would be impossible.
Through this game of differences the conversation can evolve.”
p18
Source: http://cumincad.architexturez.net/system/files/pdf/39c6.content.07058.pdf