Tuesday, 22 January 2013

Pheromone Targets

In the previous post I went over some recent work on a multi-agent approach to space planning. I mentioned "pheromone targets" as the basis for agent decision-making without going into much detail on what a pheromone actually is so I figured I'd follow up with a bit of necessary unpacking.

In the context of this model, pheromone concentration refers to a three dimensional rgb value held by each node in the network. This value is under constant modification as nodes participate in ongoing pheromone diffusion by pulling their own concentration towards that of their immediate neighbors. Through diffusion, pheromones propagate through space acting as a communication medium amongst a population of agents.

Similarly, a pheromone target is a three dimensional rgb value held by each agent that informs which node an agent decides to occupy or release at each step. If an agent hasn't reached its spatial quota, it evaluates all unoccupied nodes that lie adjacent to its current territory by comparing their pheromone concentration to its own pheromone target. The node with smallest deviation is then chosen. If an agent already has enough nodes, it evaluates each node it has accumulated in the same way before releasing the one with the largest pheromone deviation.

Pheromone targets also determine how an agent goes about modifying its local environment. Each agent pulls the pheromone concentration of all owned nodes towards its pheromone target, essentially "painting" its portion of the node network with its preferred colour. Through diffusion, other agents within the system are able to sense these changes - encouraging compatible ones (those with similar targets) to occupy space next door and the incompatible ones to settle elsewhere. To give a better sense of compatibility within an agent population, their pheromone targets can be plotted in rgb space as seen above.

Below is a look at two distinct families of agents in action. One family has pheromone targets in the cyan range while the other prefers concentrations in the magenta range. Adjacencies form within each family but the two remain distinctly separate, occupying opposite corners of the bounding volume.

The next video introduces pheromone sources as a means of templating external influences. Nodes along opposite faces of the bounding volume pull their own pheromone concentration towards white and black respectively. The gradient produced could represent the transition from public to private as imposed by a hypothetical context for example.

The final video below elaborates further on this idea of templating external influences by removing certain nodes from the network through volumetric masking. Agents are thus constrained to a subset of the total volume as they develop their territories.

Platforms: Eclipse, Processing

Friday, 18 January 2013

Thecal Matter

The last few months I've been more than a bit preoccupied with thesis work so the blog has suffered from some serious neglect. I'm sorry blog. Having defended last week though, I'm ready to get back in the swing of things and what better way to start than posting some of the fruits of my labour. Ripeness not guaranteed.

In short, my work centered on the development of bottom-up space planning software - borrowing mechanisms of self-organization from ants, termites, slime moulds and other social organisms to solve spatial adjacencies amongst elements of a given programmatic brief. The model treats each programmatic element as an autonomous agent whose goal is to territorialize some portion of a shared three dimensional node network. Agents communicate indirectly through the modification of this mutual environment via virtual "pheromones". Each agent both pursues and emits a specific pheromone concentration or "target" in its occupation of space. As such, agents with similar targets congregate while those with disparate targets avoid each other as they expand and refine their respective territories.

In due time, adjacencies between compatible programs self-organize producing an informed schematic design solution. The trick lies in defining compatibility ie. determining the appropriate pheromone target of each element in the programmatic brief. For now I've left this up to manual input - targets can be changed in real time if space is settling in undesirable ways. Moving forward however, I'd be interested in giving the agents the ability to refine their own targets if they find themselves in uncomfortable configurations.

In any case, spatial organization becomes the emergent product of a competitive ecology - agents negotiate with one another, carving out their own niches within a finite volume. The task of space planning, one that is typically carried out by a singular high-level decision-maker (aka the architect), is handed off to the distributed decision-making of low-level collective intelligence - a common approach to solving problems that exceed a certain level of complexity. Much like the slime mould mentioned a few posts back, the individuals of this collective brain are quite stupid - following only the simplest of rules - yet the result of their collective efforts can be remarkably clever.

Platforms: Eclipse, Processing