Organismic Augmentation
Organismic Augmentation
The key idea of Organismic Augmentation is to integrate technological artifacts (devices, robots) into organismic communities. The more these devices blend into the self-regulating interaction networks that exists between natural organisms, the deeper will this augmentation be anchored in the system. Social insects are especially suitable to be augmented this way, as their social interaction patterns provide already suitable "hooks" that the technological artifacts can use to anchor themselves in these systems. Besides that, social insects have a large ecological impact, due to their large worker populations, due to their role as "keystone species" and due to the centralism they offer, as augmenting one single colony can affect square kilometers or even hundreds of square kilometers of the ecosystem around the colony.
The deep integration of technological artifacts requires that they are participating in the major feedback loops that govern the collective behaviors of the natural host society. Either, these devices must participate in those feedback, what basically means that the artificial components are involved in the circular chain of causation that comprises the feedback loop. Or, alternatively, the technological artifacts modulate natural feedback loops in the augmented systems. Ideally the artifacts should do this considering also the system's past, current and/or predicted future state. In this case, these artificial components do not simply modulate an existing feedback loop, but rather generate a novel one. So in principle, the same type of basic concept is applied here, that we have already seen in Ecosystem Hacking, just on a smaller size and time scale, as the technological artifacts do not embed themselves into feedback loops of ecosystems but into feedback loops of an organismic society, like a social insect colony. In both cases there exists another type of affection, which is establishing novel, previously non existing, feedback loops. Thus in some scale, Organismic Augmentation is similar to Ecosystem Hacking, just on a smaller scale. Or, to put it differently, Organismic Augmentation on a smaller scales creates "ecosystem-active" novel biohybrid agents that can perform Ecosystem Hacking on a larger scale.
How can technological artifacts integrate themselves deeply into an organismic society? On the one hand, this can be done by bio combining biological knowledge with engineering ingenuity, a process usually called bio-inspiration, biomimetics or simply bionics. Going beyond that, Artificial Intelligence, Artificial Life and Machine Learning offer new pathways, even removing the human scientist or engineer from the loop. By applying machine learning techniques, robots could learn by themselves how to effectively integrate themselves into animal societies. In my work, I have demonstrated this together with several research partners, in the project ASSISIbf with bees and fish, in the project Hiveopolis with bees and in the project FloraRobotica with plants.
Concept of automatically tuning and refining a-priori models, which drive the microscopic and proximate behaviors of robots. Automated observation of the system behavior collects data streams, which inform sophisticated machine learning algorithms to tune these models until they produce the desired collective macroscopic behaviors. This example is shown from project RoboRoyale
A typical example for Organismic Augmentation: A group of bees was attracted by a ASSISIbf robot, and induced a symmetry breaking. This was neither done by one robot alone nor by the bees alone: It was the collective of all bees and both robots that made this decision as an end result of a negotiating behavioral and self-organizing feedback loop that includes all autonomous actors in the system
Example of Organismic Augmentation in Hiveopolis: Bristles with either variable stiffness or variable direction can switch the traffic flow between free motion and uni-directional flows of bees . Actuated bristles with motors that are affected by traffic flow sensors in the hive produce a system of self-organized traffic management