Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions by Adi Shklarsh, Gil Ariel, Elad Schneidman, and Eshel Ben-Jacob, appeared in September 2011 Issue of PLoS Computational Biology.
I mentioned to Jack Park that I had been thinking about swarms and mining semantics and he forwarded a link to the ScienceDaily article, Smart Swarms of Bacteria Inspire Robotics: Adaptable Decision-Making Found in Bacteria Communities, which was an adaptation of the PLoS Computational Biology article I cite above.
Abstract (from the PLoS article):
Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers’ influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.
This research has a number of aspects that are relevant to semantic domains.
First, although bacteria move in “complex terrains,” those terrains are no more complex and probably less so than the semantic terrains that are presented to agents (whether artificial or not). Whatever we can learn about navigation mechanisms that are successful for other, possibly less complex terrains, will be useful for semantic terrains.
Second, the notion of “performance” as increasing or decreasing influence over other agents sounds remarkably similar to “boosting” except that “boosting” is crude when compared to the mechanisms discussed in this paper.
Third, rather than complex and possibly rigid/fragile modeling (read ontologies, description logic), perhaps simpler computations in agents with short memories may be more successful.
No proof, just airy speculation at this point but experimental proof, the 19th century logicists may concede, is the best kind.