USC Viterbi School of Engineering researchers believe that some of the most complex technologies of our times, such as robotics or cognitive computing, can learn a thing or two from swarms of ants, pigeons and bacteria.
For starters, let’s look at ants. Drop a piece of sugar on the floor and wait. Sooner or later, one or two straggling ants scoping the area will stumble upon it. What happens next is perhaps one of the most elaborate orchestrations of organized teamwork in nature.
Once the food source is detected, more and more ants begin to march toward it. At first, the patterns of their movements may appear random and erratic. However, as each ant gathers and exchanges more information with its neighbors, their collective intelligence grows. They begin to orient and self-organize. They become more stable. They form a swarm.
A swarm represents agents that interact with one another, exchanging information among themselves. The goal: to be better organized, collectively smarter and more effective at performing tasks as a group.
Complex calculations
USC Viterbi PhD candidate Hana Koorehdavoudi has studied swarms the past three years. Under the mentorship of Paul Bogdan, an assistant professor in the Ming Hsieh Department of Electrical Engineering, Koorehdavoudi has developed a series of algorithms that can actually quantify the degree of complexity within swarms.
Koorehdavoudi’s calculations measure the interactions within complex systems. These calculations are unprecedented because they allow scientists to identify and evaluate the types of exchanges that result in certain forms of collective behavior — and that could help scientists engineer specific outcomes by simply tweaking the interactions that exist within a network.
The inspiration behind this unique approach was the “energy landscape,” which represents all the possible dynamic formations of agents within a swarm.
“The energy landscape helps provide an understanding of how the dynamics of the swarm evolves through time,” Koorehdavoudi said. “This lets us identify and extract how the agents relocate themselves with respect to others in the system.”
Research and development
Bogdan and Koorehdavoudi believe that the algorithms can have an impact on scientists’ efforts to understand, optimize and control complex networks. Examples of the applications are advancing research and development in the areas of robotics, urban planning or even cancer treatment.
The research by Bogdan and Koorehdavoudi could also help solve everyday problems such as traffic. Their work might even inspire new approaches for controlling the spread of cancerous cells — both scenarios involve individual agents that interact and move together (i.e., automobiles and cancer cells).
Ultimately, the phenomenon as explored by Bogdan and Koorehdavoudi is universal.
“You can also take these formulas and apply them to the brain and see how brain organizes a thought,” Bogdan said. “You can model the thinking process.”
