Single scenario results. Illustration of the design method and comparison with multifactorial simulations of the single-scenario: (a) depicts the stationary distribution and (b) the expected change. attributed to him: smart computing (2022). DOI: 10.34133/2022/9761694
Algae multiply, flock birds, and swarm insects. This collective behavior by individual organisms can provide a separate and collective benefit, such as improving reproductive opportunities for successful mating or providing security. Now, researchers have harnessed the self-organizing skills required to reap the benefits of natural swarms for robotic applications in artificial intelligence, computing, search and rescue, and more.
They published their method on August 3 at smart computing.
Corresponding author Marco Dorigo, Professor at Artificial intelligence The laboratory, called IRIDIA, is affiliated with the Free University of Brussels, Belgium. “The swarm behavior is not a single map with simple rules implemented by individual bots, but results from the complex interactions of many bots implementing the same set of rules.”
In other words, the bots must work together to achieve the overall goal of separate contributions. The problem, according to Dorigo and his co-authors Dr. Valentini and Professor Hamann, are that the traditional design of individual units to achieve a collective goal is bottom-up, requiring trial-and-error improvements that can be costly.
“To meet this challenge, we are proposing a new design approach that is global to local,” Dorigo said. “Our basic idea is to create a heterogeneous swarm using combinations of behaviorally different factors such that the resulting swarm behavior approximates user input that represents the behavior of the entire swarm.”
This configuration involves selecting individual agents with predefined behaviors that researchers know will work together to achieve the target group’s behavior. They lose out on the ability to program individual units locally, but according to Valentini, Hamann and Dorigo, the trade-off is well worth it. They cited the example of a monitoring mission, where a squadron might need to monitor a facility that requires more internal monitoring during the day and more external monitoring at night.
User provides a description of the requested swarm assignments as a file probability distribution Over the area of all potential swarms allotment — more factors inside during the day, more outside at night, or vice versa, Valentini said.
The user will define the target behavior by changing the number and position of the distribution modes, with each mode corresponding to a specific allocation, such as 80% agents indoors, 20% outside during the day, 30% indoors, and 70% outside at night. This allows the squadron to change behavior periodically and independently, predetermined by group modes, as conditions change.
“While it is difficult to find the exact control rules for the robots for the swarm to behave as we wish, the swarm required behavior It can be obtained by combining different sets of control rules that we already understand,” Dorigo said. Swarm behaviors can be modeled microscopically by mixing bots from different pre-defined rule sets. “
This isn’t the first time Dorigo has turned to nature to improve computer science methods. He previously developed an ant colony optimization algorithm, based on how ants move between their colonies and food sourcesto solve difficult computing problems involving finding a good approximation of the optimal path on a graph.
While Dorigo first proposed this approach to a relatively simple problem, it has since evolved as a way to address a variety of problems. Dorigo said he plans to take the swarm’s methodology in a similar direction.
“Our immediate next step is to validate our methodology across a larger set of swarm behaviors and beyond task assignment,” Dorigo said. “Our ultimate goal is to understand what makes this possible, and to formalize a general theory to allow researchers and engineers to design swarm behaviors without going through a painstaking trial and error process.”
Gabriel Valentini et al., Global to Local Design for Self-Organized Task Allocation in Swarms, smart computing (2022). DOI: 10.34133/2022/9761694
Introduction to Intelligent Computing
the quote: Teaching robots to be players with nature (2022, September 21) Retrieved on September 21, 2022 from https://techxplore.com/news/2022-09-robots-team-players-nature.html
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