An ant colony is probably the last place one would expect to find a maths whiz, but a study has shown that they could solve complex, dynamic problems, and may even help computer scientists develop better software.
An international team has found the ants are capable of solving difficult mathematical problems and also able to do what few computer algorithms can -- adapt optimal solution to fit a problem, the ‘Journal of Experimental Biology’ reported.
Using a novel technique, scientists tested whether Argentine ants could solve a dynamic optimisation problem by converting classic Towers of Hanoi maths puzzle into a maze.
“Although inspired by nature, the computer algorithms often do not represent the real world because they are static and designed to solve a single, unchanging problem,” said lead author Chris Reid of Sydney University.
He added: “But nature is full of unpredictability and one solution does not fit all. So we turned to ants to see how well their problem solving skills respond to change. Are they fixed to a single solution or can they adapt?”
The scientists tested the ants using the three-rod, three-disk version of the Towers of Hanoi problem -- a toy puzzle that requires players to move disks between rods while obeying certain rules and using the fewest possible moves.
But since ants cannot move disks, they converted the puzzle into a maze where the shortest path corresponds to the solution with fewest moves in the toy puzzle.
The ants at the entry point of the maze could chose between 32,768 possible paths to get to the food source on the other side, with only two of the paths being the shortest path and thus the optimal solution.
The ants were given one hour to solve the maze by creating a high traffic path between their nest and the food source, after which time the researchers blocked off paths and opened up new areas of the maze to test the ants’ dynamic problem solving ability.
After an hour, the ants solved the Towers of Hanoi by finding the shortest path around the edge of the maze. But when that path was blocked off, the ants responded first by curving their original path around the obstacle and establishing a longer, suboptimal, route.
But after a further hour, the ants had successfully resolved the maze by abandoning their suboptimal route and establishing a path that traversed through the centre of the maze on the new optimal route.
But not all the colonies’ problem solving skills were equal -- ants that were allowed to explore the maze without food for an hour prior to the test made fewer mistakes and were faster at resolving the maze compared to the ants that were naive.
Reid said: “Even simple mass-recruiting ants have much more complex and labile problem solving skills than we ever thought. Contrary to previous belief, the pheromone system of ants does not mean they get stuck in a particular path and can’t adapt.
“Having at least two separate pheromones gives them much more flexibility and helps them to find good solutions in a changing environment. Discovering how ants are able to solve dynamic problems can provide new inspiration for optimisation algorithms, which in turn can lead to better problem-solving software and hence more efficiency for human industries.”