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Originally, learning automata (LAs) were introduced to describe human behavior from both a biological and psychological point of view. In this paper, we show that a set of interconnected LAs is also able to describe the behavior of an ant colony, capable of finding the shortest path from their nest to food sources and back. The field of ant colony optimization (ACO) models ant colony behavior using artificial ant algorithms. These algorithms find applications in a whole range of optimization problems and have been experimentally proved to work very well. It turns out that a known model of interconnected LA, used to control Markovian decision problems (MDPs) in a decentralized fashion, matches perfectly with these ant algorithms. The field of LAs can thus both impart in the understanding of why ant algorithms work so well and may also become an important theoretical tool for learning in multiagent systems (MAS) in general. To illustrate this, we give an example of how LAs can be used directly in common Markov game problems.