Multi-agent research often borrows from biology, where remarkable examples of collective intelligence may be found. One interesting example is ant colonies? use of pheromones as a joint communication mechanism. In this paper we propose two pheromone-based algorithms for artificial agent foraging, trail-creation, and other tasks. Whereas practically all previous work in this area has focused on biologically-plausible but ad-hoc single pheromone models, we have developed a formalism which uses multiple pheromones to guide cooperative tasks. This model bears some similarity to reinforcement learning. However, our model takes advantage of symmetries common to foraging environments which enables it to achieve much faster reward propagation than reinforcement learning does. Using this approach we demonstrate cooperative behaviors well beyond the previous ant-foraging work, including the ability to create optimal foraging paths in the presence of obstacles, to cope with dynamic environments, and to follow tours with multiple waypoints.We believe that this model may be used for more complex problems still.