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The objective of this paper is to describe thedevelopment of a specific theory of interactions and learning amongmultiple robots performing certain tasks. One of the primaryobjectives of the research was to study the feasibility of a robotcolony in achieving global objectives, when each individual robot isprovided only with local goals and local information. In order toachieve this objective the paper introduces a novel cognitivearchitecture for the individual behavior of robots in a colony.Experimental investigation of the properties of the colonydemonstrates its ability to achieve global goals, such as thegathering of objects, and to improve its performance as a result oflearning, without explicit instructions for cooperation. Since thisarchitecture is based on representation of the “likes” and“dislikes” of the robots, it is called the Tropism System Cognitive Architecture. This paper addresseslearning in the framework of the cognitive architecture,specifically, phylogenetic and ontogenetic learning by the robots.The results show that learning is indeed possible with the TropismArchitecture, that the ability of a simulated robot colony toperform a gathering task improves with practice and that it canfurther improve with evolution over successive generations.Experimental results also show that the variability of the resultsdecreases over successive generations.