The society of mind
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
Advances in genetic programming
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning to Solve Planning Problems Efficiently by Means of Genetic Programming
Evolutionary Computation
Digital enzymes: agents of reaction inside robotic controllers for the foraging problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Exploring the evolution of internal control structure using digital enzymes
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Previous work in multi-agent systems has required the human designer to make up-front decisions about the multi-agent architecture, including the number of agents to employ and the specific tasks to be performed by each agent. This paper describes the automatic evolution of these decisions during a run of genetic programming using architecture-altering operations. Genetic programming is extended to the discovery of multi-agent solutions for a central-place foraging problem for an ant colony. In this problem each individual ant is controlled by a set of agents, where agent is used in the sense of Minsky's Society of Mind. We describe the simultaneous evolution of the number of agents needed to solve the problem and the work performing steps of each agent. Genetic programming was able to evolve time-efficient solutions to this problem by distributing the functions and terminals across successively more agents in such a way as to reduce the maximum number of functions executed per agent. The other source of time-efficiency in the evolved solution was the cooperation that emerged among the ants in the ant colony.