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
The evolution of mental models
Advances in genetic programming
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKER, a method for the automatic generation of agents that discover information about their environment, encode this information for later use, and create simple plans utilizing the stored mental models. In this method, agents are multi-part computer programs that communicate through a shared memory. Both the programs and the representation scheme are evolved using genetic programming. An illustrative problem of 'gold' collection is used to demonstrate the method in which one part of a program makes a map of the world and stores it in memory, and the other part uses this map to find the gold The results indicate that the method can evolve programs that store simple representations of their environments and use these representations to produce simple plans.