Sequential decision tasks appear in many real-world domains, including control, resource allocation, routing, and scheduling. The objective of this project is to develop a new approach to sequential decision making based on symbiotic evolution of neural networks. In symbiotic evolution, a population of neurons are evolved with genetic algorithms to cooperate and form decision-making networks. Diversity is maintained in the population as part of the task and the system can find good solutions efficiently even in difficult tasks with sparse reinforcement. In the proposed project, symbiotic evolution will be analyzed both theoretically and experimentally, the algorithm will be further developed and applied to complex real-world domains such as local-area network management, and a practical high-level interface will be developed that will allow rapid application of the method to new domains. The main scientific contributions of the research are expected to be: (1) a novel, powerful method for extracting and encoding problem-specific knowledge automatically for sequential decision making, and (2) a thorough understanding of the role of diversity and cooperation in genetic algorithms. The development of the general application interface should also benefit many practical fields, including control engineering, military science, and operations management. The main hypotheses to be tested are: (1) Pattern recognition and generalization capabilities of neural networks can be used to implement effective and robust sequential decision making strategies;(2) Genetic algorithms can discover powepful problem-specific decision-making strategies even under sparse reinforcement; 3) By making population diversity an essential part of the task, symbiotic evolution can develop solutions to harder problems and do it more efficiently than standard genetic algorithms.
