Novel Computational Intelligence Method of J* Surface Generation for Fast Optimal Decision/Control Design

Portland State University, Portland OR

Investigators

Abstract

The method known as Dynamic Programming has been known for over four decades to be the only general approach for designing optimal procedures (policies) in non-linear, stochastic decision/control con-texts. Virtually all computer decision making and/or control procedures in real-world problems stand to benefit from application of the Dynamic Programming methodology. Unfortunately, for direct application to real-world problems, its computing requirements would outstrip even present computer hardware capabilities. The good news is that in recent years, computational intelligence methods known as Adaptive Critics have been developed that implement good approximations of Dynamic Programming, and thus significantly enhance the quality of decision and control policies that are achievable in practice. This project will take the computational intelligence approach to this task one step further. The PI will try to inject more human-like intelligence into the process. The context is as follows: humans are known to be able to learn optimal solutions to complex decision/control tasks; further, the more of these they learn, i.e., the more 'experience' they accumulate, then they are able to quickly come up with a (nearly) optimal solution to a newly encountered problem, so long as it is similar to ones they have experience with. The proposed project seeks to emulate this capability. In particular, the project seeks to develop a computational intelligence methodology that efficiently designs optimal controllers for additional problems within an assumed problem context, based on knowledge of existing designs in that context. The key ingredient of this methodology will be a J* Surface Generator (J*SG). The J*SG is the device that is to contain the equivalent of human 'experience', knowledge of optimal policies for previously encountered decision/control problems, and the ability to infer from them an (approximately) optimal policy when a similar problem is encountered. The key research tasks include development of useful ways to represent the problem specification to the J*SG, and the representation of the J* surface to facilitate extraction of the associated (optimal) policy. Computational intelligence methods (e.g., neural networks, Fuzzy logic, genetic algorithms, etc.) are to be used a stools for this accomplishment, as well as for implementation of the J*SG. The venue in which the proposed research is to be performed is the NW Computational Intelligence Laboratory (NWCIL) at Portland State University. The NWCIL will expand by contributing to the training of teachers in the Oregon MESA (Mathematics, Engineering, Science Achievement) program. This program is designed to increase the numbers of African American, Hispanic, American Indian, and women students in careers in the field of mathematics, science, and engineering.