Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
This work addresses the need for more efficient and scalable hybrid cognitive capabilities, though it appears incremental as it builds on an existing algorithm.
The paper tackles the problem of improving the expressiveness, efficiency, and scaling of the Three-Weight Algorithm for hybrid cognitive processing by integrating knowledge, demonstrating these techniques on Sudoku and circle packing problems.
In this paper we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit knowledge to improve solution speed and quality. In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems. We propose novel methods by which to integrate knowledge with this algorithm to improve expressiveness, efficiency, and scaling, and demonstrate these techniques on two example problems (Sudoku and circle packing).