High Speed Approximate Cognitive Domain Ontologies for Constrained Asset Allocation based on Spiking Neurons

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). One problem that is often tasked to CDOs is asset allocation. Given the number of possible solutions in this allocation problem, determining the optimal solution via CDO can be very time and energy consuming. A grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree approximation is required to achieve the speedup. The approximate spiking approach presented in this work was able to complete nearly all allocation simulations with greater than 98% accuracy. Our results in this work show that constraining the possible solution space by creating specific rules for a scenario can alter the quality of the allocation result. We present a study compares allocation score and computation time for three different constraint implementation cases. Given the vast increase in speed, as well as the reduction computational requirements, the presented algorithm is ideal for moving asset allocation to low power embedded hardware.