Parallelizing knowledge mining in a cognitive agent for autonomous decision making

Cognitive computing can enable automatic decision making, thus enabling the design of more intelligent and autonomous systems. An autonomous cognitive decision support architecture, Cognitively Enhanced Complex Event Processing (CECEP), was developed at United States Air Force Research Laboratory to enable agent-based decision-making. CECEP has several components, which allow cognitive agents to make automatic decision without any human interaction. One of the key components, Cognitive Domain Ontologies (CDO) are a knowledge repository for CECEP agents. The knowledge mining process from CDOs in the CECEP architecture is analogous to solving a constraint-satisfaction problem (CSP). Cognitive agents are more capable when they are able to process or mine larger volume of information to make more informed and intelligent decisions. This means that the CDOs need to process large knowledge bases. Cognitive agents need to consider environmental inputs and make decisions at runtime. Hence CDOs with large knowledge bases need to be accelerated for real time cognitive agents. This study introduces an accelerated CDO solver with multiple optimizations for generating and ranking solutions for an autonomous cognitive agent. As CDOs typically generate all equally weighted valid solutions, it is impossible to find the best one from this solution set without imposing some relative importance of certain criteria. This study implemented several optimization conditions through objective functions and processed them as Multiple Constraint Optimization Problems (MCOPs) to rank solutions on a GPGPU. It achieved a speedup of 85-100 times over existing solvers for ranking the solutions.