Knowledge mining for cognitive agents through path based forward checking

Abstract

The Cognitively Enhanced Complex Event Processing (CECEP) architecture is an autonomous decision support tool that reasons and learns like humans and enables enhanced agent-based decision making. This architecture has applications in both military and civilian domains, including operations research and data mining. One of the most computationally challenging aspects of CECEP is mining domain knowledge captured in cognitive domain ontologies (CDOs). This requires massively linked knowledge databases to be searched based on a large set of constraints to generate intelligent decisions. The solution search for CDOs is a kind of Constraint Satisfaction Problem (CSP). Due to the unique nature of the CDO-to-CSP mapping, generic constraint solvers are inefficient. We introduce a novel high performance path based forward checking CSP algorithm to solve CDOs and compare it to a commonly utilized CSP solving application. Additionally our approach enables a very compact and efficient representation of the entire solution space to allow fast processing and give agents key ideas on how to further reduce the solution space. The proposed algorithm provided a speedup of about 10-25 times for generating the first solution and about half a million times for all the solutions over a Choco based conventional CSP approach.