Optimization framework and applications of training multi-state influence nets

Influence nets (INs) are proposed on the basis of causal logic for the purpose of depicting causal relationship strengths in complex systems. However, it is difficult to accurately determine the causal strength (CAST) parameters on the basis of expert knowledge only, particularly for practical problems with multiple attributes and multiple states. In this paper, the original IN with binary states is first extended into a multi-state IN, endowing the IN with the ability to model and infer under multiple states. Based on this method, an optimization framework is proposed to optimize the CAST parameters of multi-state INs. Finally, two practical cases are studied to verify the feasibility and efficiency of the proposed multi-state IN with an optimization framework in regression and classification.