I was trying to come up with a system that would evaluate bylaws for an organization as to determine their underlying logic.I think a first-order predicate system would work for representing the rules, which could be translated from the text via part-of-speech tagging and other NLP techniques.  Is there a systematic way to interpret the first-order logic rules as a whole, or some type of ML architecture that would work as a second layer to find similarities between the elements.For example,List of fun activities:golfcoffee breakpizzaBylaws:On Friday, we play golfOn Friday or Saturday, we take a quick coffee break, and if it's Saturday, we get pizzaConclusion: our group has fun on weekendsIt sounds far fetched, but I'm curious if it's possible.  I also realize that perhaps more first-order logic would be a better fit for driving the conclusions of the second layer.  
Methods to evaluate a system of written rules
machine learning;algorithms;pattern recognition;logic
The trick here is that this works well provided that your rules can be expressed in predicate form. Is golf still fun if it's raining, or if you play poorly?If you need something more flexible, you might want to look at some statistical/Baysean tools. There, you'd say that golf had a high probability of being fun, not that it was always fun all the time ever.