This is an expected behaviour. More rules impose more constraints on the feasible space. The space with more rules is hence much smaller. The only pitfall is that in the middle of calculation the feasible space may blow up before eventually shrinking. This is where the optimisation heuristics helps. Your previous question was spot on :)
Good point. Yes, it uses optimisation for various tasks.
First of all, it uses compact representatin of rules - similar to DNF, but converted to a sparse matrix.
Second, for the inference (which is a process of compiling the knowledge base) the order in which rules are compiled is crucial. We use two types of heuristic optimisations for this process, one is based on Jaccard similarity, another is so called predator-pray heuristics.
vector-logic is light-weight propositional logic inference engine. Optimised for simple queries like "what is the value of 'y' given all rules and evidences", and also allows you to iterate through the entire valid set (all valid assignments).
We also bothered to make the interface very simple and intuitive - you'll have very shallow learning curve.
Hi HN, I'm one of the creators. I've been tackling the problem of managing complex business rules for years (especially in fintech), and wanted a lightweight, Python-native tool that was built on a solid theoretical foundation.
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