Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics system observation its state transitions. So far systems that LFIT handled were mainly restricted to synchronous deterministic dynamics. However, other exist in field logical modeling, particular asynchronous semantics which is widely used biological systems. In this paper, we propose modeling discrete memory-less multi-valued dynamic as logic programs rule represents what can occur rather than will occur. This allows us represent non-determinism and an extension learn regardless update schemes, allowing capture large range semantics. We also second algorithm able whole dynamics, including semantics, form single propositional program with constraints. show through theoretical results correctness our approaches. Practical evaluation performed on benchmarks literature.