I have noob thought on the potential of these in Formal path planning. Specifically given a set of functions that basically map {State -> Actions} given precon

I have noob thought on the potential of these in Formal path planning. Specifically given a set of functions that basically map {State -> Actions} given preconditions, transition functions (heavily paraphrasing STRIPS[1]) can a correct and optionally "realistic" plan be generated[2]? I am quite interested in this. It seems clear that the issue is that there is no "guidance" like DFA on what is the correct next symbol for a Plan, but perhaps the AI can generate some kind of a probability or order on what is the best step and one can go from there... Are you guys thinking about this direction? [1] https://en.wikipedia.org/wiki/Stanford_Research_Institute_Problem_Solver#Complexity https://en.wikipedia.org/wiki/Stanford_Research_Institute_Pr... [2] Formal Planning decision problem(plan exists) given STRIPS spec is at least NP-Complete[1]. There are several mathematical, logical and statistical "tricks"(e.g. [3]) that are used to bring down the complexity and try find a plan using heuristics(thinking MDPs, POMDPs here). This is not new, everyone in LLM research knows this. [3] "Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning": https://www.sciencedirect.com/science/article/pii/S0004370299000521?ref=pdf_download https://www.sciencedirect.com/science/article/pii/S000437029...