Symbolic Planning Clause Samples

Symbolic Planning. ‌ Most state-based planners use methods that operate on explicit states, where each assignment of different values to the variable in the planning task is a different state. Representation can be beneficial for certain problems, but it has shown to be prone to scaling issues when working in domains that have many predicates, leading to a state-space explosion. This is due to the exponential growth of the state-space depending on the number of variables. Symbolic planning differs from this by representing a state as a Boolean function, leading to applying operators on sets of states, instead of one by one changes. The first big advantage of such an approach is that it is searching for a solution that uses less memory, as states are compressed based on their logical representation which now grows linearly, instead of exponentially.