SequentialExplicit

SequentialExplicit is a framework for modeling and solving sequential decision problems in which actions must follow an explicit, user-defined sequence and satisfy a set of temporal or logical constraints. It combines planning concepts that specify order with constraint satisfaction approaches that enforce feasibility. Rather than relying solely on an end-to-end policy, SequentialExplicit represents the decision process as a sequence of decision points, each with explicit constraints and observable state.

In typical implementations, the framework uses a representation that includes states, actions, and a set of constraints that link actions across time. The decision process can be planned upfront, executed step by step, or refined through iterative feedback. Solvers or differentiable approximations are employed to ensure that the resulting plan respects constraints while optimizing a given objective, such as efficiency, safety, or cost. Learning components may be incorporated to infer constraint parameters, improve initial sequences, or adapt constraint tolerance based on experience.

Applications of SequentialExplicit span robotics, automated manufacturing, process control, logistics, game AI, and workflow management. The approach offers interpretability and guarantees of constraint satisfaction, which are valuable in safety-critical or highly regulated environments. Its explicit sequencing makes plans transparent and auditable, facilitating human collaboration and verification.

Limitations include computational overhead for long horizons or highly complex constraint sets, potential brittleness when constraints are mis-specified, and challenges integrating with purely stochastic or highly uncertain environments. Ongoing research explores hybrid methods that blend explicit sequencing with flexible learning, scalability improvements, and extensions to probabilistic or soft constraints to broaden applicability.