goalconsistent

Goalconsistent, often written as goal-consistent, is a term used in artificial intelligence and decision making to describe systems, policies, or plans in which intermediate objectives and sub-goals are coherent with and supportive of a central goal throughout the task. A goal-consistent approach seeks to align actions taken at each step with the intended final outcome, avoiding strategies that yield short-term gains at the expense of longer-term objectives.

In planning and reinforcement learning, a policy is goal-consistent if the sequence of chosen subgoals, actions, and states collectively advances toward the global objective without introducing contradictions or regressions. This often involves ensuring that subgoals are feasible, non-contradictory, and hierarchically related to the overarching goal, and that progress metrics reflect both local and global progress.

Applications appear in goal-conditioned control, hierarchical planning, and robotics. For example, a goal-conditioned agent that must move an object to a target location should select intermediate subgoals (such as reorienting the object, approaching the target, and securing the grip) that do not conflict with each other or with the final move. In hierarchical systems, higher-level planners generate subgoals that are compatible with the final task, while lower-level controllers implement actions that remain aligned with those subgoals.

Challenges include defining a precise notion of consistency in stochastic or dynamic environments, balancing subgoal feasibility with overall efficiency, and measuring alignment when multiple competing objectives exist. Poorly defined or overly rigid goal-consistency can limit exploration or adaptability, while excessive flexibility may permit inconsistent behavior.

Goal-consistency is related to concepts such as goal alignment, reward shaping, and hierarchical reinforcement learning, and is of particular interest in AI safety and robust behavior design.