goalssuch

Goalssuch is a theoretical framework in artificial intelligence that describes a method for integrating explicit goal specification with search-based planning to produce action sequences that satisfy multiple, potentially conflicting objectives under given constraints. The term blends 'goals' and 'search' to emphasize the central role of goal representation in guiding the search process.

Its core idea is to treat goals and constraints as first-class entities, decomposing them into subgoals and organizing them hierarchically. The search process then explores possible plans, using heuristics and constraint propagation to prune infeasible options and prioritize plans that best satisfy the goals, possibly in a multi-objective sense.

Applications include autonomous robots, logistics and scheduling systems, game AI, and workflow optimization, where agents must balance efficiency, safety, time, and quality constraints.

History and status: goalssuch is not a standard term in established AI literatures; it appears in niche discussions and is sometimes used as a generic label for goal-driven search approaches. Related concepts include automated planning, hierarchical task network planning, and goal-conditioned reinforcement learning.

An example workflow: (1) articulate the goals and constraints, (2) decompose into subgoals, (3) generate candidate plans, (4) evaluate plans with a heuristic evaluating progress toward goals, (5) select and execute, (6) monitor progress and replan if subgoals fail or priorities change.

Advantages include improved explainability and alignment with user objectives; limitations involve computational complexity, sensitivity to goal specification quality, and challenges in scaling to very large problem spaces.

See also: Automated planning, Heuristic search, Goal-oriented planning, Goal-conditioned reinforcement learning, Workflow management.