Should the initial state be checked?

Yes, the start may already satisfy the goal and need no actions.

Is early goal testing always faster?

It can avoid later work in suitable algorithms, but the benefit depends on the problem and must not invalidate the stopping guarantee.

Can a weighted search record a goal early?

Yes. Recording a candidate is different from immediately returning it as optimal.

AI Glossary / Graphs and symbolic AI / Intermediate

Early goal test

Definition

An early goal test checks whether a newly generated search node satisfies the goal before the node is later selected from the frontier for expansion. It can let a search stop sooner when its ordering guarantees that the generated goal is an acceptable answer. The timing of the test must match the algorithm's completeness and optimality requirements.

What it means

Search algorithms often discover several possible next states and place them in a frontier, a collection awaiting further consideration. Testing a goal immediately on discovery is earlier than waiting until that node is removed from the frontier according to the search's selection rule.
The difference sounds small but can affect correctness when costs vary. A state can be a genuine goal while the route that first reaches it is unnecessarily expensive. Goal satisfaction answers whether the state qualifies; the search's stopping condition must also address whether the required quality of solution has been established.

How it works

A typical early-test pattern first checks whether the initial state already meets the goal. During expansion, it checks each generated successor and returns a solution when the algorithm permits stopping at that point. Other successors are stored for later exploration.
In breadth-first search with equal positive step costs, nodes are generated in nondecreasing depth, so the first generated goal has a minimum number of steps under the usual assumptions. Cost-based searches need different care. Uniform-cost search and A* normally rely on frontier selection and their associated conditions to justify returning an optimal goal, rather than returning the first goal generated.

Three practical examples

A breadth-first puzzle solver checks each new state immediately when the objective is a shortest sequence of equal-cost moves.
A teaching search visualizer marks the difference between discovering a goal and selecting it from the frontier.
A routing implementation tests a weighted graph to ensure it does not mistake the first generated destination for the least-cost route.

Think of it this way

Think of noticing a destination on a map while examining possible routes. You have found a way there, but you may not yet know whether it is the cheapest route. The analogy is limited because a formal search algorithm uses precise ordering rules and assumptions, rather than a traveler's informal confidence that no better route remains.

Worked example

Consider a graph with start S, intermediate node A, and goal G. The edge S-G costs 10, S-A costs 1, and A-G costs 1. Expanding S can generate G directly with total cost 10. Returning immediately at that early goal test gives a valid route, but it misses S-A-G, whose cost is 2.
Uniform-cost search instead keeps candidate nodes ordered by accumulated cost. It selects A at cost 1 before the cost-10 goal. Expanding A finds the cheaper goal route at cost 2, which can be selected before the more expensive candidate. Using A* with heuristic zero gives the same ordering, so even a consistent heuristic does not make return-on-first-generation safe here.
Now suppose every edge costs one and the objective is the fewest edges. The direct S-G path has one edge, while S-A-G has two. Breadth-first early stopping at the generated direct goal is appropriate under those assumptions. The graph shows why the algorithm's objective and edge-cost model matter as much as the goal predicate.

Illustrative case study

At fictional Brook Search, developers move a goal test earlier to reduce expansions in a route solver. Unit-cost examples pass, but a weighted example returns an expensive direct route. The team restores the stopping rule appropriate to its cost-based search and adds a regression case with a cheaper indirect route.
This fictional case illustrates a correctness issue, not a benchmark. Fewer node expansions are beneficial only if the returned answer still meets the requested solution criteria.

Common misconceptions and mistakes

Do not equate generated with optimally settled. A node can enter the frontier before the best route to it is known. A consistent heuristic does not in general make the first generated goal safe to return, as the zero-heuristic example demonstrates.
Early testing also does not make depth-first search return a shortest path. The test's timing cannot replace the search order's guarantees. Conversely, checking a goal early for bookkeeping is not inherently wrong if the algorithm continues until its valid stopping condition holds. Distinguish detecting a candidate goal from terminating the entire search.

Business use

Businesses using planning or routing components should specify whether any valid solution is enough or whether minimum cost, minimum steps, or another guarantee is required. Review goal-test placement alongside frontier ordering, duplicate handling, and edge assumptions. Include examples where a goal is found quickly by an expensive route. Document what an early returned result promises so downstream workflows do not interpret mere feasibility as proven optimality.

Check your understanding

A* uses h = 0, and the first generated goal costs 20 while a frontier node costs 2. Is consistency of h sufficient to stop at the generated goal?

Answer: No. Zero is consistent for nonnegative edge costs, yet the frontier may lead to a cheaper goal. The appropriate frontier-based stopping conditions still matter.

Related terms

Goal test
Frontier
Breadth First Search
Uniform-cost search
node generation
node expansion
A*
optimality

Learn next

Trace the worked weighted graph using a FIFO frontier and then a priority queue ordered by accumulated cost. Record when G is first generated and when the cheapest goal is selected. Explain which event supports each algorithm's guarantee under its assumptions.

Sources

Russell and Norvig, original AIMA breadth-first-search pseudocode: https://github.com/aimacode/aima-pseudocode/blob/master/md/Breadth-First-Search.md Original AIMA glossary source sentences, early and late goal testing: https://github.com/aimacode/aima-glossary/blob/master/sentences.txt

Previous: Early exit Next: Early playout termination