Cognition · Software component

Optimal Heuristic Search Planner

Software componentCognitionCognition & Memoryarc:OptimalHeuristicSearchPlanner

A graph search planner that expands nodes in order of g(n)+h(n) with an admissible heuristic, guaranteeing the lowest-cost path if one exists.

Responsibility. Finds optimal paths by expanding the frontier node with minimum estimated total cost.

Also known as: A* search, A* planner, Standard A*

Variant of Graph Search Planner abstract

When to choose. Choose when the state space admits informative admissible heuristics, optimal solutions matter more than computational efficiency, the branching factor is manageable and transitions are deterministic (e.g., route planning on a known warehouse floor plan).

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

Heuristic Estimator abstract Ch5.5

alternative to variability

Design guidance

MUST use an admissible heuristic (never overestimating remaining cost) to preserve the optimality guarantee.
SHOULD use a consistent heuristic so closed nodes never need reopening.

Quantitative guidance

As stated by the sources; verify before use.

4x4 grid example: 6 nodes expanded to find a 6-step optimal path with Manhattan distance (Ch5.6).
Grid goal 20 steps away: BFS explores ~1,257 nodes vs. ~40-60 for A* with Manhattan distance (Ch5.6).

Classification

Patterns: A* searchHeuristic-guided MCTS-style sampling in unreliable-heuristic regionsAdmissible heuristicConsistent (monotone) heuristic
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Suboptimal paths in safety-critical routing

Sources

Ch5.5: T. Nguyen, "Monte Carlo Tree Search Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.5. ISBN: 9798244538229.
Ch5.6: T. Nguyen, "A* Search and Replaning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.6. ISBN: 9798244538229.