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Explore the main themes, entities and connections around A* search algorithm. Start with the topic map, then use the sections below for research and deeper semantic analysis.
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Description
History
Variants
Overview
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
- Class
- Search algorithm
- Complete
- yes
- Data structure
- Graph
- Optimal
- yes
- Worst-case performance
- O ( | E | log | V | ) = O ( b d ) {\displaystyle O(|E|\log |V|)=O(b^{d})}
- Worst-case space complexity
- O ( | V | ) = O ( b d ) {\displaystyle O(|V|)=O(b^{d})}
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Graph traversal
- Pathfinding
- Algorithm
- Computer science
- Weighted graph
- Node Vertex (graph theory)
- Shortest path Shortest path problem
- Space complexity
- Branching factor
- Travel-routing systems Travel-routing system
- Peter Hart Peter E. Hart
- Nils Nilsson Nils Nilsson (researcher)
- Bertram Raphael
- SRI International
- Dijkstra's algorithm
- Heuristics Heuristic (computer science)
- Shortest-path tree
- Worst-case complexity
History
- The Shakey project Shakey the robot
- Mobile robot
- Admissibility Admissible heuristic
- Consistency Consistent heuristic
Description
- Search algorithm
- Best-first search
- Node Node (graph theory)
- Tree Tree (data structure)
- Heuristic
- Priority queue
- Straight-line distance Euclidean distance
- Taxicab distance
- Chebyshev distance
- Reduced cost
- Pseudocode
- Great-circle distance
- LIFO LIFO (computing)
- Depth-first search
- Binary heap
- Hash table
- Fibonacci heap
- Amortized time
Complexity
Applications
- Parsing
- Stochastic grammars Stochastic context-free grammar
- NLP Natural language processing
Relations to other algorithms
- Greedy Greedy algorithm
- Dynamic programming
- Branch and bound
- Beam search
Variants
- Anytime A*
- Field D* Any-angle path planning
- D*
- Fringe Fringe search
- Incremental heuristic search
- Jump point search
- Lifelong Planning A* (LPA*) Lifelong Planning A*
- Theta*
- Bidirectional search
Advanced semantic analysis
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.A* search algorithm
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
A* search algorithm
Top relations
Important terminology Word statistics
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Important terminology
node algorithm heuristic path goal displaystyle search nodes optimal graph cost admissible textstyle consistent shortest function case distance start solution
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| A* search algorithm | Class | Search algorithm | 1.00 | infobox |
| A* search algorithm | Complete | yes | 1.00 | infobox |
| A* search algorithm | Data structure | Graph | 1.00 | infobox |
| A* search algorithm | Optimal | yes | 1.00 | infobox |
| A* search algorithm | Worst-case performance | O ( | E | log | V | ) = O ( b d ) {\displaystyle O(|E|\log |V|)=O(b^{d})} | 1.00 | infobox |
| A* search algorithm | Worst-case space complexity | O ( | V | ) = O ( b d ) {\displaystyle O(|V|)=O(b^{d})} | 1.00 | infobox |
| video games | instance of | is often used for the common pathfinding problem in applications | 0.80 | text |
| but was originally designed as a general graph traversal algorithm | instance of | is often used for the common pathfinding problem in applications | 0.80 | text |
| A* search algorithm | related to External links | Variation | 0.60 | section |
| A* search algorithm | related to External links | Hierarchical Path-Finding | 0.60 | section |
| A* search algorithm | related to External links | HPA | 0.60 | section |
| A* search algorithm | related to External links | Brian Grinstead | 0.60 | section |
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
Bridge nodes connect otherwise separate parts of the map. Expand a row to inspect the topic groups on each side.Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.