Demo Data from EDBT 2026 / reviewers in the wild / expert
Manuel Heusner
dblp:149/1257
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Planning, search and constraint satisfaction · 100% | |
| Theoretical computer science
1 paper |
Computational complexity · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › heuristic search › best-first search
greedy best-first search |
0.7 | 2 | 2018 | Search Progress and Potentially Expanded States in Greedy Best-First Search · IJCAI 2018 Best-Case and Worst-Case Behavior of Greedy Best-First Search · IJCAI 2018 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
heuristic search |
0.7 | 2 | 2018 | Search Progress and Potentially Expanded States in Greedy Best-First Search · IJCAI 2018 Best-Case and Worst-Case Behavior of Greedy Best-First Search · IJCAI 2018 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › heuristic search
tie-breaking |
0.4 | 2 | 2018 | Best-Case and Worst-Case Behavior of Greedy Best-First Search · IJCAI 2018 Search Progress and Potentially Expanded States in Greedy Best-First Search · IJCAI 2018 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Best-Case and Worst-Case Behavior of Greedy Best-First SearchabstractWe study the impact of tie-breaking on the behavior of greedy best-first search with a fixed state space and fixed heuristic. We prove that it is NP-complete to determine the number of states that need to be expanded by greedy best-first search in the best case or in the worst case. However, the best- and worst-case behavior can be computed in polynomial time for undirected state spaces. We perform computational experiments on benchmark tasks from the International Planning Competitions that compare the best and worst cases of greedy best-first search to FIFO, LIFO and random tie-breaking. The experiments demonstrate the importance of tie-breaking in greedy best-first search. Manuel Heusner, Thomas Keller 0001, Malte Helmert |
IJCAI | 1 |
| 2018 | Search Progress and Potentially Expanded States in Greedy Best-First SearchabstractA classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress. Manuel Heusner, Thomas Keller 0001, Malte Helmert |
IJCAI | 1 |
| 2017 | Understanding the Search Behaviour of Greedy Best-First SearchabstractA classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behaviour of greedy best-first search (gbfs) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a gbfs algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are not expanded under any gbfs tie-breaking strategy. For the remaining states, we show that some are expanded by all gbfs searches, while others are expanded only if certain conditions are met. Manuel Heusner, Thomas Keller 0001, Malte Helmert |
SOCS | 1 |