EDBT 2026 Demo / reviewers in the wild / expert
Sertac Karapinar
dblp:117/4871
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 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
1 paper |
Planning, search and constraint satisfaction · 75% Knowledge representation and reasoning · 25% |
Topics — the 4 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 › plan execution
failure recovery |
0.1 | 1 | 2012 | Failure Handling In a Planning Framework · AAAI 2012 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems › rule-based systems
forward chaining |
0.1 | 1 | 2012 | Failure Handling In a Planning Framework · AAAI 2012 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
plan execution |
0.1 | 1 | 2012 | Failure Handling In a Planning Framework · AAAI 2012 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
temporal planning |
0.1 | 1 | 2012 | Failure Handling In a Planning Framework · AAAI 2012 |
Methods — techniques the papers use, named apart from their topics
TLPlan · 0.1PROBCOG · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Failure Handling In a Planning FrameworkabstractWhen an agent plans a sequence of actions, some unexpected events may occur during the execution of these actions. These unexpected events may prevent the agent to replan and achieve its goal. In this work, our purpose is to recover from plan execution failures by reasoning the causes of these faulties. We combine the TLPlan forward chaining temporal planner with the PROBCOG reasoning tool in order to handle failures. It is also quite important to decide whether the failure we are dealing with is permanent. We propose that inferring some properties of the failure source helps us handle failures and determine the failure types. Sertac Karapinar, Sanem Sariel |
AAAI | 1 |