VLDB 2026 Research / reviewers in the wild / expert
Tobias Schwartz 0002
dblp:94/7036-2
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0002-9803-385XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
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 |
Knowledge representation and reasoning · 61% Planning, search and constraint satisfaction · 39% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
hierarchical planning |
1.0 | 1 | 2026 | HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › temporal reasoning
qualitative temporal reasoning |
1.0 | 1 | 2026 | HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning |
1.0 | 1 | 2026 | HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › planning evaluation
plan verification |
0.3 | 1 | 2026 | HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
qualitative constraint networks · 1.0SAT encoding · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | HTN Plan Verification by Qualitative Temporal ReasoningabstractPlan verification is the task of checking whether a proposed plan correctly solves a given planning problem. In Hierarchical Task Network (HTN) planning, this verification problem is known to be NP-hard. Existing approaches to HTN plan verification range from SAT encodings to parser-based techniques. However, existing methods do not explicitly exploit the temporal structure inherent in hierarchical decomposition. In this paper, we establish a formal connection between HTN planning and temporal reasoning by showing how decomposition structures can be naturally represented using qualitative constraint networks. Building on this insight, we present a new top-down encoding that transforms the verification of partially ordered task networks into a temporal reasoning problem. We prove the correctness of this encoding and explain how it accounts for both the hierarchical and temporal aspects of HTN plans. By linking HTN plan verification with qualitative temporal reasoning, our approach introduces a principled formal framework for reasoning about complex temporal relationships in hierarchical plans. This connection offers new perspectives for knowledge representation in structured planning domains. Tobias Schwartz 0002, Diedrich Wolter |
AAAI | 1 |