Tobias Schwartz 0002

dblp:94/7036-2 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
hierarchical planning
1.012026
HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026
Knowledge, reasoning and agents › Knowledge representation and reasoning › temporal reasoning
qualitative temporal reasoning
1.012026
HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning
1.012026
HTN Plan Verification by Qualitative Temporal Reasoning · AAAI 2026
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › planning evaluation
plan verification
0.312026
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
YearPublicationVenuePosition
2026 HTN Plan Verification by Qualitative Temporal Reasoning
abstract
Plan 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
AAAI1