Demo Data from VLDB 2026 / reviewers in the wild / expert
Gregory Knapp
dblp:345/0004
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-0157-6509ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 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 |
Question answering and dialogue systems · 50% Knowledge representation and reasoning · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Games and playful interaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge graph |
0.7 | 1 | 2023 | Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based Approach · CHI 2023 |
Natural language and speech › Question answering and dialogue systems › dialogue generation
knowledge-grounded dialogue generation |
0.7 | 1 | 2023 | Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based Approach · CHI 2023 |
Games and playful interaction › game AI
procedural content generation |
0.2 | 1 | 2023 | Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based Approach · CHI 2023 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.3knowledge graph · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based ApproachabstractProcedural content generation (PCG) in video games offers unprecedented opportunities for customization and user engagement. Working within the specialized context of role-playing games (RPGs), we introduce a novel framework for quest and dialogue generation that places the player at the core of the generative process. Drawing on a hand-crafted knowledge base, our method grounds generated content with in-game context while simultaneously employing a large-scale language model to create fluent, unique, accompanying dialogue. Through human evaluation, we confirm that quests generated using this method can approach the performance of hand-crafted quests in terms of fluency, coherence, novelty, and creativity; demonstrate the enhancement to the player experience provided by greater dynamism; and provide a novel, automated metric for the relevance between quest and dialogue. We view our contribution as a critical step toward dynamic, co-creative narrative frameworks in which humans and AI systems jointly collaborate to create unique and user-specific playable experiences. Trevor Ashby, Braden K. Webb, Gregory Knapp, Jackson Searle, Nancy Fulda |
CHI | 3 |