Demo Data from VLDB 2026 / reviewers in the wild / expert
Francis Bisson
dblp:97/9923
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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 · 58% Knowledge representation and reasoning · 21% Reinforcement learning · 21% | |
| Human-computer interaction and pervasive computing
1 paper |
Games and playful interaction · 100% |
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 recognition |
0.3 | 2 | 2015 | Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition · IJCAI 2015 Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › theory of mind
intention recognition |
0.1 | 1 | 2011 | Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011 |
Machine learning › Reinforcement learning › multi-agent reinforcement learning
opponent modeling |
0.1 | 1 | 2011 | Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011 |
Games and playful interaction › game AI
video game agents |
0.0 | 1 | 2011 | Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011 |
Methods — techniques the papers use, named apart from their topics
plan recognition · 0.2recursive neural network · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition
Francis Bisson, Hugo Larochelle, Froduald Kabanza |
IJCAI | 1 |
| 2011 | Provoking Opponents to Facilitate the Recognition of their IntentionsabstractPossessing a sufficient level of situation awareness is essential for effective decision making in dynamic environments. In video games, this includes being aware to some extent of the intentions of the opponents. Such high-level awareness hinges upon inferences over the lower-level situation awareness provided by the game state. Traditional plan recognizers are completely passive processes that leave all the initiative to the observed agent. In a situation where the opponent's intentions are unclear, the observer is forced to wait until further observations of the opponent's actions are made to disambiguate the pending goal hypotheses. With the plan recognizer we propose, in contrast, the observer would take the initiative and provoke the opponent, with the expectation that his reaction will give cues as to what his true intentions actually are. Francis Bisson, Froduald Kabanza, Abder Rezak Benaskeur, Hengameh Irandoust |
AAAI | 1 |