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Francis Bisson

dblp:97/9923 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
plan recognition
0.322015
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
0.112011
Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011
Machine learning › Reinforcement learning › multi-agent reinforcement learning
opponent modeling
0.112011
Provoking Opponents to Facilitate the Recognition of their Intentions · AAAI 2011
Games and playful interaction › game AI
video game agents
0.012011
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
YearPublicationVenuePosition
2015 Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition
Francis Bisson, Hugo Larochelle, Froduald Kabanza
IJCAI1
2011 Provoking Opponents to Facilitate the Recognition of their Intentions
abstract
Possessing 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
AAAI1