Demo Data from SIGIR 2026 / reviewers in the wild / expert
John Funge
dblp:12/6226
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 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.
| Computer graphics and multimedia
1 paper |
Computer animation and physical simulation · 77% Visual content generation and editing · 23% | |
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 100% |
Topics — the 3 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 › planning › agent planning
action planning |
0.0 | 1 | 1999 | Cognitive Modeling: Knowledge, Reasoning and Planning for Intelligent Characters · SIGGRAPH 1999 |
Computer animation and physical simulation › procedural animation
behavioral animation |
0.0 | 1 | 1999 | Cognitive Modeling: Knowledge, Reasoning and Planning for Intelligent Characters · SIGGRAPH 1999 |
Visual content generation and editing › multimedia content creation
automated cinematography |
0.0 | 1 | 1999 | Cognitive Modeling: Knowledge, Reasoning and Planning for Intelligent Characters · SIGGRAPH 1999 |
Methods — techniques the papers use, named apart from their topics
sketch plan reasoning · 0.0interval methods · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Cognitive Modeling: Knowledge, Reasoning and Planning for Intelligent CharactersabstractRecent work in behavioral animation has taken impressive steps toward autonomous, self-animating characters for use in production animation and interactive games. It remains difficult, however, to direct autonomous characters to perform specific tasks. This paper addresses the challenge by introducing cognitive modeling. Cognitive models go beyond behavioral models in that they govern what a character knows, how that knowledge is acquired, and how it can be used to plan actions. To help build cognitive models, we develop the cognitive modeling language CML. Using CML, we can imbue a character with domain knowledge, elegantly specified in terms of actions, their preconditions and their effects, and then direct the character’s behavior in terms of goals. Our approach allows behaviors to be specified more naturally and intuitively, more succinctly and at a much higher level of abstraction than would otherwise be possible. With cognitively empowered characters, the animator need only specify a behavior outline or “sketch plan” and, through reasoning, the character will automatically work out a detailed sequence of actions satisfying the specification. We exploit interval methods to integrate sensing into our underlying theoretical framework, thus enabling our autonomous characters to generate action plans even in highly complex, dynamic virtual worlds. We demonstrate cognitive modeling applications in advanced character animation and automated cinematography. John Funge, Xiaoyuan Tu, Demetri Terzopoulos |
SIGGRAPH | 1 |