Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · cs/0410022

RRL: A Rich Representation Language for the Description of Agent Behaviour in NECA

P. Piwek·B. Krenn·M. Schroeder·M. Grice·S. Baumann·H. Pirker
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we describe the Rich Representation Language (RRL) which is used in the NECA system. The NECA system generates interactions between two or more animated characters. The RRL is an XML compliant framework for representing the information that is exchanged at the interfaces between the various NECA system modules. The full XML Schemas for the RRL are available at http://www.ai.univie.ac.at/NECA/RRL

Explore related subjects

cs.MMcs.MA

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P. Piwek, B. Krenn, M. Schroeder, M. Grice, S. Baumann, H. Pirker. 2004-10-11. RRL: A Rich Representation Language for the Description of Agent Behaviour in NECA. https://arxiv.org/abs/cs/0410022

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Trigger Timing, Deadline Readiness, and Event-Aligned Accounting for Dynamic Ad Insertion

An ad break in a live stream is a hard deadline. Once a cue arrives, the ad path has seconds to produce a state the player can use, and starting that work earlier buys time while giving the prepared state longer to expire before the break. Comparing trigger policies means scoring which of these happened; the records easiest to collect do not. We separate trigger, reach, readiness, playback, billability and measurement in an observed-event ledger, fix the reference-deadline rule before candidates are assigned, and account for contribution over every planned break-session pair, reached or not. Readiness means the declared state is valid at that deadline. We prove when scoring completion by the deadline instead reverses a policy comparison: for monotone playback with a shared latency law, a fixed state lifetime and common coefficients, the reversal occurs when the earlier policy's extra trigger cost falls between its validity gain and its completion gain. A synthetic study of 45,000 scripted sessions matches this condition on monotone paths, a check of its generator, and, outside its assumptions, finds the same reversal in mixtures with restarts, seeks, late joins and pauses. Playhead summaries misclassify up to 41% of reach indicators yet reverse no comparison in the study: maximum-position errors cancel exactly, and terminal-position errors change only magnitudes. Completion scoring reverses the comparison in three of nine short-lifetime conditions, all at the one latency setting where the proved condition holds. The evidence is synthetic by design; code, seeds and event histories are public.

cs.MM↗
arXiv2026

MMFact: Simplifying Multimodal Misinformation Detection with Shared Resources and Tools

Multimodal misinformation detection methods often rely on different preprocessing pipelines and auxiliary resources, making experimental comparisons difficult to interpret. We present MMFact, a collection of shared resources and tools that simplifies this research workflow. It brings together 47 detector implementations and baselines, three multimodal datasets, and prepared materials ranging from semantic features to OCR, image descriptions, and external evidence. Compatible methods can use the same materials, reducing variation introduced by preprocessing and supporting fairer comparisons. A modular training and evaluation framework preserves method-specific designs while providing common experimental procedures. By treating prepared materials as a shared research resource, MMFact helps researchers focus on developing and evaluating detection methods.

cs.MM↗
arXiv2026

Self-Learning Surprise Detection in Spatial Audio for Viewport-Aware 360-Video Streaming

Adaptive streaming of 360-degree video relies on viewport prediction to allocate bandwidth efficiently. Current approaches predominantly use visual saliency or historical gaze patterns, neglecting the role of spatial audio in guiding user attention. This paper presents a self-learning framework for detecting "surprising" auditory events, moments that deviate from learned temporal expectations, and demonstrates their utility for viewport prediction. The proposed architecture combines SE(3)-equivariant graph neural networks with recurrent temporal modeling, trained via a dual self-supervised objective. A key feature is the natural modeling of temporal attention decay: surprise is high at event onset but diminishes as the listener adapts. Experiments on the AVTrack360 dataset show that integrating audio surprise with visual cues reduces bitrate waste by up to 18% compared to visual-only methods.

cs.MM↗