Pith. sign in

REVIEW 4 major objections 5 minor 65 references

GRETA: Modular Platform to Create Adaptive Socially Interactive Agents

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read GRETA's extended architecture closes the loop from user perception to agent behavior in real time.

desk verdict A coherent architecture description of the GRETA platform, but the central real-time claim rests on an unmeasured 40 ms budget and the validation is largely self-referential. read the letter →

arxiv 2503.15504 v1 pith:24GWRRMV submitted 2025-01-23 cs.HC cs.RO

Michele Grimaldi , Jieyeon Woo , Fabien Boucaud , Lucie Galland , Nezih Younsi , Liu Yang , Mireille Fares , Sean Graux
show 2 more authors
Philippe Gauthier Catherine Pelachaud
This is my paper · ORCID
classification cs.HCcs.RO
keywords sociallyinteractiveagentsembodiedconversationalincrementalbehaviorrealizationframe-levelanimationreciprocaladaptationmultimodalperceptionvirtualrealitysocialtouch
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to establish that GRETA, a modular platform for animating virtual agents, has grown from a one-way animation engine into a closed-loop socially interactive agent system. Its extended architecture receives a human user's facial expressions, gaze, head movements, speech text and prosody, hand landmarks, and proximity; interprets those signals; plans the agent's next communicative intentions; and realizes the chosen nonverbal behaviors incrementally. Because behavior can be produced and revised at frame level rather than at whole-utterance level, the agent can respond to the user's conduct during the interaction instead of only after a completed turn. If the architecture works as described, it offers other SIA builders a single, modular, standard-pipeline substrate for studying and deploying adaptive virtual interlocutors in tutoring, therapy, and training applications.

What carries the argument

The load-bearing piece is the behavior-realization layer: an Incremental Realizer that cuts keyframes into chunks grouped by starting time and schedules them on a dedicated thread, an Incremental Realizer Interaction module that can interrupt, resume, stop, or clear that stream, and a Frame-level Behavior Realizer that accepts per-time-step signals and renders upper-face action units, smiles, blinks, head movements, gaze, and mouth movements. Together they let the platform turn a prediction of the next frame's user and agent state into visible behavior at frame granularity, which is what makes the adaptation loop closed.

What would settle it

Instrument a running session and timestamp each hop; if the measured time from a user's facial or audio change to the agent's displayed frame systematically exceeds one 40 ms frame period, the platform does not deliver the real-time adaptation it claims.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that GRETA now supports the full perception, interpretation, decision, and realization loop needed for adaptive social interaction. The platform takes multimodal input from facial feature extraction, speech recognition, audio prosody extraction, hand tracking, and VR tracking; converts those signals into dialog acts, image schemas, and emotional and rapport states; lets an intention planner choose the agent's next act; and then renders it either through the classic intention-to-behavior markup pipeline, an incremental keyframe-chunk realizer that can be interrupted and resumed, or a frame-level realizer that displays predicted next-frame behavior at 25 fps. The claim is that these components are integrated in one modular system, so a GRETA-controlled agent perceives human users' behavior and adapts its own behavior accordingly, including in the socially under-explored modality of touch.

Load-bearing premise

The central claim depends on the entire external-tool chain, including speech recognition, face and audio feature extraction, interpretation, behavior prediction, message passing, and rendering, completing within the stated 0.04 second loop, but the paper asserts the 25 fps rate without reporting end-to-end latency measurements.

Editorial extensions

If this is right

  • A GRETA agent can cut off or revise its own behavior mid-gesture, making interruptions, repairs, concurrent feedback, and fast turn-taking computationally reachable.
  • Frame-level rendering at 25 fps lets the agent's facial expressions and head or gaze behavior track the user's audio-visual behavior continuously rather than utterance by utterance.
  • The same perception-to-realization loop can be repurposed across scenarios by changing the content messages, so motivational interviewing, CBT, and social-skills training can share one platform.
  • In VR, the agent can recognize four types of human touch and modulate whether to touch back based on computed rapport and proxemics.
  • The modular design lets the frame-level behavior prediction model be swapped, so deeper or different adaptation models can be plugged into the same real-time loop.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper reports no end-to-end latency, a direct perception-to-render benchmark is the natural next test; the architecture's practical value depends on that measurement, not on any single module's speed.
  • The chunk-and-thread incremental realizer is a general pattern that any behavior realizer facing interruptions or repairs could adopt, even outside the GRETA system.
  • The social-touch framework suggests a transfer path to physical robots with tactile sensing, though duration and pressure sensing would need to be added for full parity.
  • The reinforcement-learning dialogue planner could become a testbed for comparing adaptation policies under identical perception and rendering conditions.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper describes the architecture of the GRETA platform, a SAIBA-based system for controlling socially interactive agents. It reports recent extensions aimed at making GRETA adaptive: perception of external human signals (facial action units, gaze, speech, hand tracking, proximity), interpretation modules (BERTIS, dialog acts, GPT-3), an adaptive behavior generation loop (IAVA/ASAP), an incremental behavior realizer that schedules keyframe chunks, a frame-level realizer, and a social-touch framework using VR, Leap Motion, and haptics. The paper presents the overall architecture, module descriptions, and several use cases (motivational interviewing, CBT, social skills training, social touch). The central claim is that the enhanced GRETA platform supports real-time reciprocal adaptation between the agent and the human user, with a claimed 25 fps adaptive behavior generation loop in Section 7.2.

Significance. If the claimed integration is real, the paper would be a useful systems contribution: it shows how perception, interpretation, adaptive generation, and incremental animation can be combined within a standard FML/BML architecture. The modular design is a strength, and the description of the incremental realizer with interrupt/resume/stop/clear operations is concrete and plausible. The paper also benefits from reusing and citing established open-source components (OpenFace, openSMILE, MediaPipe) and from naming the specific prior modules (Meaning Miner, IAVA, ASAP, AMII) on which the platform builds. However, the validation is almost entirely self-referential: there are no end-to-end measurements of the integrated system, and the only quantitative performance numbers (Section 7.3.2) are a random-forest accuracy pair with no dataset description. The real-time adaptation claim in Section 7.2 is the load-bearing assertion for the paper's central contribution, and it is currently unsupported by latency distributions, missed-frame counts, or any benchmark. The significance is therefore conditional on the authors supplying such evidence.

major comments (4)
  1. [Section 7.2 (Real-time Adaptive Behavior Generation)] The claim that 25 fps "is assured ... without any delay" is not supported by any measurement. The listed component times (approximately 0.03 s perception, 0.008 s ASAP inference, and 0.002 s communication/visualization) sum to exactly the 0.04 s frame budget, leaving zero slack for scheduling jitter, message-bus latency (ActiveMQ, ZeroMQ, OSC), or synchronization waits; throughput equal to the frame period does not imply bounded end-to-end latency. Because the reciprocal adaptation loop of Section 3.1 rests on this real-time premise, the paper must report at least an end-to-end latency distribution, a missed-frame count, and the message-passing overhead before the central claim can be accepted.
  2. [Section 7.3.2 (Social Touch, Gesture Recognition and Perception)] The social-touch random forest is reported as achieving 0.91 training accuracy and 0.80 test accuracy, but the paper gives no dataset size, class distribution, collection protocol, or validation procedure; with four touch classes (hit, tap, caress, stroke), 0.80 could reflect a heavily imbalanced set or a favorable split. Please add the dataset details, per-class precision/recall, and a clear account of how the train/test split was formed.
  3. [Section 6.1 and Section 6.2 (Incremental Behavior Realizer)] The incremental realizer splits keyframes into starting-time chunks and schedules them in a thread, but the paper does not demonstrate that this preserves the multimodal synchrony of the original BML plan. The operations Interrupt, Resume, Stop, and Clear Thread Queue are described only at the level of API behavior, with no account of how timing constraints across modalities (e.g., gesture relative to speech onset) are maintained or violated by these operations. Provide at least a small trace or controlled experiment showing scheduled versus realized onset times for a multimodal behavior, so that the claim of "incremental realization of nonverbal behaviours" can be checked.
  4. [Sections 1, 3.1, and 7.2 (Integrated validation)] The paper claims that the integrated GRETA platform can perceive, interpret, and adapt in real time, but the only quantitative evidence is drawn from previously published components (IAVA [60], ASAP [61], AMII [58]) and from the random-forest numbers in Section 7.3.2; no integrated experiment with real sensor input, recorded sessions, or latency traces is reported. Since the contribution is the integration of these components, an end-to-end demonstration (e.g., a logged interaction with timing annotations) is needed to separate novel integration claims from properties inherited from the individual modules.
minor comments (5)
  1. [Section 7.3.2] The manuscript contains the untranslated editorial note "¨: Deplacer à section User’s Input"; this appears to be an instruction to the authors and should be removed or acted on.
  2. [Section 3.2 and Section 3.3] Several citations are missing placeholders: OpenPose is cited as "[]", and Section 3.3 has empty brackets for image schemas, dialog acts, and automatic thought; these references must be completed.
  3. [Section 7.2] The units in the performance paragraph are garbled in the manuscript text (e.g., "25  ", "0.03", "0.04"); these should read frames-per-second and seconds, and the encoding artifacts should be fixed.
  4. [Section 7.4] The text refers to "fig. 6" without a clear cross-reference; ensure that the architecture figure is cited consistently where each use case is described.
  5. [Section 3.1] The term "gestuary" is used without definition; please define it explicitly (e.g., a gesture library) or use a more standard term.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a descriptive architecture report whose claims are asserted or inherited from cited prior modules, with no derivation reducing to its own inputs.

full rationale

The paper makes no predictive derivation and presents no first-principles result. It describes the GRETA platform by composing previously published modules (FAtiMA, Meaning Miner [24], ASAP [61], IAVA [60], AMII [58]) and cites those prior publications as sources of the modules' capabilities. These are ordinary references to prior work, including the authors' own work, but they are not used to force the current paper's conclusions by definition. The real-time claim in Section 7.2 asserts component times (about 0.03 s perception, 0.008 s inference, 0.002 s communication) summing to 0.04 s, and then states that 25 fps is assured 'without any delay.' That is an unsupported performance assertion and a plausible correctness risk, but it is not circular: no parameter is fitted to make the conclusion equal to the input, and the 25 fps claim is not the same statement as the assumed loop time by construction. Editorial incompleteness, such as the untranslated note in Section 7.3.2 and the missing OpenPose citation in Section 3.2, does not constitute circularity. No load-bearing step reduces to its own premise, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The paper's additions are software modules, message protocols, and an ML classifier. The only fitted component is the social-touch random forest; all other behavior generation and adaptation components are inherited from the group's prior work or external open-source tools.

free parameters (2)
  • Incremental realizer chunk timing = not reported
    Section 6.1.1 groups keyframes into time-based chunks and schedules them with thread waits. The chunk size and scheduling policy are implementation choices that determine interrupt, resume, and stop behavior, but no values or tuning procedure are given.
  • Random forest social-touch classifier parameters = not reported
    Section 7.3.2 trains a random forest on collected human-agent touch data to classify hit, tap, caress, and stroke. The model architecture, hyperparameters, dataset size, and train/test split are not described, so the reported 0.80 test accuracy cannot be reproduced.
assumptions (4)
  • domain assumption SAIBA framework and FML/BML representations are an appropriate backbone for adaptive SIA behavior generation.
    Sections 1 and 3 adopt SAIBA without questioning whether the three-stage plan-to-realize pipeline can support the reactive, incremental behaviors the paper wants to add.
  • domain assumption Third-party perception components (OpenFace, Google ASR, openSMILE, Leap Motion, VR tracking) deliver accurate, synchronized signals in real time.
    Sections 3.2 and 7.2 rely on these components for the adaptive loop; no accuracy or latency validation is provided in this paper.
  • ad hoc to paper Splitting animation keyframes into starting-time chunks and scheduling them in a thread preserves the intended multimodal synchronization of the original BML plan.
    Section 6.1.1 defines the chunking mechanism but gives no proof or evaluation that interruption, resume, and stop preserve semantic timing across speech, face, gaze, and gesture.
  • domain assumption A random forest using velocity, pressure, duration, and touched body parts can distinguish hit, tap, caress, and stroke with usable accuracy.
    Section 7.3.2 asserts this and reports 0.91 train and 0.80 test accuracy, but the implementation uses only four of six touch parameters and does not describe the dataset or cross-validation method.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GRETA: Modular Platform to Create Adaptive Socially Interactive Agents." pith.science (2026). https://pith.science/paper/24GWRRMV

@misc{pith2026250315504,
  author       = {Pith},
  title        = {Pith review of: GRETA: Modular Platform to Create Adaptive Socially Interactive Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/24GWRRMV}},
  note         = {Machine review of arXiv:2503.15504}
}
read the original abstract

The interaction between humans is very complex to describe since it is composed of different elements from different modalities such as speech, gaze, and gestures influenced by social attitudes and emotions. Furthermore, the interaction can be affected by some features which refer to the interlocutor's state. Actual Socially Interactive Agents SIAs aim to adapt themselves to the state of the interaction partner. In this paper, we discuss this adaptation by describing the architecture of the GRETA platform which considers external features while interacting with humans and/or another ECA and process the dialogue incrementally. We illustrate the new architecture of GRETA which deals with the external features, the adaptation, and the incremental approach for the dialogue processing.

Figures

Figures reproduced from arXiv: 2503.15504 by the authors.

Figure 1
Figure 1. GRETA’s Architecture Schema w.r.t. [24] 3.2 User’s multimodal signals The GRETA architecture can take several input signals to capture information related to human users interacting with GRETA virtual agents. These raw signals can be users’ facial expressions, head movements, speech but also hand landmarks and proximity. These signals are captured through a variety of media including a camera, microphone, Leap motio… view at source ↗
Figure 2
Figure 2. Computational model of emotions 4.2 Adaptive dialogue planner The GRETA framework can be utilized in conjunction with more intricate dialogue planners, as demonstrated in [21]. To simulate adaptation mechanisms that happen during interaction, we have proposed an adaptive dialogue planner. This dialogue planner can employ either task-related or social-related dialogue acts and se￾lect the next dialogue act to maximiz… view at source ↗
Figure 3
Figure 3. Behavior Planner Schema 6 BEHAVIOR REALIZER The Behavior Realizer generates the animation for the agent fol￾lowing the MPEG-4 format. The inputs are specied by the BML language. It contains the text to be spoken and/or a set of non￾verbal signals such as facial expressions, gaze, gestures, and torso movements, to be displayed. Each BML tag is transformed into a set of keyframes that are then smoothly interpolated. … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Incremental Realizer and Interaction Schema [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Social Touch Tools Schema 7.4 Gesture Generation Models 7.4.1 Facial expression and head movement generation. We propose diverse generative models for synthesizing facial gestures driven by speech and semantically-aware. We initially introduced a rule-based approach us…
Figure 6
Figure 6. Figure 6: GRETA’s General Architecture 12 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 57 canonical work pages

  1. [60]

    Jieyeon Woo, Michele Grimaldi, Catherine Pelachaud, and Catherine Achard. 2023. IA V A: Interactive and Adaptive Virtual Agent. InACM International Conference on Intelligent Virtual Agents (IV A ’23)

    work page 2023

  2. [61]

    Jieyeon Woo, Catherine Pelachaud, and Catherine Achard. 2023. ASAP: Endowing Adaptation Capability to Agent in Human-Agent Interaction. InProceedings of the 28th International Conference on Intelligent User Interfaces. 464–475

    work page 2023

  3. [58]

    Jieyeon Woo, Mireille Fares, Catherine Pelachaud, and Catherine Achard. 2023. AMII: Adaptive Multimodal Inter-personal and Intra-personal Model for Adapted Behavior Synthesis.arXiv preprint arXiv:2305.11310(2023)

    work page Pith review arXiv 2023

  4. [1]

    John R Anderson, Michael Matessa, and Christian Lebiere. 1997. ACT-R: A theory of higher level cognition and its relation to visual attention.Human–Computer Interaction12, 4 (1997), 439–462

    work page 1997

  5. [2]

    Ron Artstein, Sudeep Gandhe, Anton Leuski, and David Traum. 2008. Field testing of an interactive question-answering character. InELRA Workshop on Evaluation, Marrakech, Morocco. 36–40

    work page 2008

  6. [3]

    Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 2016. Openface: an open source facial behavior analysis toolkit. In2016 IEEE Winter Conference on Applications of Computer Vision (W ACV). IEEE, 1–10

    work page 2016

  7. [4]

    Christian Becker-Asano and Ipke Wachsmuth. 2010. W ASABI as a case study of how misattribution of emotion can be modelled computationally.A Blueprint for Aective Computing: a Sourcebook and Manual(2010), 179–193

    work page 2010

  8. [5]

    Frank Bergmann and Brian Fenton. 2015. Scene based reasoning. InArticial General Intelligence: 8th International Conference, AGI 2015, AGI 2015, Berlin, Germany, July 22-25, 2015, Proceedings 8. Springer, 25–34

    work page 2015

Show all 65 references
  1. [6]

    Timothy Bickmore, Daniel Schulman, and Langxuan Yin. 2010. Maintaining engagement in long-term interventions with relational agents.Applied Articial Intelligence24, 6 (2010), 648–666

    2010

  2. [7]

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jere...

    2020 doi arXiv

  3. [8]

    Ginevra Castellano, Ana Paiva, Arvid Kappas, Ruth Aylett, Helen Hastie, Wolmet Barendregt, Fernando Nabais, and Susan Bull. 2013. Towards empathic virtual and robotic tutors. InArticial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, Jul...

    2013

  4. [9]

    Rachel G Curtis, Bethany Bartel, Ty Ferguson, Henry T Blake, Celine Northcott, Rosa Virgara, and Carol A Maher. 2021. Improving user experience of virtual health assistants: scoping review.Journal of medical Internet research23, 12 (2021), e31737

    2021

  5. [10]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics. https: //api.semanticscholar.org/CorpusID:52967399

    2019

  6. [11]

    João Dias, Samuel Mascarenhas, and Ana Paiva. 2014. FAtiMA Modular: Towards an Agent Architecture with a Generic Appraisal Framework. 8750 (11 2014). https://doi.org/10.1007/978-3-319-12973-0_3

    2014 doi

  7. [12]

    Joao Dias and Ana Paiva. 2013. I want to be your friend: Establishing relations with emotionally intelligent agents. InProceedings of the 2013 international conference on Autonomous agents and multi-agent systems. 777–784

    2013

  8. [13]

    Paul Ekman and Wallace V Friesen. 1976. Measuring facial movement.Environ- mental psychology and nonverbal behavior1, 1 (1976), 56–75

    1976

  9. [14]

    Florian Eyben, Martin Wöllmer, and Björn Schuller. 2010. Opensmile: the munich versatile and fast open-source audio feature extractor. InProceedings of the 18th ACM international conference on Multimedia. 1459–1462

    2010

  10. [15]

    Mireille Fares. 2020. Towards multimodal human-like characteristics and ex- pressive visual prosody in virtual agents. InProceedings of the 2020 International Conference on Multimodal Interaction. 743–747

    2020

  11. [16]

    Mireille Fares, Catherine Pelachaud, and Nicolas Obin. 2022. Transformer Net- work for Semantically-Aware and Speech-Driven Upper-Face Generation. In EUSIPCO

    2022

  12. [17]

    Mireille Fares, Catherine Pelachaud, and Nicolas Obin. 2023. TranSTYLer: Multi- modal Behavioral Style Transfer for Facial and Body Gestures Generation.arXiv preprint arXiv:2308.10843(2023)

    2023 arXiv

  13. [18]

    Mireille Fares, Catherine Pelachaud, and Nicolas Obin. 2023. Zero-shot style transfer for gesture animation driven by text and speech using adversarial dis- entanglement of multimodal style encoding.Frontiers in Articial Intelligence6 (2023), 1142997

    2023

  14. [19]

    Mireille Fares, Catherine Pelachaud, and Nicolas Obin. 2023. Zero-Shot Style Transfer for Multimodal Data-Driven Gesture Synthesis. In2023 IEEE 17th In- ternational Conference on Automatic Face and Gesture Recognition (FG). IEEE, 1–4

    2023

  15. [20]

    Mireille Fares, Catherine Pelachaud, and Nicolas Obin. 2023f. META4: semantically-aligned generation of metaphoric gestures using self-supervised text and speech representation.arXiv preprint arXiv:2311.05481(2023f)

    2023 arXiv

  16. [21]

    Lucie Galland, Catherine Pelachaud, and Florian Pecune. 2022. Adapting conver- sational strategies in information-giving human-agent interaction.Frontiers in Articial Intelligence5 (2022), 1029340

    2022

  17. [22]

    Patrick Gebhard, Gregor Mehlmann, and Michael Kipp. 2012. Visual scene- maker—a tool for authoring interactive virtual characters.Journal on Multimodal User Interfaces6, 1-2 (2012), 3–11

    2012

  18. [23]

    John J Godfrey and Edward Holliman. 1997. Switchboard-1 Release 2.Linguistic Data Consortium, Philadelphia926 (1997), 927

    1997

  19. [24]

    Michele Grimaldi and Catherine Pelachaud. 2021. Generation of Multimodal Behaviors in the Greta Platform. InProceedings of the 21st ACM International Conference on Intelligent Virtual Agents(Virtual Event, Japan)(IV A ’21). As- sociation for Computing Machinery, New York, NY, ...

    2021

  20. [25]

    David Hart and Ben Goertzel. 2008. Opencog: A software framework for integra- tive articial general intelligence. InAGI. 468–472

    2008

  21. [26]

    Arno Hartholt, Ed Fast, Zongjian Li, Kevin Kim, Andrew Leeds, and Sharon Mozgai. 2022. Re-architecting the virtual human toolkit: towards an interopera- ble platform for embodied conversational agent research and development. In Proceedings of the 22nd ACM International Confer...

    2022

  22. [27]

    Arno Hartholt and Sharon Mozgai. 2022. Platforms and tools for SIA research and development. InThe Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platform...

    2022

  23. [28]

    Arno Hartholt, David Traum, Stacy C Marsella, Ari Shapiro, Giota Stratou, An- ton Leuski, Louis-Philippe Morency, and Jonathan Gratch. 2013. All together now: Introducing the virtual human toolkit. InIntelligent Virtual Agents: 13th International Conference, IV A 2013, Edinbur...

    2013

  24. [29]

    Dirk Heylen, Stefan Kopp, Stacy Marsella, Catherine Pelachaud, and Hannes Vilhjálmsson. 2008. The Next Step towards a Function Markup Language.In- ternational Workshop on Intelligent Virtual Agents, 270–280. https://doi.org/10. 1007/978-3-540-85483-8_28

    2008

  25. [30]

    Anne Kilger and Wolfgang Finkler. 1995. Incremental generation for real-time applications. https://doi.org/10.22028/D291-24965

    1995 doi

  26. [31]

    S. Kopp, B. Krenn, S. Marsella, A.N. Marshall, C. Pelachaud, H.Pirker, K.R. Thoris- son, and H.H Vilhjálmsson. 2006. Towards a common framework for multimodal generation: the behavior markup language. InIntelligent Virtual Agents - IV A. 205–217

    2006

  27. [32]

    Stefan Kopp, Thorsten Stocksmeier, and Dafydd Gibbon. 2007. Incremental multimodal feedback for conversational agents. InIntelligent Virtual Agents: 7th International Conference, IV A 2007 Paris, France, September 17-19, 2007 Proceedings

    2007

  28. [33]

    2012.Universal subgoaling and chunking: The automatic generation and learning of goal hierarchies

    John Laird, Paul Rosenbloom, and Allen Newell. 2012.Universal subgoaling and chunking: The automatic generation and learning of goal hierarchies. Vol. 11. Springer Science & Business Media

    2012

  29. [34]

    Jina Lee and Stacy Marsella. 2006. Nonverbal Behavior Generator for Embodied Conversational Agents. InIntelligent Virtual Agents, Jonathan Gratch, Michael Young, Ruth Aylett, Daniel Ballin, and Patrick Olivier (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 243–255

    2006

  30. [35]

    1993.Speaking: From intention to articulation

    Willem JM Levelt. 1993.Speaking: From intention to articulation. MIT press

    1993

  31. [36]

    Beth Logan. 2000. Mel Frequency Cepstral Coecients for Music Modeling.Proc. 1st Int. Symposium Music Information Retrieval(11 2000)

    2000

  32. [37]

    Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, et al. 2019. Mediapipe: A framework for building perception pipelines.arXiv preprint arXiv:1906.08172(2019)

    2019 arXiv

  33. [38]

    2021.Introduction to Socially Interactive Agents(1 ed.)

    Birgit Lugrin. 2021.Introduction to Socially Interactive Agents(1 ed.). Association for Computing Machinery, New York, NY, USA, 1–20. https://doi.org/10.1145/ 3477322.3477324

    2021

  34. [39]

    Birgit Lugrin, Catherine Pelachaud, and David Traum (Eds.). 2021.The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, Cognition(1 ed.). Vol. 37. Associa...

    2021

  35. [40]

    Jörn Ostermann. 2002. Face Animation in MPEG-4.MPEG-4 Facial Animation: The Standard, Implementation and Applications(2002), 17–55

    2002

  36. [41]

    Isabella Poggi, Catherine Pelachaud, Fiorella de Rosis, Valeria Caroglio, and Berardina De Carolis. 2005. GRETA. A BELIEV ABLE EMBODIED CONVERSA- TIONAL AGENT

    2005

  37. [42]

    JeRickel, Jonathan Gratch, Randall Hill, Stacy Marsella, and William Swartout

  38. [43]

    Paul S Rosenbloom, Abram Demski, and Volkan Ustun. 2016. The Sigma cognitive architecture and system: Towards functionally elegant grand unication.Journal of Articial General Intelligence7, 1 (2016), 1

    2016

  39. [44]

    Brent Rossen and Benjamin Lok. 2012. A crowdsourcing method to develop virtual human conversational agents.International Journal of Human-Computer Studies70, 4 (2012), 301–319

    2012

  40. [45]

    Takeshi Saga, Jieyeon Woo, Alexis Gerard, Hiroki Tanaka, Catherine Achard, Satoshi Nakamura, and Catherine Pelachaud. 2023. An Adaptive Virtual Agent Platform for Automated Social Skills Training. 109–111. https://doi.org/10.1145/ 3610661.3620662

    2023

  41. [46]

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Dis- tilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.ArXiv abs/1910.01108 (2019)

    2019 arXiv

  42. [47]

    David Schlangen and Gabriel Skantze. 2009. A General, Abstract Model of Incre- mental Dialogue Processing. InProceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). Association for Computational Linguistics, Athens, Greece, 710–718. https://aclanthol...

    2009

  43. [48]

    David Schlangen and Gabriel Skantze. 2011. A general, abstract model of incre- mental dialogue processing.Dialogue & Discourse2, 1 (2011), 83–111

    2011

  44. [49]

    Alexander Shoulson, Nathan Marshak, Mubbasir Kapadia, and Norman I Badler

  45. [50]

    Robert A Sottilare, Keith W Brawner, Anne M Sinatra, and Joan H Johnston. 2017. An updated concept for a Generalized Intelligent Framework for Tutoring (GIFT). GIFTtutoring. org(2017), 1–19

    2017

  46. [51]

    Michael Studdert-Kennedy. 1994. Hand and Mind: What Gestures Reveal About Thought.Language and Speech37, 2 (1994), 203–209

    1994

  47. [52]

    William R Swartout, Jonathan Gratch, Randall W Hill Jr, Eduard Hovy, Stacy Marsella, JeRickel, and David Traum. 2006. Toward virtual humans.AI Magazine 27, 2 (2006), 96–96

    2006

  48. [53]

    Thomas B Talbot and Albert Skip Rizzo. 2019. Virtual standardized patients for interactive conversational training: a grand experiment and new approach. In Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations. IGI Global, 62–86

    2019

  49. [54]

    Mark Ter Maat and Dirk Heylen. 2011. Flipper: An information state component for spoken dialogue systems. InInternational Workshop on Intelligent Virtual Agents. Springer, 470–472

    2011

  50. [55]

    Jelte van Waterschoot, Merijn Bruijnes, Jan Flokstra, Dennis Reidsma, Daniel Davison, Mariët Theune, and Dirk Heylen. 2018. Flipper 2.0: A pragmatic dialogue engine for embodied conversational agents. InProceedings of the 18th International Conference on Intelligent Virtual Ag...

    2018

  51. [56]

    Hannes Vilhjálmsson, Nathan Cantelmo, Justine Cassell, Nicolas Chafai, Michael Kipp, Stefan Kopp, Maurizio Mancini, Stacy Marsella, Andrew Marshall, Cather- ine Pelachaud, Zsóa Ruttkay, Kristinn Thórisson, Herwin Welbergen, and Rick Werf. 2007. The Behavior Markup Language: R...

    2007 doi

  52. [57]

    Lennart Wachowiak and Dagmar Gromann. 2022. Systematic analysis of image schemas in natural language through explainable multilingual neural language processing. InProceedings of the 29th International Conference on Computational Linguistics. 5571–5581

    2022

  53. [59]

    Jieyeon Woo, Michele Grimaldi, Catherine Pelachaud, and Catherine Achard

  54. [62]

    Özge Nilay Yalçın and Steve DiPaola. 2020. Modeling empathy: building a link between aective and cognitive processes.Articial Intelligence Review53, 4 (2020), 2983–3006. 11 Figure 6: GRETA’s General Architecture 12

    2020

  55. [2001]

    InAAAI spring symposium on articial intelligence and interactive entertainment, Vol

    Steve goes to Bosnia: Towards a new generation of virtual humans for interactive experiences. InAAAI spring symposium on articial intelligence and interactive entertainment, Vol. 11. 10

  56. [2013]

    InProceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games

    Adapt: the agent development and prototyping testbed. InProceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games. 9–18

  57. [2023]

    InACM International Conference on Intelligent Virtual Agents (IV A ’23)

    Conducting Cognitive Behavioral Therapy with an Adaptive Virtual Agent. InACM International Conference on Intelligent Virtual Agents (IV A ’23)

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.