pith. sign in

arxiv: 2508.07720 · v2 · pith:2ZULL7HGnew · submitted 2025-08-11 · 💻 cs.MA · cs.SY· eess.SY

Toward Goal-Oriented Communication in Multi-Agent Systems: An overview

Themistoklis Charalambous , Nikolaos Pappas , Nikolaos Nomikos , Risto Wichman This is my paper

Pith reviewed 2026-05-22 13:19 UTC · model grok-4.3

classification 💻 cs.MA cs.SYeess.SY
keywords goal-oriented communicationmulti-agent systemsinformation theorycommunication theorymachine learningswarm roboticsfederated learningedge computing
0
0 comments X

The pith

Goal-oriented communication prioritizes task-relevant information in multi-agent systems over message accuracy.

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

The paper surveys goal-oriented communication in multi-agent systems as a way to handle resource constraints by focusing on information that helps achieve shared goals. It connects concepts from information theory, communication theory, and machine learning to review foundational ideas, learning methods, and emerging protocols. Applications in swarm robotics, federated learning, and edge computing demonstrate practical uses. A sympathetic reader would care because traditional communication wastes resources on irrelevant data when agents have common tasks to accomplish. The review also points to open challenges for future work in this area.

Core claim

This review establishes goal-oriented communication as a paradigm that prioritizes the importance of information with respect to the agents' shared objectives in multi-agent systems. It bridges perspectives from information theory, communication theory, and machine learning while examining foundational concepts, learning-based approaches, emergent protocols, coordination under constraints, and applications in swarm robotics, federated learning, and edge computing, concluding with open challenges and future research directions.

What carries the argument

Goal-oriented communication, which selects messages based on their relevance to shared agent objectives rather than their fidelity to source data.

Load-bearing premise

The assumption that the chosen literature provides a comprehensive and unbiased overview of goal-oriented communication across the relevant fields without omitting key works.

What would settle it

The discovery of several important recent papers on goal-oriented communication in multi-agent systems that use approaches from the bridged fields but are absent from the survey would show incompleteness.

Figures

Figures reproduced from arXiv: 2508.07720 by Nikolaos Nomikos, Nikolaos Pappas, Risto Wichman, Themistoklis Charalambous.

Figure 1
Figure 1. Figure 1: FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: intrinsic state process extrinsic observation process Encoder Decoder FIGURE 2. Illustration of a semantic source and its lossy compression (redrawn from [52]). Distortion metrics ds [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p025_4.png] view at source ↗
read the original abstract

As multi-agent systems (MAS) become increasingly prevalent in autonomous systems, distributed control, and edge intelligence, efficient communication under resource constraints has emerged as a critical challenge. Traditional communication paradigms often emphasize message fidelity or bandwidth optimization, overlooking the task relevance of the exchanged information. In contrast, goal-oriented communication prioritizes the importance of information with respect to the agents' shared objectives. This review provides a comprehensive survey of goal-oriented communication in MAS, bridging perspectives from information theory, communication theory, and machine learning. We examine foundational concepts alongside learning-based approaches and emergent protocols. Special attention is given to coordination under communication constraints, as well as applications in domains such as swarm robotics, federated learning, and edge computing. The paper concludes with a discussion of open challenges and future research directions at the intersection of communication theory, machine learning, and multi-agent decision making.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript is a survey paper on goal-oriented communication in multi-agent systems (MAS). It contrasts traditional fidelity- or bandwidth-focused communication with goal-oriented approaches that emphasize task relevance of information under resource constraints. The review bridges information theory, communication theory, and machine learning perspectives; it covers foundational concepts, learning-based methods, emergent communication protocols, coordination challenges, and applications in swarm robotics, federated learning, and edge computing, concluding with open challenges and future directions.

Significance. If the literature selection is representative, the survey could usefully synthesize perspectives across information theory, communication theory, and machine learning to clarify how goal-oriented communication differs from classical paradigms and to identify cross-field research opportunities in constrained MAS settings. The explicit attention to applications and coordination under constraints is a constructive contribution that could help researchers navigate the intersection of these areas.

major comments (1)
  1. [Abstract and Introduction] Abstract and Introduction: The paper asserts that it provides a 'comprehensive survey' that bridges the three fields and yields a reliable view of open challenges. No literature search protocol is stated (databases, keywords, date ranges, inclusion/exclusion rules, or handling of seminal results). This directly bears on the central claim of balanced synthesis and undistorted challenges, as selection bias or omissions (e.g., specific information-theoretic bounds or ML protocol analyses) cannot be assessed from the given text.
minor comments (1)
  1. [Conclusion] The abstract and concluding section could more explicitly distinguish which open challenges are field-specific versus truly interdisciplinary.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our survey manuscript. We address the major comment below and will incorporate revisions to enhance transparency.

read point-by-point responses

  1. Referee: [Abstract and Introduction] Abstract and Introduction: The paper asserts that it provides a 'comprehensive survey' that bridges the three fields and yields a reliable view of open challenges. No literature search protocol is stated (databases, keywords, date ranges, inclusion/exclusion rules, or handling of seminal results). This directly bears on the central claim of balanced synthesis and undistorted challenges, as selection bias or omissions (e.g., specific information-theoretic bounds or ML protocol analyses) cannot be assessed from the given text.

    Authors: We agree that an explicit description of the literature selection process would strengthen the paper's claim to a balanced synthesis. In the revised version, we will insert a dedicated subsection (e.g., 'Literature Review Methodology') immediately after the Introduction that specifies: (i) the primary databases and repositories searched (IEEE Xplore, ACM DL, arXiv, Google Scholar, and selected conference proceedings); (ii) the core keyword combinations employed (e.g., 'goal-oriented communication' AND 'multi-agent systems', 'semantic communication' AND 'MAS', 'emergent communication' AND 'coordination'); (iii) the temporal scope (primarily 2018–2024 with inclusion of foundational pre-2018 works); (iv) inclusion/exclusion criteria (peer-reviewed journal/conference papers and high-quality preprints directly addressing task-relevant information exchange under constraints); and (v) the approach to seminal results (manual cross-referencing of highly cited works in information theory and ML). This addition will allow readers to evaluate coverage and potential omissions more rigorously while preserving the manuscript's focus on synthesis across the three fields. revision: yes

Circularity Check

0 steps flagged

No circularity: literature survey without derivations or fitted predictions

full rationale

This is a review paper surveying goal-oriented communication in multi-agent systems across information theory, communication theory, and machine learning. It presents no original mathematical derivations, first-principles results, predictions, or fitted parameters. The abstract and structure describe examination of foundational concepts, learning-based approaches, emergent protocols, and applications, all drawn from external literature. No load-bearing steps reduce by construction to inputs defined within the paper, self-citations, or ansatzes smuggled via prior work. As a narrative overview relying on cited external sources, the derivation chain is absent and the paper is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a survey paper; it does not introduce new free parameters, axioms, or invented entities beyond summarizing concepts already present in the cited literature.

axioms (1)
  • domain assumption Goal-oriented communication constitutes a distinct paradigm that prioritizes task relevance over traditional metrics of fidelity or bandwidth.
    Explicitly contrasted with traditional paradigms in the abstract as the foundational motivation for the survey.

pith-pipeline@v0.9.0 · 5690 in / 1184 out tokens · 50511 ms · 2026-05-22T13:19:12.152008+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AgentComm: Semantic Communication for Embodied Agents

    eess.SP 2026-04 unverdicted novelty 6.0

    AgentComm achieves nearly 50% bandwidth reduction in embodied agent communication via LLM semantic processing, importance-aware transmission, and a task knowledge base, with negligible impact on task completion.

  2. Intention-Aware Semantic Agent Communications for AI Glasses

    eess.SP 2026-04 unverdicted novelty 5.0

    An intention-aware semantic agent system for AI glasses reduces bandwidth by over 50% in simulations while preserving task performance through adaptive preprocessing guided by inferred user intentions.

  3. Sense Smarter, Think Better: Edge Perception for Next-Generation Networks

    eess.SP 2026-05 unverdicted novelty 2.0

    A structured survey of edge perception that integrates sensing modalities, edge AI, task-driven designs, and open challenges for 6G networks.

Reference graph

Works this paper leans on

127 extracted references · 127 canonical work pages · cited by 3 Pith papers

  1. [1]

    Multi-Agent Systems: A Survey,

    A. Dorri, S. S. Kanhere, and R. Jurdak, “Multi-Agent Systems: A Survey,” IEEE Access, vol. 6, pp. 28 573–28 593, 2018

    work page 2018

  2. [2]

    On the Control of Multi-Agent Systems: A Survey,

    F. Chen and W. Ren, “On the Control of Multi-Agent Systems: A Survey,” Foundations and Trends® in Systems and Control , vol. 6, no. 4, pp. 339–499, 2019

    work page 2019

  3. [3]

    H∞ State-Feedback Control of Multi-Agent Systems with Data Packet Dropout in the Communi- cation Channels: A Markovian Approach,

    A.-M. Stoica and S. C. Stoicu, “ H∞ State-Feedback Control of Multi-Agent Systems with Data Packet Dropout in the Communi- cation Channels: A Markovian Approach,” Entropy, vol. 24, no. 12, 2022

    work page 2022

  4. [4]

    Coordination of Cooperative Autonomous Ve- hicles: Toward safer and more efficient road transportation,

    R. Hult, G. R. Campos, E. Steinmetz, L. Hammarstrand, P. Falcone, and H. Wymeersch, “Coordination of Cooperative Autonomous Ve- hicles: Toward safer and more efficient road transportation,” IEEE Signal Processing Magazine , vol. 33, no. 6, pp. 74–84, 2016

    work page 2016

  5. [5]

    Perception, Planning, Control, and Coordination for Autonomous Vehicles,

    S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y . H. Eng, D. Rus, and M. H. Ang, “Perception, Planning, Control, and Coordination for Autonomous Vehicles,” Machines, vol. 5, no. 1, 2017

    work page 2017

  6. [6]

    Intersection co- ordination with priority-based search for autonomous vehicles,

    J. Li, T. A. Hoang, E. Lin, H. L. Vu, and S. Koenig, “Intersection co- ordination with priority-based search for autonomous vehicles,” Pro- ceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 10, pp. 11 578–11 585, June 2023

    work page 2023

  7. [7]

    Multi- Agent Formation Control Based on Distributed Estimation With Prescribed Performance,

    C. J. Stamouli, C. P. Bechlioulis, and K. J. Kyriakopoulos, “Multi- Agent Formation Control Based on Distributed Estimation With Prescribed Performance,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2929–2934, 2020

    work page 2020

  8. [8]

    Distributed Estimation Approach for Tracking a Mobile Target via Formation of UA Vs,

    M. Doostmohammadian, A. Taghieh, and H. Zarrabi, “Distributed Estimation Approach for Tracking a Mobile Target via Formation of UA Vs,”IEEE Transactions on Automation Science and Engineering , vol. 19, no. 4, pp. 3765–3776, 2022

    work page 2022

  9. [9]

    Distributed Estimation and Control for LTI Systems Under Finite-Time Agreement,

    C. Fioravanti, E. Makridis, G. Oliva, M. Vrakopoulou, and T. Char- alambous, “Distributed Estimation and Control for LTI Systems Under Finite-Time Agreement,” IEEE Transactions on Automatic Control, vol. 69, no. 11, pp. 7909–7916, 2024

    work page 2024

  10. [10]

    Communication-Aware Robotics: Exploiting Motion for Communication,

    A. Muralidharan and Y . Mostofi, “Communication-Aware Robotics: Exploiting Motion for Communication,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 4, pp. 115–139, 2021

    work page 2021

  11. [11]

    Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications,

    P. G. F. Dias, M. C. Silva, G. P. Rocha Filho, P. A. Vargas, L. P. Cota, and G. Pessin, “Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications,” Sensors, vol. 21, no. 6, 2021

    work page 2021

  12. [12]

    Distributed Formation Control of Multi-Robot Systems with Path Navigation via Complex Laplacian,

    X. Wu, R. Wu, Y . Zhang, and J. Peng, “Distributed Formation Control of Multi-Robot Systems with Path Navigation via Complex Laplacian,” Entropy, vol. 25, no. 11, 2023

    work page 2023

  13. [13]

    Swarm Intelligence-Based Multi-Robotics: A Com- prehensive Review,

    L. V . Nguyen, “Swarm Intelligence-Based Multi-Robotics: A Com- prehensive Review,”AppliedMath, vol. 4, no. 4, pp. 1192–1210, 2024

    work page 2024

  14. [14]

    A theory of goal-oriented communication,

    O. Goldreich, B. Juba, and M. Sudan, “A theory of goal-oriented communication,” Journal of the ACM (JACM) , vol. 59, no. 2, May

    work page

  15. [15]

    Available: https://doi.org/10.1145/2160158.2160161

    [Online]. Available: https://doi.org/10.1145/2160158.2160161

    work page doi:10.1145/2160158.2160161

  16. [16]

    Semantics-Empowered Communi- cation for Networked Intelligent Systems,

    M. Kountouris and N. Pappas, “Semantics-Empowered Communi- cation for Networked Intelligent Systems,” IEEE Communications Magazine, vol. 59, no. 6, pp. 96–102, 2021

    work page 2021

  17. [17]

    Goal- Oriented Semantic Communications for 6G Networks,

    H. Zhou, Y . Deng, X. Liu, N. Pappas, and A. Nallanathan, “Goal- Oriented Semantic Communications for 6G Networks,”IEEE Internet of Things Magazine , vol. 7, no. 5, pp. 104–110, 2024

    work page 2024

  18. [18]

    Toward Natively Intelligent Semantic Communications and Networking,

    S. E. Trevlakis, N. Pappas, and A.-A. A. Boulogeorgos, “Toward Natively Intelligent Semantic Communications and Networking,” IEEE Open Journal of the Communications Society, vol. 5, pp. 1486– 1503, 2024

    work page 2024

  19. [19]

    Less Data, More Knowledge: Building Next-Generation Semantic Communication Networks,

    C. Chaccour, W. Saad, M. Debbah, Z. Han, and H. Vincent Poor, “Less Data, More Knowledge: Building Next-Generation Semantic Communication Networks,” IEEE Communications Surveys & Tuto- rials, vol. 27, no. 1, pp. 37–76, 2025

    work page 2025

  20. [20]

    Toward Goal- Oriented Semantic Communications: New Metrics, Framework, and Open Challenges,

    A. Li, S. Wu, S. Meng, R. Lu, S. Sun, and Q. Zhang, “Toward Goal- Oriented Semantic Communications: New Metrics, Framework, and Open Challenges,” IEEE Wireless Communications , vol. 31, no. 5, pp. 238–245, 2024

    work page 2024

  21. [21]

    Semantic Communication: A Survey of Its Theoretical Development,

    G. Xin, P. Fan, and K. B. Letaief, “Semantic Communication: A Survey of Its Theoretical Development,”Entropy, vol. 26, no. 2, 2024

    work page 2024

  22. [22]

    A Survey on Goal- Oriented Semantic Communication: Techniques, Challenges, and Future Directions,

    T. M. Getu, G. Kaddoum, and M. Bennis, “A Survey on Goal- Oriented Semantic Communication: Techniques, Challenges, and Future Directions,” IEEE Access, vol. 12, pp. 51 223–51 274, 2024

    work page 2024

  23. [23]

    A Mathematical Theory of Communication,

    C. E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical Journal , vol. 27, pp. 379–423, 623–656, July, October 1948

    work page 1948

  24. [24]

    T. M. Cover and J. A. Thomas, Elements of Information Theory . Wiley, 2006

    work page 2006

  25. [25]

    Berger, Rate distortion theory; a mathematical basis for data compression, ser

    T. Berger, Rate distortion theory; a mathematical basis for data compression, ser. Prentice-Hall series in information and system sciences. Englewood Cliffs, N.J: Prentice-Hall, 1971

    work page 1971

  26. [26]

    Coding theorems for a discrete source with a fidelity criterion,

    C. E. Shannon, “Coding theorems for a discrete source with a fidelity criterion,” in IRE National Convention Record , vol. 7, March 1959, pp. 142–163

    work page 1959

  27. [27]

    Experimental Study of Transport Layer Protocols for Wireless Networked Control Systems,

    P. Kutsevol, O. Ayan, N. Pappas, and W. Kellerer, “Experimental Study of Transport Layer Protocols for Wireless Networked Control Systems,” in 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) , 2023, pp. 438–446

    work page 2023

  28. [28]

    Enabling communication and control co-design in 6g networks,

    O. Ayan, N. Pappas, M. A. G. Estevez, X. An, and W. Kellerer, “Enabling communication and control co-design in 6g networks,” in IEEE Conference on Standards for Communications and Networking (CSCN), 2024

    work page 2024

  29. [29]

    Goal-oriented middleware filtering at transport layer based on value of updates,

    P. Kutsevol, O. Ayan, N. Pappas, and W. Kellerer, “Goal-oriented middleware filtering at transport layer based on value of updates,” arXiv:2502.17350, 2025

    work page arXiv 2025

  30. [30]

    Dual effect, certainty equivalence, and separation in stochastic control,

    Y . Bar-Shalom and E. Tse, “Dual effect, certainty equivalence, and separation in stochastic control,” IEEE Transactions on Automatic Control, vol. 19, no. 5, pp. 494–500, oct 1974

    work page 1974

  31. [31]

    State-based channel access for a network of control systems,

    C. Ramesh, “State-based channel access for a network of control systems,” Ph.D. dissertation, KTH Royal Institute of Technology, Stockholm, 2014. [Online]. Available: http://www.diva-portal.org/ smash/record.jsf?pid=diva2:710642&dswid=9234

    work page 2014

  32. [32]

    K. J. Astrom, Introduction to Stochastic Control Theory . Dover Publications Inc., 2006

    work page 2006

  33. [33]

    Foundations of Control and Estimation Over Lossy Networks,

    L. Schenato, B. Sinopoli, M. Franceschetti, K. Poolla, and S. S. Sas- try, “Foundations of Control and Estimation Over Lossy Networks,” Proceedings of the IEEE , vol. 95, no. 1, pp. 163–187, Jan 2007

    work page 2007

  34. [34]

    B. D. O. Anderson and J. B. Moore, Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall, 1979

    work page 1979

  35. [35]

    Optimal sensor scheduling for multiple linear dynamical systems,

    D. Han, J. Wu, H. Zhang, and L. Shi, “Optimal sensor scheduling for multiple linear dynamical systems,” Automatica, vol. 75, pp. 260– 270, Jan. 2017

    work page 2017

  36. [36]

    Sensor Scheduling in Variance Based Event Triggered Estimation With Packet Drops,

    A. S. Leong, S. Dey, and D. E. Quevedo, “Sensor Scheduling in Variance Based Event Triggered Estimation With Packet Drops,” IEEE Transactions on Automatic Control , vol. 62, no. 4, pp. 1880– 1895, April 2017

    work page 2017

  37. [37]

    Learning Optimal Scheduling Policy for Remote State Estimation under Uncer- tain Channel Condition,

    S. Wu, X. Ren, Q.-S. Jia, K. H. Johansson, and L. Shi, “Learning Optimal Scheduling Policy for Remote State Estimation under Uncer- tain Channel Condition,” IEEE Transactions on Control of Network Systems, vol. 7, no. 2, pp. 579–591, June 2020

    work page 2020

  38. [38]

    Optimal Scheduling of Multiple Sensors over Lossy and Bandwidth Limited Channels,

    S. Wu, K. Ding, P. Cheng, and L. Shi, “Optimal Scheduling of Multiple Sensors over Lossy and Bandwidth Limited Channels,” IEEE Transactions on Control of Network Systems , vol. 7, no. 3, pp. 1188–1200, Sep. 2020

    work page 2020

  39. [39]

    Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,

    A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,”Automatica, vol. 113, p. 108759, Mar. 2020

    work page 2020

  40. [40]

    Multi- hop sensor network scheduling for optimal remote estimation,

    T. Iwaki, J. Wu, Y . Wu, H. Sandberg, and K. H. Johansson, “Multi- hop sensor network scheduling for optimal remote estimation,” Au- tomatica, vol. 127, p. 109498, May 2021

    work page 2021

  41. [41]

    Event-Based Transmission Scheduling and LQG Control Over a Packet Dropping Link,

    A. S. Leong, D. E. Quevedo, T. Tanaka, S. Dey, and A. Ahlén, “Event-Based Transmission Scheduling and LQG Control Over a Packet Dropping Link,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 8945–8950, July 2017

    work page 2017

  42. [42]

    Goal-Oriented Communication For Real-Time Tracking In Autonomous Systems,

    N. Pappas and M. Kountouris, “Goal-Oriented Communication For Real-Time Tracking In Autonomous Systems,” in IEEE International Conference on Autonomous Systems (ICAS) , 2021

    work page 2021

  43. [43]

    Real-Time Recon- struction of Markov Sources and Remote Actuation Over Wireless Channels,

    M. Salimnejad, M. Kountouris, and N. Pappas, “Real-Time Recon- struction of Markov Sources and Remote Actuation Over Wireless Channels,” IEEE Transactions on Communications , vol. 72, no. 5, pp. 2701–2715, 2024

    work page 2024

  44. [44]

    Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints,

    J. Luo and N. Pappas, “Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints,” IEEE Transactions on Communications, pp. 1–1, 2025

    work page 2025

  45. [45]

    Goal-Oriented Policies for Cost of Actuation Error Minimization in Wireless Au- VOLUME , 29 Charalambous et al.: Preparation of Papers for IEEE OPEN JOURNALS tonomous Systems,

    E. Fountoulakis, N. Pappas, and M. Kountouris, “Goal-Oriented Policies for Cost of Actuation Error Minimization in Wireless Au- VOLUME , 29 Charalambous et al.: Preparation of Papers for IEEE OPEN JOURNALS tonomous Systems,” IEEE Communications Letters , vol. 27, no. 9, pp. 2323–2327, 2023

    work page 2023

  46. [46]

    On the cost of consecutive estimation error: Significance-aware non-linear aging,

    J. Luo and N. Pappas, “On the cost of consecutive estimation error: Significance-aware non-linear aging,” IEEE Transactions on Information Theory, 2025

    work page 2025

  47. [47]

    The information bottleneck method,

    N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” 2000. [Online]. Available: https://arxiv.org/abs/physics/ 0004057

    work page 2000

  48. [48]

    Deep Variational Information Bottleneck,

    A. A. Alemi, I. Fischer, J. V . Dillon, and K. Murphy, “Deep Variational Information Bottleneck,” in International Conference on Learning Representations (ICLR) , 2017

    work page 2017

  49. [49]

    Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications,

    D. Gündüz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications,” IEEE Journal on Selected Areas in Communications , vol. 41, no. 1, pp. 5–41, 2023

    work page 2023

  50. [50]

    A Theory of Semantic Commu- nication,

    Y . Shao, Q. Cao, and D. Gündüz, “A Theory of Semantic Commu- nication,” IEEE Transactions on Mobile Computing , vol. 23, no. 12, pp. 12 211–12 228, 2024

    work page 2024

  51. [51]

    Niu and P

    K. Niu and P. Zhang, The Mathematical Theory of Semantic Com- munication. Springer Singapore, 2025

    work page 2025

  52. [52]

    A Rate-Distortion Framework for Characterizing Semantic Information,

    J. Liu, W. Zhang, and H. V . Poor, “A Rate-Distortion Framework for Characterizing Semantic Information,” in IEEE International Symposium on Information Theory (ISIT) , 2021, pp. 2894–2899

    work page 2021

  53. [53]

    An Indirect Rate- Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation,

    J. Liu, S. Shao, W. Zhang, and H. V . Poor, “An Indirect Rate- Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation,” IEEE Transactions on Communications, vol. 70, no. 9, pp. 5946–5959, 2022

    work page 2022

  54. [54]

    A Rate Distortion Approach to Goal-Oriented Communication,

    P. A. Stavrou and M. Kountouris, “A Rate Distortion Approach to Goal-Oriented Communication,” in IEEE International Symposium on Information Theory (ISIT) , 2022, pp. 590–595

    work page 2022

  55. [55]

    The Role of Fidelity in Goal-Oriented Semantic Communica- tion: A Rate Distortion Approach,

    ——, “The Role of Fidelity in Goal-Oriented Semantic Communica- tion: A Rate Distortion Approach,” IEEE Transactions on Commu- nications, vol. 71, no. 7, pp. 3918–3931, 2023

    work page 2023

  56. [56]

    A Semantic Generalization of Shannon’s Information Theory and Applications,

    C. Lu, “A Semantic Generalization of Shannon’s Information Theory and Applications,” Entropy, vol. 27, no. 5, 2025

    work page 2025

  57. [57]

    Kalman Filtering With Intermittent Observations,

    B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. Sastry, “Kalman Filtering With Intermittent Observations,” IEEE Transactions on Automatic Control , vol. 49, no. 9, pp. 1453–1464, Sep 2004

    work page 2004

  58. [58]

    Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective,

    L. Shi, M. Epstein, and R. M. Murray, “Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective,” IEEE Transactions on Automatic Control, vol. 55, no. 3, pp. 594–604, Mar. 2010

    work page 2010

  59. [59]

    On the resource allocation problem in wireless networked control systems,

    T. Charalambous, A. Ozcelikkale, M. Zanon, P. Falcone, and H. Wymeersch, “On the resource allocation problem in wireless networked control systems,” in IEEE Conference on Decision and Control (CDC), Dec. 2017

    work page 2017

  60. [60]

    Event-Based State Estimation With Variance-Based Triggering,

    S. Trimpe and R. D'Andrea, “Event-Based State Estimation With Variance-Based Triggering,” IEEE Transactions on Automatic Con- trol, vol. 59, no. 12, pp. 3266–3281, Dec. 2014

    work page 2014

  61. [61]

    Distributed Chan- nel Access for Control Over Unknown Memoryless Communication Channels,

    T. Farjam, H. Wymeersch, and T. Charalambous, “Distributed Chan- nel Access for Control Over Unknown Memoryless Communication Channels,” IEEE Transactions on Automatic Control, vol. 67, no. 12, pp. 6445–6459, 2022

    work page 2022

  62. [62]

    Distributed Channel Access for Control Over Known and Unknown Gilbert–Elliott Channels,

    ——, “Distributed Channel Access for Control Over Known and Unknown Gilbert–Elliott Channels,”IEEE Transactions on Automatic Control, vol. 68, no. 12, pp. 7405–7419, 2023

    work page 2023

  63. [63]

    A Timer-Based Distributed Channel Access Mechanism in Networked Control Sys- tems,

    T. Farjam, T. Charalambous, and H. Wymeersch, “A Timer-Based Distributed Channel Access Mechanism in Networked Control Sys- tems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 5, pp. 652–656, 2018

    work page 2018

  64. [64]

    Bikhchandani, J

    S. Bikhchandani, J. Hirshleifer, and J. G. Riley, The analytics of uncertainty and information . Cambridge University Press, 2013

    work page 2013

  65. [65]

    Risk, stochastic preference, and the value of informa- tion,

    J. P. Gould, “Risk, stochastic preference, and the value of informa- tion,” Journal of Economic Theory , vol. 8, no. 1, pp. 64–84, May 1974

    work page 1974

  66. [66]

    The Value of Side Information in Shortest Path Optimization,

    M. Rinehart and M. A. Dahleh, “The Value of Side Information in Shortest Path Optimization,” IEEE Transactions on Automatic Control, vol. 56, no. 9, pp. 2038–2049, Sep. 2011

    work page 2038

  67. [67]

    Innovations- based priority assignment for control over CAN-like networks,

    A. Molin, C. Ramesh, H. Esen, and K. H. Johansson, “Innovations- based priority assignment for control over CAN-like networks,” in IEEE Conference on Decision and Control (CDC) , Dec. 2015

    work page 2015

  68. [68]

    Scheduling networked state estimators based on Value of Information,

    A. Molin, H. Esen, and K. H. Johansson, “Scheduling networked state estimators based on Value of Information,” Automatica, vol. 110, p. 108578, Dec. 2019

    work page 2019

  69. [69]

    On the choice of the event trigger in event-based estimation,

    S. Trimpe and M. C. Campi, “On the choice of the event trigger in event-based estimation,” in International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP) . IEEE, June 2015

    work page 2015

  70. [70]

    Event-Triggered Control in Shared Networks: How the Computational Power of Sensors Affects Trans- mission Priorities,

    T. Farjam and T. Charalambous, “Event-Triggered Control in Shared Networks: How the Computational Power of Sensors Affects Trans- mission Priorities,” in IEEE Conference on Decision and Control (CDC), 2021, pp. 6224–6230

    work page 2021

  71. [71]

    Timer-Based Distributed Channel Access in Networked Control Systems over Known and Unknown Gilbert-Elliott Channels,

    T. Farjam, T. Charalambous, and H. Wymeersch, “Timer-Based Distributed Channel Access in Networked Control Systems over Known and Unknown Gilbert-Elliott Channels,” in European Control Conference (ECC), 2019, pp. 2983–2989

    work page 2019

  72. [72]

    Control-Aware Distributed Channel Access for Net- worked Control Systems,

    T. Farjam, “Control-Aware Distributed Channel Access for Net- worked Control Systems,” Ph.D. dissertation, Aalto University, Fin- land, 2022

    work page 2022

  73. [73]

    Age of Information: A New Concept, Metric, and Tool,

    A. Kosta, N. Pappas, and V . Angelakis, “Age of Information: A New Concept, Metric, and Tool,”Foundations and Trends® in Networking, vol. 12, no. 3, pp. 162–259, 2017

    work page 2017

  74. [74]

    Age of Information: An Introduction and Survey,

    R. D. Yates, Y . Sun, D. R. Brown, S. K. Kaul, E. Modiano, and S. Ulukus, “Age of Information: An Introduction and Survey,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 5, pp. 1183–1210, 2021

    work page 2021

  75. [75]

    Pappas et al., Age of Information: Foundations and Applications

    N. Pappas et al., Age of Information: Foundations and Applications . Cambridge University Press, 2023

    work page 2023

  76. [76]

    Scheduling Policies for AoI Minimization With Timely Throughput Constraints,

    E. Fountoulakis, T. Charalambous, A. Ephremides, and N. Pappas, “Scheduling Policies for AoI Minimization With Timely Throughput Constraints,” IEEE Transactions on Communications , vol. 71, no. 7, pp. 3905–3917, 2023

    work page 2023

  77. [77]

    Age of Actuation in a Wireless Power Transfer System,

    A. Nikkhah, A. Ephremides, and N. Pappas, “Age of Actuation in a Wireless Power Transfer System,” in IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) , 2023

    work page 2023

  78. [78]

    Age of Actuated Information and Age of Actuation in a Data- Caching Energy Harvesting Actuator,

    ——, “Age of Actuated Information and Age of Actuation in a Data- Caching Energy Harvesting Actuator,” in IEEE Global Communica- tions Conference (GLOBECOM) , 2024, pp. 2232–2237

    work page 2024

  79. [79]

    Remote estimation over packet-dropping wireless channels with partial state information,

    I. Tzortzis, E. Makridis, C. D. Charalambous, and T. Charalambous, “Remote estimation over packet-dropping wireless channels with partial state information,” in European Control Conference (ECC) , 2025, pp. 1414–1419

    work page 2025

  80. [80]

    Semantics-aware status updates with energy harvesting devices: Query version age of information,

    E. Delfani and N. Pappas, “Semantics-aware status updates with energy harvesting devices: Query version age of information,” in 2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) , 2024, pp. 177– 184

    work page 2024

Showing first 80 references.