Pith. sign in

REVIEW 2 major objections 3 minor 1 cited by

Signaling and Social Learning in Swarms of Robots

T0 review · 2 major / 3 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read This paper argues that all communication in learning robot swarms can be classified along two axes: information selection and physical abstraction.

desk verdict Useful review/position piece with a promising two-axis taxonomy, but the axes as defined are muddled and its own eigenspectrum example contradicts the 'lossless' physical abstraction axis; fixable, but needs revision. read the letter →

arxiv 2411.11616 v2 pith:Z3PTWKDY submitted 2024-11-18 cs.RO cs.AIcs.LGcs.MA

Leo Cazenille , Maxime Toquebiau , Nicolas Lobato-Dauzier , Alessia Loi , Loona Macabre , Nathanael Aubert-Kato , Anthony Genot , Nicolas Bredeche This is my paper · ORCID
classification cs.ROcs.AIcs.LGcs.MA
keywords signalingcommunicationswarmroboticsdecentralizedlearningandexecutionsocialcreditassignmentemergenttaxonomy
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

This paper argues that the many forms of online, decentralized learning used in swarm robotics—embodied evolution and social learning—are all instances of a single paradigm the authors call decentralized learning and execution (DLE), and that this framing should replace the usual "design then deploy" approach. Within DLE, the paper's central move is a taxonomy for communication: every signaling method, from chemical diffusion to emergent language to large language models, can be placed on a plane whose two axes are the degree of information selection (how much irrelevant content is discarded) and the degree of physical abstraction (how much the representation is transformed without loss). The taxonomy is meant to organize existing work from evolutionary robotics, multi-agent reinforcement learning, language evolution, and biophysics, and to make future work comparable. The paper also argues that communication in DLE is double-edged: it can help robots estimate their individual contribution to the collective (the credit assignment problem), yet it is itself shaped by the same evolutionary competition that can push swarms toward suboptimal outcomes. A sympathetic reader would care because the paper offers a common vocabulary and a map for a fragmented field, and it points concretely toward swarms of robots that learn while deployed and communicate through human-like language.

What carries the argument

The machinery is the two-dimensional taxonomy itself. The first axis, physical abstraction, changes how information is represented without discarding content, analogous to a change of basis in linear algebra; the second axis, information selection, reduces the amount of information transmitted by discarding what is not relevant, analogous to projection onto a subspace. The paper pairs this with the cue/signal distinction and with an evolutionary account of credit assignment, in which individual fitness, inclusive fitness, and alignment with group welfare determine whether the swarm's equilibrium approaches the socially optimal outcome for the task.

What would settle it

Take the heat-diffusion eigenspectrum method used in [53] and ask whether it is best described as physical abstraction (a change of basis that reveals the arena shape) or information selection (discarding all but the second eigenvalue). If the same implemented method can be assigned to both axes with equal justification, or if every existing signaling method shifts coordinates depending only on description, the two axes are not orthogonal and the map is not a classification. A simpler check: enumerate a broad set of published swarm communication mechanisms and see whether any cannot be placed without contradiction.

Watch

Extended reading notes

Core claim

The paper's central claim is that the problems addressed by social learning and embodied evolution in swarms are covered by the term decentralized learning and execution, and that communication in such systems should be studied through a taxonomy with two axes: information selection (lossless compression and redundancy removal at one end, lossy selection of task-relevant content at the other) and physical abstraction (raw signal transfer at one end, structured mathematical or linguistic representations at the other). The authors distinguish cues, which are unintentional and available from the environment, from signals, which are produced intentionally for receivers, and they restrict their survey to the latter. Along this plane they place biophysical processes such as reaction-diffusion, gradient broadcasting and eigenspectrum analysis, differentiable emergent communication in multi-agent reinforcement learning, grounded natural-language communication, and, at the extreme of both axes, communication mediated by large language models. The stated payoff is that existing and future signaling strategies become comparable along two dimensions, and that the credit assignment problem—estimating each robot's marginal contribution—appears as the central, unavoidable difficulty that communication can alleviate but not eliminate.

Load-bearing premise

The whole classification depends on the assumption that two axes—how much information is discarded and how abstractly the remainder is represented—are enough to meaningfully situate every signaling strategy; if some real communication method resists placement, or if the two axes turn out to be the same dimension in disguise, the taxonomy's organizing value largely disappears.

Editorial extensions

If this is right

  • Framing embodied evolution and social learning as decentralized learning and execution connects swarm robotics to the wider multi-agent reinforcement learning literature, so results about decentralized critics and networked agents become directly relevant.
  • The two-axis map gives a common coordinate system for comparing methods as different as reaction-diffusion communication and LLM-based language, making it possible to ask where a new signaling strategy sits and what neighbors it has.
  • Communication can be used for distributed credit assignment by aggregating local performance data or supporting counterfactual reasoning, but it does not remove the exponential cost of exact marginal-contribution estimation.
  • Because evolving signaling is under the same selective pressure as action policies, misaligned incentives can produce deliberately inefficient or competitive communication; shared interest or inclusive fitness is needed to keep communication aligned with the collective task.
  • At the high-abstraction extreme, robot swarms using large language models become plausible, bringing benefits in explainability and human-robot interaction while raising open problems of embodiment, deployment cost, bias, and hallucination.

Reading between the lines

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

  • A natural extension the paper does not spell out: the two axes could be operationalized quantitatively, with information selection measured through rate-distortion or mutual information and physical abstraction measured through representation distance, turning the taxonomy into a testable map rather than a qualitative diagram.
  • If the DLE framing is accepted, algorithms from decentralized optimization and gossip-based averaging could transfer directly to embodied evolution, potentially giving parameter-sharing swarms convergence guarantees they currently lack.
  • The taxonomy predicts that stigmergic trail-laying, often treated as low-level communication, can sit at very different coordinates depending on how the environmental trace is encoded; classifying a single mechanism under two readings would test the axes' robustness.
  • One testable consequence of the evolutionary argument: in a swarm of LLM-based agents without shared interest, language should drift toward private or competitive conventions, mirroring the suboptimal communication the paper describes for simpler evolving signals.
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

2 major / 3 minor

Summary. This position/review paper argues that online social learning and embodied evolution in robot swarms should be viewed under the umbrella term "decentralized learning and execution" (DLE), in which learning and execution happen simultaneously in a distributed population. The paper identifies the credit assignment problem as the central challenge of DLE and proposes a two-axis taxonomy for signaling and communication methods: the degree of information selection (lossy reduction of what is transmitted) and the degree of physical abstraction (lossless change in how information is represented). It then reviews a broad range of work—from molecular communication, reaction-diffusion, and spectral methods in swarm robotics, to emergent communication in multi-agent reinforcement learning and LLM-based agents—and maps these methods onto the proposed two-dimensional plane. The paper concludes with take-home messages for the robotics, machine learning, and complex systems communities.

Significance. If the proposed taxonomy is coherent, it would provide a rare cross-disciplinary framework for comparing communication strategies in DLE robot swarms, spanning molecular-scale physical signaling, spectral/gradient-based abstractions, emergent discrete communication, and LLM-based language. The framing around decentralized credit assignment is useful and brings together literatures that are rarely discussed side by side. The paper is an explicitly conceptual contribution: it contains no formal definitions, no mathematical model, and no experiments, so its value rests entirely on the clarity and internal consistency of its organizing axes. The extensive reference list and the explicit connection between inclusive fitness, evolutionary dynamics, and robot swarm learning are strengths, as is the paper's attempt to stake out a research agenda for a quantitatively underdeveloped area.

major comments (2)
  1. [§3 and §4.1(2)] The two axes are not independent as defined. In §3, information selection is defined as reducing information by losing what is not relevant, while physical abstraction is defined as "changing the way information is represented without loss of information." The eigenspectrum example in §4.1(2) is placed in the low-information-selection, high-physical-abstraction quadrant, but transmitting only the second eigenvalue λ2 of the graph Laplacian is a many-to-one, lossy compression of the full communication graph: it discards all spectral components except one. By the paper's own definition, that is high information selection, not low. The same problem appears with natural language, which is placed at the extreme of physical abstraction but is inherently lossy. Because the central example used to illustrate the high-abstraction quadrant violates the paper's own definition, the two axes collapse into a single lossiness dimension, and the taxonomy cannot systematically compare communication strategies as claimed. Please redefine the axes so that abstraction and selection are truly orthogonal (for example, treating the change of basis as abstraction and the choice of which components to transmit as selection), and reclassify the examples accordingly.
  2. [§3] The degree of information selection and physical abstraction is never operationalized. No scale, ordering relation, or decision rule is given for how to place a signaling method at a point in the two-dimensional plane, which makes the taxonomy non-falsifiable. The eigenspectrum example again illustrates the problem: depending on whether one focuses on the mathematical transformation or on the transmitted quantity, the same method can be assigned to different quadrants. Before the taxonomy can serve as a tool for classifying "existing and future works," the authors should specify a minimal criterion for each axis, such as defining selection by the ratio of message entropy to source entropy and abstraction by whether the representation is compositional or shared across agents.
minor comments (3)
  1. [Abstract] The abstract describes the two axes as running "from low-level lossless compression ... to high-level lossy compression," which reads as a single compression axis and conflicts with Section 3, where physical abstraction is defined as lossless and information selection is what introduces loss. Please align the abstract with the body of the paper.
  2. [§3] Typo: "by loosing information" should be "by losing information."
  3. [Figure 3] The algebraic analogy in the left panel (projection for information selection, change of basis for physical abstraction) is helpful, but the right panel places the eigenspectrum example as a change of basis even though the described method projects the graph Laplacian onto a single eigenvalue; please make the analogy and the example consistent.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the taxonomy is assembled from external work; self-citations are illustrative and not load-bearing.

full rationale

This is a review/position paper with no fitted parameters, equations, or quantitative predictions, so the fitted-input and self-definitional failure modes do not apply. The central contribution is a two-axis taxonomy (information selection, physical abstraction) proposed by explicit definition in Section 3: "We propose two axes for classification using the degree of information selection and the degree of physical abstraction," with each axis then illustrated by external works in Section 4. The DLE framing is openly a labeling proposal rather than a derived theorem: "we posit that the class of problems addressed when using such social learning or embodied evolution algorithms is covered by the umbrella term of decentralized learning and execution (DLE)." Author self-citations appear mainly as examples or background ([53] spectral swarm robotics, [36] inclusive fitness in evolutionary robotics, [10] embodied evolution review, [83,84] bio-micro-robots) and as illustrations of a quadrant rather than as premises that force the classification. Removing these citations would not change the taxonomy's structure. No uniqueness theorem from prior author work is invoked, and no ansatz is smuggled in via citation. One internal consistency concern is that physical abstraction is defined as representation change "without loss of information" whereas the paper's own eigenspectrum example (locally extracting λ2) is lossy; however, that is an issue of definitional clarity or classification coherence, not circularity, because the example is not used to derive the definition. Overall the derivation chain is not circular: the paper organizes external results under an explicitly proposed vocabulary, and its claims stand or fall on the usefulness of the taxonomy, not on a self-referential reduction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters, no new entities, and no mathematical derivations. Its axioms are conceptual framing assumptions: the validity of DLE as a category, the sufficiency of the two taxonomy axes, and the accuracy of the cited literature.

assumptions (3)
  • domain assumption Decentralized learning and execution (DLE) is a well-defined umbrella paradigm that encompasses embodied evolution, social learning, and decentralized multi-agent reinforcement learning.
    Introduced in Section 2 as a unifying label; the review's framing depends on this categorization being accepted.
  • domain assumption The two axes, information selection and physical abstraction, are sufficient to classify communication methods.
    Proposed in Section 3 without formal definitions or a proof of orthogonality or completeness; the taxonomy's usefulness depends on this.
  • domain assumption The cited works accurately represent the state of the art in their respective fields.
    The review's claims rely on the correctness and relevance of many cited references, none of which are independently verified in this paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Signaling and Social Learning in Swarms of Robots." pith.science (2026). https://pith.science/paper/Z3PTWKDY

@misc{pith2026241111616,
  author       = {Pith},
  title        = {Pith review of: Signaling and Social Learning in Swarms of Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z3PTWKDY}},
  note         = {Machine review of arXiv:2411.11616}
}
read the original abstract

This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.

Figures

Figures reproduced from arXiv: 2411.11616 by the authors.

Figure 1
Figure 1. (A) A swarm of robots is deployed in an unknown environment. Robots must learn together to solve a task. Robots interact locally with nearby robots and physical elements. (B) The decision-making process of a focal robot is based on cues from the physical world and signals from the social world. (C) Diagram of the communication and control policies for a robot, distinguishing between signals for local interactions an… view at source ↗
Figure 2
Figure 2. Alignment of Nash Equilibrium with Social Welfare with respect to the degree of inclusive fitness and the degree of shared interest among robots. The X-axis shows how aligned the individual’s interest (e.g., its local fitness function) is with that of the group, which is uniquely defined by its ability to optimally solve the task. The Y-axis shows the level of inclusive fitness experienced by each individual in the … view at source ↗
Figure 3
Figure 3. Signaling methods can be projected in a two-dimensional plane using information selection and physical abstraction as main components. Left: An algebraic analogy for information selection and physical abstraction in communication processes. Changing the degree of information selection can be done through operations like restriction to a subspace (projection with or without loss). Changing the level of physical abstr… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Kinetic theory of decentralized learning for smart active matter

    cond-mat.stat-mech 2025-01 conditional novelty 6.0 of 10

    A kinetic theory framework derives closed hydrodynamic equations for policy mean and diversity under decentralized learning, with an uncertainty relation between learning speed and policy fluctuations.

Reference graph

Works this paper leans on

170 extracted references · 77 canonical work pages · cited by 1 Pith paper

  1. [1]

    Swarm intelligence in cellular robotic systems

    G Beni and J Wang. Swarm intelligence in cellular robotic systems. In NATO ASI. 1993

    1993

  2. [2]

    A taxonomy for swarm robots

    Gregory Dudek, Michael Jenkin, Evangelos Milios, and David Wilkes. A taxonomy for swarm robots. In Pro- ceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’93), volume 1, pages 441–447. IEEE, 1993. Signaling and Social Learning in Swarms of Robots — 11/17

    1993

  3. [3]

    Swarm robotics : A review from the swarm engineering perspective

    Manuele Brambilla, Eliseo Ferrante, Mauro Birattari, and Marco Dorigo. Swarm robotics : A review from the swarm engineering perspective. Swarm Intelligence, 7(1):1–41, 2013

    2013

  4. [4]

    Swarm Robotics - A Formal Approach

    Heiko Hamann. Swarm Robotics - A Formal Approach. Springer, 2018

    2018

  5. [5]

    Reflections on the future of swarm robotics

    M Dorigo, G Theraulaz, and V Trianni. Reflections on the future of swarm robotics. Science Robotics, 2020

    2020

  6. [6]

    From individual robots to robot societies, 2021

    D Floreano and H Lipson. From individual robots to robot societies, 2021

    2021

  7. [7]

    Embodied evo- lution: Distributing an evolutionary algorithm in a pop- ulation of robots

    RA Watson, SG Ficici, and JB Pollack. Embodied evo- lution: Distributing an evolutionary algorithm in a pop- ulation of robots. Robotics and Autonomous Systems , 2002

    2002

  8. [8]

    Evolution, individual learning, and social learning in a swarm of real robots

    Jacqueline Heinerman, Massimiliano Rango, and Agos- ton Endre Eiben. Evolution, individual learning, and social learning in a swarm of real robots. In 2015 IEEE symposium series on computational intelligence, pages 1055–1062. IEEE, 2015

    2015

Show all 170 references
  1. [9]

    Social learning in swarm robotics

    N Bredeche and N Fontbonne. Social learning in swarm robotics. Philosophical Transactions of the Royal Soci- ety B, 2022

    2022

  2. [10]

    Embodied evo- lution in collective robotics: A review

    N Bredeche, E Haasdijk, and A Prieto. Embodied evo- lution in collective robotics: A review. Frontiers in Robotics and AI, 2018

    2018

  3. [11]

    Fully decentralized multi-agent rein- forcement learning with networked agents

    Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, and Tamer Basar. Fully decentralized multi-agent rein- forcement learning with networked agents. In Interna- tional Conference on Machine Learning, pages 5872–

  4. [12]

    Contrasting centralized and decentralized critics in multi-agent reinforcement learning

    Xueguang Lyu, Yuchen Xiao, Brett Daley, and Christo- pher Amato. Contrasting centralized and decentralized critics in multi-agent reinforcement learning. arXiv preprint arXiv:2102.04402, 2021

    2021 arXiv

  5. [13]

    Embodied evolution: A response to challenges in evolutionary robotics

    Sevan G Ficici, Richard A Watson, and Jordan B Pol- lack. Embodied evolution: A response to challenges in evolutionary robotics. In Proceedings of the eighth European workshop on learning robots , pages 14–22. Citeseer, 1999

    1999

  6. [14]

    Environment-driven embodied evolution in a population of autonomous agents

    Nicolas Bredeche and Jean-Marc Montanier. Environment-driven embodied evolution in a population of autonomous agents. In International Conference on Parallel Problem Solving from Nature, pages 290–299. Springer, 2010

    2010

  7. [15]

    Swarm robotic behaviors and cur- rent applications

    Melanie Schranz, Martina Umlauft, Micha Sende, and Wilfried Elmenreich. Swarm robotic behaviors and cur- rent applications. Frontiers in Robotics and AI , 7:36, 2020

    2020

  8. [16]

    Vazirani.Algorithmic Game Theory

    Noam Nisan, Tim Roughgarden, ´Eva Tardos, and Vi- jay V . Vazirani.Algorithmic Game Theory. Cambridge University Press, New York, NY , USA, 2007

    2007

  9. [17]

    A review of cooperative multi-agent deep reinforcement learning

    Afshin Oroojlooy and Davood Hajinezhad. A review of cooperative multi-agent deep reinforcement learning. Applied Intelligence, 53(11):13677–13722, 2023

    2023

  10. [18]

    An introduction to collective intelligence

    David H Wolpert and Kagan Tumer. An introduction to collective intelligence. arXiv preprint cs/9908014, 1999

    1999 arXiv

  11. [19]

    Ad hoc autonomous agent teams: Collab- oration without pre-coordination

    Peter Stone, Gal Kaminka, Sarit Kraus, and Jeffrey Rosenschein. Ad hoc autonomous agent teams: Collab- oration without pre-coordination. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 24, pages 1504–1509, 2010

    2010

  12. [20]

    Game theory and control

    Jason R Marden and Jeff S Shamma. Game theory and control. Annual review of control, robotics, and autonomous systems, 1(1):105–134, 2018

    2018

  13. [21]

    Nothing better to do? environment quality and the evolution of coopera- tion by partner choice

    P Ecoffet, N Bredeche, and JB Andr´e. Nothing better to do? environment quality and the evolution of coopera- tion by partner choice. Journal of Theoretical Biology, 2021

    2021

  14. [22]

    A Value for n-person Games

    Lloyd S Shapley. A Value for n-person Games. Annals of Mathematical Studies, 28:307–317, 1953

    1953

  15. [23]

    Multiagent systems: Algorithmic, game-theoretic, and logical foun- dations

    Yoav Shoham and Kevin Leyton-Brown. Multiagent systems: Algorithmic, game-theoretic, and logical foun- dations. Cambridge University Press, 2008

    2008

  16. [24]

    An introduction to multiagent systems

    Michael Wooldridge. An introduction to multiagent systems. John wiley & sons, 2009

    2009

  17. [25]

    Self-organisation and communication in groups of simulated and physical robots

    Vito Trianni and Marco Dorigo. Self-organisation and communication in groups of simulated and physical robots. Biological Cybernetics, 95(3):213–231, 2006

    2006

  18. [26]

    Genetic team composition and level of selection in the evolution of cooperation

    Markus Waibel, Laurent Keller, and Dario Floreano. Genetic team composition and level of selection in the evolution of cooperation. IEEE transactions on Evolu- tionary Computation, 13(3):648–660, 2009

    2009

  19. [27]

    Wolpert, Kagan Tumer, and K

    D. Wolpert, Kagan Tumer, and K. Swanson. Optimal wonderful life utility functions in multi-agent systems. 2000

    2000

  20. [28]

    Approximating the shapley value without marginal contributions

    Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, and Eyke H¨ullermeier. Approximating the shapley value without marginal contributions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 13246–13255, 2024

    2024

  21. [29]

    Co- operative and competitive multi-agent systems: From optimization to games

    Jianrui Wang, Yitian Hong, Jiali Wang, Jiapeng Xu, Yang Tang, Qing-Long Han, and J ¨urgen Kurths. Co- operative and competitive multi-agent systems: From optimization to games. IEEE/CAA Journal of Automat- ica Sinica, 9(5):763–783, 2022

    2022

  22. [30]

    Multi-agent deep reinforcement learning: a survey

    Sven Gronauer and Klaus Diepold. Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review, 55(2):895–943, 2022

    2022

  23. [31]

    The selfish gene

    Richard Dawkins. The selfish gene. Oxford university press, 2016

    2016

  24. [32]

    Social semantics: altruism, cooperation, mutualism, Signaling and Social Learning in Swarms of Robots — 12/17 strong reciprocity and group selection

    Stuart A West, Ashleigh S Griffin, and Andy Gard- ner. Social semantics: altruism, cooperation, mutualism, Signaling and Social Learning in Swarms of Robots — 12/17 strong reciprocity and group selection. Journal of evolu- tionary biology, 20(2):415–432, 2007

    2007

  25. [33]

    The genetical evolution of social behaviour

    William D Hamilton. The genetical evolution of social behaviour. ii. Journal of theoretical biology, 7(1):17–52, 1964

    1964

  26. [34]

    Not by genes alone: How culture transformed human evolution

    Peter J Richerson and Robert Boyd. Not by genes alone: How culture transformed human evolution. University of Chicago press, 2008

    2008

  27. [35]

    Communication and collective ac- tion: language and the evolution of human cooperation

    Eric Alden Smith. Communication and collective ac- tion: language and the evolution of human cooperation. Evolution and human behavior, 31(4):231–245, 2010

    2010

  28. [36]

    Surviving the tragedy of commons: emergence of altruism in a population of evolving autonomous agents

    Jean-Marc Montanier and Nicolas Bredeche. Surviving the tragedy of commons: emergence of altruism in a population of evolving autonomous agents. In European conference on artificial life, 2011

    2011

  29. [37]

    A quantitative test of hamilton’s rule for the evolution of altruism

    Markus Waibel, Dario Floreano, and Laurent Keller. A quantitative test of hamilton’s rule for the evolution of altruism. PLoS biology, 9(5):e1000615, 2011

    2011

  30. [38]

    De- centralized multi-agent reinforcement learning with net- worked agents: Recent advances

    Kaiqing Zhang, Zhuoran Yang, and Tamer Bas ¸ar. De- centralized multi-agent reinforcement learning with net- worked agents: Recent advances. Frontiers of Informa- tion Technology & Electronic Engineering, 22(6):802– 814, 2021

    2021

  31. [39]

    Learning fair policies in decentralized coop- erative multi-agent reinforcement learning

    Matthieu Zimmer, Claire Glanois, Umer Siddique, and Paul Weng. Learning fair policies in decentralized coop- erative multi-agent reinforcement learning. In Interna- tional Conference on Machine Learning, pages 12967– 12978. PMLR, 2021

    2021

  32. [40]

    Counterfactual multi-agent policy gradients

    Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual multi-agent policy gradients. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018

    2018

  33. [41]

    Historical contingency affects signaling strategies and competitive abilities in evolving populations of simu- lated robots

    Steffen Wischmann, Dario Floreano, and Laurent Keller. Historical contingency affects signaling strategies and competitive abilities in evolving populations of simu- lated robots. Proceedings of the National Academy of Sciences, 109(3):864–868, 2012

    2012

  34. [42]

    Evolutionary conditions for the emer- gence of communication in robots

    Dario Floreano, Sara Mitri, St ´ephane Magnenat, and Laurent Keller. Evolutionary conditions for the emer- gence of communication in robots. Current biology, 17(6):514–519, 2007

    2007

  35. [43]

    Flocks, herds and schools: A distributed behavioral model

    CW Reynolds. Flocks, herds and schools: A distributed behavioral model. In SIGGRAPH, 1987

    1987

  36. [44]

    Novel type of phase transition in a system of self-driven particles

    T Vicsek, A Czir ´ok, E Ben-Jacob, I Cohen, and O Shochet. Novel type of phase transition in a system of self-driven particles. Physical review letters, 1995

    1995

  37. [45]

    Animal signals

    John Maynard Smith and David Harper. Animal signals. Oxford University Press, 2003

    2003

  38. [46]

    Social robots for language learning: A review

    Rianne van den Berghe, Josje Verhagen, Ora Oudgenoeg- Paz, Sanne van der Ven, and Paul Leseman. Social robots for language learning: A review. Review of Educational Research, 89(2):259–295, 2019

    2019

  39. [47]

    In- spiration for optimization from social insect behaviour

    Eric Bonabeau, Marco Dorigo, and Guy Theraulaz. In- spiration for optimization from social insect behaviour. Nature, 406(6791):39–42, 2000

    2000

  40. [48]

    Collective decision- making and foraging patterns in ants and honeybees

    C Detrain and JL Deneubourg. Collective decision- making and foraging patterns in ants and honeybees. Advances in insect physiology, 2008

    2008

  41. [49]

    Swarm intelligence: from natural to artificial systems

    Eric Bonabeau, Marco Dorigo, and Guy Theraulaz. Swarm intelligence: from natural to artificial systems . Oxford university press, 1999

    1999

  42. [50]

    Artificial pheromone for path selec- tion by a foraging swarm of robots

    Alexandre Campo, ´Alvaro Guti´errez, Shervin Nouyan, Carlo Pinciroli, Valentin Longchamp, Simon Garnier, and Marco Dorigo. Artificial pheromone for path selec- tion by a foraging swarm of robots. Biological cybernet- ics, 103:339–352, 2010

    2010

  43. [51]

    Visual attention: The past 25 years

    Marisa Carrasco. Visual attention: The past 25 years. Vision research, 51(13):1484–1525, 2011

    2011

  44. [52]

    Dynamic programming

    Richard Bellman. Dynamic programming. science, 153(3731):34–37, 1966

    1966

  45. [53]

    Hear- ing the shape of an arena with spectral swarm robotics

    L Cazenille, N Lobato-Dauzier, A Loi, M Ito, O Mar- chal, N Aubert-Kato, N Bredeche, and AJ Genot. Hear- ing the shape of an arena with spectral swarm robotics. arXiv:2403.17147, 2024

    2024 arXiv

  46. [54]

    llama.cpp

    Georgi Gerganov. llama.cpp. https://github. com/ggerganov/llama.cpp, 2023. Accessed: 2024-05-30

    2023

  47. [55]

    Signal propagation in complex networks

    Peng Ji, Jiachen Ye, Yu Mu, Wei Lin, Yang Tian, Chit- taranjan Hens, Matja ˇz Perc, Yang Tang, Jie Sun, and J¨urgen Kurths. Signal propagation in complex networks. Physics reports, 1017:1–96, 2023

    2023

  48. [56]

    Chemical com- munication among bacteria

    Michiko E Taga and Bonnie L Bassler. Chemical com- munication among bacteria. Proceedings of the National Academy of Sciences, 100(suppl 2):14549–14554, 2003

    2003

  49. [57]

    Acoustic duetting in drosophila virilis relies on the integration of auditory and tactile signals

    Kelly M LaRue, Jan Clemens, Gordon J Berman, and Mala Murthy. Acoustic duetting in drosophila virilis relies on the integration of auditory and tactile signals. Elife, 4:e07277, 2015

    2015

  50. [58]

    A novel alarm signal in aquatic prey: familiar minnows coordinate group defences against predators through chemical disturbance cues

    Kevin R Bairos-Novak, Maud CO Ferrari, and Douglas P Chivers. A novel alarm signal in aquatic prey: familiar minnows coordinate group defences against predators through chemical disturbance cues. Journal of Animal Ecology, 88(9):1281–1290, 2019

    2019

  51. [59]

    Firefly bioluminescence: a mechanistic approach of lu- ciferase catalyzed reactions

    Simone M Marques and Joaquim CG Esteves da Silva. Firefly bioluminescence: a mechanistic approach of lu- ciferase catalyzed reactions. IUBMB life, 61(1):6–17, 2009

    2009

  52. [60]

    Electric communication in fish

    Carl D Hopkins. Electric communication in fish. Ameri- can Scientist, 62(4):426–437, 1974

    1974

  53. [61]

    Male bird song attracts females—a field experiment

    Dag Eriksson and Lars Wallin. Male bird song attracts females—a field experiment. Behavioral Ecology and Sociobiology, 19:297–299, 1986. Signaling and Social Learning in Swarms of Robots — 13/17

    1986

  54. [62]

    Bird song as a signal of aggressive intent

    William A Searcy, Rindy C Anderson, and Stephen Now- icki. Bird song as a signal of aggressive intent. Behav- ioral Ecology and Sociobiology, 60:234–241, 2006

    2006

  55. [63]

    Overcoming limited onboard sensing in swarm robotics through local communication

    Tiago Rodrigues, Miguel Duarte, Margarida Figueir ´o, Vasco Costa, Sancho Moura Oliveira, and Anders Lyhne Christensen. Overcoming limited onboard sensing in swarm robotics through local communication. In Trans- actions on Computational Collective Intelligence XX , pages 201–2...

    2015

  56. [64]

    When less is more: Robot swarms adapt better to changes with constrained communication

    Mohamed S Talamali, Arindam Saha, James AR Mar- shall, and Andreagiovanni Reina. When less is more: Robot swarms adapt better to changes with constrained communication. Science Robotics , 6(56):eabf1416, 2021

    2021

  57. [65]

    Minimal nav- igation solution for a swarm of tiny flying robots to explore an unknown environment

    KN McGuire, Christophe De Wagter, Karl Tuyls, HJ Kappen, and Guido CHE de Croon. Minimal nav- igation solution for a swarm of tiny flying robots to explore an unknown environment. Science Robotics, 4(35):eaaw9710, 2019

    2019

  58. [66]

    Timing information propagation in interactive networks

    Imane Hafnaoui, Gabriela Nicolescu, and Giovanni Bel- trame. Timing information propagation in interactive networks. Scientific Reports, 9(1):4442, 2019

    2019

  59. [67]

    The mathematics of diffusion

    John Crank. The mathematics of diffusion . Oxford university press, 1979

    1979

  60. [68]

    Modeling biological systems: the belousov–zhabotinsky reaction

    Niall Shanks. Modeling biological systems: the belousov–zhabotinsky reaction. Foundations of Chem- istry, 3(1):33–53, 2001

    2001

  61. [69]

    The chemical basis of morphogenesis

    AM Turing. The chemical basis of morphogenesis. Bul- letin of mathematical biology, 52(1-2):153–197, 1990

    1990

  62. [70]

    Information diffusion by local communication of multiple mobile robots

    Tamio Arai, Eiichi Yoshida, and Jun Ota. Information diffusion by local communication of multiple mobile robots. In Proceedings of IEEE Systems Man and Cy- bernetics Conference-SMC, volume 4, pages 535–540. IEEE, 1993

    1993

  63. [71]

    Molecular Robotics: An Introduction

    Satoshi Murata. Molecular Robotics: An Introduction. Springer, 2022

    2022

  64. [72]

    Robotic DNA nanostructures

    Sami Nummelin, Boxuan Shen, Petteri Piskunen, Qing Liu, Mauri A Kostiainen, and Veikko Linko. Robotic DNA nanostructures. ACS Synthetic Biology, 9(8):1923– 1940, 2020

    1923

  65. [73]

    A logic-gated nanorobot for targeted transport of molec- ular payloads

    Shawn M Douglas, Ido Bachelet, and George M Church. A logic-gated nanorobot for targeted transport of molec- ular payloads. Science, 335(6070):831–834, 2012

    2012

  66. [74]

    A DNA origami nanorobot controlled by nucleic acid hybridization

    Emanuela Torelli, Monica Marini, Sabrina Palmano, Luca Piantanida, Cesare Polano, Alice Scarpellini, Marco Lazzarino, and Giuseppe Firrao. A DNA origami nanorobot controlled by nucleic acid hybridization. Small, 10(14):2918–2926, 2014

    2014

  67. [75]

    Nanomechanical molecular devices made of DNA origami

    Akinori Kuzuya and Yuichi Ohya. Nanomechanical molecular devices made of DNA origami. Accounts of chemical research, 47(6):1742–1749, 2014

    2014

  68. [76]

    Folding and characterization of a bio-responsive robot from DNA origami

    Yaniv Amir, Almogit Abu-Horowitz, and Ido Bachelet. Folding and characterization of a bio-responsive robot from DNA origami. JoVE (Journal of Visualized Experi- ments), (106):e51272, 2015

    2015

  69. [77]

    Molecular robots obey- ing asimov’s three laws of robotics

    Gal A Kaminka, Rachel Spokoini-Stern, Yaniv Amir, Noa Agmon, and Ido Bachelet. Molecular robots obey- ing asimov’s three laws of robotics. Artificial life , 23(3):343–350, 2017

    2017

  70. [78]

    Switchable DNA-origami nanos- tructures that respond to their environment and their applications

    Jasleen Kaur Daljit Singh, Minh Tri Luu, Ali Abbas, and Shelley FJ Wickham. Switchable DNA-origami nanos- tructures that respond to their environment and their applications. Biophysical reviews, 10(5):1283–1293, 2018

    2018

  71. [79]

    A DNA nanorobot func- tions as a cancer therapeutic in response to a molecular trigger in vivo

    Suping Li, Qiao Jiang, Shaoli Liu, Yinlong Zhang, Yan- hua Tian, Chen Song, Jing Wang, Yiguo Zou, Gregory J Anderson, Jing-Yan Han, et al. A DNA nanorobot func- tions as a cancer therapeutic in response to a molecular trigger in vivo. Nature biotechnology, 36(3):258–264, 2018

    2018

  72. [80]

    A DNA-based molecular motor that can navigate a network of tracks

    Shelley FJ Wickham, Jonathan Bath, Yousuke Katsuda, Masayuki Endo, Kumi Hidaka, Hiroshi Sugiyama, and Andrew J Turberfield. A DNA-based molecular motor that can navigate a network of tracks. Nature nanotech- nology, 7(3):169–173, 2012

    2012

  73. [81]

    A cargo-sorting DNA robot

    Anupama J Thubagere, Wei Li, Robert F Johnson, Zibo Chen, Shayan Doroudi, Yae Lim Lee, Gre- gory Izatt, Sarah Wittman, Niranjan Srinivas, Damien Woods, et al. A cargo-sorting DNA robot. Science, 357(6356):eaan6558, 2017

    2017

  74. [82]

    Microscopic agents programmed by DNA circuits

    Guillaume Gines, AS Zadorin, J-C Galas, Teruo Fujii, A Estevez-Torres, and Y Rondelez. Microscopic agents programmed by DNA circuits. Nature nanotechnology, 12(4):351–359, 2017

    2017

  75. [83]

    Evolutionary optimization of self-assembly in a swarm of bio-micro-robots

    Nathanael Aubert-Kato, Charles Fosseprez, Guillaume Gines, Ibuki Kawamata, Huy Dinh, Leo Cazenille, An- dre Estevez-Tores, Masami Hagiya, Yannick Rondelez, and Nicolas Bredeche. Evolutionary optimization of self-assembly in a swarm of bio-micro-robots. In Pro- ceedings of the ...

    2017

  76. [84]

    Exploring self-assembling behaviors in a swarm of bio-micro-robots using surrogate-assisted map-elites

    Leo Cazenille, Nicolas Bredeche, and Nathanael Aubert- Kato. Exploring self-assembling behaviors in a swarm of bio-micro-robots using surrogate-assisted map-elites. In 2019 IEEE Symposium Series on Computational In- telligence (SSCI), pages 238–246. IEEE, 2019

    2019

  77. [85]

    Multi-scale organization in communicat- ing active matter

    Alexander Ziepke, Ivan Maryshev, Igor S Aranson, and Erwin Frey. Multi-scale organization in communicat- ing active matter. Nature communications, 13(1):6727, 2022

    2022

  78. [86]

    Swarm autonomy: From agent functionalization to machine intelligence

    Yibin Wang, Hui Chen, Leiming Xie, Jinbo Liu, Li Zhang, and Jiangfan Yu. Swarm autonomy: From agent functionalization to machine intelligence. Ad- vanced Materials, page 2312956, 2024. Signaling and Social Learning in Swarms of Robots — 14/17

    2024

  79. [87]

    Optimizing collective behav- ior of communicating active particles with machine learning

    Jens Grauer, Fabian Jan Schwarzendahl, Hartmut L¨owen, and Benno Liebchen. Optimizing collective behav- ior of communicating active particles with machine learning. Machine Learning: Science and Technology, 5(1):015014, 2024

    2024

  80. [88]

    Group formation and cohesion of active particles with visual perception– dependent motility

    Franc ¸ois A Lavergne, Hugo Wendehenne, Tobias B¨auerle, and Clemens Bechinger. Group formation and cohesion of active particles with visual perception– dependent motility. Science, 364(6435):70–74, 2019

    2019

  81. [89]

    DNA- assisted swarm control in a biomolecular motor system

    Jakia Jannat Keya, Ryuhei Suzuki, Arif Md Rashedul Kabir, Daisuke Inoue, Hiroyuki Asanuma, Kazuki Sada, Henry Hess, Akinori Kuzuya, and Akira Kakugo. DNA- assisted swarm control in a biomolecular motor system. Nature communications, 9(1):453, 2018

    2018

  82. [90]

    Cooperative cargo transportation by a swarm of molecular machines

    Mousumi Akter, JJ Keya, K Kayano, AMR Kabir, Daisuke Inoue, Henry Hess, K Sada, Akinori Kuzuya, Hiroyuki Asanuma, and Akira Kakugo. Cooperative cargo transportation by a swarm of molecular machines. Science Robotics, 7(65):eabm0677, 2022

    2022

  83. [91]

    Collective cargo transport and sorting with molecular swarms

    Nathanael Aubert-Kato, Geoff Nitschke, Ibuki Kawa- mata, and Akira Kakugo. Collective cargo transport and sorting with molecular swarms. In Artificial Life Confer- ence Proceedings 35, volume 2023, page 90. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-in...

    2023

  84. [92]

    Morpho- genesis in robot swarms

    I Slavkov, D Carrillo-Zapata, N Carranza, X Diego, F Jansson, J Kaandorp, S Hauert, and J Sharpe. Morpho- genesis in robot swarms. Science Robotics, 2018

    2018

  85. [93]

    Programmable self-assembly in a thousand-robot swarm

    M Rubenstein, A Cornejo, and R Nagpal. Programmable self-assembly in a thousand-robot swarm. Science, 2014

    2014

  86. [94]

    Er- ror cascades in collective behavior: a case study of the gradient algorithm on 1000 physical agents

    M Gauci, ME Ortiz, M Rubenstein, and R Nagpal. Er- ror cascades in collective behavior: a case study of the gradient algorithm on 1000 physical agents. In Proceed- ings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pages 1404–1412, 2017

    2017

  87. [95]

    A fast, accurate, and scal- able probabilistic sample-based approach for counting swarm size

    H Wang and M Rubenstein. A fast, accurate, and scal- able probabilistic sample-based approach for counting swarm size. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 7180–7185. IEEE, 2020

    2020

  88. [96]

    A comprehensive survey of multiagent reinforce- ment learning

    Lucian Busoniu, Robert Babuska, and Bart De Schut- ter. A comprehensive survey of multiagent reinforce- ment learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , 38(2):156–172, 2008

    2008

  89. [97]

    Numerical methods for large eigenvalue problems: revised edition

    Yousef Saad. Numerical methods for large eigenvalue problems: revised edition. SIAM, 2011

    2011

  90. [98]

    Discrete-time signal processing

    Alan V Oppenheim. Discrete-time signal processing. Pearson Education India, 1999

    1999

  91. [99]

    Mutual influence between language and perception in multi-agent communication games

    Xenia Ohmer, Michael Marino, Michael Franke, and Peter K¨onig. Mutual influence between language and perception in multi-agent communication games. PLOS Computational Biology, 18(10):1–28, 10 2022

    2022

  92. [100]

    Pereira, and William Bialek

    Naftali Tishby, Fernando C. Pereira, and William Bialek. The information bottleneck method. In Proceedings of the 37-th Annual Allerton Conference on Communica- tion, Control and Computing, pages 368–377, 1999

    1999

  93. [101]

    Compression and communication in the cultural evolution of linguistic structure

    Simon Kirby, Monica Tamariz, Hannah Cornish, and Kenny Smith. Compression and communication in the cultural evolution of linguistic structure. Cognition, 141:87–102, August 2015

    2015

  94. [102]

    Efficient compression in color naming and its evolution

    Noga Zaslavsky, Charles Kemp, Terry Regier, and Naf- tali Tishby. Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31):7937–7942, July 2018

    2018

  95. [103]

    The Talking Heads Experiment

    Luc Steels. The Talking Heads Experiment. Volume I. Words and Meanings. 1999

    1999

  96. [104]

    S. Kirby. Spontaneous evolution of linguistic structure- an iterated learning model of the emergence of regularity and irregularity. IEEE Transactions on Evolutionary Computation, 5(2):102–110, April 2001

    2001

  97. [105]

    Iter- ated learning: A framework for the emergence of lan- guage

    Kenny Smith, Simon Kirby, and Henry Brighton. Iter- ated learning: A framework for the emergence of lan- guage. Artificial Life, 9(4):371–386, October 2003

    2003

  98. [106]

    The emergence of compositional structures in perceptually grounded language games

    Paul V ogt. The emergence of compositional structures in perceptually grounded language games. Artificial Intelligence, 167(1–2):206–242, September 2005

    2005

  99. [107]

    Andrew Perfors and Daniel J. Navarro. Language evolu- tion can be shaped by the structure of the world. Cogni- tive Science, 38(4):775–793, January 2014

    2014

  100. [108]

    Christiansen and Nick Chater

    Morten H. Christiansen and Nick Chater. Language as shaped by the brain. Behavioral and Brain Sciences , 31(5):489–509, October 2008

    2008

  101. [109]

    Flexible word meaning in embodied agents

    Peter Wellens, Martin Loetzsch, and Luc Steels. Flexible word meaning in embodied agents. Connection Science, 20(2–3):173–191, September 2008

    2008

  102. [110]

    Agent-based models of strategies for the emergence and evolution of grammati- cal agreement

    Katrien Beuls and Luc Steels. Agent-based models of strategies for the emergence and evolution of grammati- cal agreement. PLoS ONE, 8(3):e58960, March 2013

    2013

  103. [111]

    Emergence of linguistic conven- tions in multi-agent systems through situated commu- nicative interactions

    J´erˆome Botoko Ekila. Emergence of linguistic conven- tions in multi-agent systems through situated commu- nicative interactions. In Proceedings of the 23rd Inter- national Conference on Autonomous Agents and Multia- gent Systems, AAMAS ’24, page 2725–2727, Richland, SC, 2024...

    2024

  104. [112]

    Reggia, Juan Uriagereka, and Gerald S

    Kyle Wagner, James A. Reggia, Juan Uriagereka, and Gerald S. Wilkinson. Progress in the simulation of emer- gent communication and language. Adaptive Behavior, 11(1):37–69, 2003

    2003

  105. [113]

    A survey of multi-agent deep reinforcement learning with Signaling and Social Learning in Swarms of Robots — 15/17 communication

    Changxi Zhu, Mehdi Dastani, and Shihan Wang. A survey of multi-agent deep reinforcement learning with Signaling and Social Learning in Swarms of Robots — 15/17 communication. Autonomous Agents and Multi-Agent Systems, 38(1), January 2024

    2024

  106. [114]

    Learning multiagent communication with backpropaga- tion

    Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus. Learning multiagent communication with backpropaga- tion. In Advances in Neural Information Processing Systems, pp. 2244–2252, 2016

    2016

  107. [115]

    Foerster, Yannis M

    Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson. Learning to communicate with deep multi-agent reinforcement learning. InProceedings of the 30th International Conference on Neural Infor- mation Processing Systems, NIPS’16, page 2145–2153, Red Hook, N...

    2016

  108. [116]

    Multi- agent bidirectionally-coordinated nets: Emergence of human-level coordination in learning to play starcraft combat games

    Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang. Multi- agent bidirectionally-coordinated nets: Emergence of human-level coordination in learning to play starcraft combat games. 2017

    2017

  109. [117]

    Fcmnet: Full communication memory net for team-level cooperation in multi-agent systems

    Yutong Wang and Guillaume Sartoretti. Fcmnet: Full communication memory net for team-level cooperation in multi-agent systems. In Proceedings of the 21st Inter- national Conference on Autonomous Agents and Multia- gent Systems, AAMAS ’22, page 1355–1363, Richland, SC, 2022. In...

    2022

  110. [118]

    Vain: Attentional multi-agent predic- tive modeling

    Yedid Hoshen. Vain: Attentional multi-agent predic- tive modeling. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 2698–2708, Red Hook, NY , USA, 2017. Curran Associates Inc

    2017

  111. [119]

    Learning attentional communication for multi-agent cooperation

    Jiechuan Jiang and Zongqing Lu. Learning attentional communication for multi-agent cooperation. In S. Ben- gio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa- Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran As- sociates, ...

    2018

  112. [120]

    TarMAC: Targeted multi-agent communication

    Abhishek Das, Th´eophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau. TarMAC: Targeted multi-agent communication. In Ka- malika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Ma- chine Learni...

    2019

  113. [121]

    Learning when to communicate at scale in multiagent cooperative and competitive tasks

    Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar. Learning when to communicate at scale in multiagent cooperative and competitive tasks. In In- ternational Conference on Learning Representations , 2019

    2019

  114. [122]

    Efficient communication in multi-agent reinforcement learning via variance based control

    Sai Qian Zhang, Qi Zhang, and Jieyu Lin. Efficient communication in multi-agent reinforcement learning via variance based control. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch´e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Sys- tem...

    2019

  115. [123]

    Learning efficient multi-agent communication: An information bottleneck approach

    Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, and Zinovi Rabinovich. Learning efficient multi-agent communication: An information bottleneck approach. In Hal Daum´e III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learn- ing, volume...

    2020

  116. [124]

    Model- based sparse communication in multi-agent reinforce- ment learning

    Shuai Han, Mehdi Dastani, and Shihan Wang. Model- based sparse communication in multi-agent reinforce- ment learning. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Sys- tems, AAMAS ’23, page 439–447, Richland, SC, 2023. International F...

    2023

  117. [125]

    Emergent com- munication through negotiation

    Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, and Stephen Clark. Emergent com- munication through negotiation. In International Con- ference on Learning Representations, 2018

    2018

  118. [126]

    Emergence of linguistic communi- cation from referential games with symbolic and pixel input

    Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, and Stephen Clark. Emergence of linguistic communi- cation from referential games with symbolic and pixel input. In International Conference on Learning Repre- sentations, 2018

    2018

  119. [127]

    Ortega, DJ Strouse, Joel Z

    Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, and Nando de Freitas. Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In Proceedings of the 36th International Conference on Ma...

    2019

  120. [128]

    Learning to schedule communication in multi-agent reinforcement learning

    Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, and Yung Yi. Learning to schedule communication in multi-agent reinforcement learning. In International Conference on Learning Representations, 2019

    2019

  121. [129]

    Emergent communication: Generalization and overfitting in lewis games

    Mathieu Rita, Corentin Tallec, Paul Michel, Jean- Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, and Florian Strub. Emergent communication: Generalization and overfitting in lewis games. In S. Koyejo, S. Mo- hamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advanc...

    2022

  122. [130]

    Emergence of grounded compositional language in multi-agent popula- tions

    Igor Mordatch and Pieter Abbeel. Emergence of grounded compositional language in multi-agent popula- tions. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Confer- ence on Artificial Intelligence, pages 1495–1502. AAAI Press, 2018

    2018

  123. [131]

    On the role of pop- ulation heterogeneity in emergent communication

    Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, and Emmanuel Dupoux. On the role of pop- ulation heterogeneity in emergent communication. In Signaling and Social Learning in Swarms of Robots — 16/17 International Conference on Learning Representations, 2022

    2022

  124. [132]

    Anti-efficient encoding in emergent communication

    Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, and Marco Baroni. Anti-efficient encoding in emergent communication. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch´e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Sys- tems, volume...

    2019

  125. [133]

    On the pitfalls of mea- suring emergent communication

    Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, and Yann Dauphin. On the pitfalls of mea- suring emergent communication. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’19, page 693–701, Richland, SC, 2019. Inter...

    2019

  126. [134]

    Emergent multi- agent communication in the deep learning era

    Angeliki Lazaridou and Marco Baroni. Emergent multi- agent communication in the deep learning era. June 2020

    2020

  127. [135]

    How agents see things: On visual representations in an emergent lan- guage game

    Diane Bouchacourt and Marco Baroni. How agents see things: On visual representations in an emergent lan- guage game. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 981–985. Association for Computational Linguis- tics, 2018

    2018

  128. [136]

    The symbol grounding problem.Physica D: Nonlinear Phenomena, 42(1):335–346, 1990

    Stevan Harnad. The symbol grounding problem.Physica D: Nonlinear Phenomena, 42(1):335–346, 1990

    1990

  129. [137]

    The physical symbol grounding problem

    Paul V ogt. The physical symbol grounding problem. Cognitive Systems Research, 3(3):429–457, September 2002

    2002

  130. [138]

    Emergent translation in multi-agent communica- tion

    Jason Lee, Kyunghyun Cho, Jason Weston, and Douwe Kiela. Emergent translation in multi-agent communica- tion. In International Conference on Learning Represen- tations, 2018

    2018

  131. [139]

    Learning to ground multi-agent com- munication with autoencoders

    Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, and Phillip Isola. Learning to ground multi-agent com- munication with autoencoders. In Advances in Neural Information Processing Systems, 2021

    2021

  132. [140]

    Abhishek Das, Satwik Kottur, Jose M. F. Moura, Stefan Lee, and Dhruv Batra. Learning cooperative visual dia- log agents with deep reinforcement learning. InProceed- ings of the IEEE International Conference on Computer Vision (ICCV), 2017

    2017

  133. [141]

    Emergence of language with multi-agent games: Learning to communicate with sequences of symbols

    Serhii Havrylov and Ivan Titov. Emergence of language with multi-agent games: Learning to communicate with sequences of symbols. In Proceedings of the 31st Inter- national Conference on Neural Information Processing Systems, NIPS’17, page 2146–2156, Red Hook, NY , USA, 2017. C...

    2017

  134. [142]

    Dynamic population-based meta-learning for multi-agent communication with natural language

    Abhinav Gupta, Marc Lanctot, and Angeliki Lazari- dou. Dynamic population-based meta-learning for multi-agent communication with natural language. In A. Beygelzimer, Y . Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Pro- cessing Systems, 2021

    2021

  135. [143]

    Sycara, Michael Lewis, and Julie Shah

    Mycal Tucker, Huao Li, Siddharth Agrawal, Dana Hughes, Katia P. Sycara, Michael Lewis, and Julie Shah. Emergent discrete communication in semantic spaces. In A. Beygelzimer, Y . Dauphin, P. Liang, and J. Wort- man Vaughan, editors, Advances in Neural Information Processing Sys...

    2021

  136. [144]

    Counter- ing language drift via visual grounding

    Jason Lee, Kyunghyun Cho, and Douwe Kiela. Counter- ing language drift via visual grounding. In Proceedings of the 2019 Conference on Empirical Methods in Natu- ral Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP- IJCNLP), p...

    2019

  137. [145]

    Multi-agent communication meets natural lan- guage: Synergies between functional and structural lan- guage learning

    Angeliki Lazaridou, Anna Potapenko, and Olivier Tiele- man. Multi-agent communication meets natural lan- guage: Synergies between functional and structural lan- guage learning. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics , pages...

    2020

  138. [146]

    On the interaction between su- pervision and self-play in emergent communication

    Ryan Lowe, Abhinav Gupta, Jakob Foerster, Douwe Kiela, and Joelle Pineau. On the interaction between su- pervision and self-play in emergent communication. In International Conference on Learning Representations, 2020

    2020

  139. [147]

    Multi-agent cooperation and the emer- gence of (natural) language

    Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. Multi-agent cooperation and the emer- gence of (natural) language. In International Conference on Learning Representations, 2017

    2017

  140. [148]

    Towards true lossless sparse communication in multi-agent systems

    Seth Karten, Mycal Tucker, Siva Kailas, and Katia Sycara. Towards true lossless sparse communication in multi-agent systems. In ICRA 2023, 2023

    2023

  141. [149]

    Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint...

    2023

  142. [150]

    Grounding Large Language Models in Interactive En- vironments with Online Reinforcement Learning

    Thomas Carta, Cl ´ement Romac, Thomas Wolf, Syl- vain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding Large Language Models in Interactive En- vironments with Online Reinforcement Learning. In Proceedings of Machine Learning Research , volume

  143. [151]

    Deep reinforcement learning from human preferences

    Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. In I. Guyon, U. V on Signaling and Social Learning in Swarms of Robots — 17/17 Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vish- wanathan...

    2017

  144. [152]

    Bootstrapping cognitive agents with a large language model

    Feiyu Zhu and Reid Simmons. Bootstrapping cognitive agents with a large language model. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1):655– 663, 2024

    2024

  145. [153]

    Tenenbaum, Tianmin Shu, and Chuang Gan

    Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, and Chuang Gan. Building cooperative embodied agents modularly with large language models. In International Conference on Learning Representations, 2024

    2024

  146. [154]

    Auto- gen: Enabling next-gen llm applications via multi-agent conversation

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. Auto- gen: Enabling next-gen llm applications via multi-agent conversation. 2023

    2023

  147. [155]

    Camel: Commu- nicative agents for ”mind” exploration of large language model society

    Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. Camel: Commu- nicative agents for ”mind” exploration of large language model society. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, Advances in Neural Information P...

    2023

  148. [156]

    Bern- stein

    Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Mered- ith Ringel Morris, Percy Liang, and Michael S. Bern- stein. Generative agents: Interactive simulacra of hu- man behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23,...

    2023

  149. [157]

    Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A

    Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Du ´e˜nez- Guzm´an, William A. Cunningham, Simon Osindero, Danny Karmon, and Joel Z. Leibo. Generative agent- based modeling with actions grounded in physical, so- cial, or digital space u...

    2023

  150. [158]

    Cultural evolution in populations of large language models

    J´er´emy Perez, Corentin L´eger, Marcela Ovando-Tellez, Chris Foulon, Joan Dussauld, Pierre-Yves Oudeyer, and Cl´ement Moulin-Frier. Cultural evolution in populations of large language models. 2024

    2024

  151. [159]

    Chain-of-thought prompting elicits reason- ing in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. Chain-of-thought prompting elicits reason- ing in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in...

    2022

  152. [160]

    Llm-powered hierarchical language agent for real-time human-ai coordination

    Jijia Liu, Chao Yu, Jiaxuan Gao, Yuqing Xie, Qingmin Liao, Yi Wu, and Yu Wang. Llm-powered hierarchical language agent for real-time human-ai coordination. In International Conference on Autonomous Agents and Multiagent Systems, 2024

    2024

  153. [161]

    William Hunt, Toby Godfrey, and Mohammad D. Soorati. Conversational language models for human- in-the-loop multi-robot coordination. In Demonstration at International Conference on Autonomous Agents and Multi-Agent Systems, 2024

    2024

  154. [162]

    Stubbersfield

    Alberto Acerbi and Joseph M. Stubbersfield. Large lan- guage models show human-like content biases in trans- mission chain experiments. Proceedings of the National Academy of Sciences, 120(44), 2023

    2023

  155. [163]

    Survey of hallucination in natural lan- guage generation

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural lan- guage generation. ACM Computing Surveys, 55(12):1– 38, 2023

    2023

  156. [164]

    Learning in games

    Drew Fudenberg and David Levine. Learning in games. European economic review, 42(3-5):631–639, 1998

    1998

  157. [165]

    Evolutionary dynamics: exploring the equations of life

    Martin A Nowak. Evolutionary dynamics: exploring the equations of life. Harvard university press, 2006

    2006

  158. [166]

    Sociophysics: an introduction

    Parongama Sen and Bikas K Chakrabarti. Sociophysics: an introduction. OUP Oxford, 2014

    2014

  159. [167]

    Socio- physics: A new approach of sociological collective be- haviour

    Serge Galam, Yuval Gefen, and Yonathan Shapir. Socio- physics: A new approach of sociological collective be- haviour. i. mean-behaviour description of a strike. Jour- nal of Mathematical Sociology, 9(1):1–13, 1982

    1982

  160. [168]

    Active Matter and Nonequilibrium Statistical Physics: Lecture Notes of the Les Houches Summer School: Volume 112, September 2018, volume 112

    Julien Tailleur, Gerhard Gompper, M Cristina Marchetti, Julia M Yeomans, and Christophe Salomon. Active Matter and Nonequilibrium Statistical Physics: Lecture Notes of the Les Houches Summer School: Volume 112, September 2018, volume 112. Oxford University Press, 2022

    2018

  161. [169]

    Introduction to evolutionary computing

    Agoston E Eiben and James E Smith. Introduction to evolutionary computing. Springer, 2015

    2015

  162. [170]

    Richard S Sutton and Andrew G. Barto. Reinforcement learning: An introduction. A Bradford Book, 2018

    2018

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.