Pith. sign in

REVIEW 2 cited by

Emergent Communication through Negotiation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1804.03980 v1 pith:KDISNYDE submitted 2018-04-11 cs.AI cs.CLcs.LGcs.MA

Kris Cao , Angeliki Lazaridou , Marc Lanctot , Joel Z Leibo , Karl Tuyls , Stephen Clark This is my paper · ORCID
classification cs.AIcs.CLcs.LGcs.MA
keywords communicationagentsagentnegotiationchannelcheapemergetalk
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -- one grounded in the semantics of the game, and one which is \textit{a priori} ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. No Press Diplomacy: Modeling Multi-Agent Gameplay

    cs.AI 2019-09 accept novelty 6.0 of 10

    A neural policy trained on 150,000 human Diplomacy games, then refined by self-play, beats rule-based bots in No Press Diplomacy.

  2. OpenSpiel: A Framework for Reinforcement Learning in Games

    cs.LG 2019-08 accept novelty 6.0 of 10

    OpenSpiel provides a unified, open-source API for coding, running, and evaluating many game types and algorithms for reinforcement learning and game theory in one framework.

Pith tools