From 93a747d1bb546ea905c20d96be1613052e67d7fb Mon Sep 17 00:00:00 2001 From: thinkwee Date: Mon, 1 Jul 2024 16:22:27 +0800 Subject: [PATCH] reformat & delete repetitions & add pv --- MultiAgentEbook/index.html | 109 +++++++++++++++++++++++++------------ MultiAgentEbook/main.js | 4 +- MultiAgentEbook/papers.csv | 36 ------------ 3 files changed, 76 insertions(+), 73 deletions(-) diff --git a/MultiAgentEbook/index.html b/MultiAgentEbook/index.html index bab1ff1..27996df 100644 --- a/MultiAgentEbook/index.html +++ b/MultiAgentEbook/index.html @@ -7,7 +7,6 @@ - Multi-Agent Research Outline @@ -42,9 +41,14 @@ Comprehensive Outline of Large Language Model-based Multi-Agent Research

- This project presents an interactive eBook that compiles an extensive collection of research papers on large language model (LLM)-based multi-agent systems. Organized into multiple chapters and continuously updated with significant research, it strives to provide a comprehensive outline for both researchers and enthusiasts in the field. We welcome ongoing contributions to expand and enhance this resource. + This project presents an interactive eBook that compiles an extensive collection of research papers on + large language model (LLM)-based multi-agent systems. Organized into multiple chapters and + continuously updated with significant research, it strives to provide a comprehensive outline for + both researchers and enthusiasts in the field. We welcome ongoing contributions to expand and enhance + this resource.

-

Initiated by the ChatDev Group at Tsinghua University.

+

Initiated by the ChatDev Group at Tsinghua + University.

Start Reading Learn More @@ -63,9 +67,11 @@ background-pattern

Multi-Agent Directions

-

- Multi-agent systems are currently classified into two categories based on whether the agents are designed to achieve specific task goals under external human instructions: task-solving-oriented systems and social-simulation-oriented systems. -

+

+ Multi-agent systems are currently classified into two categories based on whether the agents are designed to + achieve specific task goals under external human instructions: task-solving-oriented systems and + social-simulation-oriented systems. +

    @@ -79,7 +85,10 @@

    - Task solving-oriented multi-agent systems employ autonomous agents working collaboratively to tackle complex problems. Cutting-edge research in this direction revolves around three primary areas: facilitating communication among agents, designing effective organizational structures for interaction, and exploring how agents co-evolve over time. + Task solving-oriented multi-agent systems employ autonomous agents working collaboratively to tackle + complex problems. Cutting-edge research in this direction revolves around three primary areas: + facilitating communication among agents, designing effective organizational structures for interaction, + and exploring how agents co-evolve over time.

    Dataset cover
    @@ -92,7 +101,9 @@

    - Social simulation-oriented multi-agent systems concentrate on modeling and analyzing the social behaviors of agents, offering valuable insights into human dynamics and enhances the ability to analyze or predict social phenomena. + Social simulation-oriented multi-agent systems concentrate on modeling and analyzing the social + behaviors of agents, offering valuable insights into human dynamics and enhances the ability to analyze + or predict social phenomena.

    Dataset cover
    @@ -106,9 +117,10 @@
    Dive into Each Chapter
    -

    - This ebook contains research papers on the multi-agent layer and above, organized into multiple chapters based on proposed core technologies. Let's dive into each section. -

    +

    + This ebook contains research papers on the multi-agent layer and above, organized into multiple chapters based + on proposed core technologies. Let's dive into each section. +

    @@ -142,8 +154,9 @@

    Learn More

    -

    - In addition to the aforementioned resources, we also feature recent research from our lab. If you find our work of interest, we invite you to read, extend, or collaborate. +

    + In addition to the aforementioned resources, we also feature recent research from our lab. If you find our work + of interest, we invite you to read, extend, or collaborate.

    @@ -152,8 +165,10 @@ Systems cover

    ChatDev

    Multi-Agent Collaboration for Software Development

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    @@ -161,64 +176,80 @@

    iAgents

    Bijective Social Networks of Humans and Agents

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Systems cover

    AgentVerse

    General-Purpose Multi-Agent Framework

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Benchmark cover

    Co-Learning

    Cross-Task Experience Co-Leaning for Mutual Growth

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Dataset cover

    Co-Evolving

    Continuous Experience Refinement over Time

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Dataset cover

    MacNet

    Exploring Collaborative Scaling Law

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Systems cover

    CTC

    Cross-Team Multi-Agent Orchestration

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Benchmark cover

    ChatEval

    Communication for Automated Evaluation

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    Benchmark cover

    AutoForm

    Finding Effective Communication Protocals

    - PDF IconPaper - GitHub IconCode + PDF IconPaper + GitHub IconCode
    @@ -238,7 +269,8 @@

    - This ebook gathers leading research on LLM-powered multi-agent systems since 2023, categorized by key perspectives in the field. As this area rapidly evolves, updates will be ongoing. + This ebook gathers leading research on LLM-powered multi-agent systems since 2023, categorized by key + perspectives in the field. As this area rapidly evolves, updates will be ongoing.

    @@ -249,7 +281,9 @@

    - We encourage open-source collaboration on this project. You can contribute by submitting a pull request with detailed metadata for notable papers in the table. + We encourage open-source collaboration on this project. You can contribute by submitting a pull request with + detailed metadata for notable papers in the table.

    @@ -260,7 +294,8 @@

    - You can download all ebook content in CSV format directly from here. + You can download all ebook content in CSV format directly from here.

    @@ -269,11 +304,15 @@

    - Initiated by the ChatDev Group, Tsinghua University + Initiated by the ChatDev Group, Tsinghua + University
    Contact us via qianc62@gmail.com +
    + Total PV

    + \ No newline at end of file diff --git a/MultiAgentEbook/main.js b/MultiAgentEbook/main.js index 7a65408..b945961 100644 --- a/MultiAgentEbook/main.js +++ b/MultiAgentEbook/main.js @@ -15,7 +15,7 @@ hamburger.addEventListener("click", function () { hamburger.src = toggle ? srcClose : srcHam; navList.classList.toggle("active"); logoContainer.classList.toggle('active'); - document.body.style.position = toggle ? 'fixed' :'static'; + document.body.style.position = toggle ? 'fixed' : 'static'; }); tabNavList.forEach((item, index, array) => { @@ -39,4 +39,4 @@ questions.forEach((item) => { item.addEventListener("click", () => { item.classList.toggle("open"); }); -}); +}); \ No newline at end of file diff --git a/MultiAgentEbook/papers.csv b/MultiAgentEbook/papers.csv index 9e99f38..b0575ee 100755 --- a/MultiAgentEbook/papers.csv +++ b/MultiAgentEbook/papers.csv @@ -728,42 +728,6 @@ macroeconomic phenomena compared to exist- ing rule-based or learning-based agents. Our codes are released at https://github.com/ tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University -EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities,"Nian Li, Chen Gao, Mingyu Li, Yong Li, Qingmin Liao",2023.10.16,"The advent of artificial intelligence has led to a -growing emphasis on data-driven modeling in -macroeconomics, with agent-based modeling -(ABM) emerging as a prominent bottom-up -simulation paradigm. In ABM, agents (e.g., -households, firms) interact within a macroe- -conomic environment, collectively generating -market dynamics. Existing agent modeling typ- -ically employs predetermined rules or learning- -based neural networks for decision-making. -However, customizing each agent presents sig- -nificant challenges, complicating the modeling -of agent heterogeneity. Additionally, the in- -fluence of multi-period market dynamics and -multifaceted macroeconomic factors are often -overlooked in decision-making processes. In -this work, we introduce EconAgent, a large -language model-empowered agent with human- -like characteristics for macroeconomic simu- -lation. We first construct a simulation envi- -ronment that incorporates various market dy- -namics driven by agents’ decisions regarding -work and consumption. Through the perception -module, we create heterogeneous agents with -distinct decision-making mechanisms. -Fur- -thermore, we model the impact of macroeco- -nomic trends using a memory module, which -allows agents to reflect on past individual ex- -periences and market dynamics. Simulation -experiments show that EconAgent can make -realistic decisions, leading to more reasonable -macroeconomic phenomena compared to exist- -ing rule-based or learning-based agents. Our -codes are released at https://github.com/ -tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate,"Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi",2023.5.30,"Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on