UC-066 Thought-Memory Model for Multi-Agent Simulation

A 2D web-based multi-agent simulation leverages Large Language Models to model human-like interactions among generative agents. A Thought-Memory system retrieves relevant data and prior memories from a database to construct JSON-style prompts for the LLM, which outputs intended agent actions. The system allows for observable, emergent interactions between agents within the simulated space.

Publication Details

Journal
DigitalCommons - Kennesaw State University (Kennesaw State University)
Published
2025-04-15
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

UC-066 Thought-Memory Model for Multi-Agent Simulation

DigitalCommons - Kennesaw State University (Kennesaw State University)
Cell Image Analysis Techniques
article

UC-066 Thought-Memory Model for Multi-Agent Simulation

article en

Abstract

A 2D web-based multi-agent simulation leverages Large Language Models to model human-like interactions among generative agents. A Thought-Memory system retrieves relevant data and prior memories from a database to construct JSON-style prompts for the LLM, which outputs intended agent actions. The system allows for observable, emergent interactions between agents within the simulated space.

DigitalCommons - Kennesaw State University (Kennesaw State University)
Openalex Percentile: Top 44%
Cell Image Analysis Techniques
OpenAlex
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UC-066 Thought-Memory Model for Multi-Agent Simulation · DigitalCommons - Kennesaw State University (Kennesaw State University) (2025) | TGRS Research Map | TGRS