Crowd provides a configuration-driven, GUI-supported Python framework for social network agent-based simulations, demonstrated on epidemic, influence maximization, and trust game case studies.
ChatLogo: A Large Language Model-Driven Hybrid Natural-Programming Language Interface for Agent-based Modeling and Programming
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abstract
Building on Papert (1980)'s idea of children talking to computers, we propose ChatLogo, a hybrid natural-programming language interface for agent-based modeling and programming. We build upon previous efforts to scaffold ABM & P learning and recent development in leveraging large language models (LLMs) to support the learning of computational programming. ChatLogo aims to support conversations with computers in a mix of natural and programming languages, provide a more user-friendly interface for novice learners, and keep the technical system from over-reliance on any single LLM. We introduced the main elements of our design: an intelligent command center, and a conversational interface to support creative expression. We discussed the presentation format and future work. Responding to the challenges of supporting open-ended constructionist learning of ABM & P and leveraging LLMs for educational purposes, we contribute to the field by proposing the first constructionist LLM-driven interface to support computational and complex systems thinking.
fields
cs.SI 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
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Crowd: A Social Network Simulation Framework
Crowd provides a configuration-driven, GUI-supported Python framework for social network agent-based simulations, demonstrated on epidemic, influence maximization, and trust game case studies.