Agent-based modeling has been around for decades, and applied widely across
the social and natural sciences. The scope of this research method is now
poised to grow dramatically as it absorbs the new affordances provided by Large
Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just
classic Agent-Based Models (ABM)s where the agents talk to one another. Rather,
GABMs are constructed using an LLM to apply common sense to situations, act
"reasonably", recall common semantic knowledge, produce API calls to control
digital technologies like apps, and communicate both within the simulation and
to researchers viewing it from the outside. Here we present Concordia, a
library to facilitate constructing and working with GABMs. Concordia makes it
easy to construct language-mediated simulations of physically- or
digitally-grounded environments. Concordia agents produce their behavior using
a flexible component system which mediates between two fundamental operations:
LLM calls and associative memory retrieval. A special agent called the Game
Master (GM), which was inspired by tabletop role-playing games, is responsible
for simulating the environment where the agents interact. Agents take actions
by describing what they want to do in natural language. The GM then translates
their actions into appropriate implementations. In a simulated physical world,
the GM checks the physical plausibility of agent actions and describes their
effects. In digital environments simulating technologies such as apps and
services, the GM may handle API calls to integrate with external tools such as
general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar,
Email, Search, etc.). Concordia was designed to support a wide array of
applications both in scientific research and for evaluating performance of real
digital services by simulating users and/or generating synthetic data.

Agent-based modeling has evolved with the integration of Large Language Models (LLMs) to create Generative Agent-Based Models (GABMs) facilitated by Concordia, enabling language-mediated simulations in physically or digitally grounded environments.

基于 Concordia 的将行动植根于物理、社交或数字空间的生成式基于代理的建模

Generative agent-based modeling with actions grounded in physical,  social, or digital space using Concordia

Parallel neurosymbolic architectures have been applied effectively in NLP by
distilling knowledge from a logic theory into a deep model.However, prior art
faces several limitations including supporting restricted forms of logic
theories and relying on the assumption of independence between the logic and
the deep network. We present Concordia, a framework overcoming the limitations
of prior art. Concordia is agnostic both to the deep network and the logic
theory offering support for a wide range of probabilistic theories. Our
framework can support supervised training of both components and unsupervised
training of the neural component. Concordia has been successfully applied to
tasks beyond NLP and data classification, improving the accuracy of
state-of-the-art on collective activity detection, entity linking and
recommendation tasks.

该研究论文提出了一种名为 Concordia 的框架，可以有效地将逻辑理论的知识融入深度模型，支持广泛的概率性理论，并成功地应用于 NLP 以及数据分类的任务，以及集体活动检测，实体链接和推荐任务等领域，并且克服了之前其他研究所面临的限制。