PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making

• URL: http://arxiv.org/abs/2411.15998v1
• Date: Sun, 24 Nov 2024 22:36:34 GMT
• Title: PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
• Authors: Jonathan Light, Sixue Xing, Yuanzhe Liu, Weiqin Chen, Min Cai, Xiusi Chen, Guanzhi Wang, Wei Cheng, Yisong Yue, Ziniu Hu,
• Abstract summary: We propose a framework PIANIST for decomposing the world model into seven intuitive components. We show that our method works well on two different games that challenge the planning and decision making skills of the agent.
• Score: 30.46033960436517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective extraction of the world knowledge in LLMs for complex decision-making tasks remains a challenge. We propose a framework PIANIST for decomposing the world model into seven intuitive components conducive to zero-shot LLM generation. Given only the natural language description of the game and how input observations are formatted, our method can generate a working world model for fast and efficient MCTS simulation. We show that our method works well on two different games that challenge the planning and decision making skills of the agent for both language and non-language based action taking, without any training on domain-specific training data or explicitly defined world model.

Related papers

• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Large Language Models as Common-Sense Heuristics [0.9093413254392775]
  Large Language Models (LLMs) possess parametrised knowledge across a wide range of topics, enabling them to leverage the natural language descriptions of planning tasks in their solutions. We introduce a novel planning method, which leverages the parametrised knowledge of LLMs by using their output as a for Hill-Climbing Search. Our method outperforms the task success rate of similar systems within a common household environment by 22 percentage points, with consistently executable plans.
  arXiv  Detail & Related papers  (2025-01-31T00:26:38Z)
• Large Language Models are Interpretable Learners [53.56735770834617]
  In this paper, we show a combination of Large Language Models (LLMs) and symbolic programs can bridge the gap between expressiveness and interpretability. The pretrained LLM with natural language prompts provides a massive set of interpretable modules that can transform raw input into natural language concepts. As the knowledge learned by LSP is a combination of natural language descriptions and symbolic rules, it is easily transferable to humans (interpretable) and other LLMs.
  arXiv  Detail & Related papers  (2024-06-25T02:18:15Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• The Embodied World Model Based on LLM with Visual Information and Prediction-Oriented Prompts [19.00518906047691]
  VOYAGER is a well-known LLM-based embodied AI that enables autonomous exploration in the Minecraft world. It has issues such as underutilization of visual data and insufficient functionality as a world model. It was suggested that devised prompts could bring out the LLM's function as a world model.
  arXiv  Detail & Related papers  (2024-06-02T14:50:01Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• Empowering Large Language Models on Robotic Manipulation with Affordance Prompting [23.318449345424725]
  Large language models fail to interact with the physical world by generating control sequences properly. Existing LLM-based approaches circumvent this problem by relying on additional pre-defined skills or pre-trained sub-policies. We propose a framework called LLM+A(ffordance) where the LLM serves as both the sub-task planner and the motion controller.
  arXiv  Detail & Related papers  (2024-04-17T03:06:32Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning [104.58874584354787]
  In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
  arXiv  Detail & Related papers  (2023-01-27T18:59:01Z)
• Plansformer: Generating Symbolic Plans using Transformers [24.375997526106246]
  Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP) We introduce Plansformer; an LLM fine-tuned on planning problems and capable of generating plans with favorable behavior in terms of correctness and length with reduced knowledge-engineering efforts. For one configuration of Plansformer, we achieve 97% valid plans, out of which 95% are optimal for Towers of Hanoi - a puzzle-solving domain.
  arXiv  Detail & Related papers  (2022-12-16T19:06:49Z)
• Inner Monologue: Embodied Reasoning through Planning with Language Models [81.07216635735571]
  Large Language Models (LLMs) can be applied to domains beyond natural language processing. LLMs planning in embodied environments need to consider not just what skills to do, but also how and when to do them. We propose that by leveraging environment feedback, LLMs are able to form an inner monologue that allows them to more richly process and plan in robotic control scenarios.
  arXiv  Detail & Related papers  (2022-07-12T15:20:48Z)
• Universal Sentence Representation Learning with Conditional Masked Language Model [7.334766841801749]
  We present Conditional Masked Language Modeling (M) to effectively learn sentence representations. Our English CMLM model achieves state-of-the-art performance on SentEval. As a fully unsupervised learning method, CMLM can be conveniently extended to a broad range of languages and domains.
  arXiv  Detail & Related papers  (2020-12-28T18:06:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.