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Dynamic Planning with a LLM
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While Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, applications involving embodied agents remain problematic. In particular, complex plans that require multi-step reasoning become difficult and too costly as the context window grows. Planning requires understanding the likely effects of one's actions and identifying whether the current environment satisfies the goal state. While symbolic planners find optimal solutions quickly, they require a complete and accurate representation of the planning problem, severely limiting their use in practical scenarios. In contrast, modern LLMs cope with noisy observations and high levels of uncertainty when reasoning about a task. Our work presents LLM Dynamic Planner (LLM-DP): a neuro-symbolic framework where an LLM works hand-in-hand with a traditional planner to solve an embodied task. Given action-descriptions, LLM-DP solves Alfworld faster and more efficiently than a naive LLM ReAct baseline.
Forward citations
Cited by 10 Pith papers
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Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
A reinforcement-trained LLM that decomposes abstract household requests into PDDL subgoals and solves them with a symbolic planner outperforms prompting and end-to-end planning baselines on long-horizon tasks.
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Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning
TAPAS uses several specialized language-model agents to generate, correct, and adapt symbolic planning problems, reporting high benchmark accuracy and a virtual-home execution demo.
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SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs
A decision-graph SOP navigator guides LLM agents through branching and looping workflows, with reported gains on household tasks, code generation, data cleaning, and a new customer-service benchmark.
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How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective
LLM-generated plans are on average executable for only the first 2.65 of about 8.4 actions, and an NLP-based recovery pipeline plus symbolic completion raises success from 21.9% to 27.5%.
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Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning
An LLM-augmented Bayesian inverse planning model, LAIP, generates hypotheses and action likelihoods, then uses Bayes' rule to infer agent preferences, outperforming LLM-only baselines.
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FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation
Fine-tuned small language models, coached by a GPT-4 teacher through iterative prompting, can perform multi-point robot navigation on edge devices with success rates approaching larger models.
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Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents
A survey proposing a source-and-impact taxonomy (input, model, combined; security, privacy, ethics) for threats to LLM-based agents, with feature analysis and four case studies.
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One STEP at a time: Language Agents are Stepwise Planners
A stepwise planner with memory-guided execution and evaluation raises ScienceWorld scores to 67.4, outperforming CLIN and published SOTA baselines.
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Large Language Models for Planning: A Comprehensive and Systematic Survey
A structured survey of LLM planning methods, benchmarks, and interpretability work, organized around a three-way taxonomy.
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Practical Considerations for Agentic LLM Systems
This paper is a practical survey that organizes research on LLM-based agents into design considerations for planning, memory, tools, and control flow.
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