Plansformer: Generating Symbolic Plans using Transformers

Pallagani, Vishal; Muppasani, Bharath; Murugesan, Keerthiram; Rossi, Francesca; Horesh, Lior; Srivastava, Biplav; Fabiano, Francesco; Loreggia, Andrea

doi:10.48550/arxiv.2212.08681

Cited by 2 publications

(3 citation statements)

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“…Conversely, SayCanPay uses additional models trained with domain-specific knowledge collected from the current environment. There are also efforts to fine-tune LLMs like Code-T5 (Wang et al 2021) to generate plans in PDDL (Pallagani et al 2022). This requires a significant amount of training data (given LLMs' minimal PDDL exposure) which is not entirely justified by their performance.…”

Section: Related Work On Planning With Llmsmentioning

confidence: 99%

“…Existing works have demonstrated the planning abilities of both the decoder type (Pallagani et al 2022) and the encoder-decoder type architectures (Valmeekam et al 2023(Valmeekam et al , 2022. Since the generated plan is in free-form language and may contain unrecognizable (for the environment) words or incorrect syntax, it cannot be directly translated into actionable steps in the environment.…”

Section: Experimental Setup Say Modelmentioning

confidence: 99%

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SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge

Hazra,

Zuidberg Dos Martires,

De Raedt

2024

AAAI

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Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordances) and cost-effective (in plan length), remains a challenge, despite recent progress. This contrasts with heuristic planning methods that employ domain knowledge (formalized in action models such as PDDL) and heuristic search to generate feasible, optimal plans. Inspired by this, we propose to combine the power of LLMs and heuristic planning by leveraging the world knowledge of LLMs and the principles of heuristic search. Our approach, SayCanPay, employs LLMs to generate actions (Say) guided by learnable domain knowledge, that evaluates actions' feasibility (Can) and long-term reward/payoff (Pay), and heuristic search to select the best sequence of actions. Our contributions are (1) a novel framing of the LLM planning problem in the context of heuristic planning, (2) integrating grounding and cost-effective elements into the generated plans, and (3) using heuristic search over actions. Our extensive evaluations show that our model surpasses other LLM planning approaches.

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Section: Related Work On Planning With Llmsmentioning

confidence: 99%

Section: Experimental Setup Say Modelmentioning

confidence: 99%

SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge

Hazra,

Zuidberg Dos Martires,

De Raedt

2024

AAAI

View full text Add to dashboard Cite

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“…Efforts to generate multimodal, text, and image-based goalconditioned plans are exemplified by (Lu et al 2023b). Additionally, a subset of studies in this survey investigates the fine-tuning of seq2seq, code-based language models (Pallagani et al 2022(Pallagani et al , 2023b, which are noted for their advanced Application of LLMs in Planning Language Translation ( 23) Xie et al 2023;Guan et al 2023;Chalvatzaki et al 2023;Yang, Ishay, and Lee 2023;Wong et al 2023;Kelly et al 2023;Lin et al 2023c;Sakib and Sun 2023;Yang et al 2023b;Parakh et al 2023;Dai et al 2023;Yang et al 2023a;Shirai et al 2023;Ding et al 2023b;Zelikman et al 2023;Pan et al 2023;Xu et al 2023b;Brohan et al 2023;Yang, Gaglione, and Topcu 2022;Chen et al 2023a;You et al 2023) Plan Generation (53) (Sermanet et al 2023;Li et al 2023b;Pallagani et al 2022;Silver et al 2023;Pallagani et al 2023b;Arora and Kambhampati 2023;Fabiano et al 2023;Chalvatzaki et al 2023;Gu et al 2023;Silver et al 2022;Hao et al 2023a;Lin et al 2023b;Yuan et al 2023b;Gandhi, Sadigh, and Goodman 2023;…”

Section: Plan Generationmentioning

confidence: 99%

On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

Pallagani,

Muppasani,

Roy

et al. 2024

ICAPS

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Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems. We aim to keep the categorization of papers updated on https://ai4society.github.io/LLM-Planning-Viz/, a collaborative resource that allows researchers to contribute and add new literature to the categorization.

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Plansformer: Generating Symbolic Plans using Transformers

Cited by 2 publications

References 0 publications

SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge

SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge

On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

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