Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
Authors: Lin Guan
Karthik Valmeekam
Sarath Sreedharan
Subbarao Kambhampati
Venue: NeurIPS 2023 poster (2023)
Keywords: LLMs
Planning
Domain Model
LLMs for Planning
LLMs for Heuristic Guidance

Abstract: There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical due to several factors, including limited correctness of plans, strong reliance on feedback from interactions with simulators or even the actual environment, and the inefficiency in utilizing human feedback. In this work, we introduce a novel alternative paradigm that constructs an explicit world (domain) model in planning domain definition language (PDDL) and then uses it to plan with sound domain-independent planners. To address the fact that LLMs may not generate a fully functional PDDL model initially, we employ LLMs as an interface between PDDL and sources of corrective feedback, such as PDDL validators and humans. For users who lack a background in PDDL, we show that LLMs can translate PDDL into natural language and effectively encode corrective feedback back to the underlying domain model. Our framework not only enjoys the correctness guarantee offered by the external planners but also reduces human involvement by allowing users to correct domain models at the beginning, rather than inspecting and correcting (through interactive prompting) every generated plan as in previous work. On two IPC domains and a Household domain that is more complicated than commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 can be leveraged to produce high-quality PDDL models for over 40 actions, and the corrected PDDL models are then used to successfully solve 48 challenging planning tasks. Resources, including the source code, are released at: https://guansuns.github.io/pages/llm-dm.

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
Lin Guan
Karthik Valmeekam
School of Computing & AI Arizona State University
School of Computing Al Arizona State University Tempe, AZ 85281
Tempe, AZ 85281
1guan90asu. edu
kvalmeck0asu, edu
Sarath Sreedharan
Subbarao Kambhampati
Department of Computer Science Colorado State University Fort Collins, CO 80523
School Computing & Al Arizona State University
Tempe AZ 85281 raošasu edu
sertth.sreedharra@@oooooottttt. edu
Abstract
There is a growing interest in applying pre-trained large language models (LLMs) planning problems. However, methods that use LLMs directly as planners are currently impractical due to several factors, including limited correctness of plans, strong reliance on feedback from interactions with simulators or even the actual environment, and the inefficiency in utilizing human feedback. In this work, we introduce novel alternative paradigm that constructs an explicit world (domain) model in planning domain definition language (PDDL) and then uses it to plan with sound iodain--ndeependett planners. To address the fact that LLMMs may not generate fully functional PDDL model initially, we employ LLMs as an interface between PDDL and sources of corrective feedback, such as PDDL validators and humans. For users who lack background in PDDL, show that LLMs can translate PDDL into natural language and effectively encode corrective feedback back to the underlying domain model. Our framework not only enjoys the correctness guarantee offered by the external planners but also reduces human to correct domain models at the beginning, rather
involvement by allowing than inspecting and correcting (through interactive prompting) every generated plan in previous work. On two IPC domains and Household domain that is complicated than commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 be leveraged to produce high-quality PDDL models for over 40 actions, and the corrected PDDL models are then used to successfully solve 48 challenging planning tasks. Resources, including the source code, are released https:/ https://guansuns /gw.suuuunnnnggg// .githubbttt..u. io/ topagee////// /llm-dm dm.
Introduction
The field of artificial intelligence has been revolutionized with the advent of large pre-trained models. Of particular significance are transformer-bae large language models (LLMs) which have showcased remarkable performance in natural language processing tasks. Along with these tasks, LLMs have been tested perform another widely-studied crucial aspect of AI agents, namely, sequential decision-
Equul contribution
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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making or planning. Preliminary studies suggest that, in some everyday domains, LLMs are capable of suggesting sensible action plans [19, 1]. However. the correctness and executability of these plans are often limited. For instance, L.Ms may regularly overlook the physical plausibility of actions in certain states and may not effectively handle long-term dependencies across multiple actions. Several approaches have been proposed to improve the planning capabilities of L.Mss. One promising approach involves collecting feedback from the environment during plan execution and subsequently refining the plans. By incorporating various forms of feedback, such as sensory information [20], human corrections [60], or information of unmet preconditions [42, 56], the planners can re-plan and produce plans that are closer satisficing plan.
Despite the improvements in planning performance, LLMs are still far from being usable and reliable planner due to various factors:
(a) LLMs have not yet demonstrated sufficient capabilities in reasoning and planning [24, 55, 53, 54, 31J. Recent investigations show that even when provided with detailed descriptions of actions, such as PDDL domain model [33] or natural-language version of PDDL model, LLMs still struggle to produce correct and executable plans [48, 55].
(b) Existing LLMs-planning paradigms only allow for feedback collection in a fully online manner, meaning that the feedback signals are only available after the agent has started executing the plan. However, when a faithful simulator is not available or is expensive to use, collecting feedback through actual plan execution can be costly and may not fully exploit the advantages of provably sound planning, as seen in classical plannning literature [11, 13].
(c) LLMs exhibit complex behaviors that are not yet fully understood, partieularly with respect to error occurrences. LLM planners are prone to repeating the same mistakes in slightly different scenarios. Repeatedly providing the same feedback can lead to frustration for end users.
To overcome these limitations, rather than using LLMs directly as planners, we advocate a model- based paradigm, wherein PDDL world model is teased out of LLMs. We follow the identical problem setup as existing approaches, which involves providing the planner with a set of actions and their brief natural language descriptions. However, instead of directly mapping user commands to plans, we utilize LLMs to extract symbolic representation of the actions in the form of PDDL action models. This intermediate output can be used with an external Nomain-indeeennee search for feasible plans, or it can be used to validate and correct "heuristic" plans generated by an LLM planner. Additionally, our modular method essentially divides the planning process into two distinct parts, namely modeling the causal dependencies of actions and determining the appropriate sequence of actions to accomplish the goals. I.LMs, which have been trained on extensive web-scale knowledge, exhibit greater proficiency in the former task rather than the latter.
planner to reliably
Nevertheless, we still take into account the fact that the LLMs may not be able to generate error-free PDDL models at the outset. To address this, we show that LLMs can also serve as an interface between PDDL and any feedback sources that can provide corrective feedback in natural language, such as humans and the PDDL validator in VAL [18]. The LLM middle layer translates PDDL representation to natural language and presents it to users for inspection. The acquired feedback is then incorporated and archived back to the PDDL models. This conceals the complexity of PDDL from users who do not have prior knowledge of PDDL, and enables seamless inclusion of feedback. We conducted an extensive evaluation of our methodology on two IPC domains [22] from classical planning literature and a household domain that has more diverse set of actions and constraints than commonly used benchmarks such as ALFWORLD [47]. We assess the quality of the generated PDDL models through manual evaluation. Results show that GPT-4 [37] generates high-quality PDDL domain models with over 400 literals for 41 actions in total. Then, by replaying and continuing the PDDL-construction dialogue, we show that GPT-4 can readily correct all the errors according to natural language feedback from PDDL validators and humans.
We consider two use cases of the generated PDDL action models for downstream planning tasks. For one, by utilizing an LLM to translate user instructions into goal specifications in PDDL [58, 30], we can use any standard
indepeedent planner to search for a plan. On the other hand, the extracted PDDL. model can be used to validate plans suggested by an LLM planner and to provide corrective feedback in the form of unmet preconditions or goal conditions. this case, the PDDL model essentially serving as an inexpensive high-level simulator a human proxy to ensure plan correctness.

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This reduces the reliance on faithful simulators or extensive manual inspection of plans by domain experts. Compared to the first approach, the second upproach potentially offers better flexibility in incorporating both explicit and implicit user constraints in common-sensse domains because of the L.LM planner. For instance, the L.LM planner can directly incorporate ordering constraints such as "heat the potato first before mashing it" and "bring me a fork first, then a plate." OOn the contrary, an approach purely based on classical planners would require extra steps, such as introducing extra state variables in the PDDL models, in order to accommodate such constraints. However, as demonstrated in our experiments, although the validation feedback significantly improves the plan correctness on average, the performance of the second approach is still limited by the "planning capability" of LLMs.
2 Related Work
LLMs and planning. The growing interest in evaluating the emergent abilities of LLMS paved way into exploring their abilities in sequential decision-making tasks. Preliminary studies [24, 55] have shown that off-the-shelf LLMs are currently incapable of producing accurate plans. But their plans can be used as heuristics or seeds to either an external planner or human in the loop [55, 48]. score high-level actions, followed by a low-level planner that grounds these actions to determine the executability in the physical world. In similar vein, [28, 50] use LLMs to generate plans represented in Python style code. Other works have aimed to improve the planning performance of LLMs through prompt engineering [60] or collecting various forms of feedback such as sensory information [51, 20, 34), human corrections [60], self-correetions [46] or information of unmet preconditions [42, 56].
SayCan [1] and Text2Motion [29] employ an LLM as a heuristic by utilizing
Training transformers for sequential decision makinng tasks. Along with using off-the-shelf LLMs, there works that either fine-tune LLMs [55, 38] train sequence models [62, 27, 7, 43] for sequential decision making tasks. Experiments in [26] have shown that training sequence models on specifie task gives rise to internal world representation within the model. In this work, use off-the-shelf LLMS to construct symbolic world models without performing any extra training.
Learning/acquiring symbolic domain models. In classical planning, the community has explored numerous learning-based methods [59, 61, 9, 25, 4] and interactive editor-based methods [49] for acquiring symbolic domain models. For more comprehensive survey, we refer the reader to [2. 6]. Here, we are interested in leveraging the common-wort knowledge embedded in LLMs and their in-context learning ability for constructing domain models. Recent studies have shown the efficacy of LLMs in translating natural language to formal descriptions [35] or constructing PDDL goals from natural- language instructions [58, 32]. Moreover, a contemporary work [15] considers the use of LLM as a parametric world model and plan critic. However, unlike symbolic model that can simulate plan outcomes with guaranteed correctness, using LLMs directly as a world model actually adds another layer of errors. There is evidence that autoregressive models lack reliable capacity for reasoning about action effects [3, 31] and capturing errors in candidate plans [53, 54].
Language models with access to external tools. Since LLMs are approximately omniscient, they may not always outperform specialized models or tools in specific downstream tasks. To address this limitation, frameworks have been developed to enable LLMs to utilize external tools for performing sub-tasks like arithmetic [45] and logical reasoning [39, 57]. In this context, our work can be regarded as exercise in employing external sound planners to augment the capacity of LLMs for more reliable plan generation.
3 Problem Setting and Background
Our work focuses on a scenario where an intelligent agent receives high-level instructions or tasks, denoted as i, from a user. The agent is capable of only executing skills or operations that are part of a skill library I1, where each skill & has short language description 4g- We assume that the agent is equipped with the low-level control policies corresponding to these high-level skills. In order achieve the goal conditions specified in i, a planner, which can be either an LLM or an external planner [16, 12, 17]. needs to come up with a sequence of high-level skills that the agent can execute. This type of problem is referred to as a sequential decision-making or planning problem. Similar to previous works such as [60, 20], we also allow for human-in-the-lon both the modain-model construction and plan execution stages. the next subsections, we describe the formalism behind planning problems and a standard way in the literature to specify them.
feedback during
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3.1 Classical planning problems
The most fundamental planning formalism is goal-directed deterministic planning problem, referred to as classical planning problem in the planning literature. A classical planning problem [44] can be formally represented with a tuple p = ().I.g). D is referred to as the domain, is the initial state, and 9 the goal specification. The state space of a planning problem consists of the truth assignments for predicates. The domain DD is further defined by the tuple D = (F, F corresponds the set of fluents, i.e., the state variables used to define the state space with each fluent corresponding performed. Each action a; [ A (where variable could be mapped to an object) can be further defined by two components, the precondition prec[V] which describes when an action can be executed, and the effects eff [V] which defines what happens when action is executed. We assume that prec consists of set of predicates defined over the variables V. An action is assumed to be executable only if its preconditions are met, i.c, the predicates in the precondition hold in the given state. The effect set eff [ is further defined by the tuple (add [V], de][ where [dd is the set of predicates that will be set true by the action and del is the set of predicates that will be set false by the action. An action is said to be grounded we replace each of the variables with an object, else it referred to as a lifted action model. A solution to planning problem is called a plan, and is sequence of actions that once executed in the initial state would lead to a state where the goal specification holds. Classical planning problems of the simpler classes in planning and there are multiple extensions with more complex forms of preconditions, conditional effects, and also support for richer planning formalisms.
predicate with some arity. A corresponds the set of actions that can be
the set of variables used by the operator aj and each
3.2 PDDL
Planning Definition and Domain Language (PDDL) [33]. is the standard encoding language for classical planning problems. Here is example of lifted action in PDDL which corresponds to putting a block onto the table in the classical Blocksworld domain:
(:action PutDovnBlock sparaneters (?x- block) :precondition (and (robot-holding ?x))
roffect (and (not (robat holddng ?x)) (block-cless ?x) trobot-hand hhayt- (block-on on tnblable ?xx))
The parameters line provides the possible variable(s), and in this case, ?x represents the block to put down. The precondition states that the robot must be holding the block in its gripper. The effects line describes the expected outcome of this action.
Methodology
PDDL provides a succinct and standardized way to represent world model. Once a PDDL model is constructed, can be seamlessly used by any domain- indeeenden planner developed in the automated planning community to search for plan given the initial state and goal conditions. In this section, we will introduce our solution for constructing PDDL models using LLMs. We then discuss techniques for correcting enrors in the generated PDDL, models. Finally, we present the full pipeline for utilizing the generated PDDL models to solve planning problems.
4.1 Constructing PDDL models with LLMs
Our approach involves prompting pre-trained LLMs with the following information: (a) detailed instructions for the PDDL generation task, outlining components of upcoming inputs and desired outputs; (b) one or two examples from other domains (c.g.. the classical Blocksworld domain) for illustrating the input and output formats; (c) description of the current domain, including contextual information about the agent's 's tasks and physical constraints due to the specific embodiment of the agent; (d) description of the agent's 's action; and (e) dynamically updated list of predicates that the LLM can reuse to maintain consistent use symbols across multiple actions. Note that the predicate list is initialized to empty list, and thus all predicates are introduced by the LLM. The structure of the prompt is illustrated in Fig. 2, and complete prompt for the household-robog domain can be found at Appx. A.

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Step POOL Construction
Natural larcuagee desciptton meatlable actiors domain
POOL Precondtion:
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Prompt
User command
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Plane
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Oytion 1: Classical planner with LLM eoquired
Option LLM modulo planner back-prompted
PDOL model
VAL uaing LLM--ecqu red PDOL model
Figure I: An overview of our framework and existing methods that use LLMs directly as planners.
Depending on the information included in the action description or the domain context, users may gain varying levels of control over the extracted PDDL receive differing levels of support from the LLMs. On one hand, when the user provides only minimal description of the action, such as "this action enables the robot to use a microwave to heat food," " we only use the LLM PDDL constructor but also leverage the common world knowledge encoded within the model for knowledge acquisition. This particularly useful when expanding the set of actions for an AI agent. For example, robot engineer could set up training environment for skill learning by following the suggested preconditions and effects. On the other hand, when some preconditions or effects are explicitly mentioned in the prompt, we rely more on the LLM's ability to parse the knowledge provided natural language and to precisely represent it by devising a collection This capability is useful when there could be different initial setups of a skill, and the engineers have already made some assumptions on the preconditions the time designing the skill. This capability is also crucial when constructing PDDL for specialized domains. For instance, robots such as Fetch and Spot Robot have only one robot arm, which is less flexible than a human arm, and are therefore subject to many uncommon physical constraints.
predicates.
The desired output comprises the following elements: (a) the list of arguments for the action; (b) the preconditions and effects expressed PDDL; and (c) a list of any newly defined predicates and their descriptions in natural language, if applicable. An example output is shown in Fig. 2. Our algorithm generates PDDL models for each action separately, one at a time, by iterating over the set of actions. Any newly defined predicates will be added LLM can reuse existing predicates in subsequent actions without creating redundant ones. Once we obtain the initial PDDL models and the full predicate list, repeat the entire process but with all of the extracted predicates presented to the LLM. Running the generation process twice is useful because the LLMs may be unaware of some precondition(s) during the first iteration, especially the precondition(s) are not explicitly mentioned. For instance, the LLM may overlook the fact that furniture piece can be openable, but predicate created in the "open furniture piece appliance" skill can inform the LLM of this fact. One alternative to this include descriptions of all the actions in the prompt and require the LLM construct the entire domain model in single dialogue. An additional discussion on this can be found at Sec. A.2 in Appendix.
actively maintained predicate list, such that the
generation could be
is worth noting that every time new predicate defined, the LLM is required give the natural language description of it. As we will see in the following sections, this is crucial for enabling any user to easily understand and inspect the generated PDDL models without having to delve into low-level symbolic representation. Additionally, natural language descriptions allow the predicate values of the initial state automatically grounded by using I.LMS translate environment description in natural language to PDDL [30], leveraging pre-trained vision-language models
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manner, based on observations from the Instructiona for the PDDL generation taak You are defining the preconditions and affects (represented in POOL format) an AI agent's actions. Information
agent be provided the domain description Dne two examples from other donains for Illustrating the input and output fornata Hare are two examples from the classical BlocksNorld donain for denenstrating the output fornat.
Bere ie the tack.
À natural language description the donsin
Donain information: The AI here is household robot that nornally immoable furniture pieces appliances in the house to carry out household taske
various and
A natural language description the action
Action: This action enables the robot to toggle small appliancea (like hunidifiers and light toggleable The dynanically updated list of predicates
bulbs)
then ...
You can create and define new predicatea, but you may also reuse the following predicates: furaittrreppplssnee):
(robot-st
appliance ?
(objact-
houscholdfbject furotturekppllamce)
object
the furniture appliance ?f
Paraneters:
The LLM:
2. 20 householdObjact: the small appliance to be toggled on
Preconditions:
(and
(not (appliance-on ?o))
Effects:
(and
(sppliance-on Po)
New Predicates:
(epplience-on bouseboldDbject);
appliance ?o svitched
Figure 2: The prompt template for PDDL construction and an example of the LLM output for the household domain.
[41, 37, 10] and querying them in a environment.
4.2 Correcting errors in the initial PDDL models As with any Therefore, it is essential to incorporate error-correctio mechanisms. While may be easy for PDDL experts to directly inspect and correct the generated PDDL models, users possess this level of expertise. Our solution to use the L.LM as a middle layer or interface between the underlying PDDL model and any feedback source that provide corrective feedback in natural language. We consider two feedback sources this work, namely the PDDL model validation tools (e.g., the in VAL [18]) and human domain experts. The former is used to detect basic syntax errors, while the latter is mainly responsible for catching factual errors, such as missing effects. is worth noting that the feedback sources are limited to those mentioned above, and we leave the investigation of other sources for future research.
involving LLMs, there is guarantee that the output is completely error-free.
cannot assume that all end
For corrective feedback from PDDL validators, generated PDDL model is directly presented to the validator obtain brief but readable error messages. Examples of feedback messages for syntax errors are shown in Appx. A.3, For corrective feedback from users, PDDL model is translated into its language version based on the natural language descriptions of the predicates and parameters (Sec. 1). The user can then examine potentially erroneous action models. Human corrections can occur both during the construction of PDDL. models and after the models have been used for planning. Although there techniques available to assist users locate errrrs in the models (as discussed in

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Appx. A.4), this is beyond the scope of this work, since the focus here is to investigate the feasibility of using LLMs to correct PDDL models based on feedback. We also note that conrecting action models is not more cognitively demanding than correcting plans or the "reasoning traces" of an LLM planner [60]. In fact, when correcting plans, humans must also maintain the action models and their causal chains in mind in order to validate the plans. More importantly, once the action models are corrected, users no longer need to provide similar feedback repeatedly, Finally, corrective feedback is integrated by replaying and continuing the PDDL-construction dialogue. Examples of such dialogues can be found in Sec. A.7, Sec. A.9, and Sec. A. in Appendix.
4.3
Generating plans with the extracted PDDL models
Recall that given the set of extracted predicates and their natural language descriptions, we can get the grounded initial state by using LLMs to translate descriptions of the environment to PDDL, or by observing the environment and querying pre-trained lision-language models. Besides, the goal specification can be obtained by using an LLM to parse the user's command and convert it into symbolic form, as done previously in [30, 58, 32]. With this setup, the following two methods can be used generate the final plans.
Classical planner with LLM-acquired PDDL model. One straightforward approach is to employ standard domain-independerr planner to reliably find satisficing even optimal plan for the specified goal. In common-sense domains where LLMs may generate meaningful "heuristics", the LLM plans may also be used seed plans for a local-search planner such as LPG [12] accelerate the plan searching. This is similar to the approach suggested in [55], but with a higher degree of automation.
LLM modulo planner backprompted by VAL using LLM--equired PDDL model. As outlined in Sec. 1, can also use the extracted PDDL as a symbolic simulator or human proxy to provide corrective feedback based on validation information to an LLM planner. With this setup, the planner can iteratively refine the plans through re-prompting [42].
is worth noting that depending on the specific problem settings, the extracted PDDL model can also be used for tasks other than task planning. For instance, in cases where reinforcement learning is permissible, the domain model can be used to guide skill learning [21, 8] or exploration even if the model is not fully situated [14].
5 Empirical Evaluation
We conduct our experiments on an everyday household-robot domain and two more specialized IPC domains (i.e., Tyreworld and Logisties). The Household domain is similar to other commonly used benchmarks like ALFWORLD [47] and VirtualHome [40]. However, in our household domain, single-arm robot is equipped with more diverse and extended set of 22 mobile and manipulation skills. In addition, we apply more rigorous physical-plausibine description of this domain can be found at Appx. A.5. In experiments, we first evaluate the quality of PDDL models generated by the LLMs. Next, we assess the ability of the LLMs to incorporate corrective feedback from both PDDL validators and users in order obtain error-free PDDL models. Lastly, we showcase multiple ways to use the corrected PDDL model for downstream planning tasks. We present the results of GPT-4 [37] and - [36] for PDDL construction (we also conducted experiments with GPT-3 [5], and observe that its performance is comparable to that of GPT-3.5-Turee Turbbo).
constraints to each skill. A detailed
5.1 Constructing PDDL
In PDDL construction tasks, we aim to investigate the extent to which LLMs can construct accurate PDDL models before getting corrective feedback from domain experts. For all the domains, two actions from the classical Blocksworld domain are used as demonstrations in the prompt so that the end user is not required to come up with any domain-specific example. To evaluate the degree of correctness, we recruit multiple graduate students who possess expertise in PDDL. These experts are responsible for annotating and correcting any errors present in the generated PDDL models. As an evaluation metric, we count and report the total number of annotations, which may include the removal of irrelevant preconditions, the addition of missing preconditions, the replacement of incorrect predicates, the inclusion of missing parameters, and other commonly made corrections. Note
Experiments were done in May 2023.
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that the number of annotations can be viewed as the approximate distance between a generated PDDL model and its corrected version. In order provide the reader with a comprehensive understanding of the quality of the generated models, we also list all the models and collected annotations in Appendix. In each of the figures, errors that affect the functionality of the PDDL model are highlighted in yellow, while minor issues are highlighted in green. One example of minor issue is the redundant inclusion of (pi cpicuble ?o) in preconditions when (robot- holddng ?o) has already been listed. The former is unnecessary because it can be implied by the latter, but this only affects conciseness rather than functionality.
Domain
actions
# of params and literals of GPT-4 errors GPT-3.5- errors
Houschold
22
271
53
218+
Logistics
54
Tyreworld 13
108
4
94+
Table 1: The number of errors in the domain models produced by the LLMs for each of the domains. "+" mark indicates that the generated model is excessively noisy, making it challenging to determine an exact number of errors.
We first evaluate the PDDL models generated when partial constraint information is given, as this is closer to most of the practical use cases where constraints on skills in the library II are often pre-specified. In this setting, our evaluation focuses on the LLMs' ability to accurately recover a "ground truth PDDL" that captures the mentioned constraints and underlying dependencies among skills. Our results indicate that GPT-4 can produce high-quality PDDL models with significantly fewer errors when compared to GPT-3.5-Turbo. Table presents the number of errors in the generated domain models for each domain. To help the readers understand the complexities of the action models, additionally report the total number of parameters and literals in the final corrected domain models produced by GPT-4. Out of the total 59 errors made by GPT-4, three of them are syntax errors and the rest are factual errors such as missing preconditions and effects. This observation suggests that while GPT-4 demonstrates proficiency in adhering to the grammar of PDDL, it may still have an inaccurate understanding of the actions. By examining the set of predicates (listed in the Appendix), find that GPT-4 can devise a set of intuitively-named predicates that can concisely and precisely describe the states of objects and events in the domain. In contrast, GPT-3.5-Turfe produces highly noisy outputs with over 350 errors. This suggests that our framework relies heavily on GPT-4's improved capability understanding symbols, and future work may investigate how enable the use of more lightweight models (c.g., by fine-tuning on some PDDL datasets). Furthermore, recall that when the action description contains minimal information, LLMs could also be utilized to propose preconditions and effects to assist with knowledge acquisition. To verify this hypothesis, we conduct additional experiments on the Household domain that can have more open-ended action design. In this setting, the correctness of the action models is determined based on whether the preconditions and effects establish correct connections among the actions. Our results show that GPT-4 can suggest meaningful action models, and the generated PDDL models have only around 45 errors.
also
Although GPT-4 has shown improved performance in the PDDL construction task, our experiments still uncover some limitations. Firstly, GPT-4 still exhibits shallow understanding of the causal relationships between actions, particularly when it comes to tasks involving reasoning skills such as spatial reasoning. For instance, when constructing the model of action "pick up an object from furniture piece," GPT-4 fails to consider that there could be other objects stacked on top of the target object, even if relevant predicates are provided (which were created in the action "stack objects") In addition, although it occurs rarely, GPT-4 may output contradictory effects. For instance, in the action of mashing food with a blender, GPT-4 lists both (not cobject-in-reeeptacle ...)) and
...) ) as effects at the same time.
5.2 Correcting PDDL with domain experts
We proceed with the PDDL models generated by GPT-4 when the constraint information is partially given. Our objective is to demonstrate the feasibility of using GPT-4 middle layer to incorporate natural-language feedback and correct the PDDL models. As discussed in See. 4.2, we use PDDI. validators to capture basic syntax errors. In the Houschold domain, there are two syntax errors
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associated with improper usage of relevant predicates due to issues with the object types of parameters 3. As shown in Appx. A.7.1. by continuing the PDDL-construction dialogue with a feedback message "the second parameter of object-on should be furnitureAppliant householdObject was given," GPT-4 can locate the inaccurate PDDL snippet and replace it with a correct one. For the other factual errors, GPT-4 successfully corrects all of them based on the natural language feedback. An example feedback message on factual errors is "there is missing effect: the item is longer pickupable after being mashed." More PDDL-correction conversations can be found in Appendix. We also experiment with feedback written in various ways, and GPT-4 is able to understand all the messages and successfully correct the models. To quantify how effectively GPT-4 utilizes feedback from domain experts, we count the number of feedback messages concerning factual errors. Our result shows that GPT-4 required 59 feedback messages to address a total of 56 factual errors. There are three instances where additional feedback was needed. One case involved the user reiterating the error, while the other two cases involved GPT-4 introducing new errors. Furthermore, we attempt to correct the same errors using -Prrb..- Results show that GPT-3.5-Turbo not only fails to correct all the errors but also occasionally introduces new errors, again confirming its lack of ability to manipulate symbols. Some examples can be found in Appendix starting from Sec. A.7.3.
but
5.3 Generating plans with the extracted PDDL models
For planning tasks (i.e., user instructions and initial states), we use the Household domain and Logistics domain, where state-of-the-art LLM planners struggle to find valid plans. We sampled 27 tasks for Household and 21 for Logistics. For the initial states, we assume the grounding is provided, and for the goals, we leverage GPT-4 to translate user instructions into PDDL goal specifications in terms of the extracted predicates (an example prompt can be found at Appx. A.13). and send it over to a standard STRIPS planner which already has access the domain model acquired through LLMs. With this setup, a classical planner Fast Downward [16] can effectively find valid plans in 95% of the cases (the failures were only due goal translation errors). Note that in contrast earlier methods such as [30] that use LLMS only as a mechanism for translating user goals to PDDL format, and throw that over external sound planners with hand-crafted correct PDDL domain models, our approach uses LLMs themselves to develop the PDDL world model driving the external planner.
On the other hand, for the approach that utilizes PDDL models to validate LLM plans (i.e.. LLM modulo planner back-prompted by VAL using LLM- acquired domain model), we employ the state-of-the-art algorithm ReAct [60] with GPT-4 as the underlying LLM planner. However, we made two modifications to the prompt design. Firstly, we provide a detailed descrip- tion of all actions in natural language, including parameters, preconditions, and effects. These descriptions are ob- tained by using another LLM to translate the generated PDDL domain model into natural language. Secondly, we use only two fixed examples for each domain because end users might not always be able to provide large pool of examples, and the planner should rely on the action model information. The LLM plans, symbolic goal specifications, initial states and domain models are passed to a plan validation system (i.e., VAL) to check for unmet precondition(s) or goal condition(s). The validation results (given in PDDL) are then translated into natural language with GPT-4 and provided to the LLM planner by continuing the planning dialogue (see Appx. A.1 for examples). In our experiments, we limit the number of feedbacks per task to 8 due to the restricted access to GPT-4 Table 2 provides a summary of the average success rates of all approaches. Not surprisingly, the vanilla LLM planner constantly overlooks action preconditions and achieves an extremely low success rate. With the integration of validation feedback, we observe a notable improvement in plan correctness. Despite this improvement, the overall performance is still not satisfactory, as the success rate remains below
Planner Type
Household
Logistics
Only LLM Planner
15%
0%
Fast Downward with LLM-acquired
95%
100%
PDDL model
LLM backprompted by VAL using aaquirrd PDDL model
48%
33%
Table 2:
Success rates of different planning approaches in the Household domain and the Logistics domain.
$ At present, our approach only permits a fixed set of object types that are specified in the prompt. Future extensions may explore ways of enabling LLMs to create object-type hierarchy or expand the set of object types needed.

---

50%. Furthermore, we have observed that GPT-4 fails to effectively utilize the feedback, often getting stuck in doop by repeatedly generating the same plan. In some cases, may also introduce new emvors while attempting to rectify the plans.
Beyond the notion of correctness, the experiments also uncover intriguing properties of the LLM planner. In the Household domain, we intentionally introduce ordering constraints in some instructions that cannot be expressed using existing predicates (refer Appx. A.12 for examples). Remarkably, upon manual examination of the generated plans, we observe that all LLM plans adhere to the specified ordering, despite not being entirely correct or executable. Furthermore, also in the Household domain, we observe that classical planners occasionally generate physically plausible but unconventional actions, such as placing a knife on toaster when the knife not being used. In contrast, the LLM planner rarely exhibits such actions, suggesting that LLMs possess knowledge of implicit human preferences. It would be meaningful to explore methods that more effectively combine the strengths of LLL planners and the correctness guarantee provided by symbolic domain models, particularly in determining which information from LLM plans should be preserved.
6 Conclusion
We introduce a new paradigm for leveraging LLMs in planning tasks, which involves maintaining an explicit world model instead of directly mapping user prompts to plans. This is motivated by the insight that LLMs, while incapable of the combinatorial search needed to produce correct plans, may be better suited the source of world models. We present a complete pipeline that begins with generating high-quality PDDL models using GPT-4, then corrects the PDDL models with natural- language feedback, and finally utilizes the extracted domain models reliably plan in multiple ways. Our experiments demonstrate that pairing LLMs with an external planner significantly outperforms existing methods when applied two IPC domains and household-robot domain that has more action-wise constraints than commonly used benchmarks such as ALFWorld. Apart from directions for further research that we have previously mentioned, there are several exciting opportunities for extending this work. Firstly, the complexity of our evaluation domains is still lower than that of many domains used in the classical planning literature. It remains to be seen whether LLMs can effectively scale to write PDDL models that express more intricate logic. Secondly, our framework assumes full observability, meaning that the agent must fully explore the environment to acquire object states at the beginning. It would be useful to support partial observability. Finally, our experiments assume the grounding predicate values is done perfectly. However, would useful to take into account that perception can be noisy in practice.
Acknowledgement
This research was supported by ONR grants N00014-18-1-2442, N00014-18-1-2841 N00014- 19-1-2119 and N00014-23-1 ---122-----49,, AFOSR grant FA9550- 11--1-8871-7777-----11---------1 DARPA SAIL-ON grant W91 1AANN--19--00 19-2-0006, and a JP Morgan AI Faculty Research Grant Kambhampati. Sreedharan was supported in part by NSF grant 2303019.
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14


Reviews:
[{"review_id": "dbAJMmlKXS", "reviewer": "Reviewer_QSdH", "summary": "The submission investigates whether LLMs are able to create PDDL domains and problems description in three domains, two standard planning benchmarks, and one more challenging domain. The paper considers two related to the title: a) direct construction of the PDDL with an LLM, b) correcting errors in PDDLs. The paper also considers extracting plans with the obtained PDDL domains. The baseline is using a classical planner, that succeeds if the PDDL is correct. In c), they compare using the LLM as a planner, or using a ReAct style idea, but obtaining the prompts from the PDDL. This middle-ground scenario might be appealing for cases where the planning domain might not capture some elements of the problem, but an LLM can have additional insights.   The experiments are performed using GPT-4.", "questions": "- I understand that VAL was only used for checking syntax, but VAL could also be used to validate if a plan can be executed. Was that direction studied? It seems that was easy to test as providing a plan is not much harder than the information provided to build the models. \n- Which part of Fig 1 corresponds to previous approaches and which part corresponds to approaches proposed in the submission?\n", "limitations": "The work offers a methodology for building PDDL models using LLM. While the study is very preliminary, the quality is not lower that largely influential recent work like ReAct. The tasks per se is more ambitious, so further investigation is needed.\n\nThe paper ignores issues of explanability and other relevant tasks like state tracking. An incorrect model that partially tracks a state might still be useful. For instance, it might help to generate hypothesis for downstream tasks.\n", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The benchmarks used for experiments are relevant and not trivial. The household domain illustrates might be convincing for those not familiar with the planning literature.\n- Using the symbolic models as targets instead of solving the task using an LLM can help to bring the gap between LLM and symbolic models.\n- Producing a PDDL is harder than the typical implementation of LLMs plug-in. For instance, an LLM producing simple SQL might be inspected immediately, while isolated actions are not so easy to check.", "weaknesses": "- As the submission says, producing more complex PDDLs might be challenging.\n- Both the planning benchmarks and the household domain might actually be easy for an LLM than domains in practical applications. That should be discussed further in the paper.\n- The results of the manual evaluation might not extrapolate to other domains, and verification by a simulator might not be available. The submission should elaborate on the issue of explainability that is largely ignored in the document. The literature on explainability for planning should be discussed, including issues like model reconciliation.\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. Both the planning benchmarks and the household domain might actually be easy for an LLM than domains in practical applications. That should be discussed further in the paper.\n\nWe would like to highlight that our evaluation domains pose significant challenges for LLMs (if we employ them directly as planner like the baselines). A recent study that delves into the planning ability of LLMs [1] reveals low correctness rates for GPT-4 generated plans, even in seemingly simple and straightforward planning domains such as Logistics and BlocksWorld (correctness rates not exceeding 30% & being very sensitive to input changes).\n\nIt’s hard to say whether our planning benchmarks are actually easier for an LLM than practical domains (which may have more actions and more preconditions and effects). LLMs struggle significantly when planning tasks involve extensive search and complex sub-goal interactions. It is important to note that the level of search required and the complexity of sub-goal interactions are not directly related to the number of actions or the number of preconditions & effects. Nevertheless, in general, we expect to see a significant improvement in plan correctness by employing our approach to construct domain models and employing an external planner for search.\n\n[1] Valmeekam, Karthik et al. “On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark).” arXiv:2302.06706\n\n> 2. The submission should elaborate on the issue of explainability that is largely ignored in the document. The literature on explainability for planning should be discussed, including issues like model reconciliation.\n\nWe did not include a discussion on explainability in our submission due to space limits. However, we recognize the importance of this topic and plan to address it in our revision. Specifically, we will elaborate on the following points:\n1. As opposed to just directly querying LLMs for plans, generating behaviors with (intermediate) symbolic models offers additional opportunities for explanation, drawing upon existing works in explainable AI [1].\n2. In cases where the user's understanding does not align with the transcribed model, debugging and model reconciliation techniques such as D3wa+ [2] can be directly applied.\n\n[1] Kambhampati, Subbarao, et al. \"Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems.\" AAAI 2022.\n\n[2] Sreedharan, Sarath, et al. \"–d3wa+–a case study of xaip in a model acquisition task for dialogue planning.\" ICAPS 2020.\n\n> 3. I understand that VAL was only used for checking syntax, but VAL could also be used to validate if a plan can be executed. Was that direction studied? It seems that was easy to test as providing a plan is not much harder than the information provided to build the models.\n\nWe would like to clarify that VAL has been used in two ways in this paper:\n1. (Section 4.2) We use VAL to Validate the syntax of the generated PDDL model\n2. (Section 4.3 from line 245, and Section A.12.3 in Appendix) By taking a goal specification and a generated PDDL domain model, we can validate any plan derived from an LLM planner. In practice, we implement relevant functionalities with VAL.\n\nWe believe the second way is what Reviewer QSdH suggests. It is important to note that VAL can only validate plans when **provided with a PDDL domain model and the goal specification**. In other words, VAL can only be used to validate plans after obtaining the PDDL models (e.g., through our proposed approach).\n\n\n> 4. As the submission says, producing more complex PDDLs might be challenging.\n\nWe are not sure why this is listed in the weakness section. It is because producing complex PDDL models is challenging that the support of LLMs to acquire models in a semi-automated way, as done in our work, is a very promising direction. It allows for significantly accelerating the traditional knowledge engineering cycle of engineers interviewing domain experts to acquire domain models. \n\n> 5. Which part of Fig 1 corresponds to previous approaches and which part corresponds to approaches proposed in the submission?\n\nDivided by a vertical line, the left part \"Using LLMs Directly as Planners\" represents previous approaches, while the right part represents our proposed approach.\n\n> 6. The paper ignores relevant tasks like state tracking. An incorrect model that partially tracks a state might still be useful.\n\nWe would like to note that the main aim of our work is to generate domain models--which are then used by a separate external planner to do actual plan generation. As such, state tracking is not quite relevant in the context of domain model generation. In our parallel work on plan generation with the help of LLMs, we have indeed experimented with state tracking (that is enabled through a chain of thought-like prompting).\n"}, {"review_id": "pTUEuA3Z0k", "reviewer": "Reviewer_33uW", "summary": "There's been a fair amount of recent work on using LLMs to directly write plans for planning problems. In contrast, this work divides the planning process into two stages where first an LLM is used to translate natural language descriptions of actions to formal PDDL actions, then goals can be planned towards using a classic PDDL planner like FastDownward. Alternatively, the formal model can be used to validate and propose feedback on plans proposed by an LLM planner. They also explore using feedback from an automated PDDL validator (VAL) to correct things like syntax errors, as well as human feedback, both of which are ingested by the LLM which is prompted to repair the model.", "questions": "- \"GPT-3.5-Turbo produces highly noisy outputs with over 350 errors.\" (Line 296) I'm curious what sorts of errors are made by the model. You mention that GPT4 makes very few syntax errors (mostly reasoning errors). Is the same true of GPT-3.5-Turbo, or does it not even have syntax figured out?\n- I'm curious to hear more about the *newly defined predicates* (line 187) that the LLM ended up introducing in these domains. How many new predicates does it end up producing? Is it generally obvious what the \"right\" predicates are, such that we'd expect it to usually match the \"ground truth PDDL\" (line 285) exactly, or are there situations where there could be multiple reasonable choices to make for what predicates are introduced?\n- At the end of the human feedback process, are the models literally equivalent to the \"ground truth PDDL\" (line 285)? Or is there room for the LLM to make modeling choices that lead to different models (eg in the \"newly defined predicates\" mentioned above). This would be helpful for me understanding the 95% accuracy result – is it just that we've recovered the ground truth at this point by human corrections, and the ground truth can get 95%? To be clear, this is still a good result, as the idea of translating into a symbolic language for fast precise symbolic reasoning makes a lot of sense, I just want to see if I'm missing anything.\n- The exclamation point on line 345 feels a little out of place with the tone, but that's just a stylistic choice\n- To point out a piece of work that is quite different but seems in some ways to share in the spirit of this work – in \"From Word Models to World Models\" (Wong et al 2023), they similarly treat the LLM essentially as a semantic parser that translates natural language into a formal world model language (in their case, a probabilistic program) where classic tools can do most of the work. Of course, this also relates more widely to the \"LLMs with tools\" paradigm where LLMs call out to other libraries/simulators as in \"ToolFormer\" (Schick et al, 2023)  or \"Mind's Eye\" (Liu et al, 2022). I don't personally need to see all of this in the related work, but just a suggestion if you think it might be worth drawing the connections.\n- You've got a missing bullet point in Fig 1 top left box\n- I saw in Appendix A.4 you were discussing human-usable tools for debugging PDDL models – I'm curious if you've also thought about ways that this could be fully automated? For example, starting to plan with the faulty model, hitting a failure to plan, and then somehow prompting the model with this error such that it could revise the error in the model? Would love to hear if you've thought about this.", "limitations": "Limitations are adequately discussed by the authors.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The two stage process proposed here (first translating to a PDDL representation, then planning) is a nice setup. Unsurprisingly, when the PDDL model is correctly specified (e.g. after rounds of human feedback), the performance is much better than a setup that lacks this PDDL model construction step.\n- The paper flows very nicely, giving a good overview of the problem and concise summary of the method in the intro, and a nice related work and PDDL/planning background section.\n- The inclusion of detailed descriptions of the prompts is essential and much appreciated (and the full prompts given in the appendix) for fully understanding what the LLM is seeing. The authors do a great job of this. This kind of extensive appendix is invaluable for both replicating LLM work and understanding precisely what the inputs/outputs of the LLM are and its various failure modes.\n- I found a lot of the analyses / results commentary throughout the paper to be quite interesting and informative, such as the comparison between GPT-3.5-Turbo and GPT-4, or the aside about how LLMs do more \"reasonable\" things in their plans with respect to a human-like prior, for example leaving a knife on the counter instead of placing it on top of a toaster as the symbolic planner sometimes did. These little asides gave me a better feel for the strengths/weaknesses of LLMs in the context of planning, world model construction, etc.\n- The idea of translating the formal model *back* into natural language when presenting the user with errors is also a nice touch.", "weaknesses": "- The human feedback seems necessary given the errors GPT-4 introduces, and presumably this method wouldn't work well at all without that step of human correction. That means the method can't be fully automated (i.e. with just VAL feedback and no human feedback), or if this is the case then it isn't discussed or evaluated in the paper. Still, this is not a huge weakness in my view – methods involving humans are good too, and perhaps this could be a first work on which future work could be built that is more automated than just humans looking through a whole PDDL model trying to spot bugs. The authors discuss this a bit in section 4.2 and appendix A.4 which are quite nicely written.\n- Seeing an evaluation on more domains would have been nice, but I'm not super familiar with the norms in the planning literature. What they have is enough for me to get a reasonable feel for things, so this is not a big weakness.\n\n**Overall**, this seems like a good paper that I would weakly support acceptance on. Given the heavy involvement of humans in correcting the model I don't see this as an extremely groundbreaking result, but I think it's still a moderate to high impact well written piece of research that deserves to be published and would be appreciated by a NeurIPS audience. My opinion is definitely open to revision, and I may be missing some points as discussed in Questions below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. Involvement of humans: the human feedback seems necessary given the errors GPT-4 introduces, and presumably this method wouldn't work well at all without that step of human correction.\n\nAs in any use case of LLMs, LLMs are only **approximately omniscient**, and without external intervention, we can’t get guaranteed correct outputs. Our whole point is that it is easier to have the human intervention be limited to getting the model correct, and to let the sound planner ensure plan correctness, than it is to have humans in the loop for checking every single plan’s correctness. \n\nWe understand that any system would benefit from a higher degree of automation, but we believe the existing literature on leveraging LLMs for task planning tends to overlook a critical problem of how the generated (potentially erroneous) plans are going to be used --- most existing papers only end up reporting a subpar success rate/plan correctness rate. Though not explicitly mentioned in relevant papers, the users have to eventually review and correct the plans before executing them. \n\nIn contrast, what we present is a **more complete & closed-loop framework** that answers how we can obtain the final corrected plans. In fact, we posit that our approach even requires less human involvement compared to existing methods. This is because our system only necessitates the correction of the domain model once, whereas with other approaches, the user needs to inspect and correct **every plan** generated by LLMs.\n\n> 2. You mention that GPT4 makes very few syntax errors (mostly reasoning errors). Is the same true of GPT-3.5-Turbo, or does it not even have syntax figured out?\n\nWhen the number of literals increases, GPT-3.5 starts to have problems even in following PDDL syntax. We tried to characterize the failure modes, but it turns out that there are no discernible patterns regarding error types and their frequency of occurrence. Overall, GPT-4 stands as the only publicly accessible LLM that possesses sufficient capabilities at the time of submission, and users should use GPT-4 whenever possible.\n\n> 3. Regarding newly defined predicates (line 187) that the LLM ended up introducing in these domains, how many new predicates does it end up producing? Is it generally obvious what the \"right\" predicates are?\n\nPlease note that **all predicates** were introduced by the LLM in all domains. The \"dynamically updated list of predicates\" (Fig. 2) is initialized to **an empty list**. We will ensure that we provide clarification on this in our revision.\n\nRegarding the correctness of predicate design, this is indeed an open-ended problem. Our evaluation criteria are based on whether the derived predicates and the action model can precisely recover the logic of an action. For example, in the action \"toggle an appliance\", we expect to see there are predicates capturing the atomic events that the robot is close to the appliance and the appliance will be turned on as an effect. While there can be flexibility in naming the relevant predicates and parameters, it is crucial that the action model covers the required logic and functionality.\n\n> 4. At the end of the human feedback process, are the models literally equivalent to the \"ground truth PDDL\" (line 285)? This would be helpful for me understanding the 95% accuracy result – is it just that we've recovered the ground truth at this point by human corrections, and the ground truth can get 95%?\n\nWe would like to clarify that, our evaluation essentially consists of three steps:\n\n(a) Step 1: evaluating PDDL model generation. We reported (in Table 1) the number of errors in the generated domain models.\n\n(b) Step 2: iteratively correcting the generated models. As described around line 330,  GPT-4 eliminated all the errors by taking human feedback. At the end of this step, we got a fully corrected domain model that 100% recovers the logic and functionality of some corresponding \"ground truth model\".\n\n(c) Step 3: utilizing the generated model for planning. In order to plan with a PDDL model, an external planner will also require the PDDL goal specification to be provided. So there is an extra step that uses LLMs to translate natural language instruction into symbolic goal conditions. The 5% errors (in the \"95%\" result) all came from this translation process (explained in line 344). It's important to note that the domain model used in Step 3 has already been corrected in Step 2, and it is deemed free from errors.\n\nWe will make sure we provide better clarification on this in our revision.\n\n> 5. In \"From Word Models to World Models\" (Wong et al 2023), they similarly treat the LLM essentially as a semantic parser that translates natural language into a formal world model language ...\n\nWe sincerely appreciate the reviewer for sharing these works, as they exhibit clear connections and a shared spirit with our work. This is a fast-growing area, and some of these works just came out after the NeurIPS deadline. When we get more space in the paper (e.g., in camera ready), we will ensure to include more up-to-date related works.\n\n> 6. I saw in Appendix A.4 you were discussing human-usable tools for debugging PDDL models – I'm curious if you've also thought about ways that this could be fully automated?\n\nDomain model inspection tools can be valuable in reducing human effort. However, as discussed earlier in Question 1, human verification and feedback remain necessary, particularly in identifying factual errors. Please note a domain model with significant inaccuracies could still generate plans without triggering errors in search algorithms. However, these plans would be completely wrong in terms of factuality (correct executability) and would deviate greatly from the real world.\n\nNevertheless, we agree that investigating how to increase the degree of automation with existing tools can be a very promising follow-up direction.\n"}, {"review_id": "i28BAavXmf", "reviewer": "Reviewer_bNZu", "summary": "This paper proposes a translation approach to using LLMs for planning. Instead of relying on LLM prompt completion to generate plans, this approach parses a written description of the domain into a valid PDDL domain description. The user can then opt to use the PDDL domain description with an automated PDDL planner, or use the domain description along with prompting to force an LLM to generate a plan that can be parsed by a plan validator. \n\nThe evaluations were based upon International planning competition domains that are produced to push the limits of symbolic planning domains and are generally considered as challenging benchmarks within the symbolic planning community.\n\nThe key findings of the paper can be summarized as follows:\n1) Allowing LLMs to generate PDDL descriptions working in conjunction with syntax validators and human experts to create a valid PDDL description. The process was still too error-prone to completely automate without human supervision.\n2) LLMs are not capable of planning even with the restricted PDDL grammar for symbolic planning domains representative of the state-of-the-art in symbolic planning community.\n3) External symbolic modules that validate plans are capable of improving LLM planning performance, but is still well below the performance of a purpose-built automated planning algorithm", "questions": "Did the authors evaluate whether the LLM can generate PDDL domains of the same description at various levels of action abstraction? If so I am missing that from the draft.\n\nWhat is the role of the user in the proposed system?\n\nHow does your proposed framework decide if the planning domain generated is adequate?", "limitations": "NA", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper appropriately evaluates the capability of LLMs to plan and reason with domain sizes representative of the state-of-the-art in symbolic planning. As can be seen from table 1, the domains are significantly larger than ones seen in evaluating LLMs for planning. both in terms of number of actions and in terms of number of literals within the domain. \n\nThe claims of the paper are appropriately scoped, and are well supported by the experimentation. Care was taken by the authors to explicitly characterize the difficulty of the planning domain.\n\nThe key results of the paper are intuitive, in that it appears that LLMs are capable of translating information from one format (textual descriptions) to another (formal planning domain predicate and action description), but perform poorly at the actual task of planning and reasoning. Appending plan validator feedback to the prompt and replanning with an LLM is akin to backtracking in planning algorithms but is a very roundabout and computationally inefficient way of implementing search algorithms. It is essentially using the LLM output and context window as memory.", "weaknesses": "Reliance on the user: The LLM is forced to generate the domain description one action at a time. This pushes the workload of identifying the right number of actions and stopping criterion onto the user. understanding whether the domain description if complete and consistent is a major aspect of generating the PDDL domain, and right now it seems to be done by the user. \n\nExplicitly stating the contribution: I would like the authors to reflect and restate what the major contribution of this paper is. Is it a prompt structure that helps LLMs consistently translate descriptions into PDDL. Or is it the definitions of the symbolic modules used to validate the planning module developed in particular to work well with LLMs. The statement of why the paper is novel, and significant seems missing from the submission, though I appreciate the value that this work brings to the table.\n\nLLMs are new on the scene, and I have seen quite a few papers in the LLM + symbolic planning that are very recent and potentially close to the NeurIPS submission deadline. I would hope that if accepted, the authors acknowledge this work and rewrite the background and related work sections accordingly [1],[2]\n\n[1] - Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J. and Stone, P., 2023. Llm+ p: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477.\n[2] - Valmeekam, K., Olmo, A., Sreedharan, S. and Kambhampati, S., 2022. Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change). arXiv preprint arXiv:2206.10498.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. Some papers in the LLM + symbolic planning\n\nIn fact, we cite both papers and discuss them in detail in our submission:\n\n(a) LLM+P: starting from line 346, we explained the fact that  LLM+P only uses LLMs for translating user instruction into PDDL format. In contrast, our work does much more than such translation (e.g., deriving predicates and constructing domain models)\n\n(b) \"Large Language Models Still Can't Plan\": we have cited a newer version of this \"can't plan\" paper [1], authored by the same group of researchers. Their study serves as a significant motivation for our work. \n\n[1] Valmeekam, Karthik, et al. \"On the planning abilities of large language models (a critical investigation with a proposed benchmark).\" \n\n> 2. Is the LLM merely doing the translation? Various levels of action abstraction?\n\nWe would like to emphasize that our framework uses LLMs beyond simple translation. For comparison, we list here two well-known papers that merely do the translation [1,2]. Both of them **require a PDDL domain model to be given**, and then they use LLMs to translate natural-language user instruction and detailed state description to PDDL goal specification and init states.\n\nIn contrast, our framework requires the LLM to derive the set of predicates and to determine the preconditions and effects by taking partial information from the action description and leveraging the web-scale knowledge within the LLM. It is crucial to note that we do not assume the action descriptions are provided as a comprehensive list of preconditions and effects that can be directly mapped to their PDDL counterparts. The LLM needs to supply the input information with its own knowledge. In this regard, we believe our task is more akin to a program/code synthesis task rather than a mere translation task.\n\nAlso, as explained in the paper starting from line 299, we did conduct extra experiments with action descriptions at a different level of abstraction. Results confirm that GPT-4 can provide meaningful knowledge acquisition when minimal information is provided in action descriptions. The reviewer may find the descriptions we used in the Appendix starting from Section A.6.3, wherein the minimal action descriptions are given in the “Action description” box and additional details are given in the “Additional information from the user.”\n\n[1] Xie, Yaqi, et al. \"Translating natural language to planning goals with large-language models.\" arXiv:2302.05128\n\n[2] Liu, Bo, et al. \"Llm+ p: Empowering large language models with optimal planning proficiency.\" arXiv:2304.11477\n\n> 3. Reliance on the user\n\nAs in any use case of LLMs, LLMs are only **approximately omniscient**, and without external intervention, we can’t get guaranteed correct outputs. Our whole point is that it is easier to have the human intervention be limited to getting the model correct, and to let the sound planner ensure plan correctness, than it is to have humans in the loop for checking every single plan’s correctness. \n\nWe understand that any system would benefit from a higher degree of automation, but we believe the existing literature on leveraging LLMs for task planning tends to overlook a critical problem of how the generated (potentially erroneous) plans are going to be used --- most existing papers only end up reporting a subpar success rate/plan correctness rate. Though not explicitly mentioned in relevant papers, the users have to eventually review and correct the plans before executing them. \n\nIn contrast, what we present is a **more complete & closed-loop framework** that answers how we can obtain the final corrected plans. In fact, we posit that our approach even requires less human involvement compared to existing methods. This is because our system only necessitates the correction of the domain model once, whereas with other approaches, the user needs to inspect and correct **every plan** generated by LLMs. \n\n> 4. Explicitly stating the contribution\n\nOur contributions are as follows:\n\n(a). Our framework introduces a systematic approach to prompt LLMs for constructing PDDL domain models. This includes the entire workflow/algorithm, the prompt templates, and the management of derived predicates.\n\n(b). To ensure the syntax and factual correctness of the domain model, we utilize multiple sources of feedback, including the PDDL validator in VAL and human feedback. Additionally, we provide prompt templates to incorporate feedback into the generated models.\n\n(c). We explore two ways of utilizing the generated models: planning with an external planner, and back-prompting LLM planners with information on unmet preconditions and goals.\n\n> 5. What is the role of the user in the proposed system? Are they responsible for identifying the right number of actions and the stopping criterion?\n\nPlease note that in our framework, the user is not responsible for determining the right number of actions. As detailed in the problem setting, this paper operates under the assumption of a fixed-size library of actions (which is a common setting in the existing literature). \nOverall, the user is responsible for (a) making sure the domain model is producing desired policy/behaviors, and identifying factual errors if needed; (b) providing feedback on the factual errors (basically pointing out incorrect or missing preconds/effects). \n\nMost crucially, once the model is deemed correct, it is handled by a sound planner, and the user is no longer in the loop. \n\n> 6. How does your proposed framework decide if the planning domain generated is adequate?\n\nIn our framework, although tools like VAL help in pointing syntactical errors, it is eventually the user in the loop that deems the generated domain adequate by telling whether the generated behavior aligns with the user’s expectation or is executable or not. This is similar to how domains are made in the planning domain development literature (such as GIPO [1]).\n\n[1] Simpson, Ron et al. \"Planning domain definition using GIPO.\" \n\n"}, {"review_id": "QPTb744A1J", "reviewer": "Reviewer_iFiN", "summary": "This paper proposed to apply GPT LLMs to perform PDDL model construction from natural language description and correction with feedbacks, and then use classic planners or LLM planners to construct plans from the PDDL models. Experiments were conducted in the synthetic domains to verify the proposal and found that GPT-4 can construct PPDL models with less errors and classic PDDL planner outperforms LLM planners with the constructed PPDL models.", "questions": "1. Please explain the underlying models of LLMs on generating the PDDL domains and the feedback-correction mechanism; at least, explain or discuss your intuition. \n2. Have you deployed the plans to real-world fetch manipulator? If you did, how does it work? \n3. As your scenario is with fetch manipulator, have you try to use the LLMs to generate continuous parameters for the action operators? If not, please discuss the missing steps and future work is needed. \n4. What will be the solution to connect PDDL plans to real world execution? PDDL stream might be something you want to look at; explore how LLMs approach can be further extended to work with the full stack of task-and-motion planning systems like PDDL stream (https://github.com/caelan/pddlstream). \n5. Please discuss the difference between GPT-4 and GPT-3.5 rendering different results in Table 1. This will be helpful guide  readers in choosing the right LLMs to apply your proposal.", "limitations": "NA", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This is a new exploration of using LLMs to construct PDDL models (or symbolic world models for AI planning)\n2. Experiments are conducted to compare the performance of LLM planner and classic PDDL planner on the constructed PDDL models, and find that classic planner works better on PDDL model extracted by LLMs.", "weaknesses": "1. No theoretical framework is proposed to link the underlying probability models of token sequences of  LLMs to the domain and action models described by formal action description languages and its formal handling (e.g. planning, validation, correction and son). Without such a theoretical formal characterization, it is difficult to evaluate the validity and applicability/boundary of this kind of approach built on top of the LLMs, prompt-engineering, chains of feedback correction and planning and so on. \n2. A few experiments are given but without a systematic handling of metrics and the underlying hypotheses to be verified, and explanation to guide the application of the proposal and future research. The experimental methodology is ad-hoc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> 1. Clarification on the problem setting (Have you deployed the plans to real-world fetch manipulator? If you did, how does it work? What will be the solution to connect PDDL plans to real world execution?)\n\nWe are looking at the **model-based task planning** problem, where the main goal is to get the correct domain model and get the correct task plan. Within that setting, we are using LLMs to improve the efficacy of model acquisition part. We are evaluating (1) the correctness of the initial generated model itself (2) the effectiveness of the iterative human-in-the-loop improvement of the model and (3) once the model is deemed adequate by the user, the effectiveness of using the model in generating plans (with the help of an external sound model-based task planner).  \n\nThe goal of obtaining correct task plans is in line with the objectives of the current literature (that leverages LLMs for task planning [1,2,3,4]). There are several standard frameworks to connect correct task plans to execution on specific robotic/embodied agents [5,6], but this is not in the scope of this work and the targeted literature.\n\n\n[1] Yao, Shunyu, et al. \"ReAct: Synergizing Reasoning and Acting in Language Models.\" ICLR 2022.\n\n[2] Huang, Wenlong, et al. \"Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents\" ICML 2022.\n\n[3] Valmeekam, Karthik et al. “On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark).” arXiv:2302.06706\n\n[4]  Liu, Bo, et al. \"Llm+ p: Empowering large language models with optimal planning proficiency.\" arXiv:2304.11477\n\n[5] Ghallab, Malik, Dana Nau, and Paolo Traverso. Automated Planning: theory and practice. Elsevier, 2004.\n\n[6] Garrett, Caelan Reed, et al. \"Integrated task and motion planning.\" Annual review of control, robotics, and autonomous systems 4 (2021).\n\n> 2. No theoretical framework is proposed to link the underlying probability models of token sequences of LLMs to the domain and action models described by formal action description languages and its formal handling (e.g. planning, validation, correction and so on).\n\nWe believe that we are very much in tune with the current practice of carefully doing empirical evaluations on the outcomes.  While it would indeed be great to have theoretical models from the token probability level to explain what LLMs are capable of doing, we note that almost none of the work about the LLM behavior–from the AGI sparks [1] to the various planning capabilities papers [2,3] –are able to offer such models at this point.  We do note that our outcome evaluation does leverage formal checks, such as the syntax verification checks by VAL, the PDDL validator. \n\n[1] Bubeck, Sébastien, et al. \"Sparks of artificial general intelligence: Early experiments with gpt-4.\" arXiv:2303.12712.\n\n[2] Valmeekam, Karthik et al. “On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark).” arXiv:2302.06706 \n\n[3] Huang, Wenlong, et al. \"Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents\" ICML 2022.\n\n\n> 3. Please explain the underlying models of LLMs on generating the PDDL domains and the feedback-correction mechanism; at least, explain or discuss your intuition.\n\nIn studies spanning various research areas, LLMs have demonstrated strong capability in self-correcting their output by leveraging critic feedback. For instance, in program synthesis, LLMs can rectify programs by iteratively incorporating error messages generated by compilers or interpreters. In our PDDL generation task, we rely on the same type of self-correction ability of LLMs. For detailed descriptions and examples of feedback templates, we recommend the reviewer to check out our Appendix, especially Sections A.3, A.7, A.9, and A11.\n\n> 4. Please discuss the difference between GPT-4 and GPT-3.5 rendering different results in Table 1. This will be helpful guide readers in choosing the right LLMs to apply your proposal.\n\nWe compared the results of GPT-3.5 and GPT-4 in the experiment section. In our conclusion, specifically around lines 296-298, we stated that GPT-3.5 demonstrates a wide range of errors with no discernible patterns regarding error types and their frequency of occurrence. This suggests that GPT-3.5 is barely up to the task of PDDL generation, and hence GPT-4 stands as the only publicly accessible LLM that possesses sufficient capabilities at the time of submission. \n\n"}]

Author rebuttal:

Meta-review: This paper explores using large language models (LLMs) to generate PDDL domain models from natural language descriptions with a flavor of program synthesis. The models are then used with a classical PDDL planner or an LLM planner to generate plans.

The reviewers generally found the idea novel and the experiments properly evaluated, but had concerns about novelty given related concurrent work, reliance on human involvement, and lack of a theoretical framework connecting LLMs to PDDL. All reviewers recommended varying levels of acceptance, citing the challenging planning benchmarks used, value of translating textual descriptions into formal representations, and thorough experiments. In line with the reviewer-author discussion, I do feel that the lack of a theoretical grounding for the framework is in itself not a reason for rejection but do encourage the authors to pursue this line of thought as part of future work. 

In their rebuttal, the authors clarified their contributions, justified the level of human involvement, and compared to related works published near the deadline. Additional experiments adequately addressed reviewer questions about action abstraction and user roles, resulting in several reviewers raising their initial scores.

The idea of using LLMs to construct planning domain models is novel and valuable. The experiments, while limited to synthetic domains, properly stress test capabilities and analyze errors. The reliance on human verification seems necessary for now, but could potentially be alleviated through finetuning or an RLHF equivalent. I would recommend acceptance, with revisions to further clarify novelty and contributions.
Decision: Accept (poster)