Answer Set Planning: A Survey
- URL: http://arxiv.org/abs/2202.05793v1
- Date: Fri, 11 Feb 2022 17:42:47 GMT
- Title: Answer Set Planning: A Survey
- Authors: Tran Cao Son and Enrico Pontelli and Marcello Balduccini and Torsten
Schaub
- Abstract summary: The development of efficient and scalable answer set solvers has provided a boost to the development of ASP-based planning systems.
The survey explores the advantages and disadvantages of answer set planning.
It also discusses typical applications of answer set planning and presents a set of challenges for future research.
- Score: 6.348684258418859
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Answer Set Planning refers to the use of Answer Set Programming (ASP) to
compute plans, i.e., solutions to planning problems, that transform a given
state of the world to another state. The development of efficient and scalable
answer set solvers has provided a significant boost to the development of
ASP-based planning systems. This paper surveys the progress made during the
last two and a half decades in the area of answer set planning, from its
foundations to its use in challenging planning domains. The survey explores the
advantages and disadvantages of answer set planning. It also discusses typical
applications of answer set planning and presents a set of challenges for future
research.
Related papers
- EPD: Long-term Memory Extraction, Context-awared Planning and Multi-iteration Decision @ EgoPlan Challenge ICML 2024 [50.89751993430737]
We introduce a novel planning framework which comprises three stages: long-term memory Extraction, context-awared Planning, and multi-iteration Decision, named EPD.
EPD achieves a planning accuracy of 53.85% over 1,584 egocentric task planning questions.
arXiv Detail & Related papers (2024-07-28T15:14:07Z) - Ask-before-Plan: Proactive Language Agents for Real-World Planning [68.08024918064503]
Proactive Agent Planning requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction.
We propose a novel multi-agent framework, Clarification-Execution-Planning (textttCEP), which consists of three agents specialized in clarification, execution, and planning.
arXiv Detail & Related papers (2024-06-18T14:07:28Z) - Open Grounded Planning: Challenges and Benchmark Construction [44.86307213996181]
We propose a new planning task--open grounded planning.
The primary objective of open grounded planning is to ask the model to generate an executable plan based on a variable action set.
Then we test current state-of-the-art LLMs along with five planning approaches, revealing that existing LLMs and methods still struggle to address the challenges posed by grounded planning in open domains.
arXiv Detail & Related papers (2024-06-05T03:46:52Z) - Understanding the planning of LLM agents: A survey [98.82513390811148]
This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability.
Comprehensive analyses are conducted for each direction, and further challenges in the field of research are discussed.
arXiv Detail & Related papers (2024-02-05T04:25:24Z) - Learning adaptive planning representations with natural language
guidance [90.24449752926866]
This paper describes Ada, a framework for automatically constructing task-specific planning representations.
Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks.
arXiv Detail & Related papers (2023-12-13T23:35:31Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency [6.87593454486392]
We consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state.
We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain.
arXiv Detail & Related papers (2023-07-25T14:51:07Z) - Temporal Planning with Incomplete Knowledge and Perceptual Information [0.0]
This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework.
We propose a small extension to the Planning Domain Definition Language (PDDL) to model incomplete, (ii) knowledge sensing actions.
We also introduce a new set of planning domains to evaluate our solver, which has shown good performance on a variety of problems.
arXiv Detail & Related papers (2022-07-20T07:26:08Z) - Planning from video game descriptions [0.0]
Planners use these action models to get the deliberative behaviour for an agent in many different video games.
benchmarks of the domains have been produced that can be of interest to the international planning community.
arXiv Detail & Related papers (2021-09-01T15:49:09Z) - Iterative Planning with Plan-Space Explanations: A Tool and User Study [5.779503104475269]
We implement a tool for human-guided iterative planning including plan-space explanations.
The tool runs in standard Web browsers, and provides simple user interfaces for both developers and users.
We conduct a first user study, whose outcome indicates the usefulness of plan-property dependency explanations in iterative planning.
arXiv Detail & Related papers (2020-11-19T08:15:13Z) - Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning [78.65083326918351]
We consider alternatives to an implicit sequential planning assumption.
We propose Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS) for approximating the optimal plan.
We show that this algorithmic flexibility over planning order leads to improved results in navigation tasks in grid-worlds.
arXiv Detail & Related papers (2020-04-23T18:08:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.