FAPE: a Constraint-based Planner for Generative and Hierarchical
Temporal Planning
- URL: http://arxiv.org/abs/2010.13121v1
- Date: Sun, 25 Oct 2020 13:46:34 GMT
- Title: FAPE: a Constraint-based Planner for Generative and Hierarchical
Temporal Planning
- Authors: Arthur Bit-Monnot, Malik Ghallab, F\'elix Ingrand and David E. Smith
- Abstract summary: We propose a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency.
FAPE's representation coherently integrates flexible timelines with hierarchical refinement methods that can provide efficient control knowledge.
- Score: 2.771897351607068
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Temporal planning offers numerous advantages when based on an expressive
representation. Timelines have been known to provide the required
expressiveness but at the cost of search efficiency. We propose here a temporal
planner, called FAPE, which supports many of the expressive temporal features
of the ANML modeling language without loosing efficiency.
FAPE's representation coherently integrates flexible timelines with
hierarchical refinement methods that can provide efficient control knowledge. A
novel reachability analysis technique is proposed and used to develop causal
networks to constrain the search space. It is employed for the design of
informed heuristics, inference methods and efficient search strategies.
Experimental results on common benchmarks in the field permit to assess the
components and search strategies of FAPE, and to compare it to IPC planners.
The results show the proposed approach to be competitive with less expressive
planners and often superior when hierarchical control knowledge is provided.
FAPE, a freely available system, provides other features, not covered here,
such as the integration of planning with acting, and the handling of sensing
actions in partially observable environments.
Related papers
- Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation [51.06031200728449]
We propose a novel framework called mccHRL to provide different levels of temporal abstraction on listwise recommendation.
Within the hierarchical framework, the high-level agent studies the evolution of user perception, while the low-level agent produces the item selection policy.
Results observe significant performance improvement by our method, compared with several well-known baselines.
arXiv Detail & Related papers (2024-09-11T17:01:06Z) - PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes [41.47703182059505]
We propose a visual SLAM system based on planar features designed for planar ambiguous scenes.
We present an integrated data association strategy that combines plane parameters, semantic information, projection IoU, and non-parametric tests.
Finally, we design a set of multi-constraint factor graphs for camera pose optimization.
arXiv Detail & Related papers (2024-02-09T01:34:26Z) - Simple Hierarchical Planning with Diffusion [54.48129192534653]
Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets.
We introduce the Hierarchical diffuser, a fast, yet surprisingly effective planning method combining the advantages of hierarchical and diffusion-based planning.
Our model adopts a "jumpy" planning strategy at the higher level, which allows it to have a larger receptive field but at a lower computational cost.
arXiv Detail & Related papers (2024-01-05T05:28:40Z) - 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) - Efficient Planning with Latent Diffusion [18.678459478837976]
Temporal abstraction and efficient planning pose significant challenges in offline reinforcement learning.
Latent action spaces offer a more flexible paradigm, capturing only possible actions within the behavior policy support.
This paper presents a unified framework for continuous latent action space representation learning and planning by leveraging latent, score-based diffusion models.
arXiv Detail & Related papers (2023-09-30T08:50:49Z) - Extended High Utility Pattern Mining: An Answer Set Programming Based
Framework and Applications [0.0]
Rule-based languages like ASP seem well suited for specifying user-provided criteria to assess pattern utility.
We introduce a new framework that allows for new classes of utility criteria not considered in the previous literature.
We exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients.
arXiv Detail & Related papers (2023-03-23T11:42:57Z) - Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph
Construction [57.854498238624366]
We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction.
RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
arXiv Detail & Related papers (2022-10-19T16:40:28Z) - Active Learning of Abstract Plan Feasibility [17.689758291966502]
We present an active learning approach to efficiently acquire an APF predictor through task-independent, curious exploration on a robot.
We leverage an infeasible subsequence property to prune candidate plans in the active learning strategy, allowing our system to learn from less data.
In a stacking domain where objects have non-uniform mass distributions, we show that our system permits real robot learning of an APF model in four hundred self-supervised interactions.
arXiv Detail & Related papers (2021-07-01T18:17:01Z) - Finding Action Tubes with a Sparse-to-Dense Framework [62.60742627484788]
We propose a framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner.
We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets.
arXiv Detail & Related papers (2020-08-30T15:38:44Z) - A Dependency Syntactic Knowledge Augmented Interactive Architecture for
End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA.
This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn)
Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-04T14:59:32Z)
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.