617 research outputs found
Abstraction-based action ordering in planning
Many planning problems contain collections of symmetric objects, actions and structures which render them difficult to solve efficiently. It has been shown that the detection and exploitation of symmetric structure in planning problems can dramatically reduce the size of the search space and the time taken to find a solution. We present the idea of using an abstraction of the problem domain to reveal symmetric structure and guide the navigation of the search space. We show that this is effective even in domains in which there is little accessible symmetric structure available for pruning. Proactive exploitation represents a flexible and powerfulalternative to the symmetry-breaking strategies exploited in earlier work in planning and CSPs. The notion of almost symmetry is defined and results are presented showing that proactive exploitation of almost symmetry can improve the performance of a heuristic forward search planner
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at
https://github.com/matenure/GNN_planner. Data set is released at
https://github.com/IBM/IPC-graph-dat
Plan permutation symmetries as a source of inefficiency in planning
This paper briefly reviews sources of symmetry in planning and highlights one source that has not previously been tackled: plan permutation symmetry. Symmetries can be a significant problem for efficiency of planning systems, as has been previously observed in the treatment of other forms of symmetry in planning problems. We examine how plan permutation symmetries can be eliminated and present evidence to support the claim that these symmetries are an important problem for planning systems
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of
graph-based machine learning methods. We release a new data set, compiled from
International Planning Competitions (IPC), for benchmarking graph
classification, regression, and related tasks. Apart from the graph
construction (based on AI planning problems) that is interesting in its own
right, the data set possesses distinctly different characteristics from
popularly used benchmarks. The data set, named IPC, consists of two
self-contained versions, grounded and lifted, both including graphs of large
and skewedly distributed sizes, posing substantial challenges for the
computation of graph models such as graph kernels and graph neural networks.
The graphs in this data set are directed and the lifted version is acyclic,
offering the opportunity of benchmarking specialized models for directed
(acyclic) structures. Moreover, the graph generator and the labeling are
computer programmed; thus, the data set may be extended easily if a larger
scale is desired. The data set is accessible from
\url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured
Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat
The identification and exploitation of almost symmetry in planning problems
Previous work in symmetry detection for planning has identified symmetries between domain objects and shown how the exploitation of this information can help reduce search at plan time. However these methods are unable to detect symmetries between objects that are almost symmetrical: where the objects must start (or end) in slightly different configurations but for much of the plan their behaviour is equivalent. In the paper we outline a method for identifying such symmetries and discuss how this symmetry information can be positively exploited to help direct search during planning we have implemented this method and integrated it with the FF-v2.3 planner and in the paper we present results of experiments with this approach that demonstrate its potential
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Evolving macro-actions for planning
Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research into learning for planning. However, most existing work learns macros that are reusable plan fragments and so observable from planner behaviours online or plan characteristics offline. Also, there are learning methods that learn macros from domain analysis. Nevertheless, most of these methods explore restricted macro spaces and exploit specific features of planners or domains. But, the learning examples, especially that are used to acquire previous experiences, might not cover many aspects of the system, or might not always reflect that better choices have been made during the search. Moreover, any specific properties are not likely to be common with many planners or domains. This paper presents an offline evolutionary method that learns macros for arbitrary planners and domains. Our method explores a wider macro space and learns macros that are somehow not observable from the examples. Our method also represents a generalised macro learning framework as it does not discover or utilise any specific structural properties of planners or domains
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
We study how group symmetry helps improve data efficiency and generalization
for end-to-end differentiable planning algorithms when symmetry appears in
decision-making tasks. Motivated by equivariant convolution networks, we treat
the path planning problem as \textit{signals} over grids. We show that value
iteration in this case is a linear equivariant operator, which is a (steerable)
convolution. This extends Value Iteration Networks (VINs) on using
convolutional networks for path planning with additional rotation and
reflection symmetry. Our implementation is based on VINs and uses steerable
convolution networks to incorporate symmetry. The experiments are performed on
four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs)
configuration space and workspace manipulation. Our symmetric planning
algorithms improve training efficiency and generalization by large margins
compared to non-equivariant counterparts, VIN and GPPN.Comment: Restructured main text and appendix. Renamed from "Integrating
Symmetry into Differentiable Planning
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