Corpus ID: 7504474

Using Classical Planners to Solve Nondeterministic Planning Problems

@inproceedings{Kuter2008UsingCP,
  title={Using Classical Planners to Solve Nondeterministic Planning Problems},
  author={Ugur Kuter and Dana S. Nau and Elnatan Reisner and Robert P. Goldman},
  booktitle={ICAPS},
  year={2008}
}
Researchers have developed a huge number of algorithms to solve classical planning problems. We provide a way to use these algorithms, unmodified, to generate strong-cyclic solutions in fully-observable nondeterministic planning domains. Our experiments show that when using our technique with FF and SGPlan (two well-known classical planners), its performance compares quite favorably to that of MBP, one of the best-known planners for nondeterministic planning problems. 
Plan aggregation for strong cyclic planning in nondeterministic domains
We describe a planning algorithm, NDP2, that finds strong-cyclic solutions to nondeterministic planning problems by using a classical planner to solve a sequence of classical planning problems. NDP2Expand
Simple and Fast Strong Cyclic Planning for Fully-Observable Nondeterministic Planning Problems
TLDR
An NDP-motivated strong cyclic algorithm is described that can already outperform state-of-the-art FOND planners, and this algorithm is extended with a novel heuristic that addresses the challenge of dealing with the large size of the state space. Expand
Automated Generation of Diverse NPC-Controlling FSMs Using Nondeterministic Planning Techniques
We study the problem of generating a set of Finite State Machines (FSMs) modeling the behavior of multiple, distinct NPCs. We observe that nondeterministic planning techniques can be used to generateExpand
Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains
Replanners are efficient methods for solving nondeterministic planning problems. Despite showing good scalability, existing replanners often fail to solve problems involving a large number ofExpand
Fast Strong Planning for FOND Problems with Multi-root Directed Acyclic Graphs
We present a planner for addressing a difficult, yet under-investigated class of planning problems: Fully Observable Non-Deterministic planning problems with strong solutions. Our strong plannerExpand
Fast Strong Planning for FOND Problems with Multi-root Directed Acyclic Graphs
We present a planner for addressing a difficult, yet under-investigated class of planning problems: Fully Observable Non-Deterministic planning problems with strong solutions. Our strong plannerExpand
Towards Fully Observable Non-Deterministic Planning as Assumption-based Automatic Synthesis
TLDR
It is argued then that such logical characterization allows us to recast non-deterministic planning as a reactive synthesis task, and show that for a special case, recent efficient synthesis techniques can be applied. Expand
On Computing Conformant Plans Using Classical Planners: A Generate-And-Complete Approach
TLDR
The paper presents the GC[LAMA] implementation, whose classical planner is LAMA, and investigates properties of the approach, including conditions for completeness, as well as empirically evaluated against state-of-the-art conformants. Expand
Execution Monitoring to Improve Plans with Information Gathering
There has been much recent interest in planning problems with deterministic actions but stochastic observations. Examples include Mars rover planning, robot monitoring tasks and the Rocksample domainExpand
Fast strong planning for fully observable nondeterministic planning problems
TLDR
A sound and complete strong planning algorithm is presented and incorporated into the planner, FIP, which has achieved outstanding performance on strong cyclic planning problems and is on average three orders of magnitude faster than Gamer and MBP, and scales up to larger problems. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 30 REFERENCES
A Hierarchical Task-Network Planner based on Symbolic Model Checking
TLDR
This work presents a novel algorithm, called YoYo, for planning in nondeterministic domains under the assumption of full observability, that enables it to combine the power of search-control strategies as in Planning with Hierarchical Task Networks (HTNs) with techniques from the Planning via Symbolic Model-Checking (SMC). Expand
Forward-Chaining Planning in Nondeterministic Domains
TLDR
The results suggest that the general technique for taking forward-chaining planners for deterministic domains and adapting them to work in nondeterministic domains preserves many of the desirable properties of these planners, such as the ability to use heuristic techniques to achieve highly efficient planning. Expand
Planning as Model Checking for Extended Goals in Non-deterministic Domains
TLDR
A planning algorithm is defined that generates automatically plans for extended goals in nondeterministic domains and preliminary experimental results are provided based on an implementation of the planning algorithm that uses symbolic model checking techniques. Expand
Compiling Uncertainty Away: Solving Conformant Planning Problems using a Classical Planner (Sometimes)
TLDR
This work shows that this is possible by mapping conformant into classical problems that are then solved by an off-the-shelf classical planner and accommodates 'reasoning by cases' by means of an 'split-protect-and-merge' strategy. Expand
Guided Symbolic Universal Planning
TLDR
This paper introduces a general approach for guiding universal planning based on an existing method for heuristic symbolic search in deterministic domains and presents three new sound and complete algorithms for best-first strong, strong cyclic, and weak universal planning. Expand
Strong planning under partial observability
TLDR
This paper formally defines the problem of strong planning within a general framework for modeling partially observable planning domains, and proposes an effective planning algorithm, based on and-or search in the space of beliefs, that is able to outperform its competitor systems by orders of magnitude. Expand
UCPOP: A Sound, Complete, Partial Order Planner for ADL
TLDR
It is proved ucpop is both sound and complete for this representation and a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous "Yale Stacking Problem". Expand
Universal Plans for Reactive Robots in Unpredictable Environments
TLDR
This paper describes a new kind of plan, called a "universal plan", which can be synthesized automatically, yet generates appropriate behavior in unpredictable environments, and explicitly identifies predicates requiring monitoring at each moment of execution. Expand
Planning Control Rules for Reactive Agents
TLDR
This paper describes a powerful extension of this approach for handling complex goals for reactive agents by using a modal temporal logic that can express quite complex time, safety, and liveness constraints. Expand
Using Model Checking for Pre-Planning Analysis
TLDR
This paper has explored the combination of the existing pre-planning technology with standard model-checking approaches in order to extract temporal features of a planning domain that can improve planning performance. Expand
...
1
2
3
...