Interval-Based Relaxation for General Numeric Planning

@inproceedings{Scala2016IntervalBasedRF,
  title={Interval-Based Relaxation for General Numeric Planning},
  author={E. Scala and Patrik Haslum and Sylvie Thi{\'e}baux and Miquel Ram{\'i}rez},
  booktitle={European Conference on Artificial Intelligence},
  year={2016}
}
. We generalise the interval-based relaxation to sequential numeric planning problems with non-linear conditions and effects, and cyclic dependencies. This effectively removes all the limi-tations on the problem placed in previous work on numeric planning heuristics, and even allows us to extend the planning language with a wider set of mathematical functions. Heuristics obtained from the generalised relaxation are pruning-safe. We derive one such heuristic and use it to solve discrete-time… 
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References

SHOWING 1-10 OF 26 REFERENCES

Complexity of Interval Relaxed Numeric Planning

A relaxation approach with intervals for numeric planning is presented and it is shown that plan existence can be decided in polynomial time for tasks where dependencies between numeric effects are acyclic.

COLIN: Planning with Continuous Linear Numeric Change

In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL2.1.…

Using the Context-enhanced Additive Heuristic for Temporal and Numeric Planning

Temporal Fast Downward (TFD) is presented, a planning system for temporal problems that is capable of finding low-makespan plans by performing a heuristic search in a temporal search space and outperforms all state-of-the-art temporal planning systems.

SMT-Based Nonlinear PDDL+ Planning

This work presents a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems, and applies a Satisfiability Modulo Theories (SMT) solver to find plans.

A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in Planning

This work presents a heuristic for numeric planning problems building on the propositional relaxed planning graph, but using a mathematical program for numeric reasoning, and shows that the use of this heuristic enhances scalability on problems where numeric resource interaction is key in finding a solution.

Temporal Planning with Semantic Attachment of Non-Linear Monotonic Continuous Behaviours

An algorithm which builds upon existent temporal planning techniques based on linear programming to approximate non-linear continuous monotonic functions to solve a broader set of planning problems is presented.

An approach to efficient planning with numerical fluents and multi-criteria plan quality

The Metric-FF Planning System: Translating ''Ignoring Delete Lists'' to Numeric State Variables

A natural extension of "ignoring delete lists" to numeric state variables is presented, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is "monotonic".

Heuristic Planning for PDDL+ Domains

DiNo is a new planner capable of tackling complex problems with non-linear system dynamics governing the continuous evolution of states based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper.

Forward-Chaining Partial-Order Planning

This paper explores the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems, and compares POPF with the approach of constructing a sequenced plan and lifting a partial order from it.