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… 

Figures and Tables from this paper

Compiling Optimal Numeric Planning to Mixed Integer Linear Programming

This work presents a novel mixed-integer linear programming (MILP) compilation for cost-optimal numeric planning with instantaneous actions, and shows that its approach is competitive with heuristic search-based planners on domains with only simple numeric conditions.

LM-Cut Heuristics for Optimal Linear Numeric Planning

While numeric variables play an important, sometimes central, role in many planning problems arising from real world scenarios, most of the currently available heuristic search planners either do not

Landmarks for Numeric Planning Problems

The paper generalises the notion of landmarks for reasoning about planning problems involving propositional and numeric variables, and proposes a relaxationbased method for their automated extraction directly from the problem structure to infer disjunctive and additive hybrid action landmarks.

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

This paper presents a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan, and demonstrates that effective techniques for cost-optimal planning known in the classical setting can be adapted to the extended formalism.

Mixed Discrete Continuous Non-Linear Planning through Piecewise Linear Approximation

Bounding the problem using linear over and under-estimators, allows us to use scalable planners that handle linear change to find plans for non-linear domains to achieve state-of-the-art performance in non- linear planning.

A Branch-and-Cut Approach for a Mixed Integer Linear Programming Compilation of Optimal Numeric Planning

A novel compilation of numeric planning to mixed-integer linear programming (MILP) and employ a branch-and-cut algorithm to lazily generate constraints is proposed, which is faster and solves more instances in some numeric planning domains than the existing MILP based method.

Gradient-Based Mixed Planning with Discrete and Continuous Actions

A gradient-based framework to simultaneously optimize continuous parameters and actions of candidate plans and a heuristic module to estimate the best plan candidate to transit initial state to the goal based on relaxation is proposed.

Search-Guidance Mechanisms for Numeric Planning Through Subgoaling Relaxation

This paper investigates how to further exploit a new decomposition based relaxation for numeric planning problems by introducing the notion of the multi-repetition relaxed plan, and defines novel heuristics that are able to provide great guidance in problems exhibiting a pronounced numeric structure.

Interval Based Relaxation Heuristics for Numeric Planning with Action Costs

We adapt the relaxation heuristics hmax, hadd and hFF to interval based numeric relaxation frameworks, combining them with two different relaxation techniques and with two different search
...

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.