Strong planning under uncertainty in domains with numerous but identical elements (a generic approach)

Kanovich, Max I.; Vauzeilles, Jacqueline

doi:10.1016/j.tcs.2007.01.022

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…Our intention is to address the complexity issues in the next paper, as it has been done for the actions with instant effects [15,16].…”

Section: Discussionmentioning

confidence: 99%

“…In many cases this allows us to decrease significantly the combinatorial costs associated with searching large spaces [15,16].…”

Section: Introduction and Motivating Examplesmentioning

confidence: 99%

“…The complexity results of [15,16] rely upon the assumption that the actions in question cause only instant effects, so that is the duration of the actions equals zero.…”

Section: Introduction and Motivating Examplesmentioning

confidence: 99%

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Linear logic as a tool for planning under temporal uncertainty

Kanovich

Vauzeilles

2011

Theoretical Computer Science

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a b s t r a c tThe typical AI problem is that of making a plan of the actions to be performed by a controller so that it could get into a set of final situations, if it started with a certain initial situation.The plans, and related winning strategies, happen to be finite in the case of a finite number of states and a finite number of instant actions.The situation becomes much more complex when we deal with planning under temporal uncertainty caused by actions with delayed effects.Here we introduce a tree-based formalism to express plans, or winning strategies, in finite state systems in which actions may have quantitatively delayed effects. Since the delays are non-deterministic and continuous, we need an infinite branching to display all possible delays. Nevertheless, under reasonable assumptions, we show that infinite winning strategies which may arise in this context can be captured by finite plans.The above planning problem is specified in logical terms within a Horn fragment of affine logic. Among other things, the advantage of linear logic approach is that we can easily capture 'preemptive/anticipative' plans (in which a new action β may be taken at some moment within the running time of an action α being carried out, in order to be prepared before completion of action α).In this paper we propose a comprehensive and adequate logical model of strong planning under temporal uncertainty which addresses infinity concerns. In particular, we establish a direct correspondence between linear logic proofs and plans, or winning strategies, for the actions with quantitative delayed effects.

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“…Our intention is to address the complexity issues in the next paper, as it has been done for the actions with instant effects [15,16].…”

Section: Discussionmentioning

confidence: 99%

“…In many cases this allows us to decrease significantly the combinatorial costs associated with searching large spaces [15,16].…”

Section: Introduction and Motivating Examplesmentioning

confidence: 99%

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Linear logic as a tool for planning under temporal uncertainty

Kanovich

Vauzeilles

2011

Theoretical Computer Science

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“…An interesting set of planning problems, for which LinGraph will be particularly well suited, is characterized by the presence of multiple, functionally identical objects [34]. Examples include job scheduling with multiple CPUs, coordination and task planning for swarm robots or parallel assembly lines.…”

Section: Automated Reasoning and Linear Logicmentioning

confidence: 99%

LinGraph: a graph-based automated planner for concurrent task planning based on linear logic

Kortik

Saranlı

2017

Appl Intell

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In this paper, we introduce an automated planner for deterministic, concurrent domains, formulated as a graph-based theorem prover for a propositional fragment of intuitionistic linear logic, relying on the previously established connection between intuitionistic linear logic and planning problems. The new graph-based theorem prover we introduce improves planning performance by reducing proof permutations that are irrelevant to planning problems particularly in the presence of large numbers of objects and agents with identical properties (e.g. robots within a swarm, or parts in a large factory). We first present our graph-based automated planner, the Linear Logic Graph Planner (LinGraph). Subsequently we illustrate its application for planning within a concurrent manufacturing domain and provide comparisons with four existing automated planners, BlackBox, Symba-2, Metis and the Temporal Fast Downward (TFD), covering a wide range of state-of-theart automated planning techniques and implementations. We show that even though LinGraph does not rely on any heuristics, it still outperforms these systems for concurrent domains with large numbers of identical objects and agents. These gains persist even when existing methods on

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“…They define a pseudo-plan as a finite graph composed of vertices for actions and oriented edges for preconditions or effects of actions. In [24], the exponential search space problem in planning problems including functionally identical objects, such as identical balls in a box, was addressed, exploiting the resource conscious nature of linear logic. In [25], temporal concepts were incorporated into actions, allowing them to have quantitatively delayed effects.…”

Section: Related Workmentioning

confidence: 99%

Linear planning logic: An efficient language and theorem prover for robotic task planning

Kortik

Saranlı

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we introduce a novel logic language and theorem prover for robotic task planning. Our language, which we call Linear Planning Logic (LPL), is a fragment of linear logic whose resource-conscious semantics are well suited for reasoning with dynamic state, while its structure admits efficient theorem provers for automatic plan construction. LPL can be considered as an extension of Linear Hereditary Harrop Formulas (LHHF), whose careful design allows the minimization of nondeterminism in proof search, providing a sufficient basis for the design of linear logic programming languages such as Lolli. Our new language extends on the expressivity of LHHF, while keeping the resulting nondeterminism in proof search to a minimum for efficiency. This paper introduces the LPL language, presents the main ideas behind our theorem prover on a smaller fragment of this language and finally provides an experimental illustration of its operation on the problem of task planning for the hexapod robot RHex. © 2014 IEEE

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Strong planning under uncertainty in domains with numerous but identical elements (a generic approach)

Cited by 9 publications

References 17 publications

Linear logic as a tool for planning under temporal uncertainty

Linear logic as a tool for planning under temporal uncertainty

LinGraph: a graph-based automated planner for concurrent task planning based on linear logic

Linear planning logic: An efficient language and theorem prover for robotic task planning

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