LinGraph: a graph-based automated planner for concurrent task planning based on linear logic
Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies
Springer New York LLC
914 - 934
Item Usage Stats
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-the-art 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 symmetry reduction and numerical fluents are used, with LinGraph capable of handling problems with thousands of objects. Following these results, we also show that plan construction with LinGraph is equivalent to multiset rewriting systems, formally relating LinGraph to intuitionistic linear logic. © 2017, Springer Science+Business Media New York.
Intuitionistic linear logic
Published Version (Please cite this version)http://dx.doi.org/10.1007/s10489-017-0936-x
Showing items related by title, author, creator and subject.
Rudloff, B.; Ulus, F.; Vanderbei, R. (Springer, 2017)In this paper, a parametric simplex algorithm for solving linear vector optimization problems (LVOPs) is presented. This algorithm can be seen as a variant of the multi-objective simplex (the Evans–Steuer) algorithm (Math ...
Kari, D.; Vanli, N. D.; Kozat, S. S. (Elsevier B.V., 2017)We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear (piecewise linear) channel equalization algorithms that are highly efficient and provide significantly improved ...
Uyanık, İ.; Saranlı U.; Morgϋl, Ö.; Ankaralı, M. M. (Elsevier B.V., 2016)In this paper, we present a state-space system identification technique for a class of hybrid LTP systems, formulated in the frequency domain based on input-output data. Other than a few notable exceptions, the majority ...