Planning With Discrete Harmonic Potential Fields
Ahmad A. Masoud

TL;DR
This paper introduces a discrete harmonic potential field (DHPF) approach for planning on weighted graphs, demonstrating its effectiveness in developing new algorithms, enhancing existing planners, and solving dynamic routing problems in communication networks.
Contribution
It presents a novel discrete harmonic potential field method, including the M* algorithm, and shows how DHPF can improve planning and routing in dynamic, networked environments.
Findings
DHPF enables the creation of new, efficient planning algorithms.
DHPF can augment existing planners like A* to address lower bound problems.
Simulation shows DHPF supports dynamic routing with high reliability.
Abstract
In this work a discrete counterpart to the continuous harmonic potential field approach is suggested. The extension to the discrete case makes use of the strong relation HPF-based planning has to connectionist artificial intelligence (AI). Connectionist AI systems are networks of simple, interconnected processors running in parallel within the confines of the environment in which the planning action is to be synthesized. It is not hard to see that such a paradigm naturally lends itself to planning on weighted graphs where the processors may be seen as the vertices of the graph and the relations among them as its edges. Electrical networks are an effective realization of connectionist AI. The utility of the discrete HPF (DHPF) approach is demonstrated in three ways. First, the capability of the DHPF approach to generate new, abstract, planning techniques is demonstrated by constructing a…
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