Graph learning with a nearest neighbor approach

Koenig, Sven; Smirnov, Yury V.

doi:10.1145/238061.238065

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…Sven Koenig and Yuri Smirnov derived some of the previous results on which we are building our work [50]. Maxim Likhachev performed the experiments that we reported in Section 8.…”

Section: Acknowledgmentsmentioning

confidence: 98%

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Performance bounds for planning in unknown terrain

Koenig

Tovey

Smirnov

2003

Artificial Intelligence

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Planning in nondeterministic domains is typically intractable due to the large number of contingencies. Two techniques for speeding up planning in nondeterministic domains are agent-centered search and assumption-based planning. Both techniques interleave planning in deterministic domains with plan execution. They differ in how they make planning deterministic. To determine how suboptimal their plans are, we study two planning methods for robot navigation in initially unknown terrain that have successfully been used on mobile robots but not been analyzed before. The planning methods differ both in the technique they use to speed up planning and in the robot-navigation task they solve. Greedy Mapping uses agentcentered search to map unknown terrain. Dynamic A* uses assumption-based planning to navigate to a given goal location in unknown terrain. When we formalize abstractions of these planning methods on undirected graphs G = (V, E), they turn out to be similar enough that we are able to analyze their travel distance in a unified way. We discover that neither method is optimal in a worst-case sense, by a factor of Ω(log |V |/ log log |V |). We also derive factor O(|V |) upper bounds to show that these methods are not very badly sub-optimal in this sense. These results provide a first step towards explaining the good empirical results that have been reported about Greedy Mapping and Dynamic A* in the experimental literature. More generally, they show how to use tools from graph theory to analyze the plan quality of practical planning methods for nondeterministic domains.

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“…Sven Koenig and Yuri Smirnov derived some of the previous results on which we are building our work [50]. Maxim Likhachev performed the experiments that we reported in Section 8.…”

Section: Acknowledgmentsmentioning

confidence: 98%

“…Proof: Consider the planar graph G = (V, E) shown in Figure 3, which is a variation of a graph in [50]. It consists of a stem with several branches.…”

Section: Theoremmentioning

confidence: 99%

Performance bounds for planning in unknown terrain

Koenig

Tovey

Smirnov

2003

Artificial Intelligence

Self Cite

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“…It was started with a Random Walk algorithm which let a robot choose the next destination randomly through a uniform random function [Coppersmith et al, 1993;Fleischer & Trippen, 2003]. Then, the heuristic algorithm called the "Nearest Neighbor Approach" [Koenig & Smirnov, 1996] was introduced. It is a greedy algorithm that creates the shortest path from the current node to the destination based on the retrieved local information (cost or weight) of the current node.…”

Section: Graph Traversal Algorithmsmentioning

confidence: 99%

Chaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics

Changaival

Rosalie

Danoy

et al. 2017

Int. J. Bifurcation Chaos

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Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very large and/or unknown environment graphs. CHAT performance using Lozi map is proven superior than the, commonly known, Random Walk, in terms of number of nodes visited (coverage percentage) and computation time where the environment is unknown and memory usage is restricted.

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“…The list of applications of ANN and AkNN is quite extensive and also includes co-location pattern mining [31], graph-based computational learning [18], pattern recognition and classification [22], N-body simulations in astrophysical studies [10], and particle physics [23].…”

Section: Introductionmentioning

confidence: 99%