Nicolas Rivera

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In this paper, we present a conceptually simple, easy-to-implement real-time search algorithm suitable for a priori partially known environments. Instead of performing a series of searches towards the goal, like most Real-Time Heuris-tic Search Algorithms do, our algorithm follows the arcs of a tree T rooted in the goal state that is built initially using(More)
Many applications, ranging from video games to dynamic robotics, require solving single-agent, deterministic search problems in partially known environments under very tight time constraints. Real-Time Heuristic Search (RTHS) algorithms are specifically designed for those applications. As a subroutine, most of them invoke a standard, but bounded, search(More)
Information propagation on graphs is a fundamental topic in distributed computing. One of the simplest models of information propagation is the push protocol in which at each round each agent independently pushes the current knowledge to a random neighbour. In this paper we study the so-called coalescing-branching random walk (COBRA), in which each vertex(More)
Pull voting is a classic method to reach consensus among n vertices with differing opinions in a distributed network: each vertex at each step takes on the opinion of a random neighbour. This method, however, suffers from two drawbacks. Even if there are only two opposing opinions, the time taken for a single opinion to emerge can be slow and the final(More)
We consider an asynchronous voting process on graphs which we call discordant voting, and which can be described as follows. Initially each vertex holds one of two opinions, red or blue say. Neighbouring vertices with different opinions interact pair-wise. After an interaction both vertices have the same colour. The quantity of interest is T , the time to(More)