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- Nicolas Rivera, Jorge A. Baier, Carlos Hernández
- Artif. Intell.
- 2015

- Nicolas Rivera, Carlos Hernández, Jorge A. Baier
- AAAI
- 2017

- Nicolas Rivera, Jorge A. Baier, Carlos Hernández
- AAMAS
- 2013

Multiplying the heuristic function by a weight greater than one is a well-known technique in Heuristic Search. When applied to A* with an admissible heuristic it yields substantial runtime savings, at the expense of sacrificing solution optimality. Only a few works have studied the applicability of this technique to Real-Time Heuristic Search (RTHS), a… (More)

- Nicolas Rivera, Leon Illanes, Jorge A. Baier, Carlos Hernández
- SOCS
- 2013

In this paper, we present a conceptually simple, easy-toimplement 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 Heuristic Search Algorithms do, our algorithm follows the arcs of a tree T rooted in the goal state that is built initially using the… (More)

- Nicolas Rivera, Leon Illanes, Jorge A. Baier, Carlos Hernández
- J. Artif. Intell. Res.
- 2014

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)

- Tamara Fernandez, Nicolas Rivera, Yee Whye Teh
- NIPS
- 2016

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the… (More)

- Colin Cooper, Tomasz Radzik, Nicolas Rivera, Takeharu Shiraga
- SIROCCO
- 2015

- Colin Cooper, Nicolas Rivera
- ICALP
- 2016

We study voting models on graphs. In the beginning, the vertices of a given graph have some initial opinion. Over time, the opinions on the vertices change by interactions between graph neighbours. Under suitable conditions the system evolves to a state in which all vertices have the same opinion. In this work, we consider a new model of voting, called the… (More)

- Colin Cooper, Tomasz Radzik, Nicolas Rivera
- PODC
- 2016

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)