Many-objective problems are difficult to solve using conventional multi-objective evolutionary algorithms (MOEAs) as these algorithms rely primarily on Pareto ranking to guide the ...
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
In large wireless sensor networks, the problem of assigning radio frequencies to sensing agents such that no two connected sensors are assigned the same value (and will thus inter...
Ruben Stranders, Alex Rogers, Nicholas R. Jennings
A major research challenge in multi-agent systems is the problem of partitioning a set of agents into mutually disjoint coalitions, such that the overall performance of the system...
Tomasz P. Michalak, Jacek Sroka, Talal Rahwan, Mic...
Confocal fluorescence microscopy has become an important tool in biological and medical sciences for imaging thin specimen, even living ones. Due to out-of-focus blurring and noise...
We experimentally compare the two algorithms A and B by Fredman and Khachiyan [FK96] for the problem Monet--given two monotone Boolean formulas in DNF and in CNF, decide whether ...
In this paper we present an experimental study of several maximum flow algorithms in the context of unbalanced bipartite networks. Our experiments are motivated by a real world pr...
Cosmin Silvestru Negruseri, Mircea Bogdan Pasoi, B...
— Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Nearest neighbour (NN) imputation al...
We present an algorithm for the efficient and accurate computation of geodesic distance fields on triangle meshes. We generalize the algorithm originally proposed by Surazhsky e...