We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selectio...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian ...
We propose a sequential information maximization model as a general strategy for programming eye movements. The model reconstructs high-resolution visual information from a sequen...
Laura Walker Renninger, James M. Coughlan, Preeti ...
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
This paper presents an applicable model for complex multiattribute negotiations between autonomous agents. The model adopts a novel protocol which decomposes the original n-dimens...
We propose a novel approach to intelligent tutoring gaming simulations designed for both educational and inquiry purposes in complex multi-actor systems such as infrastructures or...