This paper presents a model of neural network embodiment of intentions and planning mechanisms for autonomous agents. The model bridges the dichotomy of symbolic and non-symbolic ...
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming ...
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent cl...
Many of the problems that occur in long-running systems involve the way that the system uses memory. We have developed a framework for extracting and building a model of the heap ...
The paper presents an approach to combine knowledge from memory and brain sciences with information retrieval research in the design of Web agents. An information retrieval agent f...