Reinforcement learning is bedeviled by the curse of dimensionality: the number of
parameters to be learned grows exponentially with the size of any compact encoding of a …

For a robot, an animal, and even for man, to be able to use an internal representation of the
spatial layout of its environment to position itself is a very complex task, which raises …

In sequence modeling, we often wish to represent complex interaction between labels, such
as when performing multiple, cascaded labeling tasks on the same sequence, or when long …

This article reviews map-learning and path-planning strategies within the context of map-
based navigation in mobile robots. Concerning map-learning, it distinguishes metric maps …

Chapter?? introduced hidden Markov models (HMMs), and Chapter?? introduced state
space models (SSMs), both of which are popular, but somewhat inflexible, models of …

The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov
model (HMM) that models sequences with structure at many length/time scales [FST98] …

Since 1989, East-Central Europe has witnessed a series of transformations that have
resulted in the region's geopolitical and geoeconomic repositioning within Europe. This …

The goal of the MavHome (M anaging A n Intelligent V ersa-tile Home) project is to create a
home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and …

The goal of the MavHome project is to develop technologies to Manage Adaptive Versatile
environments. In this paper, we present a complete agent architecture for a single inhabitant …

This paper considers the problem of a robot navigating in a crowded or congested
environment. A robot operating in such an environment can get easily blocked by moving …