Multi-exit architectures, in which a stack of processing layers is interleaved with early output
layers, allow the processing of a test example to stop early and thus save computation time …

In this paper, we bring techniques from operations research to bear on the problem of
choosing optimal actions in partially observable stochastic domains. We begin by …

This book is a condensation of a large body of work concerning human learning carried out
over a period of more than five years by Dr. Sun and his collaborators. In a nutshell, this …

For a d-ary Boolean function Φ:{0, 1} d→{0, 1} and an assignment to its variables x=(x 1, x
2,..., xd) we consider the problem of finding those subsets of the variables that are sufficient …

What kinds of social relationships can people have with computers? Are there activities that
computers can engage in that actively draw people into relationships with them? What are …

Building a collaborative trusting relationship with users is crucial in a wide range of
applications, such as advice-giving or financial transactions, and some minimal degree of …

We define the probabilistic planning problem in terms of a probability distribution over initial
world states, a boolean combination of propositions representing the goal, a probability …

The GERRY scheduling and rescheduling system being applied to coordinate Space Shuttle
ground processing is described. The system uses constraint-based iterative repair, a …

We are interested in the problem faced by an agent with limited computational capabilities,
embedded in a complex environment with other agents and processes not under its control …

We examine the computational complexity of testing and finding small plans in probabilistic
planning domains with both flat and propositional representations. The complexity of plan …