Most practical work on AI planningsystems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little an...
This paper presents an algorithm, called JustIn-Case Scheduling, for building robust schedules that tend not to break. The algorithm implements the common sense idea of being prep...
Sometimes inferences made at some specific time are valid at other times, too. In model-based diagnosis and monitoring as well as qualitative simulation inferences are often re-do...
An approach to nonmonotonic inference, based on preference orderings between possible worlds or states of affairs, is presented. We begin with an extant weak theory of default con...
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the mill...
Andrew J. Davenport, Edward P. K. Tsang, Chang J. ...
We consider using machine learning techniques to help understand a large software system. In particular, we describe how learning techniques can be used to reconstruct abstract Da...
This paper presents a plan-based architecture for response generation in collaborative consultation dialogues, with emphasis on cases in which the system (consultant) and user (ex...
In this paper, we describe the partially observable Markov decision process pomdp approach to nding optimal or near-optimal control strategies for partially observable stochastic ...
Anthony R. Cassandra, Leslie Pack Kaelbling, Micha...