Add to ORKGWhen planning in uncertain domains, it can be of great benefit if the planner is aware of the nature of the actions that it is dealing with. This paper investigates the use of statistical distributions of resource usage at the planning stage, and how these can be exploited to form robust plans. These statistical distributions are not always simple | they are a by-product of the execution process, formed by sub-programs that are used internally by the executive to accomplish the primitive action in hand. Techniques of forming these distributions are discussed in this paper, and methods for execution of such plans are also investigated and presented
In temporally uncertain domains, taking uncertainty into account while planning leads to problems wi...
The intent of this chapter is to review and discuss how uncertainty is handled in production plannin...
Algorithms for planning under uncertainty require accurate action models that explicitly cap-ture th...
This paper examines the problem of executing plans where there is uncertainty about resource consump...
A planning system must reason about the uncertainty of continuous variables in order to accurately p...
A planning system must reason about the uncertainty of continuous variables in order to accurately p...
AbstractUncertainty, inherent in most real-world domains, can cause failure of apparently sound clas...
The main focus of our work is the use of classical planning algorithms in service of more complex pr...
Attempts to apply classical planning techniques to realistic environments have met with two major d...
Our research area is planning under uncertainty, that is, making sequences of decisions in the face ...
We define the robustness of a sequential plan as the probability that it will execute successfully d...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
The main focus of our work is the use of classical planning algorithms in service of more complex pr...
Algorithms for planning under uncertainty require accurate action models that explicitly capture the...
Planning under uncertainty has been well studied, but usually the uncertainty is in action outcomes....
In temporally uncertain domains, taking uncertainty into account while planning leads to problems wi...
The intent of this chapter is to review and discuss how uncertainty is handled in production plannin...
Algorithms for planning under uncertainty require accurate action models that explicitly cap-ture th...
This paper examines the problem of executing plans where there is uncertainty about resource consump...
A planning system must reason about the uncertainty of continuous variables in order to accurately p...
A planning system must reason about the uncertainty of continuous variables in order to accurately p...
AbstractUncertainty, inherent in most real-world domains, can cause failure of apparently sound clas...
The main focus of our work is the use of classical planning algorithms in service of more complex pr...
Attempts to apply classical planning techniques to realistic environments have met with two major d...
Our research area is planning under uncertainty, that is, making sequences of decisions in the face ...
We define the robustness of a sequential plan as the probability that it will execute successfully d...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
The main focus of our work is the use of classical planning algorithms in service of more complex pr...
Algorithms for planning under uncertainty require accurate action models that explicitly capture the...
Planning under uncertainty has been well studied, but usually the uncertainty is in action outcomes....
In temporally uncertain domains, taking uncertainty into account while planning leads to problems wi...
The intent of this chapter is to review and discuss how uncertainty is handled in production plannin...
Algorithms for planning under uncertainty require accurate action models that explicitly cap-ture th...