Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case...

The problem of robust line planning requests for a set of origin-destination paths (lines) along with their frequencies in an underlying railway network infrastructure, which are robust to fluctuations of real-time parameters of the solution. In this work, we investigate a variant of robust line planning stemming from recent regulations in the railway sector that introduce competition and free ...

347 Robust Path Planning in the Plane Fernando De la Rosa, Christian Laugier and Jos e N ajera Abstract|This work presents an approach to plan motion strategies for robotics tasks constrained by uncertainty in position, orientation and control. Our approach operates in a (x; y; ) con guration space and it combines two local functions: a contact-based attraction function and an exploration funct...

Real-world Decision-Theoretic Planning (DTP) is a very challenging research field. A common approach is to model such problems as Markov Decision Problems (MDP) and use dynamic programming techniques. Yet, two major difficulties arise: 1dynamic programming does not scale with the number of tasks, and 2the probabilistic model may be uncertain, leading to the choice of unsafe policies. We build h...

The New Millennium Remote Agent (NMRA) will be the first on-board AI svstem to control an actual spacecraft. The ~pacecraft domain raises a number of challenges for planning and execution, ranging from extended agency and long-term planning to dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a ...

Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case...

This paper describes a novel approach to planning which takes advantage of decision theory to greatly improve robustness in an uncertain environment. We present an algorithm which computes conditional plans of maximum expected utility. This algorithm relies on a representation of the search space as an AND/OR tree and employs a depth-limit to control computation costs. A numeric robustness fact...

Recent breakthroughs in Transmission Network Expansion Planning (TNEP) have demonstrated that the use of robust optimization, as opposed to stochastic programming methods, renders the expansion planning problem computationally tractable for real systems. State-of-the-art robust methods treat this problem as stationary throughout the study period, with all the required investments on expansion o...