Research in learning from demonstration can generally be grouped into either imitation learning or intention learning. In imitation learning, the goal is to imitate the observed behavior of an expert and is typically achieved using supervised learning techniques. In intention learning, the goal is to learn the intention that motivated the expert’s behavior and to use a planning algorithm to der...

Water resources design has widely used the average return period as a concept to inform management and communication of the risk of experiencing an exceedance event within a planning horizon. Even though nonstationarity is often apparent, in practice hydrologic design often mistakenly assumes that the probability of exceedance, p, is constant from year to year which leads to an average return p...

Robust autonomy, in the sense of performing tasks in the face of dynamic changes to the environment, requires that an autonomous system be capable of responding appropriately to such changes. One such response is to effectively adapt the allocation of resources from planning to execution. By adapting the resource allocation between deliberation and execution, an autonomous system can produce sh...

We address the problem of suboptimal behavior caused by short horizons during online POMDP planning. Our solution extends potential-based reward shaping from the related field of reinforcement learning to online POMDP planning in order to improve planning without increasing the planning horizon. In our extension, information about the quality of belief states is added to the function optimized ...

A typical discrete-time sequential decision problem involves a system whose state is assumed to evolve either deterministically or probabilistically over time-periods that are often called stages. This evolution is affected by the decisions a planner makes at the beginning of each stage after observing the system state. The decision maker’s goal then is to optimize some measure of system perfor...

We study an incremental network design problem, where in each time period of the planning horizon an arc can be added to the network and a maximum flow problem is solved, and where the objective is to maximize the cumulative flowover the entire planning horizon. After presenting twomixed integer programming (MIP) formulations for this NP-complete problem, we describe several heuristics and prov...

In this paper, we consider a class of nonlinear regulator optimal control problems with an infinite planning horizon. By assuming a specific feedback structure for the controller, the resultant regular problem is reduced to an optimal parameter selection problem. Since the reduced problem is on an infinite planning horizon, we construct a sequence of finite time optimal parameter selection prob...

Decision support systems are generally geared to short-term tactical decision makkrg. As an alternative, this paper develops a mathematical progrming model to evaluate long-term strategic alternatives in the context of farm-level agricultural production where a broiler farm considers long-term implications of diversification into commercial aquiculture. The model considers a ten-year strategic ...

The paper deals with a development of the rolling horizon approach introduced recently for grouping maintenance strategies of multi-component systems by integrating two efficient optimization algorithms. The proposed approach can help to construct an optimal maintenance planning with a given availability constraint under limited repairmen. Thanks to the rolling horizon spirit, the maintenance p...