This paper presents a real-time system that guides stroke patients during upper extremity rehabilitation. The system automatically modifies exercise parameters to account for the specific needs and abilities of different individuals. We describe a partially observable Markov decision process (POMDP) model of a rehabilitation exercise that can capture this form of customization. The system will ...

Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. The performance of these algorithms can be improved by eliminating unnecessary backups or concentrating on more important points in the ...

The ever increasing capabilities and complexity of sensor networks have led to an increased interest in sensor placement and observation planning problems. Many sensor placement and planning problems, however, lead to instances of the intractable classical planning problems or (similarly intractable) partially observable Markov decision processes. We consider the problem of planning sensor acti...

A batch production process that is initially in the in-control state can fail with constant failure rate to the out-of-control state. The probability that a unit is conforming if produced while the process is in control is constant and higher than the respective constant conformance probability while the process is out of control. When production ends, the units are inspected in the order they ...

We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSat) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSat problems and the solution of stochastic planning problems. This is the first work extending the planning...

Many sequential decision-making problems require an agent to reason about both multiple objectives and uncertainty regarding the environment’s state. Such problems can be naturally modelled as multi-objective partially observable Markov decision processes (MOPOMDPs). We propose optimistic linear support with alpha reuse (OLSAR), which computes a bounded approximation of the optimal solution set...

Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world application related to the problem of navigation. However, the best achievements of Learning Classifier Systems (LCS) in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reas...

Both humans and the sensors on an autonomous vehicle have limited sensing capabilities. When these limitations coincide with scenarios involving vulnerable road users, it becomes important to account for these limitations in the motion planner. For the scenario of an occluded pedestrian crosswalk, the speed of the approaching vehicle should be a function of the amount of uncertainty on the road...

We propose a node-removal/arc-reversal algorithm for influence diagram evaluation that includes reductions that allow an influence diagram to be solved by a generalization of the dynamic programming approach to solving partially observable Markov decision processes (POMDPs). Among its potential advantages, the algorithm allows a more flexible ordering of node removals, and a POMDPinspired appro...

an elliptical tank cross-section is formulated to explore and optimization method, based on a real-coded genetic algorithm to enhance the roll stability limit of a tank vehicle. a shape genetic algorithm optimization problem is applied to minimize the overturning moment imposed on the vehicle due to c.g. height of the liquid load, and lateral acceleration and cargo load shift . the minimization...