Interactive POMDP (I-POMDP) is a stochastic optimization framework for sequential planning in multiagent settings. It represents a direct generalization of POMDPs to multiagent cases. Expectedly, I-POMDPs also suffer from a high computational complexity, thereby motivating approximation schemes. In this paper, we propose using a particle filtering algorithm for approximating the I-POMDP belief ...

Context. The order in which requirements are implemented affects the delivery of value to the end-user, but it also depends on technical constraints and resource availability. The outcome of requirements prioritization is a total ordering of requirements that best accommodates the various kinds of constraints and priorities. During requirements prioritization, some decisions on the relative imp...

We present an efficient GPU-based implementation of the Projected Tetrahedra (PT) algorithm. By reducing most of the CPU–GPU data transfer, the algorithm achieves interactive frame rates (up to 2.0 M Tets/s) on current graphics hardware. Since no topology information is stored, it requires substantially less memory than recent interactive ray casting approaches. The method uses a two-pass gpu a...

AlgoWBIs has been developed to support algorithm learning. The goal to develop this tool is to empower educators and learners with an interactive learning tool to improving algorithm’s skills. The paper focuses on how to trim down the challenges ofalgorithm learning andalso discusses how this tool will improve the effectiveness of computer facilitated interactive learning and will support in re...

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We use an interactive genetic algorithm to divide and conquer large traveling salesper-son problems. Current genetic algorithm approaches are computationally intensive and may not produce acceptable tours within the time available. Instead of applying a genetic algorithm to the entire problem, we let the user interactively decompose a problem into subproblems, let the genetic algorithm separate...

Interactive verification is a new problem, which is closely related to active learning, but aims to query as many positive instances as possible within some limited query budget. We point out the similarity between interactive verification and another machine learning problem called contextual bandit. The similarity allows us to design interactive verification approaches from existing contextua...