The identiication of evolutionary related (homologous) proteins , is a key problem in molecular biology. Here we present a inductive logic programming based method, Homology Induction (HI), which acts as a lter for existing sequence similarity searches to improve their performance in the detection of remote protein homologies. HI performs a PSI-BLAST search to generate positive, negative, and u...

In this paper we present the problem of combining optimal control with efficient information gathering in an uncertain environment. We assume that the decision maker has the ability to choose among a discrete set of sources of information, where the outcome of each source is stochastic. Different sources and outcomes determine a reduction of uncertainty, expressed in terms of constraints on sys...

Standard algorithms for finding the short­ est path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are determinis­ tic. We consider the problem of uncertain edge costs, with potential probabilistic de­ pendencies among the costs. Although these dependencies violate the standard dynamic­ programming decomposition, we identify a weaker stocha...

The paper presents a global optimization method to compute the minimum limit load factor of trusses under the unknown-but-bounded load uncertainty. We assume that the external forces consist of a part proportional to a load factor and a part that is uncertain around its nominal value. The worst-case limit load factor is introduced as the smallest limit load factor realized with some uncertain p...

Optimization problems due to noisy data are usually solved using stochastic programming or robust optimization approaches. Both requiring the explicit characterization of an uncertainty set that models the nature of the noise. Such approaches tightly depend on the modeling of the uncertainty set. In this paper, we introduce a framework that implicitly models the uncertain data. We define the ge...

Standard algorithms for nding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are determinis-tic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these dependencies violate the standard dynamic-programming decomposition, we identify a weaker stochastic con...

Today’s business has inevitably been set in the global supply chain management environment. International transportation has never played such an important role in the global supply chain network, because movement of shipments from one country to another tends to be more frequent than ever before. This paper studies international transportation problems experienced by an international transport...

In this paper we analyze the electricity portfolio problem of a big consumer in a multi-stage stochastic programming framework. Stochasticity enters the model via the uncertain spot price process and is represented by a scenario tree. The decision that has to be taken is how much energy should be bought in advance, and how large the exposition to the uncertain spot market, as well as the relati...

Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this paper, we introduce a framework that considers the uncertain dat...

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and underactuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty i...