Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment algorithm for multi-target air-borne threats.

In many real-world planning scenarios, the users are interested in optimizing multiple objectives (such as makespan and execution cost), but are unable to express their exact tradeoff between those objectives. When a planner encounters such partial preference models, rather than look for a single optimal plan, it needs to present the pareto set of plans and let the user choose from them. This idea of presenting the full pareto set is fraught with both computational and user-interface challenges. To make it practical, we propose the approach of finding a representative subset of the pareto set. We measure the quality of this representative set using the Integrated Convex Preference (ICP) model, originally developed in the OR community. We implement several heuristic approaches based on the Metric-LPG planner to find a good solution set according to this measure. We present empirical results demonstrating the promise of our approach.

This paper deals with  multiobjective optimization in the context of multiattribute utility theory. The alternatives (feasible solutions) are seen as elements of a product set of attributes and preferences over solutions are represented by generalized additive decomposable (GAI) utility functions modeling individual preferences or criteria. Due to decomposability, utility vectors attached to solutions can be compiled into a graphical structure closely related to junction trees, the so-called GAI net. We first show how the structure of the GAI net can be used to determine efficiently the exact set of Pareto-optimal solutions in a product set and provide numerical tests on random instances. Since the exact determination of the Pareto set is intractable in worst case, we propose a near admissible algorithm with performance guarantee, exploiting the GAI structure to approximate the set of Pareto optimal solutions. We present numerical experimentations, showing that both utility decomposition and approximation significantly improve resolution times in multiobjective search problems.

The problem of environment design considers a setting in which an interested party aims to influence an agent's decisions by making limited changes to the agent's environment.  Zhang and Parkes [2008] first introduced the environment design concept for a specific problem in the Markov Decision Process setting. In this paper, we present a general framework for the formulation and solution of environment design problems. We consider both the case in which the agent's local decision model is known and partially unknown to the interested party, and illustrate the framework and results on a linear programming setting.  For the latter problem, we formulate an active, indirect elicitation method and provide conditions for convergence and logarithmic convergence. We relate to the problem of inverse optimization and also offer a game-theoretic interpretation of our methods.

Real-time dynamic programming (RTDP) solves Markov decision processes (MDPs) when the initial state is restricted, by focusing dynamic programming on the envelope of states reachable from an initial state set. RTDP often provides performance guarantees without visiting the entire state space.  Building on RTDP, recent work has sought to improve its efficiency through various optimizations, including maintaining upper and lower bounds to both govern trial termination and prioritize state exploration. In this work, we take a Bayesian perspective on these upper and lower bounds and use a value of perfect information (VPI) analysis to govern trial termination and exploration in a novel algorithm we call VPI-RTDP.  VPI-RTDP leads to an improvement over state-of-the-art RTDP methods, empirically yielding up to a three-fold reduction in the amount of time and number of visited states required to achieve comparable policy performance.

Feature selection can be conducted in a supervised or unsupervised manner, in terms of whether the label information We consider the problem of semi-supervised feature is utilized to guide the selection of relevant features. Generally, selection, where we are given a small amount supervised feature selection methods require a large of labeled examples and a large amount of unlabeled amount of labeled training data. It however could fail to identify examples. Since a small number of labeled the relevant features that are discriminative to different samples are usually insufficient for identifying the classes, provided the number of labeled samples is small. On relevant features, the critical problem arising from the other hand, while unsupervised feature selection methods semi-supervised feature selection is how to take could work well with unlabeled training data, they ignore advantage of the information underneath the unlabeled the label information and therefore are often unable to identify data. To address this problem, we propose the discriminative features. Given the high cost in manually a novel discriminative semi-supervised feature labeling data, and at the same time abundant unlabeled selection method based on the idea of manifold data are often easily accessible, it is desirable to develop feature regularization. The proposed method selects selection methods that are capable of exploiting both labeled features through maximizing the classification margin and unlabeled data.

Kernel methods have been applied successfully in many applications. The kernel matrix plays an important role in kernel-based learning methods, but the ideal kernel matrix is usually unknown in practice and needs to be estimated. In this paper, we propose to directly learn the ideal kernel matrix (called the optimal neighborhood kernel matrix) from a pre-specified kernel matrix for improved classification performance. We assume that the pre-specified kernel matrix generated from the specific application is a noisy observation of the ideal one. The resulting optimal neighborhood kernel matrix is shown to be the summation of the pre-specified kernel matrix and a rank-one matrix. We formulate the problem of learning the optimal neighborhood kernel as a constrained quartic problem, and propose to solve it using two methods: level method and constrained gradient descent. Empirical results on several benchmark data sets demonstrate the efficiency and effectiveness of the proposed algorithms.

Sparse coding is an unsupervised learning algorithm for finding concise, slightly higher-level representations of inputs, and has been successfully applied to self-taught learning, where the goal is to use unlabeled data to help on a supervised learning task, even if the unlabeled data cannot be associated with the labels of the supervised task (Raina et al., 2007). However, sparse coding uses a Gaussian noise model and a quadratic loss function, and thus performs poorly if applied to binary valued, integer valued, or other non-Gaussian data, such as text. Drawing on ideas from generalized linear models (GLMs), we present a generalization of sparse coding to learning with data drawn from any exponential family distribution (such as Bernoulli, Poisson, etc). This gives a method that we argue is much better suited to model other data types than Gaussian. We present an algorithm for solving the L1-regularized optimization problem defined by this model, and show that it is especially efficient when the optimal solution is sparse. We also show that the new model results in significantly improved self-taught learning performance when applied to text classification and to a robotic perception task.

Calendar management tools assist users with coordinating their daily life. Different tasks have to be scheduled according to the user preferences. In many cases, tasks are at different locations and travel times have to be considered. Therefore, these kinds of calendar management problems can be regarded as spatio-temporal optimisation problems and are often variants of traveling salesman problems (TSP) or vehicle routing problems. While standard TSPs require a solution to include all tasks, prize-collecting TSPs are more suited for calendar management problems as they require a solution that optimises the total sum of prizes we assigned to tasks at different locations. If we now add time windows that limit when tasks can occur, these prize-collecting TSPs with time windows (TW-TSP) are excellent abstractions of spatio-temporal optimisation problems such as calendar management. Due to the inherent complexity of TW-TSPs, the existing literature considers mainly approximation algorithms or special cases. We present a novel algorithm for TW-TSPs that enables us to find the optimal solution to TW-TSP problems occurring in real-world calendar management applications efficiently. Our algorithm is a fixed-parameter tractable algorithm that depends on the maximal number of tasks that can be revisited from some other task, a parameter which is small in the application scenario we consider.

Kidneys are the most prevalent organ transplants, but demand dwarfs supply. Kidney exchanges enable willing but incompatible donor-patient pairs to swap donors. These swaps can include cycles longer than two pairs as well, and chains triggered by altruistic donors. Current kidney exchanges address clearing(deciding who gets kidneys from whom) as an offline problem: they optimize the current batch. In reality, clearing is an online problem where patient-donor pairs and altruistic donors appear and expire over time. In this paper, we study trajectory-based online stochastic optimization algorithms (which use a recent scalable optimal offline solver as a subroutine) for this. We identify tradeoffs in these algorithms between different parameters. We also uncover the need to set the batch size that the algorithms consider an atomic unit. We develop an experimental methodology for setting these parameters, and conduct experiments on real and generated data. We adapt the REGRETS algorithm of Bent and van Hentenryck for the setting. We then develop a better algorithm. We also show that the AMSAA algorithm of Mercier and van Hentenryck does not scale to the nationwide level. Our best online algorithm saves significantly more lives than the current practice of solving each batch separately.