93,831 research outputs found
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
An improved bees algorithm local search mechanism for numerical dataset
Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any
optimum solutions for the problem mainly because of lack of accuracy. To solve this
issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this
study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods
Supplier Choice: Market Selection under Uncertainty.
Suppliers and Manufacturers generally have some say in which subset of all possible demand they will meet. In some cases that choice is implicit through pricing decisions and feature selection. Other times it is made explicitly by choosing only specific regions to stock a product in. This thesis includes models using both approaches and incorporates random demands. We present several methods for choosing a subset of all candidate customers given uncertain demands.
In this thesis we consider four models of demand selection. The first two research problems consider market selection, which has been studied in the literature. The Selective Newsvendor Problem (SNP) looks at a decision maker choosing a subset of candidate markets to serve, and then receiving revenues and paying newsvendor-type costs based on the selected collection. In this thesis we consider a generalization with normally distributed demands which includes a multi-period problem as a special case and develop both exact and heuristic algorithms to solve it. When demands are not normally distributed, the problem is considerably more complex and is in general NP-hard. We develop an approximation algorithm using sample average approximation and a rounding approach to efficiently solve the problem. In addition to the work on market selection, we propose two other models for demand selection. We study auctions as a tool for a supplier with a fixed capacity to allocate the limited supply to retailers with newsvendor-type costs. Finally, we present a model for a supplier who must ensure demand is met in all markets, but has the option to work with subsidiary suppliers to meet that demand.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120864/1/zstrinka_1.pd
Validating Network Value of Influencers by means of Explanations
Recently, there has been significant interest in social influence analysis.
One of the central problems in this area is the problem of identifying
influencers, such that by convincing these users to perform a certain action
(like buying a new product), a large number of other users get influenced to
follow the action. The client of such an application is a marketer who would
target these influencers for marketing a given new product, say by providing
free samples or discounts. It is natural that before committing resources for
targeting an influencer the marketer would be interested in validating the
influence (or network value) of influencers returned. This requires digging
deeper into such analytical questions as: who are their followers, on what
actions (or products) they are influential, etc. However, the current
approaches to identifying influencers largely work as a black box in this
respect. The goal of this paper is to open up the black box, address these
questions and provide informative and crisp explanations for validating the
network value of influencers.
We formulate the problem of providing explanations (called PROXI) as a
discrete optimization problem of feature selection. We show that PROXI is not
only NP-hard to solve exactly, it is NP-hard to approximate within any
reasonable factor. Nevertheless, we show interesting properties of the
objective function and develop an intuitive greedy heuristic. We perform
detailed experimental analysis on two real world datasets - Twitter and
Flixster, and show that our approach is useful in generating concise and
insightful explanations of the influence distribution of users and that our
greedy algorithm is effective and efficient with respect to several baselines
Mixed Integer Linear Programming for Feature Selection in Support Vector Machine
This work focuses on support vector machine (SVM) with feature selection. A
MILP formulation is proposed for the problem. The choice of suitable features
to construct the separating hyperplanes has been modelled in this formulation
by including a budget constraint that sets in advance a limit on the number of
features to be used in the classification process. We propose both an exact and
a heuristic procedure to solve this formulation in an efficient way. Finally,
the validation of the model is done by checking it with some well-known data
sets and comparing it with classical classification methods.Comment: 37 pages, 20 figure
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