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Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.
An Asynchronous Branch-and-Bound DCOP Algorithm

*Correspondence: aljadda@uga.edu 1Department of Computer Science, University of Georgia, Athens,GA, USA Full list of author information is available at the end of the article †Equal contributor Abstract Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in machine learning and data mining, demonstrates this limitation when the training data consists of random variables, each of them has a large set of possible values. In the big data era, one would expect new extensions to the existing PGMs to handle the massive amount of data produced these days by computers, sensors and other electronic devices. With hierarchical data data that is arranged in a treelike structure with several levels one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian Networks become infeasible for representing the probability distributions. In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves perfect precision of 1.0 and high recall of 0.93 when it is used as multi-label classifier for the annotation of mass spectrometry data. On another data set of 1.5 billion search logs provided by CareerBuilder.com the model was able to predict latent semantic relationships between search keywords with accuracy up to 0.80.
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model

Deep learning (DL) became the method of choice in recent years for solving problems ranging from object recognition and speech recognition to robotic perception and human disease prediction. In this paper, we present a hybrid architecture of convolutional neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of actions that nonlinearly transforms an input shape or distribution into a target shape or distribution with the same support. While such a framework can be useful in a variety of problems such as robotic path planning, sequential decision-making in games and identifying material processing pathways to achieve desired microstructures, this paper focuses on controlling fluid deformations in a microfluidic channel by deliberately placing a sequence of pillars, which has a significant impact on manufacturing for biomedical and textile applications where highly targeted shapes are desired. We propose an architecture which simultaneously predicts the intermediate shape lying in the nonlinear transformation pathway between the undeformed and desired flow shape, then learns the causal action–the single pillar which results in the deformation of the flow–one at a time. The learning of stage-wise transformations provides deep insights into the physical flow deformation. Results show that under the current framework, our model is able to predict a sequence of pillars that reconstructs the flow shape which highly resembles the desired shape.

Deep Action Sequence Learning for Causal Shape Transformation