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Multi-objective vehicle routing with automated negotiation. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03329-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractThis paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders for each company, tries to find the set of all truck load exchanges that are Pareto-optimal and individually rational. Unlike existing approaches, it does this without relying on any kind of trusted central server, so the companies do not need to disclose their private cost models to anyone. The idea is that the companies can then use automated negotiation techniques to negotiate which of these truck load exchanges will truly be carried out. Furthermore, this paper presents a new, multi-objective, variant of And/Or search that forms part of our approach, and it presents experiments based on real-world data, as well as on the commonly used Li & Lim data set. These experiments show that our algorithm is able to find hundreds of solutions within a matter of minutes. Finally, this paper presents an experiment with several state-of-the-art negotiation algorithms to show that the combination of our search algorithm with automated negotiation is viable.
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Wilson N, Marinescu R. An axiomatic framework for influence diagram computation with partially ordered preferences. Int J Approx Reason 2020. [DOI: 10.1016/j.ijar.2020.06.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Cano A, Gómez-Olmedo M, Moral S, Moral-García S. MPE Computation in Bayesian Networks Using Mini-Bucket and Probability Trees Approximation. INT J UNCERTAIN FUZZ 2020. [DOI: 10.1142/s0218488520500348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Given a set of uncertain discrete variables with a joint probability distribution and a set of observations for some of them, the most probable explanation is a set or configuration of values for non-observed variables maximizing the conditional probability of these variables given the observations. This is a hard problem which can be solved by a deletion algorithm with max marginalization, having a complexity similar to the one of computing conditional probabilities. When this approach is unfeasible, an alternative is to carry out an approximate deletion algorithm, which can be used to guide the search of the most probable explanation, by using A* or branch and bound (the approximate+search approach). The most common approximation procedure has been the mini-bucket approach. In this paper it is shown that the use of probability trees as representation of potentials with a pruning of branches with similar values can improve the performance of this procedure. This is corroborated with an experimental study in which computation times are compared using randomly generated and benchmark Bayesian networks from UAI competitions.
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Affiliation(s)
- Andrés Cano
- Dpto. Computer Science and Artificial Intelligence, University of Granada, Granada, 18071, Spain
| | - Manuel Gómez-Olmedo
- Dpto. Computer Science and Artificial Intelligence, University of Granada, Granada, 18071, Spain
| | - Serafín Moral
- Dpto. Computer Science and Artificial Intelligence, University of Granada, Granada, 18071, Spain
| | - Serafín Moral-García
- Dpto. Computer Science and Artificial Intelligence, University of Granada, Granada, 18071, Spain
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Shi N, Zhu Z, Tang K, Parker D, He S. ATEN: And/Or tree ensemble for inferring accurate Boolean network topology and dynamics. Bioinformatics 2020; 36:578-585. [PMID: 31368481 DOI: 10.1093/bioinformatics/btz563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 07/02/2019] [Accepted: 07/24/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Inferring gene regulatory networks from gene expression time series data is important for gaining insights into the complex processes of cell life. A popular approach is to infer Boolean networks. However, it is still a pressing open problem to infer accurate Boolean networks from experimental data that are typically short and noisy. RESULTS To address the problem, we propose a Boolean network inference algorithm which is able to infer accurate Boolean network topology and dynamics from short and noisy time series data. The main idea is that, for each target gene, we use an And/Or tree ensemble algorithm to select prime implicants of which each is a conjunction of a set of input genes. The selected prime implicants are important features for predicting the states of the target gene. Using these important features we then infer the Boolean function of the target gene. Finally, the Boolean functions of all target genes are combined as a Boolean network. Using the data generated from artificial and real-world gene regulatory networks, we show that our algorithm can infer more accurate Boolean network topology and dynamics from short and noisy time series data than other algorithms. Our algorithm enables us to gain better insights into complex regulatory mechanisms of cell life. AVAILABILITY AND IMPLEMENTATION Package ATEN is freely available at https://github.com/ningshi/ATEN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ning Shi
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Ke Tang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - David Parker
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.,Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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Sufficient and necessary conditions for solution finding in valuation-based systems. Int J Approx Reason 2019. [DOI: 10.1016/j.ijar.2019.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Pan Y, Dong Y, Zhou J, Hallen M, Donald BR, Zeng J, Xu W. cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design. J Comput Biol 2016; 23:737-49. [PMID: 27154509 PMCID: PMC5586165 DOI: 10.1089/cmb.2015.0234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches.
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Affiliation(s)
- Yuchao Pan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Yuxi Dong
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Jingtian Zhou
- Department of Pharmacology and Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Mark Hallen
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Wei Xu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
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Zhou Y, Wu Y, Zeng J. Computational Protein Design Using AND/OR Branch-and-Bound Search. J Comput Biol 2016; 23:439-51. [DOI: 10.1089/cmb.2015.0212] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Yichao Zhou
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Yuexin Wu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
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Morariu VI, Harwood D, Davis LS. Tracking people's hands and feet using mixed network AND/OR search. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013; 35:1248-1262. [PMID: 23520262 DOI: 10.1109/tpami.2012.187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We describe a framework that leverages mixed probabilistic and deterministic networks and their AND/OR search space to efficiently find and track the hands and feet of multiple interacting humans in 2D from a single camera view. Our framework detects and tracks multiple people's heads, hands, and feet through partial or full occlusion; requires few constraints (does not require multiple views, high image resolution, knowledge of performed activities, or large training sets); and makes use of constraints and AND/OR Branch-and-Bound with lazy evaluation and carefully computed bounds to efficiently solve the complex network that results from the consideration of interperson occlusion. Our main contributions are: 1) a multiperson part-based formulation that emphasizes extremities and allows for the globally optimal solution to be obtained in each frame, and 2) an efficient and exact optimization scheme that relies on AND/OR Branch-and-Bound, lazy factor evaluation, and factor cost sensitive bound computation. We demonstrate our approach on three datasets: the public single person HumanEva dataset, outdoor sequences where multiple people interact in a group meeting scenario, and outdoor one-on-one basketball videos. The first dataset demonstrates that our framework achieves state-of-the-art performance in the single person setting, while the last two demonstrate robustness in the presence of partial and full occlusion and fast nontrivial motion.
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Affiliation(s)
- Vlad I Morariu
- Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA.
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Netzer A, Grubshtein A, Meisels A. Concurrent forward bounding for distributed constraint optimization problems. ARTIF INTELL 2012. [DOI: 10.1016/j.artint.2012.09.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Silberstein M, Weissbrod O, Otten L, Tzemach A, Anisenia A, Shtark O, Tuberg D, Galfrin E, Gannon I, Shalata A, Borochowitz ZU, Dechter R, Thompson E, Geiger D. A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees. Bioinformatics 2012; 29:197-205. [PMID: 23162081 DOI: 10.1093/bioinformatics/bts658] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes. RESULTS Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome. AVAILABILITY Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website. CONTACT omerw@cs.technion.ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Silberstein
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
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