Download or read book Exploiting Problem Structure in Distributed Constraint Optimisation with Complex Local Problems written by David A. Burke and published by . This book was released on 2008 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today{u2019}s world, networks are ubiquitous, e.g. supply chain networks, computational grids, telecom networks and social networks. In many situations, the individual entities or {u2018}agents{u2019} that make up these networks need to coordinate their actions in order to make some group decision. Distributed Constraint Optimisation (DisCOP) considers algorithms explicitly designed to handle such problems, searching for globally optimal solutions while balancing communication load with processing time. However, most research on DisCOP algorithms only considers simplified problems where each agent has a single variable, i.e. only one decision to make. This is justified by two problem reformulations, by which any DisCOP with complex local problems (multiple variables per agent) can be transformed to give exactly one variable per agent. The restriction to a single variable has been an impediment to practical applications of DisCOP, since few problems naturally fit into that framework. Furthermore, there has been no research showing whether the standard reformulations are actually effective. In this dissertation, we address this issue. We evaluate the standard reformulation techniques and show that one of them is rarely competitive. We demonstrate that explicitly considering the structure of DisCOPs with complex local problems in the design of algorithms allows problems to be solved more efficiently. In particular, we show the benefits of distinguishing between the public (between agents) and private (within one agent) search spaces. Furthermore, we identify the public variables (those involved in inter-agent constraints) as a critical factor affecting how DisCOPs with complex local problems are solved. From this, we propose a number of novel techniques based on interchangeability, symmetry, relaxation, aggregation and domain reduction. These methods exploit the problem structure and act on the public variables to enable more efficient solving of Dis-COPs with complex local problems, thus greatly extending the range of problems that can be solved using DisCOP algorithms.

Download or read book Exploiting the Structure of Distributed Constraint Optimization Problems with Applications in Smart Grids written by Ferdinando Fioretto and published by . This book was released on 2016 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent multi-agent architectures to govern the agents’ autonomous behavior in a Multi-Agent System (MAS), where several agents coordinate with each other to optimize a global cost function. They represent a powerful approach to the description and resolution of many practical problems and serve several applications such as distributed scheduling, coordination of unmanned air vehicles, smart grid electric networks, and sensor networks. Typical real world applications are characterized by complex dynamics and interactions among a large number of entities, which translate into hard combinatorial problems, posing significant challenges from a computational point of view. The adoption of DCOPs on large instances of problems faces two main limitations: (1) Modeling limitations, as current resolution methods detach the model from the resolution process, imposing limiting assumptions on the capabilities of an agent (e.g., that it controls a single variable of the problem, and that it operates solely on the resolution of a global problem, ignoring the presence of private objectives); and (2) Solving capabilities, as the inability of current approaches to capitalize on the presence of structural information which may allow incoherent/unnecessary data to reticulate among the agents as well as to exploit latent structure of the agent’s local problems, and/or of the problem of interest. The objective of the proposed dissertation is to address such limitations, studying how to adapt and integrate insights gained from centralized solving techniques, and from Graphic Processing Units (GPUs) parallel architectures, in order to design practical algorithms to efficiently solve large, complex, DCOPs, enabling their use for the resolution of real-world complex problems, such as those arising within the smart electricity grid context. To do so, we hypothesize that one can exploit the structure of DCOPs in both problem modeling and problem resolution phases.

Download or read book AIxIA 2021 Advances in Artificial Intelligence written by Stefania Bandini and published by Springer Nature. This book was released on 2022-07-18 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book constitutes revised selected papers from the refereed proceedings of the 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021, which was held virtually in December 2021. The 36 full papers included in this book were carefully reviewed and selected from 58 submissions; the volume also contains 12 extended and revised workshop contributions. The papers were organized in topical sections as follows: Planning and strategies; constraints, argumentation, and logic programming; knowledge representation, reasoning, and learning; natural language processing; AI for content and social media analysis; signal processing: images, videos and speech; machine learning for argumentation, explanation, and exploration; machine learning and applications; and AI applications.

Download or read book Principles and Practice of Multi Agent Systems written by Nirmit Desai and published by Springer Science & Business Media. This book was released on 2012-01-09 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2010, held in Kolkata, India, in November 2010. The 18 full papers presented together with 15 early innovation papers were carefully reviewed and selected from over 63 submissions. They focus on practical aspects of multiagent systems and cover topics such as agent communication, agent cooperation and negotiation, agent reasoning, agent-based simulation, mobile and semantic agents, agent technologies for service computing, agent-based system development, ServAgents workshop, IAHC workshop, and PRACSYS workshop.

Download or read book Encyclopedia of Business Analytics and Optimization written by Wang, John and published by IGI Global. This book was released on 2014-02-28 with total page 2862 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Download or read book A Class of Algorithms for Distributed Constraint Optimization written by Adrian Petcu and published by IOS Press. This book was released on 2009 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Addresses three major issues that arise in Distributed Constraint Optimization Problems (DCOP): efficient optimization algorithms, dynamic and open environments, and manipulations from self-interested users. This book introduces a series of DCOP algorithms, which are based on dynamic programming.

Download or read book Algorithmic and Domain Centralization in Distributed Constraint Optimization Problems written by John P. Davin and published by . This book was released on 2005 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "A class of problems known as Distributed Constraint Optimization Problems (DCOP) has become a growing research interest in computer science because of its difficulty (NP-Complete) and many real-world applications (meeting scheduling, sensor networks, military planning). In this thesis we identify two types of centralization relevant to DCOPs: algorithmic centralization, in which a DCOP algorithm actively centralizes part (or all) of the problem structure, and domain centralization, in which inherent centralization already exists in the domain specification. We explore algorithmic centralization by empirically studying Adopt and OptAPO, two DCOP algorithms which differ in the amount of centralization they use. Our results show that centralizing a problem's structure decreases communication overhead, but increases local computation. We compare the algorithms through our contribution of a new performance metric, Cycle-Based Runtime, which takes both communication costs and local computation time into account. We then explore domain centralization by studying meeting scheduling, which has problem structure clustered at scheduling agents. We present a novel variant of Adopt, called AdoptMVA, which uses a centralized search within agents to take advantage of the partially centralized structure. We show that when agent ordering is controlled for, AdoptMVA outperforms Adopt in situations where communication costs are high. We contribute a Branch & Bound search heuristic which works well for meeting scheduling problems with multiple variables per agent. We also empirically experiment with meeting scheduling, showing that meeting size is in some cases a better indicator of solution difficulty than the number of agents in a problem."

Download or read book Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

Download or read book Principles and Practice of Constraint Programming written by John Hooker and published by Springer. This book was released on 2018-08-22 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Principles and Practice of Constraint Programming, CP 2018, held in Lille, France, in August 2018.The 41 full and 9 short papers presented in this volume were carefully reviewed and selected from 114 submissions. They deal with all aspects of computing with constraints including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, scheduling, configuration, and planning. The papers were organized according to the following topics/tracks: main technical track; applications track; CP and data science; CP and music; CP and operations research; CP, optimization and power system management; multiagent and parallel CP; and testing and verification.

Download or read book Evolutionary Computation in Combinatorial Optimization written by Francisco Chicano and published by Springer. This book was released on 2016-03-15 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 17 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic algorithms, variable neighborhood search, particle swarm optimization, hyperheuristics, mat-heuristic and other adaptive approaches. Applications included both traditional domains, such as graph coloring, vehicle routing, the longest common subsequence problem, the quadratic assignment problem; and new(er) domains such as the traveling thief problem, web service location, and finding short addition chains. The theoretical studies involved fitness landscape analysis, local search and recombination operator analysis, and the big valley search space hypothesis. The consideration of multiple objectives, dynamic and noisy environments was also present in a number of articles.

Download or read book Ant Colony Optimization written by Marco Dorigo and published by MIT Press. This book was released on 2004-06-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Download or read book Hybrid Algorithms for Distributed Constraint Satisfaction written by David Alexander James Lee and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is dividedinto several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problemtogether with the constraints relating its own variables (intra-agent constraints) andthe constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting schedulingand sensor networks. Existing approaches to Distributed Constraint Satisfaction canbe mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local searchalgorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completenessof systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate tosolve DisCSPs. Three new hybrid approaches which combine both systematic and localsearch for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyband; (iii) Multi-HDCS. These approaches use distributed local search to gather informationabout difficult variables and best values in the problem. Distributed systematic search isrun with a variable and value ordering determined by the knowledge learnt through localsearch. Two implementations of each of the three approaches are presented: (i) using penaltiesas the distributed local search strategy and; (ii) using breakout as the distributed localsearch strategy. The three approaches are evaluated on several problem classes. Theempirical evaluation shows these distributed hybrid approaches to significantly outperformboth systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributedproblem solving with distributed systematic search taking less time to run by using theinformation learnt by distributed local search. As a consequence, larger problems can nowbe solved in a more practical timeframe.

Download or read book ECAI 2014 written by T. Schaub and published by IOS Press. This book was released on 2014-08 with total page 1264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The role of artificial intelligence (AI) applications in fields as diverse as medicine, economics, linguistics, logical analysis and industry continues to grow in scope and importance. AI has become integral to the effective functioning of much of the technical infrastructure we all now take for granted as part of our daily lives. This book presents the papers from the 21st biennial European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. Included here are the 158 long papers and 94 short papers selected for presentation at the conference. Many of the papers cover the fields of knowledge representation, reasoning and logic as well as agent-based and multi-agent systems, machine learning, and data mining. The proceedings of PAIS 2014 and the PAIS System Demonstrations are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.

Download or read book Readings in Agents written by Michael N. Huhns and published by Morgan Kaufmann. This book was released on 1998 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects the most significant literature on agents in an attempt top forge a broad foundation for the field. Includes papers from the perspectives of AI, databases, distributed computing, and programming languages. The book will be of interest to programmers and developers, especially in Internet areas.

Download or read book Principles and Practice of Constraint Programming written by Gilles Pesant and published by Springer. This book was released on 2015-08-12 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 21st International Conference on Principles and Practice of Constraint Programming, CP 2015, held in Cork, Ireland, in August/September 2015. This edition of the conference was part of George Boole 200, a celebration of the life and work of George Boole who was born in 1815 and worked at the University College of Cork. It was also co-located with the 31st International Conference on Logic Programming (ICLP 2015). The 48 revised papers presented together with 3 invited talks and 16 abstract papers were carefully selected from numerous submissions. The scope of CP 2014 includes all aspects of computing with constraints, including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, schedulling, configuration, and planning.

Download or read book Essays in Large Scale Optimization Algorithm and Its Application in Revenue Management written by Mingxi Zhu (Researcher in optimization algorithms) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on the large-scale optimization algorithm and its application in revenue management. It comprises three chapters. Chapter 1, Managing Randomization in the Multi-Block Alternating Direction Method of Multipliers for Quadratic Optimization, provides theoretical foundations for managing randomization in the multi-block alternating direction method of multipliers (ADMM) method for quadratic optimization. Chapter 2, How a Small Amount of Data Sharing Benefits Distributed Optimization and Learning, presents both the theoretical and practical evidences on sharing a small amount of data could hugely benefit distributed optimization and learning. Chapter 3, Dynamic Exploration and Exploitation: The Case of Online Lending, studies exploration/ exploitation trade-offs, and the value of dynamic extracting information in the context of online lending. The first chapter is a joint work with Kresimir Mihic and Yinyu Ye. The Alternating Direction Method of Multipliers (ADMM) has gained a lot of attention for solving large-scale and objective-separable constrained optimization. However, the two-block variable structure of the ADMM still limits the practical computational efficiency of the method, because one big matrix factorization is needed at least once even for linear and convex quadratic programming. This drawback may be overcome by enforcing a multi-block structure of the decision variables in the original optimization problem. Unfortunately, the multi-block ADMM, with more than two blocks, is not guaranteed to be convergent. On the other hand, two positive developments have been made: first, if in each cyclic loop one randomly permutes the updating order of the multiple blocks, then the method converges in expectation for solving any system of linear equations with any number of blocks. Secondly, such a randomly permuted ADMM also works for equality-constrained convex quadratic programming even when the objective function is not separable. The goal of this paper is twofold. First, we add more randomness into the ADMM by developing a randomly assembled cyclic ADMM (RAC-ADMM) where the decision variables in each block are randomly assembled. We discuss the theoretical properties of RAC-ADMM and show when random assembling helps and when it hurts, and develop a criterion to guarantee that it converges almost surely. Secondly, using the theoretical guidance on RAC-ADMM, we conduct multiple numerical tests on solving both randomly generated and large-scale benchmark quadratic optimization problems, which include continuous, and binary graph-partition and quadratic assignment, and selected machine learning problems. Our numerical tests show that the RAC-ADMM, with a variable-grouping strategy, could significantly improve the computation efficiency on solving most quadratic optimization problems. The second chapter is a joint work with Yinyu Ye. Distributed optimization algorithms have been widely used in machine learning and statistical estimation, especially under the context where multiple decentralized data centers exist and the decision maker is required to perform collaborative learning across those centers. While distributed optimization algorithms have the merits in parallel processing and protecting local data security, they often suffer from slow convergence compared with centralized optimization algorithms. This paper focuses on how small amount of data sharing could benefit distributed optimization and learning for more advanced optimization algorithms. Specifically, we consider how data sharing could benefit distributed multi-block alternating direction method of multipliers (ADMM) and preconditioned conjugate gradient method (PCG) with application in machine learning tasks of linear and logistic regression. These algorithms are commonly known as algorithms between the first and the second order methods, and we show that data share could hugely boost the convergence speed for this class of the algorithms. Theoretically, we prove that a small amount of data share leads to improvements from near-worst to near-optimal convergence rate when applying ADMM and PCG methods to machine learning tasks. A side theory product is the tight upper bound of linear convergence rate for distributed ADMM applied in linear regression. We further propose a meta randomized data-sharing scheme and provide its tailored applications in multi-block ADMM and PCG methods in order to enjoy both the benefit from data-sharing and from the efficiency of distributed computing. From the numerical evidences, we are convinced that our algorithms provide good quality of estimators in both the least square and the logistic regressions within much fewer iterations by only sharing 5% of pre-fixed data, while purely distributed optimization algorithms may take hundreds more times of iterations to converge. We hope that the discovery resulted from this paper would encourage even small amount of data sharing among different regions to combat difficult global learning problems. The third chapter is a joint work with Haim Mendelson. This paper studies exploration and exploitation tradeoffs in the context of online lending. Unlike traditional contexts where the cost of exploration is an opportunity cost of lost revenue or some other implicit cost, in the case of unsecured online lending, the lender effectively gives away money in order to learn about the borrower's ability to repay. In our model, the lender maximizes the expected net present value of the cash flow she receives by dynamically adjusting the loan amounts and the interest (discount) rate as she learns about the borrower's unknown income. The lender has to carefully balance the trade-offs between earning more interest when she lends more and the risk of default, and we provided the optimal dynamic policy for the lender. The optimal policy support the classic "lean experimentation" in certain regime, while challenge such concept in other regime. When the demand elasticity is zero (the discount rate is set exogenously), or the elasticity a decreasing function of the discount rate, the optimal policy is characterized by a large number of small experiments with increasing repayment amounts. When the demand elasticity is constant or when it is an increasing function of the discount rate, we obtain a two-step optimal policy: the lender performs a single experiment and then, if the borrower repays the loan, offers the same loan amount and discount rate in each subsequent period without any further experimentation. This result sheds light in how to take into account the market churn measured by elasticity, in the dynamic experiment design under uncertain environment. We further provide the implications under the optimal policies, including the impact of the income variability, the value of information and the consumer segmentation. Lastly, we extend the methodology to analyze the Buy-Now-Pay-Later business model and provide the policy suggestions.

Download or read book Advances in Information Systems written by Tatyana Yakhno and published by Springer Science & Business Media. This book was released on 2004-10-12 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Advances in Information Systems, ADVIS 2004, held in Izmir, Turkey in October 2004. The 61 revised full papers presented were carefully reviewed and selected from 203 submissions. The papers are organized in topical sections on databases and datawarehouses, data mining and knowledge discovery, Web information systems development, information systems development and management, information retrieval, parallel and distributed data processing, multimedia information systems, information privacy and security, evolutionary and knowledge-based systems, software engineering and business process modeling, and network management.