The Experts below are selected from a list of 23829 Experts worldwide ranked by ideXlab platform
Makoto Yokoo - One of the best experts on this subject based on the ideXlab platform.
-
sat based Automated Mechanism design for false name proof facility location
Pacific Rim International Conference on Multi-Agents, 2019Co-Authors: Nodoka Okada, Taiki Todo, Makoto YokooAbstract:In the literature of Mechanism design, market Mechanisms have been developed by professionals based on their experience. The concept of Automated Mechanism design (AMD), initiated by Sandholm (2002), is a ground-breaking computer-aided framework to develop market Mechanisms. In this paper, we apply a very recent AMD approach based on Boolean Satisfiability (SAT) to the Mechanism design of false-name-proof facility location. We first provide a general theoretical characteristic of false-name-proof Mechanisms, which enables a quite compact representation of target Mechanisms. Our approach successfully reproduces several known results in the literature on false-name-proof facility locations over discrete structures. Furthermore, some unknown Mechanisms are discovered for locating a public good on a 2-by-2 grid, and an impossibility result is revealed for locating a public bad, with an additional mild assumption, on a 2-by-3 grid. Finally, we demonstrate the extendability of our approach, by providing a new false-name-proof Mechanism for a slightly modified problem of locating a public good.
-
PRIMA - SAT-Based Automated Mechanism Design for False-Name-Proof Facility Location
PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019Co-Authors: Nodoka Okada, Taiki Todo, Makoto YokooAbstract:In the literature of Mechanism design, market Mechanisms have been developed by professionals based on their experience. The concept of Automated Mechanism design (AMD), initiated by Sandholm (2002), is a ground-breaking computer-aided framework to develop market Mechanisms. In this paper, we apply a very recent AMD approach based on Boolean Satisfiability (SAT) to the Mechanism design of false-name-proof facility location. We first provide a general theoretical characteristic of false-name-proof Mechanisms, which enables a quite compact representation of target Mechanisms. Our approach successfully reproduces several known results in the literature on false-name-proof facility locations over discrete structures. Furthermore, some unknown Mechanisms are discovered for locating a public good on a 2-by-2 grid, and an impossibility result is revealed for locating a public bad, with an additional mild assumption, on a 2-by-3 grid. Finally, we demonstrate the extendability of our approach, by providing a new false-name-proof Mechanism for a slightly modified problem of locating a public good.
-
AAMAS - Social Decision with Minimal Efficiency Loss: An Automated Mechanism Design Approach
2015Co-Authors: Mingyu Guo, Hong Shen, Taiki Todo, Yuko Sakurai, Makoto YokooAbstract:We study the problem where a group of agents need to choose from a finite set of social outcomes. We assume every agent's valuation for every outcome is bounded and the bounds are public information. For our model, no Mechanism simultaneously satisfies strategy-proofness, individual rationality, non-deficit, and efficiency. In light of this, we aim to design Mechanisms that are strategy-proof, individually rational, non-deficit, and minimize the worst-case efficiency loss. We propose a family of Mechanisms called the shifted Groves Mechanisms, which are generalizations of the Groves Mechanisms. We first show that if there exist Mechanisms that are strategy-proof, individually rational, and non-deficit, then there exist shifted Groves Mechanisms with these properties. Our main result is an Automated Mechanism Design (AMD) approach for identifying the (unique) optimal shifted Groves Mechanism, which minimizes the worst-case efficiency loss among all shifted Groves Mechanisms. Finally, we prove that the optimal shifted Groves Mechanism is globally optimal among all deterministic Mechanisms that are strategy-proof, individually rational, and non-deficit.
-
AAMAS - Parametric Mechanism Design via Quantifier Elimination
2015Co-Authors: Atsushi Iwasaki, Mingyu Guo, Taiki Todo, Etsushi Fujita, Hidenao Iwane, Hirokazu Anai, Makoto YokooAbstract:This paper proposes an alternative Automated Mechanism design approach called parametric Mechanism design via quantifier elimination (PMD-QE), which utilizes QE, a symbolic formula manipulation technique. In PMD-QE, we start from a skeleton of Mechanisms, which is characterized by a set of parameters, e.g., critical values. The range of parameters where the given constraints are satisfied is automatically identified by QE. To demonstrate the potential of this idea, we are able to identify a non-trivial dominant-strategy incentive compatible Mechanism for a setting where a bidder has a publicly known budget limit.
Linda J. Broadbelt - One of the best experts on this subject based on the ideXlab platform.
-
Reactive Flow Simulation Based on the Integration of Automated Mechanism Generation and On-the-Fly Reduction
Energy & Fuels, 2014Co-Authors: Shuliang Zhang, Linda J. Broadbelt, Ioannis P. Androulakis, Marianthi G. IerapetritouAbstract:A novel computational framework that enables reactive flow simulation without using an a priori reaction Mechanism is proposed in this work. The proposed computational framework is based on the integration of Automated Mechanism generation and on-the-fly reduction based on element flux analysis. Stepwise implementation of the integrated framework is developed to perform reactive flow simulations. The computational framework starts the simulation without any prior knowledge of the Mechanism with only fuel and air as the initial species and performs the stepwise Mechanism generation and on-the-fly reduction iteratively to obtain the entire simulation results. In each local computation step, a Mechanism is generated automatically starting with the reduced Mechanism at the end of the previous step, and flux-based on-the-fly reduction is applied to the generated Mechanism to obtain reactive flow simulation results for the current step. The reduced Mechanism at the end of the current step is then used as the st...
-
Automated Mechanism generation. Part 2: application to atmospheric chemistry of alkanes and oxygenates
Journal of Atmospheric Chemistry, 2009Co-Authors: Shumaila S. Khan, Linda J. BroadbeltAbstract:In this study, an Automated Mechanism generation framework was applied to atmospheric chemistry of volatile organic compounds (VOCs) and nitrogen oxides (NO_ x ). The framework generates reactions with minimal input based on a small set of reaction operators and includes a hierarchy for specifying rate constants for every reaction created. Mechanisms were generated for formaldehyde-air-NO_ x , acetaldehyde-formaldehyde- n -octane-air-NO_ x , and acetone-air-NO_ x , and the model results were compared to experimental data obtained from smog chambers and to the SAPRC-99 lumped models. The models generated captured the experimental data very well, and their mechanistic formulation provided new insights into the controlling reaction pathways to pollutant formation. The approach applied here is sufficiently general that it can be applied to a wide range of alkane and oxygenate mixtures.
-
Automated Mechanism generation. Part 1: Mechanism development and rate constant estimation for VOC chemistry in the atmosphere
Journal of Atmospheric Chemistry, 2009Co-Authors: Shumaila S. Khan, Qizhi Zhang, Linda J. BroadbeltAbstract:A framework for Automated Mechanism generation for modeling atmospheric chemistry at the mechanistic level was developed. In part 1, categorization of reactions into reaction families and determination of rate coefficients using a hierarchical approach that uses experimental data and kinetic correlations are described. The main focus was to develop kinetic correlations for estimating rate coefficients that are not available experimentally, and the main correlation used was the Evans–Polanyi relationship that relates the activation energy to the heat of reaction. A hierarchical scheme for calculating heats of reaction and other thermodynamic properties was developed. The rate constants calculated using the proposed correlations are in most cases within an order of magnitude of available experimental values, and 82% are within a factor of five.
-
Mechanistic Modeling of Lubricant Degradation. 2. The Autoxidation of Decane and Octane
Industrial & Engineering Chemistry Research, 2008Co-Authors: Jim Pfaendtner, Linda J. BroadbeltAbstract:Automated Mechanism generation was used to study the condensed-phase oxidation of decane and octane as a means to gain insights into the degradation of lubricating oils. Part 1 of this series established a library of structure−reactivity relationships that enables the estimation of all kinetic data required for solving large reaction Mechanisms that model lubricant degradation. Specific reaction rules are proposed that enable Automated Mechanism generation to be used, for the first time, to study condensed-phase free-radical oxidation of large substrates. Models of decane oxidation were generated, and good agreement with available experimental data was achieved. The optimized parameters were then used to generate predictive models of octane autoxidation.
-
Detailed Kinetic Modeling of Silicon Nanoparticle Formation Chemistry via Automated Mechanism Generation
The Journal of Physical Chemistry A, 2004Co-Authors: Hsi-wu Wong, Mark T. Swihart, Linda J. BroadbeltAbstract:Thermal decomposition of silane can be used to produce silicon nanoparticles, which have attracted great interest in recent years because of their novel optical and electronic properties. However, these silicon nanoparticles are also an important source of particulate contamination leading to yield loss in conventional semiconductor processing. In both cases, a fundamental knowledge of the reaction kinetics of particle formation is needed to understand and control the nucleation of silicon particles. In this work, detailed kinetic modeling of silicon nanoparticle formation chemistry was carried out using Automated reaction Mechanism generation. Literature values, linear free-energy relationships (LFERs), and a group additivity approach were incorporated to specify the rate parameters and thermochemical properties of the species in the system. New criteria for terminating the Mechanisms generated were also developed and compared, and their suitability for handling an unbounded system was evaluated. Four di...
Nodoka Okada - One of the best experts on this subject based on the ideXlab platform.
-
sat based Automated Mechanism design for false name proof facility location
Pacific Rim International Conference on Multi-Agents, 2019Co-Authors: Nodoka Okada, Taiki Todo, Makoto YokooAbstract:In the literature of Mechanism design, market Mechanisms have been developed by professionals based on their experience. The concept of Automated Mechanism design (AMD), initiated by Sandholm (2002), is a ground-breaking computer-aided framework to develop market Mechanisms. In this paper, we apply a very recent AMD approach based on Boolean Satisfiability (SAT) to the Mechanism design of false-name-proof facility location. We first provide a general theoretical characteristic of false-name-proof Mechanisms, which enables a quite compact representation of target Mechanisms. Our approach successfully reproduces several known results in the literature on false-name-proof facility locations over discrete structures. Furthermore, some unknown Mechanisms are discovered for locating a public good on a 2-by-2 grid, and an impossibility result is revealed for locating a public bad, with an additional mild assumption, on a 2-by-3 grid. Finally, we demonstrate the extendability of our approach, by providing a new false-name-proof Mechanism for a slightly modified problem of locating a public good.
-
PRIMA - SAT-Based Automated Mechanism Design for False-Name-Proof Facility Location
PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019Co-Authors: Nodoka Okada, Taiki Todo, Makoto YokooAbstract:In the literature of Mechanism design, market Mechanisms have been developed by professionals based on their experience. The concept of Automated Mechanism design (AMD), initiated by Sandholm (2002), is a ground-breaking computer-aided framework to develop market Mechanisms. In this paper, we apply a very recent AMD approach based on Boolean Satisfiability (SAT) to the Mechanism design of false-name-proof facility location. We first provide a general theoretical characteristic of false-name-proof Mechanisms, which enables a quite compact representation of target Mechanisms. Our approach successfully reproduces several known results in the literature on false-name-proof facility locations over discrete structures. Furthermore, some unknown Mechanisms are discovered for locating a public good on a 2-by-2 grid, and an impossibility result is revealed for locating a public bad, with an additional mild assumption, on a 2-by-3 grid. Finally, we demonstrate the extendability of our approach, by providing a new false-name-proof Mechanism for a slightly modified problem of locating a public good.
Thanh N. Truong - One of the best experts on this subject based on the ideXlab platform.
-
performance of first principles based reaction class transition state theory
Journal of Physical Chemistry B, 2016Co-Authors: Artur Ratkiewicz, Lam K Huynh, Thanh N. TruongAbstract:Performance of the Reaction Class Transition State Theory (RC-TST) for prediction of rates constants of elementary reactions is examined using data from its previous applications to a number of different reaction classes. The RC-TST theory is taking advantage of the common structure denominator of all reactions in a given family combined with structure activity relationships to provide a rigorous theoretical framework to obtain rate expression of any reaction within a reaction class in a simple and cost-effective manner. This opens the possibility for integrating this methodology with an Automated Mechanism generator for “on-the-fly” generation of accurate kinetic models of complex reacting systems.
-
Automated Mechanism generation: From symbolic calculation to complex chemistry
International Journal of Quantum Chemistry, 2005Co-Authors: Artur Ratkiewicz, Thanh N. TruongAbstract:Different aspects of the symbolic algebra computations for generating elementary reactions of complex systems are reviewed. Such calculations are the heart of each Automated Mechanism generator system and are employed extensively in different stages of Mechanism generation. The range of symbolic calculation topics and basic ideas of these implementations, together with some specific examples, are given. Particular attention is devoted to the transition between the symbolic calculation and the real complex chemistry. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2006
-
Application of Chemical Graph Theory for Automated Mechanism Generation
Journal of Chemical Information and Computer Sciences, 2002Co-Authors: Artur Ratkiewicz, Thanh N. TruongAbstract:We present an application of the chemical graph theory approach for generating elementary reactions of complex systems. Molecular species are naturally represented by graphs, which are identified by their vertices and edges where vertices are atom types and edges are bonds. The Mechanism is generated using a set of reaction patterns (sub-graphs). These subgraphs are the internal representations for a given class of reaction thus allowing for the possibility of eliminating unimportant product species a priori. Furthermore, each molecule is canonically represented by a set of topological indices (Connectivity Index, Balaban Index, Schulz TI Index, WID Index, etc.) and thus eliminates the probability for regenerating the same species twice. Theoretical background and test cases on combustion of hydrocarbons are presented.
Tuomas Sandholm - One of the best experts on this subject based on the ideXlab platform.
-
Sample Complexity of Automated Mechanism Design
arXiv: Learning, 2016Co-Authors: Maria-florina Balcan, Tuomas Sandholm, Ellen VitercikAbstract:The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, Automated Mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable Automated Mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class. In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in Automated Mechanism design. In particular, we provide tight sample complexity bounds on the number of samples needed to guarantee that the empirical revenue of the designed Mechanism on the samples is close to its expected revenue on the underlying, unknown distribution over bidder valuations, for each of the auction classes in the hierarchy. In addition to helping set Automated Mechanism design on firm foundations, our results also push the boundaries of learning theory. In particular, the hypothesis functions used in our contexts are defined through multi-stage combinatorial optimization procedures, rather than simple decision boundaries, as are common in machine learning.
-
NIPS - Sample Complexity of Automated Mechanism Design
2016Co-Authors: Maria-florina Balcan, Tuomas Sandholm, Ellen VitercikAbstract:The design of revenue-maximizing combinatorial auctions, i.e. multi item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, Automated Mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable Automated Mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class. In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in Automated Mechanism design. In particular, we provide tight sample complexity bounds on the number of samples needed to guarantee that the empirical revenue of the designed Mechanism on the samples is close to its expected revenue on the underlying, unknown distribution over bidder valuations, for each of the auction classes in the hierarchy. In addition to helping set Automated Mechanism design on firm foundations, our results also push the boundaries of learning theory. In particular, the hypothesis functions used in our contexts are defined through multi stage combinatorial optimization procedures, rather than simple decision boundaries, as are common in machine learning.
-
Automated online Mechanism design and prophet inequalities
National Conference on Artificial Intelligence, 2007Co-Authors: Mohammadtaghi Hajiaghayi, Robert Kleinberg, Tuomas SandholmAbstract:Recent work on online auctions for digital goods has explored the role of optimal stopping theory -- particularly secretary problems -- in the design of approximately optimal online Mechanisms. This work generally assumes that the size of the market (number of bidders) is known a priori, but that the Mechanism designer has no knowledge of the distribution of bid values. However, in many real-world applications (such as online ticket sales), the opposite is true: the seller has distributional knowledge of the bid values (e.g., via the history of past transactions in the market), but there is uncertainty about market size. Adopting the perspective of Automated Mechanism design, introduced by Conitzer and Sandholm, we develop algorithms that compute an optimal, or approximately optimal, online auction Mechanism given access to this distributional knowledge. Our main results are twofold. First, we show that when the seller does not know the market size, no constant-approximation to the optimum efficiency or revenue is achievable in the worst case, even under the very strong assumption that bid values are i.i.d. samples from a distribution known to the seller. Second, we show that when the seller has distributional knowledge of the market size as well as the bid values, one can do well in several senses. Perhaps most interestingly, by combining dynamic programming with prophet inequalities (a technique from optimal stopping theory) we are able to design and analyze online Mechanisms which are temporally strategyproof (even with respect to arrival and departure times) and approximately efficiency (revenue)-maximizing. In exploring the interplay between Automated Mechanism design and prophet inequalities, we prove new prophet inequalities motivated by the auction setting.
-
Automated Mechanism Design
2005Co-Authors: Tuomas SandholmAbstract:Mechanisms design has traditionally been a manual endeavor. In 2002, Conitzer and Sandholm introduced the Automated Mechanism design (AMD) approach, where the Mechanism is computationally created for the specific problem instance at hand. This has several advantages: 1) it can yield better Mechanisms than the ones known to date, 2) it applies beyond the problem classes studied manually to date, 3) it can circumvent seminal economic impossibility results that hold for classes of problems but not all instances, and 4) it shifts the burden of design from man to machine. In this talk I will overview our results on AMD to date. I will cover problem representations and the computational complexity of different variants of the design problem. Initial applications include revenue-maximizing combinatorial auctions and (combinatorial) public goods problems. Algorithms for AMD will be discussed. To reduce the computational complexity of designing optimal combinatorial auctions, I introduce an incentive compatible, individually rational subfamily called Virtual Valuations Combinatorial Auctions. The auction Mechanism's revenue can be boosted (started, for example, from the VCG) by hill-climbing in this subspace. I will also present computational complexity and communication complexity results that motivate multi-stage and non-truth-promoting Mechanisms. Finally, I present our first steps toward automatically designing multi-stage Mechanisms.
-
Computing and Markets - Automated Mechanism Design
2005Co-Authors: Tuomas SandholmAbstract:Mechanisms design has traditionally been a manual endeavor. In 2002, Conitzer and Sandholm introduced the Automated Mechanism design (AMD) approach, where the Mechanism is computationally created for the specific problem instance at hand. This has several advantages: 1) it can yield better Mechanisms than the ones known to date, 2) it applies beyond the problem classes studied manually to date, 3) it can circumvent seminal economic impossibility results that hold for classes of problems but not all instances, and 4) it shifts the burden of design from man to machine. In this talk I will overview our results on AMD to date. I will cover problem representations and the computational complexity of different variants of the design problem. Initial applications include revenue-maximizing combinatorial auctions and (combinatorial) public goods problems. Algorithms for AMD will be discussed. To reduce the computational complexity of designing optimal combinatorial auctions, I introduce an incentive compatible, individually rational subfamily called Virtual Valuations Combinatorial Auctions. The auction Mechanism's revenue can be boosted (started, for example, from the VCG) by hill-climbing in this subspace. I will also present computational complexity and communication complexity results that motivate multi-stage and non-truth-promoting Mechanisms. Finally, I present our first steps toward automatically designing multi-stage Mechanisms.
