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Learning in MultiAgent Systems
, 2000
"... There is an increased interest in multiagent systems (MASs) for computing robust solutions to complex real world problems. In this paper we analyze dierent aspects of multiagent systems, in particular multiagent architectures, multiagent problems, and optimization algorithms for MASs. Further ..."
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There is an increased interest in multiagent systems (MASs) for computing robust solutions to complex real world problems. In this paper we analyze dierent aspects of multiagent systems, in particular multiagent architectures, multiagent problems, and optimization algorithms for MASs. Furthermore, we present a scheme for mapping multiagent problems to architectures which can be used for solving them and a mapping from multiagent problem features to optimization algorithms. Finally, we review the solutions of previous work on many dierent multiagent problems. 1 Introduction Multiagent systems (MASs). The study of multiagent systems enables us to come up with robust solutions to complex problems. In the past many monolithic approaches have been constructed to solve such tasks. As problems have become more complex during the last decades, more modular systems have been developed for solving them. The study of multiagent systems (MASs) is becoming an active eld of res...
Improving Constrained Nonlinear Search Algorithms Through Constraint Relaxation
, 2001
"... In this thesis we study constraint relaxations of various nonlinear programming (NLP) algorithms in order to improve their performance. For both stochastic and deterministic algorithms, we study the relationship between the expected time to find a feasible solution and the constraint relaxation leve ..."
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In this thesis we study constraint relaxations of various nonlinear programming (NLP) algorithms in order to improve their performance. For both stochastic and deterministic algorithms, we study the relationship between the expected time to find a feasible solution and the constraint relaxation level, build an exponential model based on this relationship, and develop a constraint relaxation schedule in such a way that the total time spent to find a feasible solution for all the relaxation levels is of the same order of magnitude as the time spent for finding a solution of similar quality using the last relaxation level alone.
A Survey on Reinforcement Learning in Global Optimization
, 1998
"... This report surveys recent development on the global combinatorial optimization using reinforcement learning methods. It introduces the general background of combinatorial optimization problems and reinforcement learning techniques, describes observations and previous works in this area, and focuses ..."
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This report surveys recent development on the global combinatorial optimization using reinforcement learning methods. It introduces the general background of combinatorial optimization problems and reinforcement learning techniques, describes observations and previous works in this area, and focuses on Boyan and Moore's recent work, the STAGE algorithm with the assistant of reinforcement learning. 1 Introduction Combinatorial optimization is a family of problems with great theoretical and economical importance. Applications range from boolean formula satisfiability, binpacking, Bayesian network structure finding, to VLSI design and NASA's space shuttle payload planning. The goal of each of these problems is to find the best possible configuration from a large space of possible configurations. Problems in the combinatorial optimization domain can be formulated as: given a finite state space X; and an objective function f : X ! !, find an optimal state x = argmin x2X f(x). The hug...
Placement and Routing for 3DFPGAs using Reinforcement Learning and Support Vector Machines
"... The primary advantage of using 3DFPGA over 2DFPGA is that the vertical stacking of active layers reduce the Manhattan distance between the components in 3DFPGA than when placed on 2DFPGA. This results in a considerable reduction in total interconnect length. Reduced wire length eventually leads ..."
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The primary advantage of using 3DFPGA over 2DFPGA is that the vertical stacking of active layers reduce the Manhattan distance between the components in 3DFPGA than when placed on 2DFPGA. This results in a considerable reduction in total interconnect length. Reduced wire length eventually leads to reduction in delay and hence improved performance and speed. Design of an efficient placement and routing algorithm for 3DFPGA that fully exploits the above mentioned advantage is a problem of deep research and commercial interest. In this paper, an efficient placement and routing algorithm is proposed for 3DFPGAs which yields better results in terms of total interconnect length and channelwidth. The proposed algorithm employs two important techniques, namely, Reinforcement Learning (RL) and Support Vector Machines (SVMs), to perform the placement. The proposed algorithm is implemented and tested on standard benchmark circuits and the results obtained are encouraging. This is one of the very few instances where reinforcement learning is used for solving a problem in the area of VLSI.
Optimizing Neural Networks For The Generation Of Block Designs
 JAGOTA A
, 1997
"... This work describes the evaluation of several search algorithms, based on optimizing neural networks, as applied to a family of problems : the generation of blockdesigns. Given a set (v; b; u) of parameters (v rows, b columns and u ones), a block design is any v 2 bbinary configuration that has th ..."
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This work describes the evaluation of several search algorithms, based on optimizing neural networks, as applied to a family of problems : the generation of blockdesigns. Given a set (v; b; u) of parameters (v rows, b columns and u ones), a block design is any v 2 bbinary configuration that has the following properties: u ones, r ones per row, k ones per column, and correlation between pairs of rows. The values [u; r; k; ] are called here the descriptors of the design and, since they have to be integers, they impose admissibility constraints on the independent parameters. Admissiblity, though, does not imply existence. An optimizing algorithm can be decomposed into a cost function, that conforms the search landscape, and a search strategy that defines the way to explore it. This work proposes a set of cost functions, based on the number of pairs as a measure of the distribution of each of the properties of a design. The resulting structure, then, is straightforwardly mapped onto a...
Design Space Characterization in MicroArchitecture Design and Implementation
"... Abstract—Modern VLSI designs contain both microarchitecture parameters and implementation parameters. These can be used to facilitate verification and relaxed design specifications. We concentrate on extending prior work in understanding design parameterization and using those design knobs to make g ..."
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Abstract—Modern VLSI designs contain both microarchitecture parameters and implementation parameters. These can be used to facilitate verification and relaxed design specifications. We concentrate on extending prior work in understanding design parameterization and using those design knobs to make global optimizations. This paper discusses the application of machine learning techniques to improve the efficiency and quality of the design space characterization and optimization. Specifically, we propose improvements to the circuit energy vs delay characterization.