scholarly journals Tree Pruning for New Search Techniques in Computer Games

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kieran Greer
Keyword(s):  
Computer Games ◽  
Imperfect Information ◽  
Search Space ◽  
Search Process ◽  
Game Tree ◽  
Search Game ◽  
Computer Chess ◽  
Small Set ◽  
Search Reduction ◽  
Tree Pruning

This paper proposes a new mechanism for pruning a search game tree in computer chess. The algorithm stores and then reuses chains or sequences of moves, built up from previous searches. These move sequences have a built-in forward-pruning mechanism that can radically reduce the search space. A typical search process might retrieve a move from a Transposition Table, where the decision of what move to retrieve would be based on the position itself. This algorithm stores move sequences based on what previous sequences were better, or caused cutoffs. The sequence is then returned based on the current move only. This is therefore position independent and could also be useful in games with imperfect information or uncertainty, where the whole situation is not known at any one time. Over a small set of tests, the algorithm was shown to clearly out perform Transposition Tables, both in terms of search reduction and game-play results. Finally, a completely new search process will be suggested for computer chess or games in general.

Development and research of a genetic method for the analysis and determination of the location of power grid objects

2020 ◽  
Vol 25 (1) ◽  
pp. 20-42
Author(s):  
Fedorchenko I. ◽  
◽  
Oliinyk A. ◽  
Korniienko S. ◽  
Kharchenko A. ◽  
...  
Keyword(s):  
Power Supply ◽  
Narrow Range ◽  
Search Space ◽  
Distribution Networks ◽  
Power Supplies ◽  
Search Process ◽  
Stable Convergence ◽  
Genetic Method

The problem of combinatorial optimization is considered in relation to the choice of the location of the location of power supplies when solving the problem of the development of urban distribution networks of power supply. Two methods have been developed for placing power supplies and assigning consumers to them to solve this problem. The first developed method consists in placing power supplies of the same standard sizes, and the second - of different standard sizes. The fundamental difference between the created methods and the existing ones is that the proposed methods take into account all the material of the problem and have specialized methods for coding possible solutions, modified operators of crossing and selection. The proposed methods effectively solve the problem of low inheritance, topological unfeasibility of the found solutions, as a result of which the execution time is significantly reduced and the accuracy of calculations is increased. In the developed methods, the lack of taking into account the restrictions on the placement of new power supplies is realized, which made it possible to solve the problem of applying the methods for a narrow range of problems. A comparative analysis of the results obtained by placing power supplies of the same standard sizes and known methods was carried out, and it was found that the developed method works faster than the known methods. It is shown that the proposed approach ensures stable convergence of the search process by an acceptable number of steps without artificial limitation of the search space and the use of additional expert information on the feasibility of possible solutions. The results obtained allow us to propose effective methods to improve the quality of decisions made on the choice of the location of power supply facilities in the design of urban electrical.

ANN softmax

2021 ◽  
Vol 15 (1) ◽  
pp. 1-10
Author(s):  
Kang Zhao ◽  
Liuyihan Song ◽  
Yingya Zhang ◽  
Pan Pan ◽  
Yinghui Xu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Training Sample ◽  
Locality Sensitive Hashing ◽  
Search Process ◽  
Inverted File ◽  
Deep Model ◽  
Learning Tasks ◽  
Improve Accuracy ◽  
Small Set ◽  
Kernel Design

Thanks to the popularity of GPU and the growth of its computational power, more and more deep learning tasks, such as face recognition, image retrieval and word embedding, can take advantage of extreme classification to improve accuracy. However, it remains a big challenge to train a deep model with millions of classes efficiently due to the huge memory and computation consumption in the last layer. By sampling a small set of classes to avoid the total classes calculation, sampling-based approaches have been proved to be an effective solution. But most of them suffer from the following two issues: i) the important classes are ignored or only partly sampled, such as the methods using random sampling scheme or retrieval techniques of low recall (e.g., locality-sensitive hashing), resulting in the degradation of accuracy; ii) inefficient implementation owing to incompatibility with GPU, like selective softmax. It uses hashing forest to help select classes, but the search process is implemented in CPU. To address the above issues, we propose a new sampling-based softmax called ANN Softmax in this paper. Specifically, we employ binary quantization with inverted file system to improve the recall of important classes. With the help of dedicated kernel design, it can be totally parallelized in mainstream training framework. Then, we find the size of important classes that are recalled by each training sample has a great impact on the final accuracy, so we introduce sample grouping optimization to well approximate the full classes training. Experimental evaluations on two tasks (Embedding Learning and Classification) and ten datasets (e.g., MegaFace, ImageNet, SKU datasets) demonstrate our proposed method maintains the same precision as Full Softmax for different loss objectives, including cross entropy loss, ArcFace, CosFace and D-Softmax loss, with only 1/10 sampled classes, which outperforms the state-of-the-art techniques. Moreover, we implement ANN Soft-max in a complete GPU pipeline that can accelerate the training more than 4.3X. Equipped our method with a 256 GPUs cluster, the time of training a classifier of 300 million classes on our SKU-300M dataset can be reduced to ten days.

Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

scholarly journals Evolutionary Squeaky Wheel Optimization: A New Framework for Analysis

2011 ◽  
Vol 19 (3) ◽  
pp. 405-428 ◽  
Author(s):  
Jingpeng Li ◽  
Andrew J. Parkes ◽  
Edmund K. Burke
Keyword(s):  
Simulated Annealing ◽  
Search Space ◽  
Simple Problem ◽  
Problem Instance ◽  
Search Process ◽  
Distribution Probability ◽  
Formal Framework ◽  
Recent Extension ◽  
New Framework ◽  
Real World Problems

Squeaky wheel optimization (SWO) is a relatively new metaheuristic that has been shown to be effective for many real-world problems. At each iteration SWO does a complete construction of a solution starting from the empty assignment. Although the construction uses information from previous iterations, the complete rebuilding does mean that SWO is generally effective at diversification but can suffer from a relatively weak intensification. Evolutionary SWO (ESWO) is a recent extension to SWO that is designed to improve the intensification by keeping the good components of solutions and only using SWO to reconstruct other poorer components of the solution. In such algorithms a standard challenge is to understand how the various parameters affect the search process. In order to support the future study of such issues, we propose a formal framework for the analysis of ESWO. The framework is based on Markov chains, and the main novelty arises because ESWO moves through the space of partial assignments. This makes it significantly different from the analyses used in local search (such as simulated annealing) which only move through complete assignments. Generally, the exact details of ESWO will depend on various heuristics; so we focus our approach on a case of ESWO that we call ESWO-II and that has probabilistic as opposed to heuristic selection and construction operators. For ESWO-II, we study a simple problem instance and explicitly compute the stationary distribution probability over the states of the search space. We find interesting properties of the distribution. In particular, we find that the probabilities of states generally, but not always, increase with their fitness. This nonmonotonocity is quite different from the monotonicity expected in algorithms such as simulated annealing.

scholarly journals Smoothing Method for Approximate Extensive-Form Perfect Equilibrium

2017 ◽  
Author(s):  
Christian Kroer ◽  
Gabriele Farina ◽  
Tuomas Sandholm
Keyword(s):  
Nash Equilibrium ◽  
Imperfect Information ◽  
Large Scale ◽  
Nash Equilibria ◽  
Convex Polytope ◽  
Solution Concept ◽  
Extensive Form ◽  
Game Tree ◽  
Equilibrium Refinements ◽  
Perfect Equilibria

Nash equilibrium is a popular solution concept for solving imperfect-information games in practice. However, it has a major drawback: it does not preclude suboptimal play in branches of the game tree that are not reached in equilibrium. Equilibrium refinements can mend this issue, but have experienced little practical adoption. This is largely due to a lack of scalable algorithms.Sparse iterative methods, in particular first-order methods, are known to be among the most effective algorithms for computing Nash equilibria in large-scale two-player zero-sum extensive-form games. In this paper, we provide, to our knowledge, the first extension of these methods to equilibrium refinements. We develop a smoothing approach for behavioral perturbations of the convex polytope that encompasses the strategy spaces of players in an extensive-form game. This enables one to compute an approximate variant of extensive-form perfect equilibria. Experiments show that our smoothing approach leads to solutions with dramatically stronger strategies at information sets that are reached with low probability in approximate Nash equilibria, while retaining the overall convergence rate associated with fast algorithms for Nash equilibrium. This has benefits both in approximate equilibrium finding (such approximation is necessary in practice in large games) where some probabilities are low while possibly heading toward zero in the limit, and exact equilibrium computation where the low probabilities are actually zero.

scholarly journals FLC control for tuning exploration phase in bio-inspired metaheuristic

2017 ◽  
Vol 16 (2) ◽  
pp. 32
Author(s):  
Kazimierz Kiełkowicz ◽  
Damian Grela
Keyword(s):  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Bat Algorithm ◽  
Search Space ◽  
Search Process ◽  
Control Parameters ◽  
Local Search Methods ◽  
Multidimensional Search ◽  
Exploration Phase

<p>Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.</p>

scholarly journals A View on Deep Reinforcement Learning in Imperfect Information Games

2020 ◽  
Vol 65 (2) ◽  
pp. 31
Author(s):  
T.V. Pricope
Keyword(s):  
Reinforcement Learning ◽  
Imperfect Information ◽  
Large Scale ◽  
Traditional Approach ◽  
Search Space ◽  
Fictitious Play ◽  
Learning Agents ◽  
Real World Applications ◽  
Imperfect Information Games ◽  
Human Player

Many real-world applications can be described as large-scale games of imperfect information. This kind of games is particularly harder than the deterministic one as the search space is even more sizeable. In this paper, I want to explore the power of reinforcement learning in such an environment; that is why I take a look at one of the most popular game of such type, no limit Texas Hold’em Poker, yet unsolved, developing multiple agents with different learning paradigms and techniques and then comparing their respective performances. When applied to no-limit Hold’em Poker, deep reinforcement learning agents clearly outperform agents with a more traditional approach. Moreover, if these last agents rival a human beginner level of play, the ones based on reinforcement learning compare to an amateur human player. The main algorithm uses Fictitious Play in combination with ANNs and some handcrafted metrics. We also applied the main algorithm to another game of imperfect information, less complex than Poker, in order to show the scalability of this solution and the increase in performance when put neck in neck with established classical approaches from the reinforcement learning literature.

A Spatial Grammar Method for the Computational Design Synthesis of Virtual Soft Locomotion Robots

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Merel van Diepen ◽  
Kristina Shea
Keyword(s):  
Degrees Of Freedom ◽  
Computational Design ◽  
Simulation Method ◽  
Search Space ◽  
Search Process ◽  
Design Synthesis ◽  
Spatial Grammar ◽  
Three Stages ◽  
Computational Design Synthesis ◽  
Simulation Feedback

Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. They lack rigid components that provide them with higher flexibility, and they have no joints that need protection from liquids or dirt. However, the hand-design of soft robots is often a lengthy trail-and-error process. This work presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The computational approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization. Here, designs are generated using a spatial grammar to explicitly guide the type of solutions generated and exclude infeasible designs. The soft material simulation method developed and integrated is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the method capabilities. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.

Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games

2006 ◽  
pp. 21-34 ◽  
Author(s):  
Darse Billings ◽  
Aaron Davidson ◽  
Terence Schauenberg ◽  
Neil Burch ◽  
Michael Bowling ◽  
...  
Keyword(s):  
Imperfect Information ◽  
Tree Search ◽  
Game Tree ◽  
Imperfect Information Games ◽  
Game Tree Search ◽  
Information Games
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