scholarly journals Mice in a labyrinth exhibit rapid learning, sudden insight, and efficient exploration

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Matthew Rosenberg ◽  
Tony Zhang ◽  
Pietro Perona ◽  
Markus Meister
Keyword(s):  
Search Algorithm ◽  
Forced Choice ◽  
Learning Rate ◽  
Global Memory ◽  
Complex Tasks ◽  
Rapid Learning ◽  
Efficient Exploration

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

scholarly journals Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration

2021 ◽  
Author(s):  
Matthew Rosenberg ◽  
Tony Zhang ◽  
Pietro Perona ◽  
Markus Meister
Keyword(s):  
Search Algorithm ◽  
Forced Choice ◽  
Learning Rate ◽  
Global Memory ◽  
Complex Tasks ◽  
Rapid Learning ◽  
Efficient Exploration

AbstractAnimals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal quickly discovers the location of a reward in the maze and executes correct 10-bit choices after only 10 reward experiences – a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

SUPPLY CHAIN FORMATION IN OPEN, MARKET-BASED MULTI-AGENT SYSTEMS

2002 ◽  
Vol 02 (03) ◽  
pp. 349-363 ◽  
Author(s):  
ANEURIN M. EASWARAN ◽  
JEREMY PITT
Keyword(s):  
Supply Chain ◽  
Service Providers ◽  
Search Algorithm ◽  
Multiple Criteria ◽  
Simultaneous Optimization ◽  
Open Market ◽  
Multi Agent Systems ◽  
Complex Tasks ◽  
Hierarchical Task Network ◽  
Service Allocation

Efficient allocation of services to form a supply chain to solve complex tasks is a crucial problem. Optimal service allocation based on a single criterion is NP-Complete. Furthermore, complex tasks in general have multiple criteria that may be conflicting and non-commensurable. This paper presents a two-stage brokering algorithm for optimal anytime service allocation based on multiple criteria. In the first stage, a hierarchical task network planner is used to identify the services required to solve a task. In the second stage, a genetic algorithm (GA) determines service providers based on multiple criteria to provide the services identified by the planner. We present our algorithm and results from various experiments conducted to analyze the effect of various parameters that influence the complexity of the problem. In general, the results show the GA finds optimal solutions much quicker than a standard search algorithm. The empirical results also indicate the performance of the algorithm is sub-linear or polynomial time for various parameters. The algorithm has the ability to deal with any number of criteria. By addressing this problem, we expand the range of problems being addressed to any that require simultaneous optimization of multiple criteria and/or planning.

scholarly journals An Evolutionary Approach to Improve the Halftoning Process

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1636
Author(s):  
Noé Ortega-Sánchez ◽  
Diego Oliva ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Angel A. Juan
Keyword(s):  
Visual Information ◽  
Search Algorithm ◽  
Harmony Search ◽  
Similarity Index ◽  
Structural Similarity ◽  
Search Space ◽  
Adaptive Method ◽  
Harmony Search Algorithm ◽  
Efficient Exploration

The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; a possible benefit of this task is the reduction of ink when graphics are printed. The human eye is not able to detect the absence of information, but the printed image stills have good quality. The most used method for halftoning is called Floyd-Steinberger, and it defines a specific matrix for the halftoning conversion. However, most of the proposed techniques in halftoning use predefined kernels that do not permit adaptation to different images. This article introduces the use of the harmony search algorithm (HSA) for halftoning. The HSA is a popular evolutionary algorithm inspired by the musical improvisation. The different operators of the HSA permit an efficient exploration of the search space. The HSA is applied to find the best configuration of the kernel in halftoning; meanwhile, as an objective function, the use of the structural similarity index (SSIM) is proposed. A set of rules are also introduced to reduce the regular patterns that could be created by non-appropriate kernels. The SSIM is used due to the fact that it is a perception model used as a metric that permits comparing images to interpret the differences between them numerically. The aim of combining the HSA with the SSIM for halftoning is to generate an adaptive method that permits estimating the best kernel for each image based on its intrinsic attributes. The graphical quality of the proposed algorithm has been compared with classical halftoning methodologies. Experimental results and comparisons provide evidence regarding the quality of the images obtained by the proposed optimization-based approach. In this context, classical algorithms have a lower graphical quality in comparison with our proposal. The results have been validated by a statistical analysis based on independent experiments over the set of benchmark images by using the mean and standard deviation.

Eidetic Wolf Search Algorithm with a global memory structure

2016 ◽  
Vol 254 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Simon Fong ◽  
Suash Deb ◽  
Thomas Hanne ◽  
Jinyan (Leo) Li
Keyword(s):  
Search Algorithm ◽  
Memory Structure ◽  
Global Memory

Near Optimal Learning Rate BP Algorithm and its Application in Traffic Forecast

2011 ◽  
Vol 121-126 ◽  
pp. 4513-4517
Author(s):  
Li Ming Qin ◽  
Hai Tao Zhang
Keyword(s):  
Search Algorithm ◽  
Optimization Theory ◽  
Learning Rate ◽  
Bp Algorithm ◽  
Optimal Learning ◽  
One Dimensional ◽  
Traffic Forecast ◽  
Optimal Learning Rate

Aimed at the inadequacy of the standard BP algorithm, a near optimal learning rate BP algorithm (NOLRBP) is presented. Selecting the learning rate of the algorithm based on one-dimensional search algorithm of optimization theory avoids the blindness in determining the learning rate. Simulations show that the algorithm is superior to the standard BP algorithm (SDBP), momentum BP algorithm (MOBP) and variable learning rate BP algorithm (VLBP).

scholarly journals Z katalogu zadań pamięcioznawczych

Stylistyka ◽  
2019 ◽  
Vol 28 ◽  
pp. 7-24
Author(s):  
Wojciech Chlebda
Keyword(s):  
Memory Studies ◽  
Systemic Approach ◽  
Global Memory ◽  
Collective Memories ◽  
Complex Tasks ◽  
Concise Review ◽  
Linguistic Research ◽  
With Memory

The author gives a concise review of the achievements of memory studies, especially Polish ones, focusing the attention on the new sub-discipline of knowledge, namely linguistic memory studies. After recalling the assumptions of his own concept of “linguified memory,” the author formulates five complex tasks which, in his opinion, are now faced by linguists dealing with memory studies. They are determined by the following: (1) the need for metamemory scientific arrangements; (2) the need for description and classification of linguistic memory exponents; (3) the need for a systemic approach to linguistic memory exponents, i.e. their analysis in clusters (constellations) allowing mapping of collective memories; (4) the need for linguistic research on collective oblivion; (5) the need to search for locations of collective memories/oblivions in order to answer the question of the possibility of transnational (Slavic, European, global) memory/oblivion.

scholarly journals Hybridizing the Cuckoo Search Algorithm with Different Mutation Operators for Numerical Optimization Problems

2018 ◽  
Vol 29 (1) ◽  
pp. 1043-1062 ◽  
Author(s):  
Bilal H. Abed-alguni ◽  
David J. Paul
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Mutation Operators ◽  
Efficient Exploration ◽  
Polynomial Mutation

Abstract The Cuckoo search (CS) algorithm is an efficient evolutionary algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species. Mutation is an operator used in evolutionary algorithms to maintain the diversity of the population from one generation to the next. The original CS algorithm uses the Lévy flight method, which is a special mutation operator, for efficient exploration of the search space. The major goal of the current paper is to experimentally evaluate the performance of the CS algorithm after replacing the Lévy flight method in the original CS algorithm with seven different mutation methods. The proposed variations of CS were evaluated using 14 standard benchmark functions in terms of the accuracy and reliability of the obtained results over multiple simulations. The experimental results suggest that the CS with polynomial mutation provides more accurate results and is more reliable than the other CS variations.

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