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exploration exploitation tradeoff

Frazer Meacham, Carl T Bergstrom
Normal anxiety is considered an adaptive response to the possible presence of danger, but is susceptible to dysregulation. Anxiety disorders are prevalent at high frequency in contemporary human societies, yet impose substantial disability upon their sufferers. This raises a puzzle: why has evolution left us vulnerable to anxiety disorders? We develop a signal detection model in which individuals must learn how to calibrate their anxiety responses: they need to learn which cues indicate danger in the environment...
2016: Evolution, Medicine, and Public Health
Hossam M Zawbaa, E Emary, Crina Grosan
BACKGROUND: Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used...
2016: PloS One
Timothé Collet, Olivier Pietquin
Active learning is the problem of interactively constructing the training set used in classification in order to reduce its size. It would ideally successively add the instance-label pair that decreases the classification error most. However, the effect of the addition of a pair is not known in advance. It can still be estimated with the pairs already in the training set. The online minimization of the classification error involves a tradeoff between exploration and exploitation. This is a common problem in machine learning for which multiarmed bandit, using the approach of Optimism in the Face of Uncertainty, has proven very efficient these last years...
2015: Computational Intelligence and Neuroscience
Stéphane Ross, Joelle Pineau
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeoff in classical reinforcement learning. Unfortunately, the applicability of this type of approach has been limited to small domains due to the high complexity of reasoning about the joint posterior over model parameters. In this paper, we consider the use of factored representations combined with online planning techniques, to improve scalability of these methods...
July 2008: Uncertainty in Artificial Intelligence: Proceedings of the ... Conference
Laurel S Morris, Kwangyeol Baek, Prantik Kundu, Neil A Harrison, Michael J Frank, Valerie Voon
We focus on exploratory decisions across disorders of compulsivity, a potential dimensional construct for the classification of mental disorders. Behaviors associated with the pathological use of alcohol or food, in alcohol use disorders (AUD) or binge-eating disorder (BED), suggest a disturbance in explore-exploit decision-making, whereby strategic exploratory decisions in an attempt to improve long-term outcomes may diminish in favor of more repetitive or exploitatory choices. We compare exploration vs exploitation across disorders of natural (obesity with and without BED) and drug rewards (AUD)...
March 2016: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Elizabeth Mannix, Margaret A Neale
-As the workplace has become increasingly diverse, there has been a tension between the promise and the reality of diversity in team process and performance. The optimistic view holds that diversity will lead to an increase in the variety of perspectives and approaches brought to a problem and to opportunities for knowledge sharing, and hence lead to greater creativity and quality of team performance. However, the preponderance of the evidence favors a more pessimistic view: that diversity creates social divisions, which in turn create negative performance outcomes for the group...
October 2005: Psychological Science in the Public Interest: a Journal of the American Psychological Society
Herman De Beukelaer, Geert De Meyer, Veerle Fack
BACKGROUND: Over the last decade genetic marker-based plant breeding strategies have gained increasing attention because genotyping technologies are no longer limiting. Now the challenge is to optimally use genetic markers in practical breeding schemes. For simple traits such as some disease resistances it is possible to target a fixed multi-locus allele configuration at a small number of causal or linked loci. Efficiently obtaining this genetic ideotype from a given set of parental genotypes is known as the marker-assisted gene pyramiding problem...
2015: BMC Genetics
Robert C Wilson, Andra Geana, John M White, Elliot A Ludvig, Jonathan D Cohen
All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our "Horizon task," participants made explore-exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon)...
December 2014: Journal of Experimental Psychology. General
Andrew S Kayser, Jennifer M Mitchell, Dawn Weinstein, Michael J Frank
Whether to continue to exploit a source of reward, or to search for a new one of potentially greater value, is a fundamental and underconstrained decision. Recent computational studies of this exploration-exploitation tradeoff have found that variability in exploration across individuals is influenced by a functional polymorphism (Val158Met) in the catechol-O-methyltransferase (COMT) gene, whose protein product degrades synaptically released dopamine. However, these and other genotype-phenotype associations have rarely been causally tested...
January 2015: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Morgan W B Kirzinger, Geetanchaly Nadarasah, John Stavrinides
Plant and human pathogens have evolved disease factors to successfully exploit their respective hosts. Phytopathogens utilize specific determinants that help to breach reinforced cell walls and manipulate plant physiology to facilitate the disease process, while human pathogens use determinants for exploiting mammalian physiology and overcoming highly developed adaptive immune responses. Emerging research, however, has highlighted the ability of seemingly dedicated human pathogens to cause plant disease, and specialized plant pathogens to cause human disease...
2011: Genes
Daniella Laureiro-Martínez, Nicola Canessa, Stefano Brusoni, Maurizio Zollo, Todd Hare, Federica Alemanno, Stefano F Cappa
An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time)...
2013: Frontiers in Human Neuroscience
Silvia García Díez, Jérôme Laforge, Marco Saerens
This paper proposes a simple extension of the celebrated MINIMAX algorithm used in zero-sum two-player games, called Rminimax. The Rminimax algorithm allows controlling the strength of an artificial rival by randomizing its strategy in an optimal way. In particular, the randomized shortest-path framework is applied for biasing the artificial intelligence (AI) adversary toward worse or better solutions, therefore controlling its strength. In other words, our model aims at introducing/implementing bounded rationality to the MINIMAX algorithm...
February 2013: IEEE Transactions on Cybernetics
Chao Wang, Alvin T Yeh
The inverse relationship between two-photon excited fluorescence (TPEF) and laser pulse duration suggests that two-photon microscopy (TPM) performance may be improved by decreasing pulse duration. However, for ultrashort pulses of sub-10 femtosecond (fs) in duration, its spectrum contains the effective gain bandwidth of Ti:Sapphire and its central wavelength is no longer tunable. An experimental study was performed to explore this apparent tradeoff between untuned sub-10 fs transform-limited pulse (TLP) and tunable 140 fs pulse for TPEF...
February 2012: Journal of Biomedical Optics
Winter Mason, Duncan J Watts
Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration...
January 17, 2012: Proceedings of the National Academy of Sciences of the United States of America
Thomas N Wisdom, Robert L Goldstone
We implemented a problem-solving task in which groups of participants simultaneously played a simple innovation game in a complex problem space, with score feedback provided after each of a number of rounds. Each participant in a group was allowed to view and imitate the guesses of others during the game. The results showed the use of social learning strategies previously studied in other species, and demonstrated benefits of social learning and nonlinear effects of group size on strategy and performance. Rather than simply encouraging conformity, groups provided information to each individual about the distribution of useful innovations in the problem space...
April 2011: Nonlinear Dynamics, Psychology, and Life Sciences
Peter Forthmann, Thomas Koehler, Michel Defrise, Patrick La Riviere
PURPOSE: A CT scanner measures the energy that is deposited in each channel of a detector array by x rays that have been partially absorbed on their way through the object. The measurement process is complex and quantitative measurements are always and inevitably associated with errors, so CT data must be preprocessed prior to reconstruction. In recent years, the authors have formulated CT sinogram preprocessing as a statistical restoration problem in which the goal is to obtain the best estimate of the line integrals needed for reconstruction from the set of noisy, degraded measurements...
November 2010: Medical Physics
Theresa M Desrochers, Dezhe Z Jin, Noah D Goodman, Ann M Graybiel
Habits and rituals are expressed universally across animal species. These behaviors are advantageous in allowing sequential behaviors to be performed without cognitive overload, and appear to rely on neural circuits that are relatively benign but vulnerable to takeover by extreme contexts, neuropsychiatric sequelae, and processes leading to addiction. Reinforcement learning (RL) is thought to underlie the formation of optimal habits. However, this theoretic formulation has principally been tested experimentally in simple stimulus-response tasks with relatively few available responses...
November 23, 2010: Proceedings of the National Academy of Sciences of the United States of America
Shiva K Das
The IMRT treatment planning process typically follows a path that is based on the manner in which the planner interactively adjusts the target and organ-at-risk (OAR) constraints and priorities. The time-intensive nature of this process restricts the planner from fully understanding the dose tradeoff between structures, making it unlikely that the resulting plan fully exploits the extent to which dose can be redistributed between anatomical structures. Multiobjective Pareto optimization has been used in the past to enable the planner to more thoroughly explore alternatives in dose trade-off by combining pre-generated Pareto optimal solutions in real time, thereby potentially tailoring a plan more exactly to requirements...
May 2009: Medical Physics
Daoyi Dong, Chunlin Chen, Hanxiong Li, Tzyh-Jong Tarn
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL...
October 2008: IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics
Guanqi Guo, Y Shouyi
This paper proposes a kind of evolutionary parallel local search technique (EPLS) that integrates the reproduction mechanisms from evolutionary algorithms and simplex method. The major aim is to explore the tradeoff between exploration and exploitation for optimizing multimodal functions. It has been cost-efficiently reached by means of parallel local search using simplex method. In each generation, EPLS partitions the population into a group of subpopulations, each of which consists of several individuals with adjacent space locations...
2003: IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics
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