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  • 1
    Electronic Resource
    Electronic Resource
    Prioritized sweeping: Reinforcement learning with less data and less time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Keywords: Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993104
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  • 2
    Electronic Resource
    Electronic Resource
    Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time (1993)
    Springer
    Machine learning 13 (1993), S. 103-130 
    Details
    ISSN: 0885-6125
    Keywords: Memory-based learning ; learning control ; reinforcement learning ; temporal differencing ; asynchronous dynamic programming ; heuristic search ; prioritized sweeping
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slower, but more accurate, because they make full use of the observations. Prioritized sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems. It successfully solves large state-space real-time problems with which other methods have difficulty.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022635613229
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  • 3
    Electronic Resource
    Electronic Resource
    The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces (1995)
    Springer
    Machine learning 21 (1995), S. 199-233 
    Details
    ISSN: 0885-6125
    Keywords: Reinforcement Learning ; Curse of Dimensionality ; Learning Control ; Robotics ; kd-trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022656217772
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  • 4
    Electronic Resource
    Electronic Resource
    The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces (1995)
    Springer
    Machine learning 21 (1995), S. 199-233 
    Details
    ISSN: 0885-6125
    Keywords: Reinforcement Learning ; Curse of Dimensionality ; Learning Control ; Robotics ; kd-trees
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that neither planning nor exploration occurs uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been rested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993591
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  • 5
    Electronic Resource
    Electronic Resource
    Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98 (1999)
    [s.l.] : Macmillian Magazines Ltd.
    Nature 401 (1999), S. 356-360 
    Details
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] Climate variability in the Indian Ocean region seems to be, in some aspects, independent of forcing by external phenomena such as the El Niño/Southern Oscillation. But the extent to which, and how, internal coupled ocean–atmosphere dynamics determine the state of the Indian Ocean ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/43848
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  • 6
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    Unknown
    Murphy, D. J., ed., "The Big Strikes: Queensland, 1889-1965" (Book Review) (1987)
    Boston : Periodicals Archive Online (PAO)
    Business History Review. 61:4 (1987:Winter) 672 
    Details
    ISSN: 0007-6805
    Topics: History , Economics
    Notes: BOOK REVIEWS
    URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pao:&rft_dat=xri:pao:article:3071-1987-061-04-000030
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  • 7
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 11-73 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; distance functions ; smoothing parameters ; weighting functions ; global tuning ; local tuning ; interference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006559212014
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  • 8
    Electronic Resource
    Electronic Resource
    Locally Weighted Learning for Control (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 75-113 
    Details
    ISSN: 1573-7462
    Keywords: locally weighted regression ; LOESS ; LWR ; lazy learning ; memory-based learning ; least commitment learning ; forward models ; inverse models ; linear quadratic regulation (LQR) ; shifting setpoint algorithm ; dynamic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006511328852
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  • 9
    Electronic Resource
    Electronic Resource
    The Racing Algorithm: Model Selection for Lazy Learners (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 193-225 
    Details
    ISSN: 1573-7462
    Keywords: lazy learning ; model selection ; cross validation ; optimization ; attribute selection
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Given a set of models and some training data, we would like to find the model that best describes the data. Finding the model with the lowest generalization error is a computationally expensive process, especially if the number of testing points is high or if the number of models is large. Optimization techniques such as hill climbing or genetic algorithms are helpful but can end up with a model that is arbitrarily worse than the best one or cannot be used because there is no distance metric on the space of discrete models. In this paper we develop a technique called “racing” that tests the set of models in parallel, quickly discards those models that are clearly inferior and concentrates the computational effort on differentiating among the better models. Racing is especially suitable for selecting among lazy learners since training requires negligible expense, and incremental testing using leave-one-out cross validation is efficient. We use racing to select among various lazy learning algorithms and to find relevant features in applications ranging from robot juggling to lesion detection in MRI scans.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006556606079
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  • 10
    Electronic Resource
    Electronic Resource
    Characterization and inhibition of a cholecystokinin-inactivating serine peptidase (1996)
    [s.l.] : Nature Publishing Group
    Nature 380 (1996), S. 403-409 
    Details
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] A cholecystokinin (CCK)-inactivating peptidase was purified and identified as a membrane-bound isoform of tripeptidyl peptidase II (EC 3.4.14.10), a cytosolic subtilisin-like peptidase of previously unknown functions. The peptidase was found in neurons responding to cholecystokinin, as well as in ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/380403a0
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