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93
Intelligence by Design: Principles of Modularity and Coordination for Engineering Complex Adaptive Agents
, 2001
"... All intelligence relies on search  for example, the search for an intelligent agent's next action. Search is only likely to succeed in resourcebounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This d ..."
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Cited by 69 (25 self)
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All intelligence relies on search  for example, the search for an intelligent agent's next action. Search is only likely to succeed in resourcebounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation
Linear and Order Statistics Combiners for Pattern Classification
 Combining Artificial Neural Nets
, 1999
"... Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification resul ..."
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Cited by 65 (7 self)
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Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the "added" error. If N unbiased classifiers are combined by simple averaging, the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based nonlinear combiners, we derive expressions that indicate how much the median, the maximum and in general the ith order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.
Shifting Inductive Bias with SuccessStory Algorithm, Adaptive Levin Search, and Incremental SelfImprovement
 MACHINE LEARNING
, 1997
"... We study task sequences that allow for speeding up the learner's average reward intake through appropriate shifts of inductive bias (changes of the learner's policy). To evaluate longterm effects of bias shifts setting the stage for later bias shifts we use the "successstory algorithm" (SSA). SSA ..."
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Cited by 62 (30 self)
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We study task sequences that allow for speeding up the learner's average reward intake through appropriate shifts of inductive bias (changes of the learner's policy). To evaluate longterm effects of bias shifts setting the stage for later bias shifts we use the "successstory algorithm" (SSA). SSA is occasionally called at times that may depend on the policy itself. It uses backtracking to undo those bias shifts that have not been empirically observed to trigger longterm reward accelerations (measured up until the current SSA call). Bias shifts that survive SSA represent a lifelong success history. Until the next SSA call, they are considered useful and build the basis for additional bias shifts. SSA allows for plugging in a wide variety of learning algorithms. We plug in (1) a novel, adaptive extension of Levin search and (2) a method for embedding the learner's policy modification strategy within the policy itself (incremental selfimprovement). Our inductive transfer case studies...
Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning
 Neural Computation
, 2001
"... Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Errordriven backpropagation is computationally powerful, and has proven useful for modeling a range of psycholo ..."
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Cited by 45 (5 self)
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Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Errordriven backpropagation is computationally powerful, and has proven useful for modeling a range of psychological data, but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This paper demonstrates two main points about these errordriven interactive networks: (a) they generalize poorly due to attractor dynamics that interfere with the network's ability to systematically produce novel combinatorial representations in response to novel inputs; and (b) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independent...
Latent Variable Models for Neural Data Analysis
, 1999
"... The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 1011 neurons, each making an average of 10 3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. ..."
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Cited by 42 (5 self)
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The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 1011 neurons, each making an average of 10 3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis. It is divided
Active Learning with Multiple Views
, 2002
"... Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multiview domains, in which there are several disjoint subsets of features (views), each of which i ..."
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Cited by 41 (1 self)
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Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multiview domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce CoTesting, which is the first approach to multiview active learning. Second, we extend the multiview learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that CoTesting outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing. 1.
Key Concepts in Model Selection: Performance and Generalizability
 Journal of Mathematical Psychology
, 2000
"... methods of model selection, and how do they work? Which methods perform better than others, and in what circumstances? These questions rest on a number of key concepts in a relatively underdeveloped field. The aim of this essay is to explain some background concepts, highlight some of the results in ..."
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Cited by 39 (12 self)
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methods of model selection, and how do they work? Which methods perform better than others, and in what circumstances? These questions rest on a number of key concepts in a relatively underdeveloped field. The aim of this essay is to explain some background concepts, highlight some of the results in this special issue, and to add my own. The standard methods of model selection include classical hypothesis testing, maximum likelihood, Bayes method, minimum description length, crossvalidation and Akaike’s information criterion. They all provide an implementation of Occam’s razor, in which parsimony or simplicity is balanced against goodnessoffit. These methods primarily take account of the sampling errors in parameter estimation, although their relative success at this task depends on the circumstances. However, the aim of model selection should also include the ability of a model to generalize to predictions in a different domain. Errors of extrapolation, or generalization, are different from errors of parameter estimation. So, it seems that simplicity and parsimony may be an additional factor in managing these errors, in which case the standard methods of model selection are incomplete implementations of Occam’s razor. 1. WHAT IS MODEL SELECTION? William of Ockham (1285 1347/49) will always be remembered for his famous postulations of Ockham’s razor (also spelled ‘Occam’), which states that entities are not to be multiplied beyond necessity. In a similar vein, Sir Isaac Newton’s first rule of hypothesizing instructs us that we are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances. While they This paper is derived from a presentation at the Methods of Model Selection symposium at Indiana University
A computer scientist’s view of life, the universe, and everything
 Foundations of Computer Science: Potential  Theory  Cognition
, 1997
"... Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, genera ..."
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Cited by 38 (15 self)
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Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, generalization, and learning in a given universe. Preliminaries Assumptions. A long time ago, the Great Programmer wrote a program that runs all possible universes on His Big Computer. “Possible ” means “computable”: (1) Each universe evolves on a discrete time scale. (2) Any universe’s state at a given time is describable by a finite number of bits. One of the many universes is ours, despite some who evolved in it and claim it is incomputable. Computable universes. Let TM denote an arbitrary universal Turing machine with unidirectional output tape. TM’s input and output symbols are “0”, “1”, and “, ” (comma). TM’s possible input programs can be ordered
Internal Models and Anticipations in Adaptive Learning Systems
 In Proceedings of the Workshop on Adaptive Behavior in Anticipatory Learning Systems
"... The explicit investigation of anticipations in relation to adaptive behavior is a recent approach. This chapter first provides psychological background that motivates and inspires the study of anticipations in the adaptive behavior field. Next, a basic framework for the study of anticipations in ada ..."
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Cited by 34 (7 self)
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The explicit investigation of anticipations in relation to adaptive behavior is a recent approach. This chapter first provides psychological background that motivates and inspires the study of anticipations in the adaptive behavior field. Next, a basic framework for the study of anticipations in adaptive behavior is suggested. Different anticipatory mechanisms are identified and characterized. First fundamental distinctions are drawn between implicit anticipatory behavior, payoff anticipatory behavior, sensory anticipatory behavior, and state anticipatory behavior. A case study allows further insights into the drawn distinctions.