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Humanoid Robots That Behave, Speak, and Think Like Humans: A
Humanoid Robots That Behave, Speak, and Think Like Humans: A

... mode). Note that TSMs are associated with each level of the HTD shown in Figure 2. Each TSM, at each level is activated by the TSM at a level above it. Each TSM consists of pattern recognition circuits that recognize TITwords and sentences, and thereby transmit them to another TSM-level either for f ...
An Integrated Approach of Learning, Planning, and Execution
An Integrated Approach of Learning, Planning, and Execution

... of the environment refers to a mapping between perceived situations, performed actions, and expected new situations. This representation would be closer to the one used in reactive planners (Brooks, 1986), than to high-level models of the environment used by deliberative planners, such as the STRIPS ...
A Survey of Current Practice and Teaching of AI
A Survey of Current Practice and Teaching of AI

... would like to teach. The survey suggested an apparent disparity between what is taught in many AI courses and what many AI colleagues would like to teach. We decided to study this issue by conducting surveys of both educators and practitioners. There have been at least two events in the past twenty ...
Preference Handling – An Introductory Tutorial
Preference Handling – An Introductory Tutorial

... but that is about it. One can find various discussions in the literature as to when and whether total or weak orderings are appropriate (for an entry point, see, e.g., (Hansson, 2001b)), but this debate is mostly inconsequential from our perspective, and we make no commitment to one or the other. De ...
Wollowski, M., Selkowitz, R., Brown, L., Goel, A
Wollowski, M., Selkowitz, R., Brown, L., Goel, A

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Extended Hidden Number Problem and Its

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... Initially, each processor is assigned one of the numbers to be added and, at the end of the computation, one of the processors stores the sum of all the numbers.  Assuming n = 16, Processors as well as numbers are labeled from 0 to 15. The sum of numbers with consecutive labels from i to j is denot ...
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What are the chances… - Advocate Health Care

... Bayes’ Rule underlies reasoning systems in artificial intelligence, decision analysis, and everyday medical decision making we often know the probabilities on the right hand side of Bayes’ Rule and wish to estimate the probability on the left. ...
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... enabling systems to learn, adapt and develop solutions to problems on their own. Various AI-related technologies, such as natural language processing (NLP), computer vision, robotics, machine learning and speech recognition, have substantially progressed over the years to coalesce into systems that ...
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Planning Algorithms for Interactive Storytelling

... nonoptimal plans, however, may produce unintelligent characters, which is most undesirable. So while optimality is not a strict requirement for a planning algorithm in an IS system, algorithms that are “too nonoptimal” are best avoided. We emphasize that care must be taken when discussing optimality ...
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The Unconscious Mind as a Means for Authentication - E

... or a few members of the individual’s IUP set. It is therefore crucial to choose an authentication task intended to measure IUPs that maximizes its stability and minimizes any temporal fluctuations. For example, let’s assume that it is viable to measure unconsciously motivated social judgments (i.e. ...
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... precise numerical probabilities. U sing qualitative probabilities could substantially reduce the effort for knowledge engineering and improve the robustness of results. We examine experimentally how well infinitesimal probabilities (the kappa-calculus of Goldszmidt and Pearl) perform a diagnostic ta ...
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6-up - SEAS

...  To generate a sequence of n words given a 1st order Markov model (i.e. conditioned on one previous word): • Fix some ordering of the vocabulary v1 v2 v3 …vk. • Use unigram method to generate an initial word w1 • For each remaining wi , 2 ≤ i ≤ n —Choose a random value ri between 0 and 1 j ...
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An Extension of the ICP Algorithm Considering Scale Factor

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The SAS System and Meta Knowledge

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EFFICIENCY OF LOCAL SEARCH WITH
EFFICIENCY OF LOCAL SEARCH WITH

... Second, we shall deal with the inverse problem which consists of estimating the number of local maxima from information deduced from the covering. Direct problem (section 4). One puts M points randomly in the search space. The question is the following: Given the statistical distribution of the rela ...
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Solving Complex Logistics Problems with Multi

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Lebeltel2000

... application of the marginalization rule. The denominator appears to be a normalization term. Consequently, by convention, we will replace it by Σ . It is well known that general Bayesian inference is a very difficult problem, which may be practically intractable. Exact inference has been proved to b ...
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Physics 6C - UCSB C.L.A.S.
Physics 6C - UCSB C.L.A.S.

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New Insights Into Emission Tomography Via Linear Programming

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Disco – Novo – GoGo Meinolf Sellmann Carlos Ans´otegui

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integrated security in cloud computing environment

... multi-tenancy, service availability, long-term viability, privileged user access and regulatory compliance. Multi-tenancy shows sharing of resources, services, storage and applications with other users, residing on same physical or logical platform at cloud provider’s premises. Defense-in-depth appr ...
Paper - George Karypis
Paper - George Karypis

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Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled ""training"" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern, while machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics, and they've become increasingly similar by integrating developments and ideas from each other.In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is ""spam"" or ""non-spam""). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform ""most likely"" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.
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