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Improving  Learning  Performance  Through  Rational ... Jonathan  Gratch*,  Steve  Chien+,  and ...
Improving Learning Performance Through Rational ... Jonathan Gratch*, Steve Chien+, and ...

... examp1e.l The challenge is to choose examples and evaluations in such a way as to maximize the likelihood of a good selection with a minimum of learning cost. Choosing the best hypothesis is problematic as the underlying probability distributions are typically unavailable. Rather than requiring a h ...
Learning Text Similarity with Siamese Recurrent
Learning Text Similarity with Siamese Recurrent

... models, but some go beyond the word level and represent text as a sequence of characters (Kim et al., 2015; Ling et al., 2015). In this paper we take the latter approach for the flexibility it affords us in dealing with out-of-vocabulary words. Representation learning through neural networks has rec ...
Computational Discovery of Communicable Knowledge
Computational Discovery of Communicable Knowledge

...  What are the conditions on the learned clauses/methods?  If the subproblem involved skill chaining, they are the conditions of the first subskill clause.  If the subproblem involved concept chaining, they are the subconcepts that held at the subproblem’s outset. ...
An e-Learning Theoretical Framework
An e-Learning Theoretical Framework

... E-Learning concept trends Today the e-learning concept, apart from technology, includes learning strategies, learning methods, and lately is very much directed to the vast possibilities of content diffusion and connection. The concept trend no longer means simply the use of a computer as an artifact ...
MS PowerPoint format
MS PowerPoint format

... – Example: “The can will rust.” (auxiliary verb versus noun) – Label: vj  s  correct word sense (e.g., s  {auxiliary verb, noun}) – Representation: m examples (labeled attribute vectors <(w1, w2, …, wn), s>) – Return: classifier f: X  V that disambiguates new x  (w1, w2, …, wn) ...
Training a Cognitive Agent to Acquire and Represent Knowledge
Training a Cognitive Agent to Acquire and Represent Knowledge

... other sources of information. Thelwall, Prabowo and Fairclough [3] mention that RSS feeds are one of the very few sources on the internet, which may be able to offer quality information relevant to specific subjects. The Knowledge Representation (KR) meta-structure used is Sowa’s CG [9], which are f ...
Lecture-09-20050914 - Kansas State University
Lecture-09-20050914 - Kansas State University

... Department of Computing and Information Sciences, KSU ...
Computational Discovery of Communicable Knowledge
Computational Discovery of Communicable Knowledge

... Each memory holds different content the agent uses in activities. ...
e-Learning and e-Ethics - International Journal of the Computer, the
e-Learning and e-Ethics - International Journal of the Computer, the

... theory about e-ethics to be proposed. Being conscious of theories and subjecting them to examination is essential because they are particularly important to change and learning. [9] Such a decisive role is to be defined for e-ethics in e-learning. E-ethics theory contains some abstract or at best sc ...
What connectionist models can learn from music
What connectionist models can learn from music

... ARTIST's responses when subject to the probe-tone technique are very similar to that of humans, and show the same evolution as children's responses as both are more and more exposed to music. Besides making predictions and testing hypotheses for psychological or neurobiological theories, we can imag ...
PDF version - PCP-net
PDF version - PCP-net

... Guttman’s (1950) unidimensional scaling technique. It is a way of displaying multivariate data in a two-dimensional space. The rows of a data matrix are plotted so that the partial order defined on those rows is maximally preserved. Interestingly, Bell (1986) developed a program for obtaining partia ...
PDF file
PDF file

... image, and the other pixels are set to be uniform gray. There may be more than one object in each window image, but for the purpose of object identification, the image is assigned with only one label. The labeled radar windows create a set of selected areas, whereas the rest of the image is ignored. ...
A New Local Move Operator for Reconstructing Gene Regulatory
A New Local Move Operator for Reconstructing Gene Regulatory

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The speed of learning instructed stimulus
The speed of learning instructed stimulus

... simplest solution consistent with neurophysiological knowledge. Additional experiments are suggested to test the hypothesis underlying the model and also to explore forgetting effects for which there were indications for the longer delay conditions. ...
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... though we see ourselves as creatures of reason, the entire field of behavioral economics and a good chunk of cognitive psychology argue that we survive using a loose collection of decent heuristics and a complete misunderstanding of statistics. Given our history, it is not surprising that we are now ...
available here - Moving AI Lab
available here - Moving AI Lab

... boundary of human knowledge on machine intelligence. We are looking for exceptional individuals from all area of artificial intelligence, who have a track record of research excellence or rich experience of building AI systems, a passion to shape the future of AI, and the potential to become an outs ...
Competitive learning
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... In contrast to supervised learning, unsupervised or self-organised learning does not require an external teacher. During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data into ...
Perception, Action, and Utility: The Tangled Skein
Perception, Action, and Utility: The Tangled Skein

... enjoyment I would derive from having a picnic in sunny weather, independently of my belief that it will be sunny tomorrow. 2.  The integral over s in equations (13.1) and (13.2) is generally intractable. Although most investigations of decision theory in psychology and economics make Gaussian or mul ...
APPLICATION OF AN EXPERT SYSTEM FOR ASSESSMENT OF
APPLICATION OF AN EXPERT SYSTEM FOR ASSESSMENT OF

... In contrast to supervised learning, unsupervised or self-organised learning does not require an external teacher. During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data into ...
An Abductive-Inductive Algorithm for Probabilistic
An Abductive-Inductive Algorithm for Probabilistic

... abducibles that express the categorical variables relevant to explain the given observations. An adaptation of the Gibbs sampling algorithm [4] is used to learn the probability distribution over the categorical variables. Abduction has also been shown to be useful in inductive learning. The XHAIL a ...
pdf (paper) - Pascal Hitzler
pdf (paper) - Pascal Hitzler

... a considerable extent, and some of this work uses FCA as a main component [9–11, 21, 31, 50]. More recently, FCA has also found application for refining, completing, and improving ontologies [3, 4, 6, 13, 19, 20, 38, 40–42, 46–48]. Indeed, it turns out that FCA is indeed used as an ontology engineer ...
Knowledge Building in User-Generated Online Virtual Realities
Knowledge Building in User-Generated Online Virtual Realities

... The services of Web 2.0 offer great opportunities for such a knowledge-building discourse. Through active user participation in the production of content, individuals have the opportunity to participate in a collective development of knowledge and, at the same time, benefit from a vast amount of kno ...
Animated Agents for Language Conversation
Animated Agents for Language Conversation

... appraisal for emotions’ model of Ortony, Clore, and Collins, also known as the OCC model (Ortony et al. 1988). Here, emotions are seen as valenced reactions to events, other agents’ actions, and objects, qualified by the agent’s goals, standards, and preferences. The OCC model groups emotion types a ...
experiments in the variety of being - Home page-
experiments in the variety of being - Home page-

... of the universe… – runs counter to the prevailing scientific and analytic spirit. There has developed a tradition that there is no room in this spirit or world view for religious sentiment. The concept of God inherited from religion is so invested, on the one hand with institution, sentiment and tra ...
INTELLIGENT AGENT PLANNING WITH QUASI
INTELLIGENT AGENT PLANNING WITH QUASI

... the agent can adapt in real-time to the changing conditions of its execution environment. There are many inductive learning algorithms that address the problem of classification. We can mention three main classes of such algorithms: decision trees, which provide an explicit symbolic result, similar ...
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Concept learning

Concept learning, also known as category learning, concept attainment, and concept formation, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as ""the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories."" More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.
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