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Fall Symposium Series
Fall Symposium Series

... difficulty of both natural language processing and computer vision has discouraged researchers from attempting integration, although in some cases it may simplify individual tasks like collateral-based vision, resolving ambiguous sentences through the use of visual information. Developing a bridge b ...
[pdf]
[pdf]

... It also provides transparent and modular specification mechanisms for them. Using RO LL, a programmer can specify an experience class relevant for a given learning task by adding a perception mechanism for it, a routine for performing physical actions to gather such experiences, a critic that decide ...
Learning to Evaluate Conditional Partial Plans
Learning to Evaluate Conditional Partial Plans

... in order for learning to be successful. One of the problems is the closed world semantics used by most ILP algorithms. Deductor, in order to deal with incomplete knowledge that the agent has about the world, employs open-world semantics — from the mere fact that the agent is unable to prove somethin ...
Fact Sheet: Bringing Artificial Intelligence to Life
Fact Sheet: Bringing Artificial Intelligence to Life

... year1, machine learning is the fastest growing field of AI and a key computational method for expanding the field of AI. At its core, machine learning is the use of computer algorithms to make predictions based on data, allowing machines to act or think without being explicitly directed to perform s ...
DM533 Artificial Intelligence
DM533 Artificial Intelligence

... Artificial Intelligence is concerned with the general principles of rational agents and on the components for constructing them ...
Observational Learning Based on Models of - FORTH-ICS
Observational Learning Based on Models of - FORTH-ICS

... SI, SPL, AIPmotor, VIP and MT, and is assigned two tasks:(i) to form the neural codes that represent the motion of the fingers of our cognitive agent and (ii) to build a correspondence between the agent’s and the demonstrator’s actions (through the VIPSPL circuit). The process that allows the format ...
Research on Statistical Relational Learning at the
Research on Statistical Relational Learning at the

... We believe that the goal of SRL should be to learn statistical models of any type of structured information, not just (for example) relational databases or Horn knowledge bases. This includes statistical models of procedures performed by humans, and of programs in procedural languages (e.g., Java, P ...
APLICACIóN DE REDES NEuRONALES ARTIFICIALES A
APLICACIóN DE REDES NEuRONALES ARTIFICIALES A

... J. Jerez, L. Franco, E. Alba, A. Llombart-Cussac, A. Lluch, N. Ribelles, B. Munárriz and M. Martín. Improvement of Breast Cancer Relapse Prediction in High Risk Intervals Using Artificial Neural Networks. Breast Cancer Research and Treatment, 94, pp. 265--272 ...
A Relational Representation for Procedural Task Knowledge
A Relational Representation for Procedural Task Knowledge

... a robot to develop a visually grounded grammar yet rely on predefined spatial relationships and objects. These approaches are task dependent and do not easily support generalization through a unified learning approach. This related work does, however, emphasize that useful categorization of sensorim ...
Nicolas Boulanger-Lewandowski
Nicolas Boulanger-Lewandowski

... • Incorporation of large-scale multimodal data to improve music metadata quality, user experience and recommendations via machine learning. Adobe Systems, San Francisco, CA, United States Creative Technologies Lab Intern ...
Transfer Learning of Latin and Greek Characters in
Transfer Learning of Latin and Greek Characters in

... as well as new learning methods and network architectures. The ancestor of the modern neural network was the perceptron, created by Rosenblatt in 1957[1]. Perceptrons and thus connectionist models fell out of vogue with artificial intelligence researchers after their limitations were analysed by Min ...
Why Generality Is Key to Human-Level Artificial Intelligence
Why Generality Is Key to Human-Level Artificial Intelligence

... the weaker assumption that computability provides an appropriate conceptual apparatus for theories of the mind. That is, computational models can simulate human information processes, thereby either providing tools that take over specific functions or allow detailed and consistent generative descrip ...
as a PDF
as a PDF

... In the visual servoing task, BECCA demonstrated its ability to achieve better than random performance on an RL task with a 58-dimensional observationaction space, about which it had no prior knowledge. The visual servoing task was trivial and has many straightforward solutions that incorporate some ...
PDF file
PDF file

... instead of images that the brain senses and produces (e.g., sensory images and effector images). This paper informally introduces recent advances along the line of a new type of, brain-anatomy inspired, neural networks —Developmental Networks (DNs). The new theoretical results discussed here include ...
記錄 編號 6668 狀態 NC094FJU00392004 助教 查核 索書 號 學校
記錄 編號 6668 狀態 NC094FJU00392004 助教 查核 索書 號 學校

... pp. 154-156. [14] J. Y. Kuo, “A document-driven agent-based approach for business”, processes management. Information and Software Technology, 2004, Vol. 46, pp. 373-382. [15] J. Y. Kuo, S.J. Lee and C.L. Wu, N.L. Hsueh, J. Lee. Evolutionary Agents for Intelligent Transport Systems, International Jo ...
Enhancing the Explanatory Power of Intelligent, Model
Enhancing the Explanatory Power of Intelligent, Model

... Expertise might be scarce in some organizations (can propagate the expertise through the use of an ES). An ES might also be used to enhance the role of an expert by providing the necessary assistance. ...
記錄編號 6668 狀態 NC094FJU00392004 助教查核 索書號 學校名稱
記錄編號 6668 狀態 NC094FJU00392004 助教查核 索書號 學校名稱

... (eds.) Proceedings in the Third International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS'04), ACM, p. 922-929, 2004. [9] M. D'Inverno, K. Hindriks, M. Luck, “A Formal Architecture for the 3APL Agent Programming Language”, in ZB2000 ,Lecture Notes in Computer Science, Spring ...
Extinction Learning
Extinction Learning

... Western Illinois University, Macomb, IL, USA ...
lec3 - Department of Computer Science
lec3 - Department of Computer Science

... • Replace the top layer of the causal network by an RBM – This eliminates explaining away at the top-level. – It is nice to have an associative memory at the top. • Replace the sleep phase by a top-down pass starting with the state of the RBM produced by the wake phase. – This makes sure the recogni ...
Why Neurons Cannot be Detectors: Shifting Paradigms from Sherlock Holmes... Elvis Presley? Nancy A. Salay ()
Why Neurons Cannot be Detectors: Shifting Paradigms from Sherlock Holmes... Elvis Presley? Nancy A. Salay ()

... a solid information theoretic account of representation is supposed to play exactly this bridging role. Indeed, it‘s because of a sensitivity to this abstractness of the concept of representation that researchers are typically careful to call neurons detectors, primitive representations, rather than ...
Rachel Hogue`s presentation on Big Data in Education
Rachel Hogue`s presentation on Big Data in Education

... report on big data and its use in the US among consumers and businesses "Big data and other technological innovations, including new online course platforms that provide students real time feedback, promise to transform education by personalizing learning. At the same time, the federal government mu ...
artificial intelligence and life in 2030
artificial intelligence and life in 2030

... – is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason, and take action. – Computer vision and AI planning, for example, drive the video games that are now a ...
PDF file
PDF file

... can serve as class supervision [7], attention [2], [3], and storage of time information [33]. Foreseeably, there are many other functions to which we can attribute feed-backward connections to. Gallistel reviewed [5]: “This problem-specific structure, they argue, is what makes learning possible.” “N ...
Werbos_IECON05_tutorial
Werbos_IECON05_tutorial

... Hebb 1949: Intelligence As An Emergent Phenomenon or Learning ...
MS Word 97 format
MS Word 97 format

... Prerequisite: CIS 300 (Algorithms and Data Structures) and instructor permission, or CIS 500 (Analysis of Algorithms and Data Structures); basic courses in probability and statistics, databases recommended Textbook: none (course notes) Venue: Monday-Friday 8:00-10:00am, 236 Nichols Hall (lecture) an ...
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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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