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Machine Condition Monitoring Using Artificial Intelligence: The
Machine Condition Monitoring Using Artificial Intelligence: The

... system. Natural intelligence is difficult to reproduce, for example, a person may reach a conclusion but at some later date may be unable to re-create the reasoning process that led to that conclusion or to even recall the assumption that were a part of the decision. ...
A Comparative Utility Analysis of Case
A Comparative Utility Analysis of Case

... crucial for the study of the utility problem because many utility problems arise due to interactions between the functional level of the system and the way that functional computation is actually implemented. For a comparative analysis to be successful, the AI systems being studied must be modeled w ...
Aalborg Universitet Parameter learning in MTE networks using incomplete data
Aalborg Universitet Parameter learning in MTE networks using incomplete data

... represented by an MTE having the same structure as in Equation 3. The problem is that the split points would then be (linear) functions of at least one of the continuous variables, which is not consistent with the MTE framework (see ...
Ontology Integration Experienced on Medical Terminologies
Ontology Integration Experienced on Medical Terminologies

... should occur only once in an integrated ontology. The existence of synonyms and homonyms causes problems for this kind of integration. However, a much bigger problem is the existence of subtle differences between implemented concepts that have the same name and stand, vaguely, for the same (concrete ...
Complete Workshop Proceedings
Complete Workshop Proceedings

... The term higher cognitive abilities can be identified with all forms of cognition which essentially include a deliberative aspect like reasoning, planning, game playing, learning, problem solving etc. In particular, purely reactive behaviors or behaviors which can be reduced to mere conditioning are ...
Emotion and Robotics
Emotion and Robotics

... •  Observe a human and infer his/her emotion •  Approaches:–  Speech Tone Recognition –  Facial Expression Recognition ...
Spike-timing-dependent plasticity: common themes
Spike-timing-dependent plasticity: common themes

... Miller 1986) leading to rate based learning rules. Markram et al. (1997), however, showed that synapses can be robustly weakened if the presynaptic spike arrived shortly after the postsynaptic spike and that the transition between potentiation and depression is very sharp (Fig. 1B). Later studies co ...
Identifying and Accounting for Task-Dependent Bias in Crowdsourcing
Identifying and Accounting for Task-Dependent Bias in Crowdsourcing

... opinion of a large number of individuals may be incorrect. Such systematic errors can be viewed as being analogous to erroneous, universally perceived optical illusions that are induced by certain visual patterns. Task-dependent biases can be based upon visual or other properties of tasks. Worker bi ...
Aalborg Universitet The Meaning of Action
Aalborg Universitet The Meaning of Action

... the development of robust approaches for representing and recognizing the actions. There is strong neurobiological evidence that human actions and activities are directly connected to the motor control of the human body [43, 94, 95]. When viewing other agents performing an action, the human visual s ...
IV. Model Application: the UAV Autonomous Learning in Unknown
IV. Model Application: the UAV Autonomous Learning in Unknown

... that from real brains. And this will help analyzing the relations between neuron activities and biological rules. (3) Brain-inspired models are built using spiking neural network, in which the spike is a strong information-coding unit. Information is also encoded by spikes within brains. This inform ...
Fuzzy Logic and Neural Nets
Fuzzy Logic and Neural Nets

... Fuzzy Rules Example (from Gems) • Rules for controlling a car: – Variables are distance to car in front and how fast it is changing, delta, and acceleration to apply – Sets are: • Very small, small, perfect, big, very big - for distance • Shrinking fast, shrinking, stable, growing, growing fast for ...
Using Reinforcement Learning to Spider the Web Efficiently
Using Reinforcement Learning to Spider the Web Efficiently

... The majority of the pages in many computer science department web sites do not contain links to research papers, but instead are about courses, homework, schedules and admissions information. Avoiding whole branches and neighborhoods of departmental web graphs can significantly improve efficiency an ...
Neuro-fuzzy systems
Neuro-fuzzy systems

... Fuzzy logic systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. These limitations have been a central driving force behind the creation of intelligent hybrid systems where two o ...
PDF - Tuan Anh Le
PDF - Tuan Anh Le

... models as programs that include sample and observe statements (Gordon et al., 2014). Both sample and observe are functions that specify random variables in this generative model using probability distribution objects as an argument, while observe, in addition, specifies the conditioning of this rand ...
Connectionism and Information Processing Abstractions
Connectionism and Information Processing Abstractions

... even subscribe to any kind of information processing or representational language in talking about mental phenomena. Those who do accept the need for information processing of some type nevertheless reject processing of labeled symbols and look to analog, or continuous, processes as the natural medi ...
Prediction of lower extremities` movement by angle–angle diagrams
Prediction of lower extremities` movement by angle–angle diagrams

... mainly due their prospective application in the myoelectrical prostheses’ control systems. In our study of gait, we apply new methods that are based on the analysis of gait angles by cyclograms (also called angle–angle diagrams or cyclokinograms) and artificial intelligence and allow us to predict t ...
Learning Grounded Language through Situated Interactive Instruction
Learning Grounded Language through Situated Interactive Instruction

... of the message. This static parse of the sentence is categorized further as an action-command, goaldescription, descriptive-sentence, etc. 2. Intention Association: Based on the categorization of the sentence and the information contained in it, the agent associates an intention (purpose) with the s ...
Goal-Based Action Priors - Humans to Robots Laboratory
Goal-Based Action Priors - Humans to Robots Laboratory

... manipulating objects in an environment, an object can be placed anywhere in a large set of locations. The size of the state space explodes exponentially with the number of objects, which bounds the placement problems that the robot is able to expediently solve. Depending on the goal, any of these st ...
www.tech.plym.ac.uk
www.tech.plym.ac.uk

... • Several possible representations, but the choice fell on ‘adaptive networks’ Tony Belpaeme VUB AI-lab ...
2. Computers: The Machines Behind Computing.
2. Computers: The Machines Behind Computing.

... Copyright ©2016 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly acce ssible website, in whole or in part. ...
over deliver
over deliver

... As Management Consultant Tom Peters says, “Quality is important, to be sure, so is absolute response time, and price, but at the top of most lists, by far, is keeping your word.” With uncertainty rising, if you ‘under promise, over deliver,’ you will not only keep the customers satisfied; you'll kee ...
Birds, primates, and spoken language origins: behavioral
Birds, primates, and spoken language origins: behavioral

... Vocal learners such as humans and songbirds can learn to produce elaborate patterns of structurally organized vocalizations, whereas many other vertebrates such as non-human primates and most other bird groups either cannot or do so to a very limited degree. To explain the similarities among humans ...
deep variational bayes filters: unsupervised learning of state space
deep variational bayes filters: unsupervised learning of state space

... latent system variables? These two tasks are competing: A more powerful representation of system requires more computationally demanding inference, and efficient inference, such as the well-known Kalman filters, Kalman & Bucy (1961), can prohibit sufficiently complex system classes. Leveraging a rec ...
The neural correlates of implicit and explicit sequence learning
The neural correlates of implicit and explicit sequence learning

... logic also naturally yields dichotomous characterizations of the contrast between implicit and explicit learning. However, studies based on this logic overlook the fact that even carefully designed learning and testing conditions can hardly be considered as “process-pure” (Reingold and Merikle 1988; ...
IT7005B-Artificial Intelligence UNIT WISE Important Questions
IT7005B-Artificial Intelligence UNIT WISE Important Questions

... 2. Write the two functions of KB agent. 3. Define inference. 4. Write short notes on unification. 5. Define logic. 6. Define entailment. 7. Define truth preserving in logic. 8. Differentiate forward and backward chaining. 9. Define inference procedure. 10. Define logical inference or deduction. 11. ...
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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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