alison@ Fri Aug 19 10:42:17 BST 1994 Artificial Intelligence
... Ultimate goal of AI is to imitate human thought-- artificial neural networks attempt to replicate the connectivity and functioning of biological neural networks (i.e. the human brain). Theory is that replicating the brain’s structure, the artificial network will, in turn, possess the ability to lear ...
... Ultimate goal of AI is to imitate human thought-- artificial neural networks attempt to replicate the connectivity and functioning of biological neural networks (i.e. the human brain). Theory is that replicating the brain’s structure, the artificial network will, in turn, possess the ability to lear ...
Simulating Mirror Neurons
... wvi (t) = wvi (t) + γΦ(u, v, t)(s(r) − wvi (t)) wvc (t) = wvc (t) + γΦ(u, v, t)(q(t) − wvc (t)) After the training stage, the scalar output of a neuron is calculated by combining that neuron’s input vector and context vector. The output itself yv (t) is simply yv (t) = e−dv (t) where dv (t) = (1 − α ...
... wvi (t) = wvi (t) + γΦ(u, v, t)(s(r) − wvi (t)) wvc (t) = wvc (t) + γΦ(u, v, t)(q(t) − wvc (t)) After the training stage, the scalar output of a neuron is calculated by combining that neuron’s input vector and context vector. The output itself yv (t) is simply yv (t) = e−dv (t) where dv (t) = (1 − α ...
Artificial Intelligence
... • NPCs use path finding • NPCs respond to sounds, lights, signals • NPCs co-ordinate with each other; squad tactics • Some natural language processing • Randomness can be useful ...
... • NPCs use path finding • NPCs respond to sounds, lights, signals • NPCs co-ordinate with each other; squad tactics • Some natural language processing • Randomness can be useful ...
An Evolutionary Artificial Neural Network Time Series Forecasting
... Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may ...
... Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may ...
Higher Coordination with Less Control * A Result of Information
... learning and adaption rules • Most are based on an underlying model – so they are limited to the model • They use intrinsically generated reinforcement signals [prediction errors] as an input to a learning algorithm • Need of a learning rule independent of model structure, requires less assumptions ...
... learning and adaption rules • Most are based on an underlying model – so they are limited to the model • They use intrinsically generated reinforcement signals [prediction errors] as an input to a learning algorithm • Need of a learning rule independent of model structure, requires less assumptions ...
UNIT-5 - Search
... 1. A correct answer for each example or instance is available. 2. Learning is done from known sample input and output. Unsupervised learning It is a learning pattern is which correct answers are not given for the input. It is mainly used in probabilistic learning system. Reinforcement learning Here ...
... 1. A correct answer for each example or instance is available. 2. Learning is done from known sample input and output. Unsupervised learning It is a learning pattern is which correct answers are not given for the input. It is mainly used in probabilistic learning system. Reinforcement learning Here ...
Character Recognition using Spiking Neural Networks
... IV. R ESULTS For initial testing, the network was trained using only four characters (’A’, ’B’, ’C’, and ’D’). There were 15 input neurons and 4 output neurons for this case. The training parameters used are described in the appendix. The weights were initialized to random values between 0.5 and 1.0 ...
... IV. R ESULTS For initial testing, the network was trained using only four characters (’A’, ’B’, ’C’, and ’D’). There were 15 input neurons and 4 output neurons for this case. The training parameters used are described in the appendix. The weights were initialized to random values between 0.5 and 1.0 ...
Networked Nature of Society - the Department of Computer and
... each vertex divides their current cash equally among their neighbors (or chooses a random neighbor to give it all to) each vertex thus also receives some cash from its neighbors repeat ...
... each vertex divides their current cash equally among their neighbors (or chooses a random neighbor to give it all to) each vertex thus also receives some cash from its neighbors repeat ...
School Report - Pace University Webspace
... Using this technique, the system is able to learn the components that make up each of the digits from 0 – 9 that might appear in a German zip code. After the initial learning has taken place, the similar numbers, in different order, are passed through the processor, to test the error rate. In this s ...
... Using this technique, the system is able to learn the components that make up each of the digits from 0 – 9 that might appear in a German zip code. After the initial learning has taken place, the similar numbers, in different order, are passed through the processor, to test the error rate. In this s ...
Cognitive Neuropsychology and Computational Cognitive Science
... • Units affect other units by exciting or inhibiting them • The unit takes the weighted sum of all the input links and generates a single output to another unit if the integrated input sum is above some threshold • Different rules used to change the strengths of the connections between units (learni ...
... • Units affect other units by exciting or inhibiting them • The unit takes the weighted sum of all the input links and generates a single output to another unit if the integrated input sum is above some threshold • Different rules used to change the strengths of the connections between units (learni ...
applications of artificial intelligence in structural engineering a.k.l
... assessment of flexural behaviour, multi-layer feed forward ANNs were trained to learn the relationship between input and output data generated from the available experimental data. The error correcting back propagation algorithm was used to map the relationship. The flexural behaviour of two differe ...
... assessment of flexural behaviour, multi-layer feed forward ANNs were trained to learn the relationship between input and output data generated from the available experimental data. The error correcting back propagation algorithm was used to map the relationship. The flexural behaviour of two differe ...
UNIT-5 - Search
... 1. A correct answer for each example or instance is available. 2. Learning is done from known sample input and output. Unsupervised learning It is a learning pattern is which correct answers are not given for the input. It is mainly used in probabilistic learning system. Reinforcement learning Here ...
... 1. A correct answer for each example or instance is available. 2. Learning is done from known sample input and output. Unsupervised learning It is a learning pattern is which correct answers are not given for the input. It is mainly used in probabilistic learning system. Reinforcement learning Here ...
ICT619 Intelligent Systems
... Network treated as a black box and its response to a series of test cases is evaluated ...
... Network treated as a black box and its response to a series of test cases is evaluated ...
Kristin Völk – Curriculum Vitae
... Working Title Predictiveness and prediction in classical conditioning: a Bayesian statistical model Description Classical conditioning is a rather pure form of prediction learning. Here we focus on one of its critical facets that still lacks a statistical treatment, namely, that conditioned stimuli ...
... Working Title Predictiveness and prediction in classical conditioning: a Bayesian statistical model Description Classical conditioning is a rather pure form of prediction learning. Here we focus on one of its critical facets that still lacks a statistical treatment, namely, that conditioned stimuli ...
Hydrological Neural Modeling aided by Support Vector Machines
... Modern ANNs are rooted in many disciplines, like neurosciences, mathematics, statistics, physics and engineering. They find many successful applications in such diverse fields as modeling, time series analysis, pattern recognition and signal processing, due to their ability to learn from input data ...
... Modern ANNs are rooted in many disciplines, like neurosciences, mathematics, statistics, physics and engineering. They find many successful applications in such diverse fields as modeling, time series analysis, pattern recognition and signal processing, due to their ability to learn from input data ...
Adaptive probabilistic networks - EECS Berkeley
... algorithm [5]. Our algorithm can be seen as as a variant of EM in which the \maximize" phase is carried out by a gradient-following method. Lauritzen [9] also considers the application of EM to belief networks. Spiegelhalter, Dawid, Lauritzen and Cowell [13] provide a thorough analysis of the statis ...
... algorithm [5]. Our algorithm can be seen as as a variant of EM in which the \maximize" phase is carried out by a gradient-following method. Lauritzen [9] also considers the application of EM to belief networks. Spiegelhalter, Dawid, Lauritzen and Cowell [13] provide a thorough analysis of the statis ...
DATA MINING OF INPUTS: ANALYSING MAGNITUDE AND
... simple weighted link. The network is trained using a training set of input patterns with desired outputs, using the back-propagation of error measures. The network training is terminated using a test set of patterns which are never seen by the network during training. That is, when the error on the ...
... simple weighted link. The network is trained using a training set of input patterns with desired outputs, using the back-propagation of error measures. The network training is terminated using a test set of patterns which are never seen by the network during training. That is, when the error on the ...
Toward Human-Level (and Beyond) Artificial Intelligence
... The coefficients are the most expensive part of the computation. They all involved exponentials and cost roughly 10 – 14 floating point operations each. That’s 60-84 ops/neuron/timestep (just for coefs). However, these can be efficiently computed using table lookups, i.e. precompute them and then lo ...
... The coefficients are the most expensive part of the computation. They all involved exponentials and cost roughly 10 – 14 floating point operations each. That’s 60-84 ops/neuron/timestep (just for coefs). However, these can be efficiently computed using table lookups, i.e. precompute them and then lo ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.