Evolutionary Connectionism and Mind/Brain Modularity - laral
... Brain/mind modularity is a contentious issue in cognitive science. Cognitivists tend to conceive of the mind as a set of distinct specialized modules and they believe that this rich modularity is basically innate. Cognitivist modules are theoretical entities which are postulated in “boxes-and-arrows ...
... Brain/mind modularity is a contentious issue in cognitive science. Cognitivists tend to conceive of the mind as a set of distinct specialized modules and they believe that this rich modularity is basically innate. Cognitivist modules are theoretical entities which are postulated in “boxes-and-arrows ...
Bounded Seed-AGI
... instance of the right-hand pattern is observed (such an instance being a goal), then a sub-goal patterned after the left-hand pattern is produced. Additionally, when an input (other than a goal or a prediction) matches a RT, an assumption is produced, patterned after the LT. Notice that multiple ins ...
... instance of the right-hand pattern is observed (such an instance being a goal), then a sub-goal patterned after the left-hand pattern is produced. Additionally, when an input (other than a goal or a prediction) matches a RT, an assumption is produced, patterned after the LT. Notice that multiple ins ...
PowerPoint-Präsentation
... (n-1) < t < n. This choice requires a central clock or pacemaker and is sensitive to timing errors. Asynchronous or Sequential (more natural for both brains and artificial networks) All neurons are updated one by one, where one can proceed in either of two ways: at each time step, select at random a ...
... (n-1) < t < n. This choice requires a central clock or pacemaker and is sensitive to timing errors. Asynchronous or Sequential (more natural for both brains and artificial networks) All neurons are updated one by one, where one can proceed in either of two ways: at each time step, select at random a ...
Guest Editorial Applications Of Artificial Neural Networks To Image
... the selection of input data, the number of the hidden units and the learning problems are studied. Experimental results are presented for real scene imagery. Finally, the last three papers focus primarily on applications of neural networks to image prediction and compression. In the paper by Wang et ...
... the selection of input data, the number of the hidden units and the learning problems are studied. Experimental results are presented for real scene imagery. Finally, the last three papers focus primarily on applications of neural networks to image prediction and compression. In the paper by Wang et ...
Representing Probabilistic Rules with Networks of
... play. The former method provides no more than a general impression; the latter forces the human to redo the entire learning process. It would be extremely helpful if it was possible to automatically construct readable higher level descriptions of the stored network knowledge. So far we only discusse ...
... play. The former method provides no more than a general impression; the latter forces the human to redo the entire learning process. It would be extremely helpful if it was possible to automatically construct readable higher level descriptions of the stored network knowledge. So far we only discusse ...
Does machine learning really work?
... which customers were later found to purchase certain items. Universities have data on which students were later found to successfully graduate. Manufacturers have time-series data on which process parameters later produced flawed or optimal products. Figure 2 illustrates a number of time-series pred ...
... which customers were later found to purchase certain items. Universities have data on which students were later found to successfully graduate. Manufacturers have time-series data on which process parameters later produced flawed or optimal products. Figure 2 illustrates a number of time-series pred ...
A Taxonomy of the Evolution of Artificial Neural Systems Helmut A
... parallel search, as complete ANN structures are evolved and evaluated based on the performance of the trained ANN structure. Besides the definition of the fitness function, the other main design issue in all applications of EAs is the genetic encoding of an individual (solution). As pointed out abov ...
... parallel search, as complete ANN structures are evolved and evaluated based on the performance of the trained ANN structure. Besides the definition of the fitness function, the other main design issue in all applications of EAs is the genetic encoding of an individual (solution). As pointed out abov ...
Hive Collective Intelligence for Cloud Robotics
... deployed through the Robotic Applications Platform, as a means to create a hive controller that will be able to learn from stimuli from different interactions, whilst outperforming traditional architectures. Robots, and especially humanoid robots, lack computational performance, an inherent hinderin ...
... deployed through the Robotic Applications Platform, as a means to create a hive controller that will be able to learn from stimuli from different interactions, whilst outperforming traditional architectures. Robots, and especially humanoid robots, lack computational performance, an inherent hinderin ...
IOSR Journal of Computer Engineering (IOSRJCE)
... amount of knowledge but a human has to read and interpret the knowledge for it to be used. A computer program designed to model the problem solving ability of a human expert. Expert systems and Artificial intelligence encompasses such diverse activities as game playing, automated reasoning, natural ...
... amount of knowledge but a human has to read and interpret the knowledge for it to be used. A computer program designed to model the problem solving ability of a human expert. Expert systems and Artificial intelligence encompasses such diverse activities as game playing, automated reasoning, natural ...
Cognitive Primitives for Automated Learning
... processed response to an input. Thus we could say learning is a process through which external inputs enable acquiring knowledge and/or skill that affect the state of an organism such that its subsequent behavior may be altered. Many have addressed the ways and means by which humans receive external ...
... processed response to an input. Thus we could say learning is a process through which external inputs enable acquiring knowledge and/or skill that affect the state of an organism such that its subsequent behavior may be altered. Many have addressed the ways and means by which humans receive external ...
1 HYBRID EXPERT SYSTEM OF ROUGH SET AND NEURAL
... this paper a new method for pre-processing data for neural network based on rough set has been developed and merged with neural expert system. The process consists of acquisition of data, formalization of decision tables, reduction of attributes, and binarization of input data [7, 15]. First decisio ...
... this paper a new method for pre-processing data for neural network based on rough set has been developed and merged with neural expert system. The process consists of acquisition of data, formalization of decision tables, reduction of attributes, and binarization of input data [7, 15]. First decisio ...
Spiking Neurons with Boltzmann-like Properties to
... and N o is the winner otherwise. The answer is thus easily determined correct or not. This was all done for a given network, but networks are quite random so, it was run over 100 networks per test condition. In table 2, different numbers of neurons in the Gas subnet are shown. Neurons in Gas % Corre ...
... and N o is the winner otherwise. The answer is thus easily determined correct or not. This was all done for a given network, but networks are quite random so, it was run over 100 networks per test condition. In table 2, different numbers of neurons in the Gas subnet are shown. Neurons in Gas % Corre ...
Swarm intelligence
... A group of birds are randomly searching food in an area. There is only one piece of food in this area. None of the birds know the location of the food, but they know how far away they are at any time. ...
... A group of birds are randomly searching food in an area. There is only one piece of food in this area. None of the birds know the location of the food, but they know how far away they are at any time. ...
Protocol S1
... are not likelihood equivalent since the predictive densities disagree, f(q1)f(y1|q1)f(y2|y1,q2)f(q2)≠ f(q2)f(y2|q2)f(y1|y2,q1)f(q1). Therefore, we distinguish between models M*1 and M*2 using a LOD score that conditions on genotypes at multiple QTL (derived from earlier gene mapping of phenotypes). ...
... are not likelihood equivalent since the predictive densities disagree, f(q1)f(y1|q1)f(y2|y1,q2)f(q2)≠ f(q2)f(y2|q2)f(y1|y2,q1)f(q1). Therefore, we distinguish between models M*1 and M*2 using a LOD score that conditions on genotypes at multiple QTL (derived from earlier gene mapping of phenotypes). ...
Neural-Symbolic Learning and Reasoning: Contributions and
... d'Avila Garcez, Lamb, and Gabbay (2009) for an overview). Meanwhile, there has been some suggestive recent work showing that neural networks can learn entire sequences of actions, thus amounting to "mental simulation" of some concrete, temporally extended activity. There is also a very well develope ...
... d'Avila Garcez, Lamb, and Gabbay (2009) for an overview). Meanwhile, there has been some suggestive recent work showing that neural networks can learn entire sequences of actions, thus amounting to "mental simulation" of some concrete, temporally extended activity. There is also a very well develope ...
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.