A Case Study in Developmental Robotics
... a common one. For example, in an article (Barto et al., 1995) it was shown that majority of reinforcement learning (RL) algorithms are properly treated within the dynamic programming framework. RL methods which use dynamic programming as mathematical framework, require that environment be represente ...
... a common one. For example, in an article (Barto et al., 1995) it was shown that majority of reinforcement learning (RL) algorithms are properly treated within the dynamic programming framework. RL methods which use dynamic programming as mathematical framework, require that environment be represente ...
A Fast and Accurate Online Sequential Learning Algorithm for
... as soon as the learning procedure for that particular (single or chunk of) observation(s) is completed. 4) The learning algorithm has no prior knowledge as to how many training observations will be presented. OS-ELM originates from the batch learning extreme learning machine (ELM) [20]–[22], [27], [ ...
... as soon as the learning procedure for that particular (single or chunk of) observation(s) is completed. 4) The learning algorithm has no prior knowledge as to how many training observations will be presented. OS-ELM originates from the batch learning extreme learning machine (ELM) [20]–[22], [27], [ ...
Substitutive competition: Virtual pets as competitive buffers to
... provide pupils with avatars will engage the pupils’ interest in the virtual learning environment, a method suggested by previous studies (Feldon & Kafai, 2008). Each pupil owns a My-Pet, and his or her game goal is to take good care of it. The pet’s current status is indicated by several numerical a ...
... provide pupils with avatars will engage the pupils’ interest in the virtual learning environment, a method suggested by previous studies (Feldon & Kafai, 2008). Each pupil owns a My-Pet, and his or her game goal is to take good care of it. The pet’s current status is indicated by several numerical a ...
Feature Markov Decision Processes
... (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). It is an art performed by human designers to extract the right state representation out of the bare observations, i. ...
... (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). It is an art performed by human designers to extract the right state representation out of the bare observations, i. ...
iat.9.05 - Web Intelligence Consortium
... For each memory, a cognitive architecture also commits to: the encoding of contents in that memory; the organization of structures within the memory; the connections among structures across memories. Most cognitive architectures rely upon formalisms similar to predicate calculus that express r ...
... For each memory, a cognitive architecture also commits to: the encoding of contents in that memory; the organization of structures within the memory; the connections among structures across memories. Most cognitive architectures rely upon formalisms similar to predicate calculus that express r ...
The Non-Action-Centered
... Psychologically oriented cognitive architectures: “intelligent” systems that are cognitively realistic; detailed cognitive theories that have been tested through capturing and explaining psychological data; and so on They help to shed new light on human cognition and therefore they are useful to ...
... Psychologically oriented cognitive architectures: “intelligent” systems that are cognitively realistic; detailed cognitive theories that have been tested through capturing and explaining psychological data; and so on They help to shed new light on human cognition and therefore they are useful to ...
REASONING ANd dECISION - Université Paul Sabatier
... The goal of this theory is to explain and predict social actors’ choices in strategic interaction contexts, that is, when the choice of a given agent depends on what other agents decide to do. Classical game theory is based on a very simple conceptual frame including the concepts of preference and a ...
... The goal of this theory is to explain and predict social actors’ choices in strategic interaction contexts, that is, when the choice of a given agent depends on what other agents decide to do. Classical game theory is based on a very simple conceptual frame including the concepts of preference and a ...
Hebbian learning - Computer Science | SIU
... 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 i ...
... 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 i ...
Read paper The big issues
... environment declaratively . . . [A declarative can] be used by the machine even for purposes unforeseen by the machine's designer, it [can] more easily be modified than could knowledge embodied in programs, and it facilitate[s] communication between machine and other machines and humans. [33] For Ni ...
... environment declaratively . . . [A declarative can] be used by the machine even for purposes unforeseen by the machine's designer, it [can] more easily be modified than could knowledge embodied in programs, and it facilitate[s] communication between machine and other machines and humans. [33] For Ni ...
Motivated Learning for Machine Intelligence_ Nov
... Exploration is needed in order to learn and to model the environment. But is exploration the only motivation we need to develop EI? Can we find a more efficient mechanism for learning? ...
... Exploration is needed in order to learn and to model the environment. But is exploration the only motivation we need to develop EI? Can we find a more efficient mechanism for learning? ...
Artificial Neural Networks - Introduction -
... The strength of the interconnections between neurons is implemented by means of the synaptic weights used to store the knowledge. The learning process is a procedure of the adapting the weights with a learning algorithm in order to capture the knowledge. On more mathematically, the aim of the lear ...
... The strength of the interconnections between neurons is implemented by means of the synaptic weights used to store the knowledge. The learning process is a procedure of the adapting the weights with a learning algorithm in order to capture the knowledge. On more mathematically, the aim of the lear ...
An information-theoretic approach to curiosity
... The partition function Z(x) = a∈A pπt (a)e λ Q (x,a) ensures normalization. This solution is similar to Boltzmann exploration, also known as softmax action selection [23]. The only difference is that here, we have an additional “complexity penalty”, log pπt (a). We note that by a similar calculation ...
... The partition function Z(x) = a∈A pπt (a)e λ Q (x,a) ensures normalization. This solution is similar to Boltzmann exploration, also known as softmax action selection [23]. The only difference is that here, we have an additional “complexity penalty”, log pπt (a). We note that by a similar calculation ...
- EdShare
... seek examples, cope with multiple concepts and seek its own classification by an oracle, experiments, or clustering ...
... seek examples, cope with multiple concepts and seek its own classification by an oracle, experiments, or clustering ...
Neural Networks, Fuzzy Models and Dynamic Logic. Chapter in R
... Modeling field theory [40], summarized below, associates lower-level signals with higher-level concept-models (or internal representations), resulting in understanding of signals, while overcoming the difficulties of CC described in Section 2. It is achieved by using measures of similarity between t ...
... Modeling field theory [40], summarized below, associates lower-level signals with higher-level concept-models (or internal representations), resulting in understanding of signals, while overcoming the difficulties of CC described in Section 2. It is achieved by using measures of similarity between t ...
Adding Data Mining Support to SPARQL via Statistical
... the category of a WSDL web service and to recommend it to the user for further annotation. They treated the determination of a web service’s category as a text classification problem and applied traditional data mining algorithms, such as Naive Bayes and Support Vector Machines. Our second experimen ...
... the category of a WSDL web service and to recommend it to the user for further annotation. They treated the determination of a web service’s category as a text classification problem and applied traditional data mining algorithms, such as Naive Bayes and Support Vector Machines. Our second experimen ...
Robot Learning, Future of Robotics
... "So... what does the thinking?" "You're not understanding, are you? The brain does the thinking. The meat.“ "Thinking meat! You're asking me to believe in thinking meat!“ "Yes, thinking meat! Conscious meat! Loving meat. Dreaming meat. The meat is the whole deal! Are you getting the ...
... "So... what does the thinking?" "You're not understanding, are you? The brain does the thinking. The meat.“ "Thinking meat! You're asking me to believe in thinking meat!“ "Yes, thinking meat! Conscious meat! Loving meat. Dreaming meat. The meat is the whole deal! Are you getting the ...
A physics approach to classical and quantum machine learning
... – artificial intelligence (AI) and its applications – projective simulation (PS) model, a physical approach to AI ◦ Standard (classical) PS agent – benchmarking (grid-world and mountain-car problems) – generalization within PS Model ◦ Quantum PS agent – implementation of a quantum agent ...
... – artificial intelligence (AI) and its applications – projective simulation (PS) model, a physical approach to AI ◦ Standard (classical) PS agent – benchmarking (grid-world and mountain-car problems) – generalization within PS Model ◦ Quantum PS agent – implementation of a quantum agent ...
NeuralNets
... • Analogy to biological neural systems, the most robust learning systems we know. • Attempt to understand natural biological systems through computational modeling. ...
... • Analogy to biological neural systems, the most robust learning systems we know. • Attempt to understand natural biological systems through computational modeling. ...
PDF
... In reinforcement learning, there is a tradeoff between spending time acting in the environment and spending time planning what actions are best. Model-free methods take one extreme on this question— the agent updates only the state most recently visited. On the other end of the spectrum lie classica ...
... In reinforcement learning, there is a tradeoff between spending time acting in the environment and spending time planning what actions are best. Model-free methods take one extreme on this question— the agent updates only the state most recently visited. On the other end of the spectrum lie classica ...
Spike-Timing-Dependent Hebbian Plasticity as
... is conveyed back to the dendritic locations of synapses by backpropagating action potentials from the soma (Stuart & Sakmann, 1994). In this article, we explore the hypothesis that recurrent excitation in neocortical circuits subserves the function of prediction and generation of temporal sequences ...
... is conveyed back to the dendritic locations of synapses by backpropagating action potentials from the soma (Stuart & Sakmann, 1994). In this article, we explore the hypothesis that recurrent excitation in neocortical circuits subserves the function of prediction and generation of temporal sequences ...
Intelligent Systems - Teaching-WIKI
... "test set”, which must not be used during training. – The test set must represent the cases that the ANN should generalize to. A re-run with the test set provides an unbiased estimate of the generalization error, provided that the test set was chosen randomly. – The disadvantage of split-sample vali ...
... "test set”, which must not be used during training. – The test set must represent the cases that the ANN should generalize to. A re-run with the test set provides an unbiased estimate of the generalization error, provided that the test set was chosen randomly. – The disadvantage of split-sample vali ...
AI AND MACHINE LEARNING TECHNIQUES FOR MANAGING
... Abstract: The application of pattern recognition (PR) techniques, expert systems (ESs), artificial neural networks (ANNs), fuzzy systems (FSs) and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. On ...
... Abstract: The application of pattern recognition (PR) techniques, expert systems (ESs), artificial neural networks (ANNs), fuzzy systems (FSs) and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. On ...
Dynamically Adaptive Tutoring Systems: Bottom-Up or Top
... but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules pla ...
... but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules pla ...
Incremental Ensemble Learning for Electricity Load Forecasting
... approaches was also presented in the literature. The best known methods for homogeneous ensemble learning are bagging [6] and boosting [13]. These approaches have been shown to be very effective in improving the accuracy of base models. To accomplish adaptive ensemble learning for online stream envi ...
... approaches was also presented in the literature. The best known methods for homogeneous ensemble learning are bagging [6] and boosting [13]. These approaches have been shown to be very effective in improving the accuracy of base models. To accomplish adaptive ensemble learning for online stream envi ...