Search Techniques in AI and Robotics AI and
... States are not given, continuous and often hard to characterize On-line search Planning and execution have to be interleaved Real-time constraints exist Search space might or might not fit into memory ...
... States are not given, continuous and often hard to characterize On-line search Planning and execution have to be interleaved Real-time constraints exist Search space might or might not fit into memory ...
Evolving Connectionist and Fuzzy-Connectionist Systems for
... The complexity and the dynamics of many real-world problems, especially in engineering and manufacturing, require using sophisticated methods and tools for building on-line, adaptive decision making and control systems (OLADECS). Such systems should be able to 'grow' as they work. They should be abl ...
... The complexity and the dynamics of many real-world problems, especially in engineering and manufacturing, require using sophisticated methods and tools for building on-line, adaptive decision making and control systems (OLADECS). Such systems should be able to 'grow' as they work. They should be abl ...
Supervised learning - TKK Automation Technology Laboratory
... • Build and train a multilayer feedforward backpropagation neural network to estimate optimal controls for a flying robot! • Data (matrices P and T) is in “superdata.mat” • Input data (P) is recorded from four successful runs through a certain zig-zag route (Red Bull Air Race etc) using a simulator. ...
... • Build and train a multilayer feedforward backpropagation neural network to estimate optimal controls for a flying robot! • Data (matrices P and T) is in “superdata.mat” • Input data (P) is recorded from four successful runs through a certain zig-zag route (Red Bull Air Race etc) using a simulator. ...
Invited Paper Neural networks in engineering D.T. Pham Intelligent
... Introduction Neural networks are computational models of the brain. There are over 50 different neural network models, some based more closely on current understanding of the brain operation than others. However, in general neural networks all have two of the brain's important characteristics: a par ...
... Introduction Neural networks are computational models of the brain. There are over 50 different neural network models, some based more closely on current understanding of the brain operation than others. However, in general neural networks all have two of the brain's important characteristics: a par ...
Artifical Neural Networks (ANN) - In data pattern recognition for
... 6.2 Tips and ideas for future work ............................................... 39 7 References ..................................................................................... 41 ...
... 6.2 Tips and ideas for future work ............................................... 39 7 References ..................................................................................... 41 ...
What is a Neural Network?
... • It can organize its own structure (connected neurons) to perform certain computations much faster than current computers Neural Networks and Learning Machines, Third Edition Simon Haykin ...
... • It can organize its own structure (connected neurons) to perform certain computations much faster than current computers Neural Networks and Learning Machines, Third Edition Simon Haykin ...
Speech to UML: An Intelligent Modeling Tool for Software Engineering
... UML is supported by several CASE-tools where the models are presented, constructed, and manipulated as diagrams. According to our experience, many of the potential UML CASE-tool users feel that the tools do not offer enough support for software engineering, reducing the tools to the level of ordinar ...
... UML is supported by several CASE-tools where the models are presented, constructed, and manipulated as diagrams. According to our experience, many of the potential UML CASE-tool users feel that the tools do not offer enough support for software engineering, reducing the tools to the level of ordinar ...
OpenCog: A Software Framework for Integrative Artificial General
... they result in human-level intelligence. In this vein, Steven Mithen [2] has provided powerful albeit somewhat speculative vision of modern human intelligence as the integration of components that evolved relatively discretely in prehuman minds. On the other hand, most of the work in the AI field to ...
... they result in human-level intelligence. In this vein, Steven Mithen [2] has provided powerful albeit somewhat speculative vision of modern human intelligence as the integration of components that evolved relatively discretely in prehuman minds. On the other hand, most of the work in the AI field to ...
The Format of the IJOPCM, first submission
... Time – Delay Single Layer Artificial Neural Network Models for Estimating Shelf Life of Burfi 1. Introduction Artificial neural network (ANN) consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases an ANN i ...
... Time – Delay Single Layer Artificial Neural Network Models for Estimating Shelf Life of Burfi 1. Introduction Artificial neural network (ANN) consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases an ANN i ...
Pathfinding in Computer Games
... There are four neighbouring nodes to (1,1) which are E(1,0), (2,1), (1,2), (2,2) respectively. Since E(1,0) is the only node, which is not on either of the lists, it is now looked at. Given that all the neighbours of (1,1) have been looked at, it is added to the Closed list. Since E(1,0) is the end ...
... There are four neighbouring nodes to (1,1) which are E(1,0), (2,1), (1,2), (2,2) respectively. Since E(1,0) is the only node, which is not on either of the lists, it is now looked at. Given that all the neighbours of (1,1) have been looked at, it is added to the Closed list. Since E(1,0) is the end ...
Introduction to Artificial Neural Networks (ANNs)
... IBM’s DECtalk: several man years of work → Reading machine. NETtalk: 10 hours of backprop training on a 1000-word text, T1000. 95% accuracy on T1000; 78% accuracy on novel text. Improvement during training sounds like a child learning to read. Concept layer is key. 79 different (overlapping) clouds ...
... IBM’s DECtalk: several man years of work → Reading machine. NETtalk: 10 hours of backprop training on a 1000-word text, T1000. 95% accuracy on T1000; 78% accuracy on novel text. Improvement during training sounds like a child learning to read. Concept layer is key. 79 different (overlapping) clouds ...
Michael Arbib: CS564 - Brain Theory and Artificial Intelligence
... Peter Dominey, Michael Arbib, and Amanda Alexander Supplementary Reading in the NSL Book: Crowley-Arbib Saccade Model M. Crowley, E. Oztop, and S. Marmol ...
... Peter Dominey, Michael Arbib, and Amanda Alexander Supplementary Reading in the NSL Book: Crowley-Arbib Saccade Model M. Crowley, E. Oztop, and S. Marmol ...
now
... components of the system. Moreover, the rules specifying interactions among the system's components are executed using only local information, without reference to the global pattern” ...
... components of the system. Moreover, the rules specifying interactions among the system's components are executed using only local information, without reference to the global pattern” ...
495-210
... such areas and reaches any problem wherein large groups of states – combinatorial problems -- must be generated or scanned by search algorithms. This versatility relies upon the way constraint programming attack problems, separating their definition from the solving method employed. Models are defin ...
... such areas and reaches any problem wherein large groups of states – combinatorial problems -- must be generated or scanned by search algorithms. This versatility relies upon the way constraint programming attack problems, separating their definition from the solving method employed. Models are defin ...
Artificial Neural Networks
... • After training for a long time on a string of half a billion characters from English Wikipedia, he got it to generate new text. – It generates by predicting the probability distribution for the next character and then sampling a character from this distribution. ...
... • After training for a long time on a string of half a billion characters from English Wikipedia, he got it to generate new text. – It generates by predicting the probability distribution for the next character and then sampling a character from this distribution. ...
Chapter 13 Multimedia and Artificial Intelligence
... Cognitive Science Applications An expert system attempts to embody human expertise in a particular field. • Authorities in a field of study develop expert systems with the help of a knowledge engineer, a special type of programmer who specializes in building a knowledge base consisting of a set of r ...
... Cognitive Science Applications An expert system attempts to embody human expertise in a particular field. • Authorities in a field of study develop expert systems with the help of a knowledge engineer, a special type of programmer who specializes in building a knowledge base consisting of a set of r ...
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 ...
Learning Macro-Actions in Reinforcement Learning
... McCallum, 1995; Hansen, Barto & Zilberstein, 1997; Burgard et aI., 1998). In this work we are not specially concerned with non-Markov problems. However the results in this paper suggest that some methods for partially observable MDP could be applied to MDPs and result in faster learning. The difficu ...
... McCallum, 1995; Hansen, Barto & Zilberstein, 1997; Burgard et aI., 1998). In this work we are not specially concerned with non-Markov problems. However the results in this paper suggest that some methods for partially observable MDP could be applied to MDPs and result in faster learning. The difficu ...
Lecture 14
... At first sight there are a number of problems with evolving program code, including ...
... At first sight there are a number of problems with evolving program code, including ...
pdf file - Plymouth University
... cells) and migration (the new cells can move in 2D space). The genotype-tophenotype process starts with a single cell which, by undergoing a number of duplication and migration processes, produces a collection of neurons arranged in a 2D space. At the end of this stage the neurons grow their axons a ...
... cells) and migration (the new cells can move in 2D space). The genotype-tophenotype process starts with a single cell which, by undergoing a number of duplication and migration processes, produces a collection of neurons arranged in a 2D space. At the end of this stage the neurons grow their axons a ...
SPECTR1
... simulate complex situations by means of stochastic variables are called Monte Carlo simulations. One can use the Monte Carlo simulation to simulate a manufacturing process, a queueing process, the life cycle of a vehicle, or almost any well-defined process. The Monte Carlo simulation is a very power ...
... simulate complex situations by means of stochastic variables are called Monte Carlo simulations. One can use the Monte Carlo simulation to simulate a manufacturing process, a queueing process, the life cycle of a vehicle, or almost any well-defined process. The Monte Carlo simulation is a very power ...
Towards comprehensive foundations of computational intelligence.
... high level cognition, using the symbolic knowledge modeling to solve problems that require sequential reasoning, planning and understanding of language, but ignoring learning and associative memories. High-level cognition requires different approach than perception-action sequences at the lower cog ...
... high level cognition, using the symbolic knowledge modeling to solve problems that require sequential reasoning, planning and understanding of language, but ignoring learning and associative memories. High-level cognition requires different approach than perception-action sequences at the lower cog ...
USC Brain Project Specific Aims
... Our current representation of hand state defines a 7-dimensional trajectory F(t) with the following components F(t) = (d(t), v(t), a(t), o1(t), o2(t), o3(t), o4(t)): d(t): distance to target at time t v(t): tangential velocity of the wrist a(t): Aperture of the virtual fingers involved in grasping a ...
... Our current representation of hand state defines a 7-dimensional trajectory F(t) with the following components F(t) = (d(t), v(t), a(t), o1(t), o2(t), o3(t), o4(t)): d(t): distance to target at time t v(t): tangential velocity of the wrist a(t): Aperture of the virtual fingers involved in grasping a ...