Pathfinding in Computer Games
... generated by such algorithms are composed of convex polygons which when assembled together represent the shape of the map analogous to a floor plan. The polygons in a mesh have to be convex since this guarantees that the AI agent can move in a single straight line from any point in one polygon to th ...
... generated by such algorithms are composed of convex polygons which when assembled together represent the shape of the map analogous to a floor plan. The polygons in a mesh have to be convex since this guarantees that the AI agent can move in a single straight line from any point in one polygon to th ...
Artificial Neural Networks - Introduction -
... ANNs are a powerful technique (Black Box) to solve many real world problems. They have the ability to learn from experience in order to improve their performance and to adapt themselves to changes in the environment. In addition, they are able to deal with incomplete information or noisy data and ca ...
... ANNs are a powerful technique (Black Box) to solve many real world problems. They have the ability to learn from experience in order to improve their performance and to adapt themselves to changes in the environment. In addition, they are able to deal with incomplete information or noisy data and ca ...
Short-term memory
... the function of neuronal networks. These variations can result in long-lasting (and maybe permanent) alterations in neuronal operations, for instance through activity-dependent changes in synaptic transmission. There is now strong evidence for a complementary process, acting over an intermediate tim ...
... the function of neuronal networks. These variations can result in long-lasting (and maybe permanent) alterations in neuronal operations, for instance through activity-dependent changes in synaptic transmission. There is now strong evidence for a complementary process, acting over an intermediate tim ...
nn1-02
... What are biological neuron networks? (see next lectures for more details) • UNITs: nerve cells called neurons, many different types and are extremely complex, around 1011 neurons in the brain ...
... What are biological neuron networks? (see next lectures for more details) • UNITs: nerve cells called neurons, many different types and are extremely complex, around 1011 neurons in the brain ...
Cognitive Learning
... receive rewards and punishments. Learning a behavior and performing it are not the same thing • Tenet 1: Response consequences (such as rewards or punishments) influence the likelihood that a person will perform a particular behavior again • Tenet 2: Humans can learn by observing others, in addition ...
... receive rewards and punishments. Learning a behavior and performing it are not the same thing • Tenet 1: Response consequences (such as rewards or punishments) influence the likelihood that a person will perform a particular behavior again • Tenet 2: Humans can learn by observing others, in addition ...
Cognitive/Observational Learning
... rewards and punishments. Learning a behavior and performing it are not the same thing • Tenet 1: Response consequences (such as rewards or punishments) influence the likelihood that a person will perform a particular behavior again • Tenet 2: Humans can learn by observing others, in addition to lear ...
... rewards and punishments. Learning a behavior and performing it are not the same thing • Tenet 1: Response consequences (such as rewards or punishments) influence the likelihood that a person will perform a particular behavior again • Tenet 2: Humans can learn by observing others, in addition to lear ...
On the Prediction Methods Using Neural Networks
... The iterated prediction method is the most common method and consists in training a predictor for the single step prediction, predictor that is subsequently used recursively for the corresponding multi step ahead problem. The outputs corresponding for the next step are provided to the inputs of the ...
... The iterated prediction method is the most common method and consists in training a predictor for the single step prediction, predictor that is subsequently used recursively for the corresponding multi step ahead problem. The outputs corresponding for the next step are provided to the inputs of the ...
The Implementation of Artificial Intelligence and Temporal Difference
... Evaluation function will start out such that choices are random ...
... Evaluation function will start out such that choices are random ...
Machine learning and Neural Networks
... everything it needs to know already? Many programs or computer-controlled robots must be prepared to deal with things that the creator would not know about, such as game-playing programs, speech programs, electronic “learning” pets, and robotic explorers. Here, they would have access to a range of u ...
... everything it needs to know already? Many programs or computer-controlled robots must be prepared to deal with things that the creator would not know about, such as game-playing programs, speech programs, electronic “learning” pets, and robotic explorers. Here, they would have access to a range of u ...
Real-Time Credit-Card Fraud Detection using Artificial Neural
... “Figure 6” depicts a basic model for the training process of ANN. In this paper we have used supervised learning [17], so our data consist of both input and desired output. A random weight is generated for each connection and output is calculated based on current weight & input. Obviously, in the in ...
... “Figure 6” depicts a basic model for the training process of ANN. In this paper we have used supervised learning [17], so our data consist of both input and desired output. A random weight is generated for each connection and output is calculated based on current weight & input. Obviously, in the in ...
Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden
... networks trained with the standard back propagation algorithm. They are supervised networks so they require a desired response to be trained. They learn how to transform input data into a desired response, so they are widely used for pattern classification. With one or two hidden layers, they can ap ...
... networks trained with the standard back propagation algorithm. They are supervised networks so they require a desired response to be trained. They learn how to transform input data into a desired response, so they are widely used for pattern classification. With one or two hidden layers, they can ap ...
Introduction I have been interested in artificial intelligence and
... I have been interested in artificial intelligence and artificial life for years and I read most of the popular books printed on the subject. I developed a grasp of most of the topics yet neural networks always seemed to elude me. Sure, I could explain their architecture but as to how they actually w ...
... I have been interested in artificial intelligence and artificial life for years and I read most of the popular books printed on the subject. I developed a grasp of most of the topics yet neural networks always seemed to elude me. Sure, I could explain their architecture but as to how they actually w ...
Learning Text Similarity with Siamese Recurrent
... At each time step t ∈ {1, . . . , T }, the hiddenstate vector ht is updated by the equation ht = σ(W xt + U ht−1 ), in which xt is the input at time t, W is the weight matrix from inputs to the hidden-state vector and U is the weight matrix on the hidden-state vector from the previous time step ht−1 ...
... At each time step t ∈ {1, . . . , T }, the hiddenstate vector ht is updated by the equation ht = σ(W xt + U ht−1 ), in which xt is the input at time t, W is the weight matrix from inputs to the hidden-state vector and U is the weight matrix on the hidden-state vector from the previous time step ht−1 ...
Chapter 12: Artificial Intelligence and Modeling the Human State
... – The strengths of the connections are then modified so as to minimize errors in succeeding input/output pairs. • Example: Back propagation: This method of learning is divided into two phases: 1. The inputs are applied to the network, and the outputs compared with the correct output. 2. The resultin ...
... – The strengths of the connections are then modified so as to minimize errors in succeeding input/output pairs. • Example: Back propagation: This method of learning is divided into two phases: 1. The inputs are applied to the network, and the outputs compared with the correct output. 2. The resultin ...
Artificial Intelligence - Florida State University
... 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 ...
A differentiable approach to inductive logic programming
... and examples, typically in the form of database relations or knowledge graphs. Inductive logic programming is often combined with use of probabilistic logics, and is a useful technique for knowledge base completion and other relational learning tasks [2]. However, past inductive logic programming ap ...
... and examples, typically in the form of database relations or knowledge graphs. Inductive logic programming is often combined with use of probabilistic logics, and is a useful technique for knowledge base completion and other relational learning tasks [2]. However, past inductive logic programming ap ...
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.