Implementing feedback in creative systems: A - CEUR
... was a sticky bookmark, created by Arthur Fry. In the Writers Workshop, authors similarly have the opportunity to share things that they find interesting, but that they are not certain about. The author may want to ask a specific question about their creation: Does x work better than y? They may flag ...
... was a sticky bookmark, created by Arthur Fry. In the Writers Workshop, authors similarly have the opportunity to share things that they find interesting, but that they are not certain about. The author may want to ask a specific question about their creation: Does x work better than y? They may flag ...
6pp - Stanford University
... • Supporting all of these models is machine learning. • In the non-machine learning approach, one would write a complex program (remember, we are solving tasks of significant complexity), but this gets very tedious. For example, how should a spellchecker know that for ”hte”, ”the” (transposition) is ...
... • Supporting all of these models is machine learning. • In the non-machine learning approach, one would write a complex program (remember, we are solving tasks of significant complexity), but this gets very tedious. For example, how should a spellchecker know that for ”hte”, ”the” (transposition) is ...
13. Intelligent Information Systems.
... • Consists of related technologies that try to simulate and reproduce human thought and behavior • Includes thinking, speaking, feeling, and reasoning ...
... • Consists of related technologies that try to simulate and reproduce human thought and behavior • Includes thinking, speaking, feeling, and reasoning ...
Deep neural networks - Cambridge Neuroscience
... computational framework. However, neural network technology was not sufficiently advanced to take on realworld tasks such as object recognition from photographs. As a result, neural networks did not initially live up to their promise as AI systems and in cognitive science, modelling was restricted t ...
... computational framework. However, neural network technology was not sufficiently advanced to take on realworld tasks such as object recognition from photographs. As a result, neural networks did not initially live up to their promise as AI systems and in cognitive science, modelling was restricted t ...
Constructive neural-network learning algorithms for pattern
... that are deemed redundant. Constructive algorithms offer several significant advantages over pruning-based algorithms including, the ease of specification of the initial network topology, better economy in terms of training time and number of training examples, and potential for converging to a smal ...
... that are deemed redundant. Constructive algorithms offer several significant advantages over pruning-based algorithms including, the ease of specification of the initial network topology, better economy in terms of training time and number of training examples, and potential for converging to a smal ...
preprint
... science. Many of such Bayesian models postulate that cognitive processes perform some form of Bayesian inference.1 Examples of such models can be found in several different cognitive domains, including vision (Yuille & Kersten, 2006), language (Chater & Manning, 2006), decision making (Sloman & Hagm ...
... science. Many of such Bayesian models postulate that cognitive processes perform some form of Bayesian inference.1 Examples of such models can be found in several different cognitive domains, including vision (Yuille & Kersten, 2006), language (Chater & Manning, 2006), decision making (Sloman & Hagm ...
Artificial Intelligence in Network Intrusion Detection
... goals) of AI research include reasoning, knowledge representation, automated planning and scheduling, ML, natural language processing, computer vision, robotics and general intelligence (strong AI) [4]. In this paper we will mainly focus on ML as it seems to be the most promising AI sub-field for in ...
... goals) of AI research include reasoning, knowledge representation, automated planning and scheduling, ML, natural language processing, computer vision, robotics and general intelligence (strong AI) [4]. In this paper we will mainly focus on ML as it seems to be the most promising AI sub-field for in ...
Learning Symbolic Models of Stochastic Domains
... falls over; pulling on a drawer usually opens it, but sometimes it sticks; moving a box does not typically break the items inside it. Building agents to perform these common tasks is a challenging problem. In this work, we attack it with two of the tools of modern AI: machine learning and probabilis ...
... falls over; pulling on a drawer usually opens it, but sometimes it sticks; moving a box does not typically break the items inside it. Building agents to perform these common tasks is a challenging problem. In this work, we attack it with two of the tools of modern AI: machine learning and probabilis ...
Lecture 11 - Chapter 7
... The Artificial Intelligence Field • Artificial intelligence systems form a broad and diverse set of systems that can replicate human decision making for certain types of well-defined problems – Identify the major components of the artificial intelligence field and provide one example of each type of ...
... The Artificial Intelligence Field • Artificial intelligence systems form a broad and diverse set of systems that can replicate human decision making for certain types of well-defined problems – Identify the major components of the artificial intelligence field and provide one example of each type of ...
On the Sample Complexity of Reinforcement Learning with a Generative Model
... Also, the action-value function Qπ is the unique fixed-point of the Bellman operator T π which is defined as (T π Q)(z) , r(z) + γ(P π Q)(z) for all z ∈ Z. One can also define the Bellman optimality operator and the Bellman operator on the value function as (T V )(x) , r(x, π ∗ (x)) + γ(Pπ∗ V )(x) a ...
... Also, the action-value function Qπ is the unique fixed-point of the Bellman operator T π which is defined as (T π Q)(z) , r(z) + γ(P π Q)(z) for all z ∈ Z. One can also define the Bellman optimality operator and the Bellman operator on the value function as (T V )(x) , r(x, π ∗ (x)) + γ(Pπ∗ V )(x) a ...
Building Knowledge Bases through Multistrategy Learning and
... learning techniques, relying on the knowledge engineer, machine learning research has generally avoided involving a human expert in the learning loop. We think that neither approach is sufficient, and that the automation of knowledge acquisition should be based on a direct interaction between a huma ...
... learning techniques, relying on the knowledge engineer, machine learning research has generally avoided involving a human expert in the learning loop. We think that neither approach is sufficient, and that the automation of knowledge acquisition should be based on a direct interaction between a huma ...
AS THE PAPER FOLDS, THE MIND UNFOLDS
... of kindergarten who dedicated most of his life to exploring young children’s learning processes, realized that children’s games are educational tools of great value. Origami, in that sense, has the characteristics of a game: it is an enjoyable activity; it follows certain rules; it involves emotions ...
... of kindergarten who dedicated most of his life to exploring young children’s learning processes, realized that children’s games are educational tools of great value. Origami, in that sense, has the characteristics of a game: it is an enjoyable activity; it follows certain rules; it involves emotions ...
Document
... 15. Define Ideal Rational Agent. For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built in knowledge the agent has. 16. Define Omniscience. An Omniscien ...
... 15. Define Ideal Rational Agent. For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built in knowledge the agent has. 16. Define Omniscience. An Omniscien ...
Automatically Building Special Purpose Search Engines with
... is part of a noun phrase is “Wisniewski” is in a list of city names is under node X in WordNet part of ends in is in bold font noun phrase “-ski” is indented O t 1 is in hyperlink anchor last person name was female next two words are “and Associates” ...
... is part of a noun phrase is “Wisniewski” is in a list of city names is under node X in WordNet part of ends in is in bold font noun phrase “-ski” is indented O t 1 is in hyperlink anchor last person name was female next two words are “and Associates” ...
Visualizing Inference Henry Lieberman and Joe Henke MIT Media Lab
... clue that some important knowledge is missing from the knowledge base. Finally, incorrect assertions can also appear when inference is “too liberal” – it concluded something without sufficient evidence. ...
... clue that some important knowledge is missing from the knowledge base. Finally, incorrect assertions can also appear when inference is “too liberal” – it concluded something without sufficient evidence. ...
A Piagetian Model of Early Sensorimotor Development
... development pathways is not possible. Again, it is inspired by principles from Piaget’s theory. Similarly for the CALM system (Perotto et al., 2007) which has schemas similar to Drescher’s and runs in an abstract domain. The work of Mugan and Kuipers (2007) does experiment in a domain where a baby i ...
... development pathways is not possible. Again, it is inspired by principles from Piaget’s theory. Similarly for the CALM system (Perotto et al., 2007) which has schemas similar to Drescher’s and runs in an abstract domain. The work of Mugan and Kuipers (2007) does experiment in a domain where a baby i ...
On the Sample Complexity of Reinforcement Learning with a Generative Model
... the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sample complexity of estimating the action-value function. The main idea to improve the upper bo ...
... the above-mentioned gap between the lower bound and the upper bound, guarantee that no learning method, given the generative model of the MDP, can be significantly more efficient than QVI in terms of the sample complexity of estimating the action-value function. The main idea to improve the upper bo ...
Learning Domain-Specific Control Knowledge from Random Walks Alan Fern
... tion simulation” algorithm, that, given state s and action a, returns a next state t. The fourth component C is an actioncost function that maps S × A to real-numbers, and I is a randomized “initial state” algorithm, that returns a state in S. Throughout this section, we assume a fixed planning doma ...
... tion simulation” algorithm, that, given state s and action a, returns a next state t. The fourth component C is an actioncost function that maps S × A to real-numbers, and I is a randomized “initial state” algorithm, that returns a state in S. Throughout this section, we assume a fixed planning doma ...
applying artificial neural networks in slope stability related
... handle the non-linear multivariate characteristics of the landslide phenomena that is assumed to be due to the spatial and temporal variability, scale dependency, and complicated interrelationship of the factors affecting landslide manifestation. Statistical models, such as multiple regression and d ...
... handle the non-linear multivariate characteristics of the landslide phenomena that is assumed to be due to the spatial and temporal variability, scale dependency, and complicated interrelationship of the factors affecting landslide manifestation. Statistical models, such as multiple regression and d ...
Placing prediction into the fear circuit
... fear to a CS is negatively accelerated across learning trials, so that fear of a CS increases most during early CS–US pairings (when the US is unexpected) and least during later pairings (when the CS has come to predict the US). To explain such findings, learning theories have posited that fear cond ...
... fear to a CS is negatively accelerated across learning trials, so that fear of a CS increases most during early CS–US pairings (when the US is unexpected) and least during later pairings (when the CS has come to predict the US). To explain such findings, learning theories have posited that fear cond ...
Review Reward, Motivation, and Reinforcement Learning
... However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concern ...
... However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concern ...
The Impact of Sample Reduction on PCA-based Feature Mykola Pechenizkiy Seppo Puuronen
... and (2) the problem of poor RS caused by the presence of some irrelevant or indirectly relevant individual features. We consider these problems further in Section 2 and different types of FE techniques for SL in Section 3, including Principal Component Analysis (PCA), and two class-conditional appro ...
... and (2) the problem of poor RS caused by the presence of some irrelevant or indirectly relevant individual features. We consider these problems further in Section 2 and different types of FE techniques for SL in Section 3, including Principal Component Analysis (PCA), and two class-conditional appro ...
PDF file
... principles of developing a single mind might be of great value for studying the science for social development, human and robotic. A. AMD Directly Related Research Fields Research on how the mind works can be categorized in different ways. The following is a way proposed by Weng & McClelland [12]. 1 ...
... principles of developing a single mind might be of great value for studying the science for social development, human and robotic. A. AMD Directly Related Research Fields Research on how the mind works can be categorized in different ways. The following is a way proposed by Weng & McClelland [12]. 1 ...
Artificial Intelligence: The new frontier for hedge funds
... pricing model just like physics, 3) the model must have some capability to learn and adjust to the changes in the market environment whilst maintaining its predictive capability. Creating a new mathematical model took many years of using physics theories, and in addition AI/Machine Learning methodol ...
... pricing model just like physics, 3) the model must have some capability to learn and adjust to the changes in the market environment whilst maintaining its predictive capability. Creating a new mathematical model took many years of using physics theories, and in addition AI/Machine Learning methodol ...
A Bayesian network primer
... distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure. A local probabilistic model is attached to each node to quantify the stochasti ...
... distribution and optionally the causal structure of the domain. In an intuitive causal interpretation, the nodes represent the uncertain quantities, the edges denote direct causal influences, defining the model structure. A local probabilistic model is attached to each node to quantify the stochasti ...