Adaptive Practice of Facts in Domains with Varied Prior Knowledge
... developed models are not easily applicable in educational setting, where prior knowledge can be an important factor. There are also many implementations of the spaced repetition principle using “flashcard software” (well known example is SuperMemo), but these implementations usually use scheduling a ...
... developed models are not easily applicable in educational setting, where prior knowledge can be an important factor. There are also many implementations of the spaced repetition principle using “flashcard software” (well known example is SuperMemo), but these implementations usually use scheduling a ...
The calculus of self-modifiable algorithms: planning, scheduling and
... designed to be a universal theory for intelligent and parallel systems, integrating various styles of programming and applied in different domains of future generation computers. The use of artificial intelligence in future generation computers requires different forms of parallelism, learning, reas ...
... designed to be a universal theory for intelligent and parallel systems, integrating various styles of programming and applied in different domains of future generation computers. The use of artificial intelligence in future generation computers requires different forms of parallelism, learning, reas ...
data mining for predicting the military career choice
... information that can contribute to the ECM development effectiveness. An example of the ECM used for ships to defend themselves from “fire-and-forget anti-ships missiles”, is the employment of chaff rockets, as described in [8]. This type of rocket (Chaff rockets) are loaded with of metallized filam ...
... information that can contribute to the ECM development effectiveness. An example of the ECM used for ships to defend themselves from “fire-and-forget anti-ships missiles”, is the employment of chaff rockets, as described in [8]. This type of rocket (Chaff rockets) are loaded with of metallized filam ...
Chapter 1: Introduction to AI
... Can Computers Learn and Adapt ? • Learning and Adaptation – consider a computer learning to drive on the freeway – we could teach it lots of rules about what to do – or we could let it drive and steer it back on course when it heads ...
... Can Computers Learn and Adapt ? • Learning and Adaptation – consider a computer learning to drive on the freeway – we could teach it lots of rules about what to do – or we could let it drive and steer it back on course when it heads ...
Pathfinding in Computer Games
... E – Edges: A set of connections between the vertices, which can be either directed or not ...
... E – Edges: A set of connections between the vertices, which can be either directed or not ...
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 ...
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... Wind energy industry depends on wind speed forecasts to help determine facility location, facility layout, as well as the optimal use of turbines in day today operations [5]. There are physical, statistical, artificial neural and hybrid ...
... Wind energy industry depends on wind speed forecasts to help determine facility location, facility layout, as well as the optimal use of turbines in day today operations [5]. There are physical, statistical, artificial neural and hybrid ...
ShimonWhiteson - Homepages of UvA/FNWI staff
... My research is focused on artificial intelligence. I believe that intelligent agents are essential to improving our ability to solve complex, real-world problems. Consequently, my research focuses on the key algorithmic challenges that arise in developing control systems for such agents. These syste ...
... My research is focused on artificial intelligence. I believe that intelligent agents are essential to improving our ability to solve complex, real-world problems. Consequently, my research focuses on the key algorithmic challenges that arise in developing control systems for such agents. These syste ...
This Is a Publication of The American Association for Artificial
... In the area of semantic interpretation, there have been a number of interesting uses of corpus-based techniques. Some researchers have used empirical techniques to address a difficult subtask of semantic interpretation, that of developing accurate rules to select the proper meaning, or sense, of a s ...
... In the area of semantic interpretation, there have been a number of interesting uses of corpus-based techniques. Some researchers have used empirical techniques to address a difficult subtask of semantic interpretation, that of developing accurate rules to select the proper meaning, or sense, of a s ...
INTRODUCTION
... Unsupervised learning is the great promise of the future. Currently, this learning method is limited to networks known as self-organizing maps. These kinds of networks are not in widespread use. They are basically an academic novelty. Yet, they have shown they can provide a solution in a few instanc ...
... Unsupervised learning is the great promise of the future. Currently, this learning method is limited to networks known as self-organizing maps. These kinds of networks are not in widespread use. They are basically an academic novelty. Yet, they have shown they can provide a solution in a few instanc ...
Bootstrap Planner: an Iterative Approach to Learn Heuristic
... it to solve a set of training instances. There is a time limit assigned for solving each training instance. If enough training instances have been solved in an iteration, i.e., a number of instances above a fixed threshold, the planner learns a better heuristic from the solved instances and repeats ...
... it to solve a set of training instances. There is a time limit assigned for solving each training instance. If enough training instances have been solved in an iteration, i.e., a number of instances above a fixed threshold, the planner learns a better heuristic from the solved instances and repeats ...
TRADITIONAL LEARNING THEORIES
... affective as well as cognitive dimensions of learning was informed in part by Freud's psychoanalytic approach to human behavior. Although most would not label Freud a learning theorist, aspects of his psychology, such as the influence of the subconscious mind on behavior, as well as the concepts of ...
... affective as well as cognitive dimensions of learning was informed in part by Freud's psychoanalytic approach to human behavior. Although most would not label Freud a learning theorist, aspects of his psychology, such as the influence of the subconscious mind on behavior, as well as the concepts of ...
Supervised and Unsupervised Neural Networks
... It is an inherently multiprocessor-friendly architecture and without much modification, it goes beyond one or even two processors of the von Neumann architecture. It has ability to account for any functional dependency. The network discovers (learns, models) the nature of the dependency without need ...
... It is an inherently multiprocessor-friendly architecture and without much modification, it goes beyond one or even two processors of the von Neumann architecture. It has ability to account for any functional dependency. The network discovers (learns, models) the nature of the dependency without need ...
09-unsupervised - The University of Iowa
... • Despite weaknesses, k-means is still the most popular algorithm due to its simplicity, efficiency and – other clustering algorithms have their own lists of weaknesses. • No clear evidence that any other clustering algorithm performs better in general – although they may be more suitable for some s ...
... • Despite weaknesses, k-means is still the most popular algorithm due to its simplicity, efficiency and – other clustering algorithms have their own lists of weaknesses. • No clear evidence that any other clustering algorithm performs better in general – although they may be more suitable for some s ...
Extending Universal Intelligence Models with Formal Notion of
... space (any algorithm has non-zero probability and can be learned), and models are naturally ordered by their complexity (it is impossible to specify such universal machine that reverts this order). Apparently, the universal agent based on the algorithmic probability (such as AIξ [2]) may require exe ...
... space (any algorithm has non-zero probability and can be learned), and models are naturally ordered by their complexity (it is impossible to specify such universal machine that reverts this order). Apparently, the universal agent based on the algorithmic probability (such as AIξ [2]) may require exe ...
CHAPTER TWO
... the network is provided with inputs but not with desired outputs. The system itself must then decide what features it will use to group the input data. This is often referred to as self-organization or adaption. At the present time, unsupervised learning is not well understood. This adaption to the ...
... the network is provided with inputs but not with desired outputs. The system itself must then decide what features it will use to group the input data. This is often referred to as self-organization or adaption. At the present time, unsupervised learning is not well understood. This adaption to the ...
Automated Endoscope Navigation and Advisory System from
... of not needing to consider all the possible trees that could be constructed from purely objective data. However, the possible ignorance of some interacting variables will generate many probabilistic networks that could closely approximate the given observed data. ...
... of not needing to consider all the possible trees that could be constructed from purely objective data. However, the possible ignorance of some interacting variables will generate many probabilistic networks that could closely approximate the given observed data. ...
Scaling Kernel-Based Systems to Large Data Sets
... a committee member, more weight is put on data that are misclassified by previously trained committee members. Schapire (1990) developed the original boosting approach, boosting by filtering. Here, three learning systems are used and the existence of an oracle that can produce an arbitrary quantity ...
... a committee member, more weight is put on data that are misclassified by previously trained committee members. Schapire (1990) developed the original boosting approach, boosting by filtering. Here, three learning systems are used and the existence of an oracle that can produce an arbitrary quantity ...
CV - Chris Gatti
... of Dr. Kai-Nan An, Mayo Orthopedic Research Alumni Association, Mayo Clinic, Rochester, MN, August 3. Gatti C. J. and Embrechts M. J., 2014. An application of the temporal difference algorithm to the truck backer-upper problem. Proceedings of the 22nd European Symposium on Artificial Neural Networks, ...
... of Dr. Kai-Nan An, Mayo Orthopedic Research Alumni Association, Mayo Clinic, Rochester, MN, August 3. Gatti C. J. and Embrechts M. J., 2014. An application of the temporal difference algorithm to the truck backer-upper problem. Proceedings of the 22nd European Symposium on Artificial Neural Networks, ...
Artificial Intelligence – an Overview
... examples – To avoid over generalization, negative examples are given, which are used to specialize the knowledge. 6. Learning by Induction through experimentation - The system generates examples itself by designing experiments on the environment. ...
... examples – To avoid over generalization, negative examples are given, which are used to specialize the knowledge. 6. Learning by Induction through experimentation - The system generates examples itself by designing experiments on the environment. ...
Machine learning
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition ""can be viewed as two facets ofthe same field.""When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.