Human-Based Computation for Microfossil Identification
... Fossilized shells are used to map hydrocarbon ...
... Fossilized shells are used to map hydrocarbon ...
Reinforcement Learning in the Presence of Rare Events
... methods is to find a change of measure such that the variance in the rare event probability estimator is minimized. Finding an optimal change of measure is a difficult problem. If the frequency of the rare events is increased too much, then the estimator is starved of samples from the normal states, ...
... methods is to find a change of measure such that the variance in the rare event probability estimator is minimized. Finding an optimal change of measure is a difficult problem. If the frequency of the rare events is increased too much, then the estimator is starved of samples from the normal states, ...
Piagetian Autonomous Modeler
... Ryszard Michalski [1] [3] [12] has long been involved in multistrategy learning and inference. His work has largely focused on logical models of inference in Artificial Intelligence systems. He and his co-authors have developed a method of inference involving Dynamically Interlaced Hierarchies. The ...
... Ryszard Michalski [1] [3] [12] has long been involved in multistrategy learning and inference. His work has largely focused on logical models of inference in Artificial Intelligence systems. He and his co-authors have developed a method of inference involving Dynamically Interlaced Hierarchies. The ...
Learning Domain-Specific Control Knowledge from Random Walks Alan Fern
... we were able to bootstrap API, without human assistance, by using the domain-independent FF heuristic (Hoffmann & Nebel 2001). This approach, however, is limited both by the heuristic’s ability to provide useful “bootstrapping” guidance, which can vary widely across domains, as well as by the learne ...
... we were able to bootstrap API, without human assistance, by using the domain-independent FF heuristic (Hoffmann & Nebel 2001). This approach, however, is limited both by the heuristic’s ability to provide useful “bootstrapping” guidance, which can vary widely across domains, as well as by the learne ...
Automated Modelling and Solving in Constraint Programming
... set, which is given, for instance, as a set of examples of its solutions and non-solutions. This kind of learning is called constraint acquisition (Bessiere et al. 2005). The motivations for constraint acquisition are many. For example, in order to solve partially defined constraints more efficient ...
... set, which is given, for instance, as a set of examples of its solutions and non-solutions. This kind of learning is called constraint acquisition (Bessiere et al. 2005). The motivations for constraint acquisition are many. For example, in order to solve partially defined constraints more efficient ...
Beyond Keywords: The Revolution in Search
... quick education on the subject. With the rise of the web and now smartphone apps, users expect more and more of their information needs to be satisfied through a search. ...
... quick education on the subject. With the rise of the web and now smartphone apps, users expect more and more of their information needs to be satisfied through a search. ...
Neuro-fuzzy systems
... The weighted inputs xi o wi, where o is a t-norm and tconorm, can be general fuzzy relations too, not just simple products as in standard neurons The transfer function g can be a non-linear such as a sigmoid ...
... The weighted inputs xi o wi, where o is a t-norm and tconorm, can be general fuzzy relations too, not just simple products as in standard neurons The transfer function g can be a non-linear such as a sigmoid ...
A Neural Network of Adaptively Timed Reinforcement
... One of the main tasks of the present work is to show how processes such as adaptive timing, reinforcement learning, attention, and motor learning differ, yet are linked in the control of behavior. Thus the exposition needs to describe several different types of circuits that form part of a larger ne ...
... One of the main tasks of the present work is to show how processes such as adaptive timing, reinforcement learning, attention, and motor learning differ, yet are linked in the control of behavior. Thus the exposition needs to describe several different types of circuits that form part of a larger ne ...
Business Process Modelling Examples
... way the human brain works • Neural networks are most useful for decisions that involve patterns or image recognition • For example its use in the finance industry to discover credit card fraud finding common elements in millions of fraudulent transactions ...
... way the human brain works • Neural networks are most useful for decisions that involve patterns or image recognition • For example its use in the finance industry to discover credit card fraud finding common elements in millions of fraudulent transactions ...
Connecting Conscious and Unconscious - Axel Cleeremans
... classical and connectionist representations, namely that the former are inherently passive whereas the latter are continuously active. Indeed, the symbolic, propositional representations characteristic of classical models of cognition (i.e., production rules and declarative knowledge) are intrinsica ...
... classical and connectionist representations, namely that the former are inherently passive whereas the latter are continuously active. Indeed, the symbolic, propositional representations characteristic of classical models of cognition (i.e., production rules and declarative knowledge) are intrinsica ...
Artificial intelligence - University of London International Programmes
... Most work in AI focuses on smaller components thought to be necessary for producing intelligent programs. The major subfields, some of which will be considered in the following chapters, are: 1. Problem solving, where an agent is given a problem setting and a goal and must determine how to realize t ...
... Most work in AI focuses on smaller components thought to be necessary for producing intelligent programs. The major subfields, some of which will be considered in the following chapters, are: 1. Problem solving, where an agent is given a problem setting and a goal and must determine how to realize t ...
An Overview of Some Recent Developments in Bayesian Problem
... that the mainstream AI community at that time considered probabilistic approaches impractical for building intelligent systems. Since then the workshop has become the Conference on Uncertainty in AI, attracting high-quality contributions from researchers in a broad array of disciplines, including AI ...
... that the mainstream AI community at that time considered probabilistic approaches impractical for building intelligent systems. Since then the workshop has become the Conference on Uncertainty in AI, attracting high-quality contributions from researchers in a broad array of disciplines, including AI ...
The challenge of complexity for cognitive systems
... can help to establish relations between formal and computational models of cognitive systems and human cognitive processes and they also can be used exploratory to give general suggestions about cognitive inspired algorithms. The second way may be called “psychonics” in analogy to bionics where engi ...
... can help to establish relations between formal and computational models of cognitive systems and human cognitive processes and they also can be used exploratory to give general suggestions about cognitive inspired algorithms. The second way may be called “psychonics” in analogy to bionics where engi ...
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.