AI and Machine Learning

Hybrid AI and Machine Learning Systems

Many different Artificial Intelligence and Machine Learning architectures and algorithms have been developed, each with their own strengths and weaknesses that make them particularly suited to certain classes of problems. Two approaches to artificial intelligence are dealt with here: subsumption architecture, and neural networks. S.A.’s are good at building up complex behaviors from sets of sim...

Imprecision in Machine Learning and AI

I this note we consider five different relevant problems in AI and machine learning. We argue that possible solutions to such problems might be achieved by replacing the probability distributions in the systems with sets of them. Such a robust approach is based on the so-called impreciseprobabilistic framework. The proposed solutions provide a persuasive justification of the imprecise framework...

On AI, Markets and Machine Learning

What is fun about artificial intelligence (AI) is that it is fundamentally constructive! Rather than theorizing about the behavior of an existing, say social system, or understanding the way in which decisions are currently being made, one gets to ask a profound question: how should a system for making intelligent decisions be designed? In multi-agent systems we’re often interested, in particul...

Machine Learning Interpretability with H2O Driverless AI

Introducing Machine Learning from an Ai Perspective

1 Ingrid Russell, University of Hartford, Department of Computer Science, West Hartford, CT 06117, [email protected], 860-768-4191. 2 Zdravko Markov, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, [email protected], 860-832-2723 3 Neli Zlatareva, Central Connecticut State University, Department of Computer Science, New Britain, CT 06050, zlatarev...