CS Department - Machine Learning

MACHINE LEARNING AND AI

Artificial Intelligence (AI) seeks to understand the fundamental nature of intelligence and how to make computers exhibit intelligent behavior. Machine Learning addresses the problem of how to automatically learn concepts and behaviors from data. With seven faculty in AI and machine learning, UCF CS is highly active in these areas. Major research strengths include complex systems, data mining, diagnostics, evolutionary computation, intelligent simulation, knowledge representation, learning from observation of human performance, multi-agent systems, natural language processing, neural networks, neuroevolution, robotics, and social informatics.

FACULTY

Ladislau Boloni Ladislau Boloni

Avelino J. Gonzalez Avelino J. Gonzalez

Haiyan Hu Haiyan Hu

Fei Liu Fei Liu

Guo-Jun Qi Guo-Jun Qi

Kenneth Stanley Kenneth Stanley

Gita Sukthankar Gita Sukthankar

Annie Wu Annie Wu

Wei Zhang Wei Zhang

EVOLUTIONARY COMPLEXITY (EPLEX) RESEARCH GROUP
LAB DIRECTOR: STANLEY

The Evolutionary Complexity (EPlex) Research Group at the University of Central Florida abstracts the essential properties of natural evolution that made it possible to discover astronomically complex structures such as the human brain into computer algorithms for artificial intelligence and machine learning. If such properties can be abstracted effectively, then they can be leveraged to automate the discovery of large-scale neural networks (which is called neuroevolution), robot morphologies, building and vehicle architectures, art, and music. Lab director Kenneth Stanley is an inventor of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, and numerous subsequent high-impact technologies have been invented at EPlex: CPPNs (compositional pattern producing networks), HyperNEAT (Hypercube-based NEAT), novelty search, ES-HyperNEAT (evolvable structure HyperNEAT), adaptive HyperNEAT, Picbreeder, Galactic Arms Race, MaestroGenesis, Petalz, and many others.
Evolutionary Complexity Research Group

EVOLUTIONARY COMPUTATION LAB
LAB DIRECTOR: WU

The Evolutionary Computation Lab at the University of Central Florida conducts research in the areas of Evolutionary Computation and Natural Computation. Evolutionary algorithms, including genetic algorithms, evolutionary programming, and evolutionary strategies, are search and learning tools which are based on principles from genetics and evolutionary biology and have been successfully applied to a wide range of problems. Natural computation focuses on computational mechanisms based on natural phenomena as well as complex adaptive systems that involve multiple interacting entities. The research in our lab includes both theoretical studies on how these algorithms work and real world applications.
Evolutionary Computation Lab

INTELLIGENT AGENTS LAB
LAB DIRECTOR: SUKTHANKAR

The Intelligent Agents Lab is dedicated to investigating social models within RAP systems (Robots, Agents, and People), with a particular emphasis on studying the behavior of people within games and simulation environments where people and agents can interact on an "equal footing". In these social-computational systems where autonomous agents must interact with larger groups of people, it is insufficient to model and infer the activity of one person; we must be able to understand larger social structures such as teams, groups, and crowds. To study these problems, IAL lab members conduct basic research on agent-based modeling, machine learning, and search; we aim to improve on the state of the art methods used in:
• agents for games and simulations
• human motion analysis and path prediction
• social simulations of groups and teams
• human-robot interaction
Intelligent Agents Lab

NATURAL LANGUAGE PROCESSING GROUP
LAB DIRECTOR: LIU

The Natural Language Processing Group at the University of Central Florida focuses on innovative natural language technologies that improve human language understanding and processing. NLP enables computers to derive meaning from human language and generate natural language from a logical form. Specifically, we conduct research in the areas of text summarization, language generation, social media analytics, natural language semantics, and machine Learning for NLP. Much of the technologies involve probabilistic models, machine learning, and deep learning as applied to large-scale text data.
Natural Language Processing Group