Mission of the Machine Learning and Inference Laboratory

Mission of the MLI Laboratory

The Machine Learning and Inference (MLI) Laboratory conducts fundamental and experimental research on the development of intelligent systems capable of advanced forms of learning, inference, and knowledge generation, and applies them to real-world problems.

Major research areas include:
· theory and computational models of learning and inference
· data mining and knowledge discovery
· machine learning and natural induction
· inductive databases and knowledge scouts
· behavior modeling and computer intrusion detection
· non-Darwinian evolutionary computation
· multistrategy learning and knowledge mining
· intelligent systems for education
· models of human plausible reasoning
· machine vision with learning capabilities

The developed methods and systems are experimentally applied to a wide spectrum of problems in science and industry. Application areas of special interest include complex engineering design and optimization, computer security, medicine, bioinformatics, earth sciences, sociology, biochemistry, communication networks, geographic information systems, world economy, computer vision, education and software engineering.

The Laboratory supports education, scholarship, and research in these areas. It has a highly international team of researchers and the state-of-the-art computer facilities.

A Brief History for the Curious

The Machine Learning and Inference Laboratory (MLI) is one of the most active and accomplished laboratories in the world in the fields of machine learning and computational intelligence. It traces its roots to the Intelligent Systems Group (ISG) that Dr. Michalski established at the University of Illinois at Champaign-Urbana in 1972.

Since then, Dr. Michalski and his collaborators have been continuously publishing original research results in such areas as inductive learning, pattern recognition, advisory systems, machine inference, data mining and knowledge discovery, computer vision, plausible reasoning, and others. In 1980, they co-organized the first workshop in machine learning, and then several subsequent workshops/conferences in this field. These conferences now continue as International Conferences on Machine Learning, the most important conferences in this field.

In 1988, Dr. Michalski accepted an invitation from George Mason University (GMU) to move his research group there, and renamed it to the Machine Learning and Inference Laboratory (MLI). In 1991, MLI has co-organized the First International Workshop on Mulistrategy (MSL’91) in Harpers Ferry; and then subsequent MSL workshops (MSL’93 and MSL’96 in Harper’s Ferry, MSL’98 in Desenzano del Garda, Italy; and MSL’00 in Guimarães, Portugal). In 1992, it organized the first workshop on learning and vision.

MLI has pioneered several research directions, such as symbolic rule learning, variable-valued logic, constructive induction, conceptual clustering, variable-precision logic, multistrategy learning, learnable evolution model, inductive databases and knowledge scouts.

With over 650 papers and 14 books and conference proceedings, MLI is one of the most published and continuously contributing new ideas research groups in the areas of its expertise.

Research and Educational Contributions in the Nutshell

MLI has originated many innovative ideas and methods in the areas of machine learning, knowledge discovery, plausible inference, advisory systems, and computer vision. Among specific methodological contributions are:
· Aq algorithm for solving complex covering problems that pioneered the progressive covering approach to inductive learning (a.k.a. separate and conquer),
· variable-valued logic and attributional calculus
· constructive induction
· conceptual clustering
· variable-precision logic (with Patrick Winston from MIT)
· the core theory of human plausible reasoning (with Alan Collins from BBN)
· multistrategy learning
· inductive databases and knowledge scouts
· learnable evolution model (LEM).

To support education, MLI has established several Ph.D. level courses including CSI777: Principles of Knowledge Mining, and CSI 873: Computational Learning and Discovery.

MLI organized numerous conferences and workshops, and has trained a large number of students and researchers in the areas of its activities. It has also been a place of frequent visits by international scholars and students from around the world.

Main Systems Developed

Over the years, MLI has developed many experimental systems in the areas of machine learning, inference, knowledge discovery, and others:
· AQ family of rule learning systems (AQVAL, AQ11, AQ15, AQ18, AQ19, and most recently, AQ21 - under development)
· PLANT/s--the first agricultural expert system, and the first practical expert system with learning capabilities,
· INDUCE family for structural learning (INDUCE 1,..,4)
· CLUSTER family for conceptual clustering (CLUSTER 1, 2)
· ABACUS systems for equation discovery (ABACUS 1, 2)
· SPARC qualitative prediction systems (SPARC/E, SPARC/G)
· QUIN and ADVISE general purpose advisory systems
· MIST system for applying machine learning to vision
· INLEN family for multistrategy data mining and inference
· LEM systems for evolutionary computation, based on Learnable Evolution Model--a form non-Darwinian evolutionary computation, specially oriented for complex function optimization and engineering design.

On the invitation of the Boston Science Museum, our group has also developed ILLIAN system for demonstrating learning and discovery capabilities of computers for the public. The system toured major U.S. science museums. An extended version of this system, EMERALD-SUN, has been used in teaching AI/CI courses. A new implementation, iAQ, has been presented at many conferences and meetings, and is being distributed world-wide to academic and industrial organizations. It is available through internet (www.mli.gmu.edu/software.html).