Experimental software developed in the Machine Learning and Inference Laboratory

The figure above is the opening screen of the 1999 JAVA Version of the EMERALD system developed in the Machine Learning and Inference Laboratory. The EMERALD system (Experimental Machine Example-based Reasoning and Learning Disciple) integrates five modules ("robots") each displaying a capability for some form of learning and discovery. An earlier version of EMERALD was presented at a national exhibit "Robots and Beyond: Age of Intelligent Machines" which toured eight major U.S. museums of science.

The MLI Laboratory has developed many experimental programs for machine learning, data mining and knowledge discovery, machine inference, knowledge visualization and related tasks. Among its major programs are: ABACUS, AQ11, AQ15c, AQ16 (POSEIDON), AQ17-DCI, AQ18, AQ19, AQ21, CLUSTER, EMERALD-AQ, EMERALD-SUN, iAQ, INDUCE1..4, INLEN, VINLEN, KV1 and KV2, LEM1, LEM2, LEM3, RT, SPARC/E and SPARC/G. These programs are briefly described below.

Some programs have already been arranged to be directly downloadable from this website. They were placed at the beginning of the list. As time permits, we will try to make other programs downloadable too.

iAQ program demonstrates Natural Induction, that is, an ability of a computer program to learn knowledge from data in forms natural to people, and by that easy to understand and interpret. In iAQ, discovered rules are expressed verbally and also as natural language text. The program has an entertaining introduction, accompanied by music. iAQ can be run on any Windows XP system. Because of the voice and sound output, to run the program it is necessary to attach speakers to the computer. An important new feature of iAQ (not yet fully completed) is the "Your data" option, which allows the user to apply the learning program to the user's own data. To learn about the AQ learning methodology, click on "Papers" on the left, and then search for papers (numerous) that have "AQ" in the title. To download iAQ click here.

LEM3 system implements a novel, non-Darwinian methodology for evolutionary computation, called Learnable Evolution Model or LEM. LEM employs a learning program to guide the evolutionary computation. Instead of conventional random mutations and recombinations, LEM employs hypothesis formation and generation operators to create new populations of individuals. Initial experiments have shown that LEM can very significantly speed up evolutionary computation in terms of number of fitness functions evaluations, and can be particularly useful for very complex optimization and design applications in which such evaluation is not trivial. LEM3 uses the AQ21 learning module for hypothesis formulation to guide the evolution process. LEM3 runs on Linux and Windows platforms. To download LEM3 click here. To learn about the LEM methodology, click on "Papers" on the left, and search for papers that have "Learnable Evolution Model" in the title. The LEM3 version available here is created 2004. To obtain the most recent version, please contact Dr. Janusz Wojtusiak.

AQ21 can be viewed as a laboratory for performing experiments in machine learning and knowledge discovery. It is the newest member of the AQ family of systems developed over the years in the Machine Learning and Inference Laboratory. It consists of a learning module that generates general indutive hypotheses from data, and a testing module that applies the learned hypotheses to testing data. The learned hypotheses are in the form of rulesets in attributional calculus, a logic system that combines elements of propositional, predicate and multi-valued logic. AQ21 can generate many different types of descriptions, such as complete and consistent generalizations, approximate theories, strong patterns, discriminant or characteristic descriptions, or descriptions with exceptions. The descriptions are optimized according to task-dependent criteria. The testing module, which is fully integrated with the learning module, includes a variety of methods for testing the learned hypotheses. AQ21 runs on Linux and Windows platforms. To download AQ21 click here. The AQ21 version available here is created in 2004. To obtain the most recent version, please contact Dr. Janusz Wojtusiak.

ABACUS 2 is a program for integrated quantitative and qualitative discovery. Specifically, given data consisting of numeric and possibly also symbolic characterizations of some phenomenon (an object, a process, a system), ABACUS will generate mathematical equations characterizing this phenomenon and qualitative conditions under which these equations apply. These equations can then be used for predicting the behavior of this system or process. For example, given data characterizing an electric circuit (voltage, current, resistance, and any other relevant or irrelevant properties, ABACUS will generate the Ohm's Law)

AQ Family: All of the programs in the AQ family learn general decision rules from examples of decision classes. Here are standard features of the "base" version of the AQ program The learned decision rules are optimized according to user-defined criteria or a default optimality criterion. The criteria refer to syntactic simplicity of the rules (measured by the number of rules, number of conditions in the rules, the simplicity of the conditions,or a combination of these factors), and/or the evaluation cost of the rules (the cost of measuring the attributes involved in the rules). Programs allow the user to generate different types of desriptions ("rulesets"), such as discriminant (that discriminate among given decision classes), or characteristic (that specify common features of the objects in the individual classes. The programs can also generate rulesets that have different relations among the rules -- intersecting (rules of different classes may logicaly intersect over areas not covering training examples), disjoint (rules or different classes are logically disjoint) or ordered (rules for each class are totally ordered and must be executed in the given order when applied to a given object). Learned rules are evaluated either by a strict match or by a flexible match. Individual versions of AQ programs have some additional features above the "base" version of the program. AQ15c: a plain version of the AQ learning program (implemented in the ANSI C). This version is available for SunOS 4.1, MacOS 7.5 and DOS 6.x AQ16 (POSEIDON): Plain AQ with mechanisms for optimizing rules by applying rule modification mechanisms. There are two mechanisms: TRUNC--that truncates insignificant rules (which corresponds to performing a form of ruleset specialization) or TRUNC/SG that modifies rules conditions and truncates insignificant rules (which corresponds to performing of both specialization and generalization of rules). Rules are evaluated either by a strict match or by a flexible match. These version is oriented toward learning concepts from noisy data or learning "flexible" concepts, that lack precise definition. The program applies som simple froms of "two-tiered" concept representation. A two-tiered represetnation consist of a base concept representation (BCR) that captures typical concept properties, and inferential concept representation that captures non-typical, variable, or exceptional concept properties. (See MLI papers on two-tiered concept representation). This version is available for SunOS 4.1 AQ17-DCI: AQ program with Data-driven constructive induction capabilities. These capabilities allow the program to autmatically modify the representation of the problem, e.g. adding or removing attributes or removing attribute-values. This version is available for SunOS 4.1. AQ17-HCI: AQ program with Hypothesis-driven constructive induction capabilities. These capabilities allow the program to autmatically modify the representation of the problem, e.g. adding or removing attributes. AQ18 (STAR): An environment for symbolic learning that integrates a large set of modules such as ruleset learning, dicision structure learning, constructive induction, ruleset testing and knowledge visualization.

CLUSTER creates meaningful categories and classifications of given entities, and formulates descriptions of these created categories. Each class description is given in conjunctive form involving selected object attributes.CLUSTER has been applied to varied practical problems including classifying Spanish Folksongs, microcomputers, and reconstructing soybean disease categories.

DIAV: Diagrammatic Visualization of learning algorithms and discrete knowledge transmutations.

EMERALD: SUN Version: Integrated Learning Systems for Research and Education. An earlier version of this system, called ILLIAN, was presented at eight major U.S. Museums od Science.

Knowledge Visualizer: a software system similar to DIAV but developed using Java 1.0.2 and suitable for large problems.

MIST: Software system supporting an application of symbolic learning in computer vision. Current system applies AQ learning methodology for determining rules that characterize different objects in a visual scene.

INDUCE learns structural descriptions of groups of objects, and determines important distinctions between the groups.

Sparc/G: Predicts possible future objects or events by discovering rules characterizing the sequence of objects or events observed so far. Individual objects or events are described by sets of multitype attributes. Sparc/E: Discovers rules for predicting sequences in EULESIS, a card game that models scientific discovery.

A List of Recipients of MLI Software

ABB Corporate Research, Norway
Abo AKademi University, Finland
Anna University, India
Austrian Research Institute for AI (ARIAI), Austria
Automazione Management, Italy
Bangladesh University of Engineering and Technology, Bangladesh
Beckman Institute, USA
Bilkent University, Turkiye
Blue Cross / Blue Shield of Hawaii, USA
Bologne University, Italy
Bhartiya Vidyapeeth College Of Engineering, India
Brigham Young University, USA
British Columbia Cancer Agency, Canada
Brooklyn College of CUNY , USA
CAD, Philip Morris Research Center, USA
Carnegie Mellon University, USA
Carleton University, Canada
Deakin University, Australia
Centre dEstudis Avancats de Blanes, Spain
CSIRO, Australia
Department of Engergy, USA
Domaine Universitaire de St Jerome, France
DSO National Laboratories, Singapore
Fakultaet fur Informatik, Germany
Elasis - Fiat, Italy
Environmental Protection Agency, Australia
Foundation on Cardiac Surgery Development, Poland
European Computer-Industry, Germany
Galaxy Technology, China
George Mason University, USA
German National Research Center for Computer Science (GMD), Germany
Graz University of Technology, Austria
Hawaii Medical Service Association, USA
I2B Technologies SA, Chile
Information Technology Institute, Republic of Singapore
INPRO, Germany
INRIA, France
International Intelligent Systems (INIS),Inc. Institute of Computer Science, Greece
Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
Institute of Fundamental Problems of Technology, Polish Academy of Sciences, Warsaw, Poland
Institute of Informatics, Bulgaria
Institute of Statistics, Poland
ISX Corporation, USA
ITMI, France
KAIST Research Institute, KOREA
Karlsruhe University, Germany
Kielce University of Technology, Poland
Laval University, Canada
LENZE, Inc., Germany
Landcare Research N2 Ltd., New Zealand
Landcare Research, New Zealand
Lawrence Berkeley Laboratory, USA
Middle Tennessee State University, USA
MIT, Cambridge, MA, USA
MITRE Corporation, USA
Mostra D'Oltremare, Italy
Monash University, Australia
National Institute of Health, Japan
National Institute of Health, USA
National Institute of Standards and Technology, USA
National Institute of Technology, Dept. of Information and Management, Kaohsiung, Taiwan
National Security Agency
Norwegian Defense, Norway
Novell Inc., USA
Osaka Univeristy, Japan
Oklahoma State University, USA
Purdue University, USA
Queens University, Canada
Sao Paulo University, Brazil
Sciences of Lisbon, Portugal
The Silesia University of Technology, Dept. of Fundamentals of Machine Design, Gliwice, Poland
Soong Sil University, Korea
Southwestern Bell Telephone, Co., USA
SRA, USA
Stanford University, USA
Syracuse University, USA
TASC, USA
Technical University of Lodz, Poland
Technion-Israel Institute of Technology, Israel
TECSIEL, Italy
The Norwegian Institute of Technology, Norway
The University of Manchester, UK
Tsinghua University, P.R. China
United Technologies Research Center, USA
Universidad Autonoma de Baja California, Mexico
Universita' Di Modena, Facolta' d' Ingegneria, Italy
Universidade Nova de Lisboa, Portugal
Universidad de Granada, Spain
Universitat Politecnica, Spain
Universitat Rovira i Virgili, Spain
Universite de Valenciennes, France
Universite de Paris Sud - Laboratoire de Recherche en Informatique, France
Universite Paris VI, France
University of Aberdeen, UK
University of Arizona, USA
University of British Columbia, Canada
University of Buckingham, UK
University of Calgary, Canada
Univeristy of California at Berkley, USA
University of California at Irvine, USA
University of Dortmund, Germany
University of Florida, USA
University of Gliwice, Poland
University of Illinois-Urbana Champaign, USA
University of Karlsruhe, Germany
University of Keal, UK
University of Manchester, USA
University of Muenster - Information Science, Germany
University of Newcastle Upon Tyne, UK
University of North Carolina-Charlotte, USA
University of North Texas, USA
University of Otago, New Zealand
University of Ottawa, Canada
University of Portsmouth, United Kingdom
University of Siena, Italy
University of South Florida, USA
University of Stellenbosch, South Africa
University of Strathclyde, UK
University of Sydney, Australia
University of the Aegean, Greece
University of Tampere, Finland
University of Toledo, USA
University of Virginia, USA
University of Waterloo, Canada
US NAVY, USA
USDA-Natural Resources Conservation Service, USA
Virginia Commonwelth University, USA
VP Technology at Eidetic Systems, USA
Warsaw School of Economics, Poland
Wrocaw University Of Technology, Poland
Yale University, USA