ALEX: Towards Adaptive Learning in a Multi-Algorithm Environment

In the eld of machine learning a large number of learning algorithms which can be used for extracting knowledge from the contents of databases have been developed. The acquired knowledge can be employed in expert systems to speed up database access and/or to predict answers to queries for which no information could be provided by the database system. But most of the learning algorithms are of restricted use in these kind of systems since usually each learning algorithm is based on it's speci c learning approach which is very well suited for a particular problem domain, but which is less successful in various other domains. To overcome this problem e orts in the eld of machine learning have been directed towards creating integrated learning environments which incorporate several learning algorithms within one learning tool eg. , The Machine Learning Toolbox (Uszynski, 1992) and as such can provide a combination of the di erent capabilities of their component algorithms in a common environment. Putting di erent algorithms together into one tool is only the rst step towards building a fully integrated learning environment. The goal must be to create a system which makes synergy e ects between di erent algorithms possible, resulting in a multi-algorithm system which exceeds the capabilities of a system only providing access to a set of learning techniques. Over recent years ALEX, a multi-algorithm environment for automated knowledge acquisition has been developed at IIASA (Winkelbauer and Fedra, 1991). ALEX has been designed and implemented in a modular fashion integrating three di erent learning algorithms in a common environment (Winkelbauer and Berka, 1993). As a generic learning environment, ALEX can be used for a number of possible applications ranging from \rule-mining" in data bases to extracting rule-based representations from algorithmic software systems. To enable automatic adaptation of the learning environment to a given problem domain the introduction of a feedback loop between the results of a learning step and the input of the next learning step is proposed. Such a feedback loop is also expected to improve the overall performance of the learning environment by utilizing the synergy e ects in the multi-algorithm system, making ALEX more than the sum of its components. 2 Introduction Extracting knowledge from data has always been one of the key issues in the el d of machine learning. A large number of learning algorithms have been developed which can serve as tools for automated knowledge acquisition from databases. They can transform the implicit (static) knowledge which is hidden in the contents of database systems into explicit (dynamic) knowledge which can be used in expert systems to speed up database access and to produce and/or predict answers to queries for which no information could be provided by the database system. An example for systems which successfully integrate data base, machine learning and expert system tools in an e cient and problem-oriented way are case based reasoning systems which became quite popular recently (Trappl, 1992). But most of the machine learning algorithms developed so far are of restricted use in these kind of systems, mainly because they are \stand-alone" implementations. Each learning algorithm is based one speci c learning approach which usually is very well suited for the problem domain it has been designed to work in, but which is less successful in various other domains. To overcome this problem e orts have been directed towards creating integrated learning environments which incorporate several learning algorithms within one learning tool eg. , The Machine Learning Toolbox (Uszynski, 1992) and as such can provide a combination of the di erent capabilities of their component algorithms in a common environment. Combining di erent learning algorithms is not an easy task, mainly because there never has been an overall accepted form of knowledge representation and almost every algorithm is based on it's own and mostly unique form of internal representation of the acquired knowledge. Thus the basic problem in creating an integrated learning environment is to nd a standardized knowledge and data representation which ts the algorithms to be combined. Speci c important features of a particular algorithm (eg., uncertainty measures) which are not included in the other algorithms even increase the complexity of this task. But putting di erent algorithms together into one tool is only the rst step towards building a fully integrated learning environment. The goal must be to create a system which makes synergy e ects between di erent algorithms possible, resulting in a multi-algorithm system which exceeds the capabilities of a system only providing access to a set of di erent learning techniques. 3 The Learning Environment ALEX Over recent years the Automatic Learning Environment ALEX has been developed at the International Institute for Applied Systems Analysis (Winkelbauer and Fedra, 1991; Winkelbauer and Berka, 1993). The main aim in the ALEX project was to develop a exible, integrated learning environment to enable the application and evaluation of di erent learning strategies for di erent problem domains. Figure 1: The Learning Environment ALEX Thus ALEX has been based on a exible structure (Figure 1) and consists of the following four components: the module for Example Generation and Interpretation, which generates examples for training as well as for evaluation (eg., by accessing 4 a database or running a simulation software package) and transforms them into an internal representation suited for the learning algorithms, the component for Statistical Analysis, i.e., statistical a priori analysis of training examples, the Learning Algorithms, performing the actual machine learning, the Performance Component, applying and evaluating the knowledge base generated by the learning algorithms. But the modularity of the above structure alone does not guarantee for easy exchange of its components. Most critical is the design of the representation forms of the di erent kinds of knowledge in the system which implicitly de ne the interfaces between the modules. The overall knowledge representation scheme of ALEX consists of the following three parts: Examples: Generated by the Example Generator and (if necessary) interpreted (ie., prepared) by the Example Interpreter and the Statistical Analysis module, the examples are stored in the system as sequential list of attribute/value tuples. Domain Speci c Knowledge: Domain speci c knowledge in ALEX comprises information on how to assign linguistic variables to numeric value ranges of example attributes and is aimed at guiding the learning process and making the resulting rules easier to understand and possibly more meaningful. This information is given in the form of a list of linguistic variables together with their proposed numeric ranges for each continuous example attribute. Acquired Knowledge: As already stated above, the basic problem in creating an integrated learning environment is to nd a standardized knowledge and data representation which ts the algorithms to be combined. 5 Production rules have been selected as standard representation form for the generated knowledge because: { they are easy to understand and as such ideal for the performance component of the system, { they can be easily obtained from many other knowledge representation forms without loss of information and accuracy, { they can be used as a \neutral" basis for comparison of results from di erent learning algorithms. Throughout the whole system the user is supported by a Graphics User Interface (implemented in X-Windows the "X" of the ALEX acronym) which is completely menu driven and makes extensive use of graphics aiming at making the learning process and the application and evaluation of the generated knowledge transparent and easy to understand. The Application Domain The application of ALEX has been directed towards model abstraction from simulation software systems, ie. , the translation of a complex procedural simulation model into a set of rules that can directly be used as the knowledge base of the performance component (a similar task is described for a medical domain by Lavrac and Mozetic, 1992). Simulation or optimization models do contain a considerable amount of knowledge, represented in the algorithmic structure and data sets of this software. Models are, however, di cult to use and also di cult to integrate into rule-based expert systems. Using the ALEX learning environment, a given model can be run repeatedly in a Monte-Carlo framework to generate examples which constitute the input for the learning algorithm. The learning algorithm then generates rules from these examples, such representing the model's behavior in the form of rules that can be directly used as the knowledge base of the performance component and of any rule-based expert system, relating the range of plausible input conditions in a qualitative way to discrete model outcomes. The river model component of TOXSCREEN, a system of dynamic simulation models, which has been adapted as part of an environmental risk 6 assessment system at IIASA/ACA (Fedra, 1992), has been selected to become the tutor software system for ALEX. The model system TOXSCREEN, developed at Oak Ridge National Laboratory, is designed to assess the potential environmental fate of toxic chemicals released to air, water, or soil. It evaluates the potential of chemicals to accumulate in environmental media and is intended for use as a screening device. Since TOXSCREEN is a numerical simulation package the examples produced from its runs consist of continuous attributes only. A sample training example as produced by TOXSCREEN is shown below: EXAMPLE Ex-000 spill 0.39 kg/day photolysis 84.04 %/day hydrolysis 35.33 %/day oxidation 44.65 %/day biodegradation 31.86 %/day volatilization 88.64 %/day sediment_diameter 0