systems Abstract. The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions. 1.

Key words: machine learning, concept acquisition, explanation-based learning Abstract. In the last issue of this journal Mitchell, Keller, and Kedar-Cabelli presented a unifying framework for the explanation-based approach to machine learning. While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata, and learning from observation. This paper outlines six specific problems with the previously proposed framework and presents an alternative generalization method to perform explanation-based learning of new concepts.

Analogical reasoning has a long history in artificial intelligence research, primarily because of its promise for Ike acquisition unit effective use of knowledge. Defined as a representational mapping from a known "source " domain into a novel "target" domain, analogy provides a basic mechanism for effectively connecting a reasoner's past and present experience. Using a four-component process model of analogical reasoning, this paper reviews sixteen computational studies of analogy. These studies are organized chronologically within broadly defined task domains of automated deduction, problem solving and planning, natural language comprehension, and machine learning. Drawing on these detailed reviews, a comparative analysis of diverse contributions to basic analogy processes identifies recurrent problems for studies of analogy and common approaches to their solution. The paper concludes by arguing that computational studies of analogy are in a slate of adolescence: looking to more mature research areas in artificial intelligence for robust accounts of basic reasoning processes and drawing upon a long tradition of research in other disciplines.

Information retrieval using probabilistic techniques has at-tracted significant attention on the part of researchers in information and computer science over the past few de-cades. In the 198Os, knowledge-based techniques also made an impressive contribution to “intelligent ” informa-tion retrieval and indexing. More recently, information sci-ence researchers have turned to other newer artificial-in-telligence-based inductive learning techniques including neural networks, symbolic learning, and genetic algo-rithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and re-trieval capabilities of current information storage and re-trieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these tech-niques, we present three popular methods: the connec-tionist Hopfield network; the symbolic ID3/ID5R; and evolu-tion-based genetic algorithms. We discuss their knowl-edge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users ’ infor-mation needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques.

Solutions to the problem of learning from examples will have far-reaching benefits, and therefore, the problem is one of the most widely studied in the field of machine learning. The purpose of this study is to investigate a general solution method for the problem, the automatic generation of decision (or classification) trees. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort or faults, where "high" was defined to be in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112,000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort...

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible...

Massively parallel networks of relatively simple computing elements offer an attractive and versatile framework for exploring a variety of learning structures and processes for intelligent systems. This paper briefly summarizes the popular learning structures and processes used in such networks. It outlines a range of potentially more powerful alternatives for pattern-directed inductive learning in such systems. It motivates and develops a class of new learning algorithms for massively parallel networks of simple computing elements. We call this class of learning processes generative for they offer a set of mechanisms for constructive and adaptive determination of the network architecture - the number of processing elements and the connectivity among them - as a function of experience. Generative learning algorithms attempt to overcome some of the limitations of some approaches to learning in networks that rely on modification of weights on the links within an otherwise fixed network t...

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial...

This thesis is about inductive learning, or learning from examples. The goal has been to investigate ways of improving learning algorithms. The chess end-game "King and Rook against King" (KRK) was chosen, and a number of benchmark learning tasks were defined within this domain, sufficient to over-challenge stateof -the-art learning algorithms. The tasks comprised learning rules to distinguish (1) illegal positions and (2) legal positions won optimally in a fixed number of moves. From our experimental results with task (1) the best-performing algorithm was selected and a number of improvements were made. The principal extension to this generalisation method was to alter its representation from classical logic to a non-monotonic formalism. A novel algorithm was developed in this framework to implement rule specialisation, relying on the invention of new predicates. When experimentally tested this combined approach did not at first deliver the expected performance gains due to restrictio...

Non-Axiomatic Reasoning System (NARS) is an intelligent reasoning system, where intelligence means working and adapting with insu cient knowledge and resources. NARS uses a new form of term logic, or an extended syllogism, in which several types of uncertainties can be represented and processed, and in which deduction, induction, abduction, and revision are carried out in a uni ed format. The system works in an asynchronously parallel way. The memory of the system is dynamically organized, and can also be interpreted as a network. After present the major components of the system, its implementation is brie y described. An example is used to show howthe system works. The limitations of the system are also discussed. 1