In the last few years, we have witnessed a major growth in the use of empirical methods in AI. In part, this growth has arisen from the availability of fast networked computers that allow certain problems of a practical size to be tackled for the first time. There is also a growing realization that results obtained empirically are no less valuable than theoretical results. Experiments can, for example, offer solutions to problems that have defeated a theoretical attack and provide insights that are not possible from a purely theoretical analysis. I identify some of the emerging trends in this area by describing a recent workshop that brought together researchers using empirical methods as far apart as robotics and knowledge-based systems.

In computational cognitive modeling, we hypothesize internal mental processes of human cognitive activities and express such activities by computer programs. Such computational models often consist of many components and aspects. Claims are often made that certain aspects play a key role in modeling, but such claims are sometimes not well justified or explored. In this article, we first review some fundamental distinctions and issues in computational modeling. We then discuss, in principle, systematic ways of identifying the source of power in models.

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.

This paper presents a comprehensive approach for model-based diagnosis which includes proposals for characterizing and computing preferred diagnoses, assuming that the system description is augmented with a system structure (a directed graph explicating the interconnections between system components). Specifically, we first introduce the notion of a consequence, which is a syntactically unconstrained propositional sentence that characterizes all consistency-based diagnoses and show that standard characterizations of diagnoses, such as minimal conflicts, correspond to syntactic variations on a consequence. Second, we propose a new syntactic variation on the consequence known as negation normal form (NNF) and discuss its merits compared to standard variations. Third, we introduce a basic algorithm for computing consequences in NNF given a structured system description. We show that if the system structure does not contain cycles, then there is always a linear-size consequence in NNF which can be computed in linear time. For arbitrary system structures, we show a precise connection between the complexity of computing consequences and the topology of the underlying system structure. Finally, we present an algorithm that enumerates the preferred diagnoses characterized by a consequence. The algorithm is shown to take linear time in the size of the consequence if the preference criterion satisfies some general conditions.

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.

Introduction to this Special Issue on Innovative Applications of AI

This article describes a one-day workshop entitled AI and Knowledge Management that was held at the Fourteenth National Conference on Artificial Intelligence. The workshop was successful in identifying areas where AI techniques can be used to help those working on knowledge management and identifying areas for future work in this area.

CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library.

We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text for a recognizer shipping in new models of Apple Computer's NEWTON MESSAGEPAD and EMATE. Combining an artificial neural network (ANN) as a character classifier with a context-driven search over segmentation and word-recognition hypotheses provides an effective recognition system. Long-standing issues relative to training, generalization, segmentation, models of context, probabilistic formalisms, and so on, need to be resolved, however, to achieve excellent performance. We present a number of recent innovations in the application of ANNs as character classifiers for word recognition, including integrated multiple representations, normalized output error, negative training, stroke warping, frequency balancing, error emphasis, and quantized weights.