This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.

It is common to view programs as a combination of logic and control: the logic part defines what the program must do, the control part -- how to do it. The Logic Programming paradigm was developed with the intention of separating the logic from the control. Recently, extensive research has been conducted on automatic generation of control for logic programs. Only a few of these works considered the issue of automatic generation of control for improving the efficiency of logic programs. In this paper we present a novel algorithm for automatic finding of lowest-cost subgoal orderings. The algorithm works using the divide-and-conquer strategy. The given set of subgoals is partitioned into smaller sets, based on co-occurrence of free variables. The subsets are ordered recursively and merged, yielding a provably optimal order. We experimentally demonstrate the utility of the algorithm by testing it in several domains, and discuss the possibilities of its cooperation with other existing methods.

Research on constraints and agents is emerging at the intersection of the communities studying constraint computation and software agents. Constraint- based reasoning systems can be enhanced by using agents with multiple problem-solving approaches or diverse problem representations. The constraint computation paradigm can be used to model agent consultation, cooperation, and competition. An interesting theme in agent interaction, which is studied here in constraint-based terms, is confronting ignorance: the agent's own ignorance or its ignorance of other agents.

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.

It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. Our physical bodies are in many ways overdetermined, unnecessarily complex, and inefficiently designed, that is, the predictable product of the blind search that is evolution. Natural intelligence is unlikely to be limited by principles of parsimony and is likely to be overdetermined, unnecessarily complex, and inefficiently designed. One example is the view that thinking is in part visual, and hence it might prove useful to develop representations and reasoning mechanisms that reason with diagrams (not just about them) and that take seriously their visual nature.

This article, derived from the 1996 Association for the Advancement of Artificial Intelligence Presidential Address, explores the notion of intelligence from a variety of perspectives and finds that it "are" many things. It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. One goal of this article is to go back to basics, reviewing the things that we, individually and collectively, have taken as given, in part because we have taken multiple different and sometimes inconsistent things for granted. I believe it will prove useful to expose the tacit assumptions, models, and metaphors that we carry around as a way of understanding both what we're about and why we sometimes seem to be at odds with one another. Intelligence are also many things in the sense that is a product of evolution. Our physical bodies are in many ways overdetermined, unnecessarily complex, and inefficiently designed, that is, the predictable product of the blind search that is evolution. What's manifestly true of our anatomy is also likely true of our cognitive architecture. Natural intelligence is unlikely to be limited by principles of parsimony and is likely to be overdetermined, unnecessarily complex, and inefficiently designed. In this sense, intelligence are many things because is composed of the many elements that have been thrown together over evolutionary timescales. I suggest that in the face of that, searching for minimalism and elegance may be a diversion, for it simply may not be there. Somewhat more crudely put: The human mind is a 400,000-year-old legacy application -- and you expected to find structured programming? I end with a number of speculations, suggesting that there are some niches in the design space of intelligences that are currently underexplored. One example is the view that thinking is in part visual, and hence it might prove useful to develop representations and reasoning mechanisms that reason with diagrams (not just about them) and that take seriously their visual nature. I speculate as well that thinking may be a form of reliving, that re-acting out what we have experienced is one powerful way to think about and solve problems in the world. In this view, thinking is not simply the decontextualized manipulation of abstract symbols, powerful though that may be. Instead, some significant part of our thinking may be the reuse or simulation of our experiences in the environment. In keeping with this, I suggest that it may prove useful to marry the concreteness of reasoning in a model with the power that arises from reasoning abstractly.

Understanding the mind is one of the great "holy grails" of twentieth-century research. Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees -- and, in general, he succeeds.

A biologically motivated model of cortical self-organization is proposed. Contextis combined with bottom-up information via a maximum likelihood cost function. Clusters of one or more units are modulated by a common contextual gating Signal; they thereby organize themselves into mutually supportive predictors of abstract contextual features. The model was tested in its ability to discover viewpoint-invariant classes on a set of real image sequences of centered, graduallyrotating faces. It performed considerably better than supervised back-propagation at generalizing to novel views from a small number of training examples.

The genetic algorithm (GA) is a heuristic search procedure based on mechanisms abstracted from population genetics. In a previous paper [Baluja & Caruana, 1995], we showed that much simpler algorithms, such as hillcIimbing and Population Based Incremental Learning (PBIL), perform comparably to GAs on an optimization problemcustom designed to benefit from the GA's operators. This paper extends these results in two directions. First, in a large-scale empirical comparison of problems that have been reported in GA literature, we show that on many problems, simpleralgorithms can perform significantly better than GAs. Second, we describe when crossover is useful, and show how it can be incorporated into PBIL. 1 IMPLICIT VS.

The genetic algorithm (GA) is a heuristic search procedure based on mechanisms abstracted from population genetics. In a previous paper [Baluja & Caruana, 1995], we showed that much simpler algorithms, such as hillcIimbing and Population Based Incremental Learning (PBIL), perform comparably to GAs on an optimization problem custom designed to benefit from the GA's operators. This paper extends these results in two directions. First, in a large-scale empirical comparison of problems that have been reported in GA literature, we show that on many problems, simpler algorithms can perform significantly better than GAs. Second, we describe when crossover is useful, and show how it can be incorporated into PBIL. 1 IMPLICIT VS.