haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276 298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke 6 N. J. Blair, 2000, Psychonomic Bulletin 6 Review, 7, 636 645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other ``irrational' ' phenomena in associative learning, including base rate effects, perseveration of attention through relevance

. This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal bestfirst search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal eithe...

Efficient data mining algorithms are crucial for effective knowledge discovery. We present the Multi-Stream Dependency Detection (MSDD) data mining algorithm that performs a systematic search for structure in multivariate time series of categorical data. The systematicity of MSDD's searchmakes implementation of both parallel and distributed versions straightforward. Distributing the search for structure over multiple processors or networked machines makes mining of large numbers of databases or very large databases feasible. We present results showing that MSDD efficiently finds complex structure in multivariate time series, and that the distributed version finds the same structure in approximately 1/n of the time required by MSDD, where n is the number of machines across which the search is distributed. MSDD differs

Capture-recapture (CR) models have been proposed as an objective method for controlling software inspections. CR models were originally developed to estimate the size of animal populations. They have also been used to estimate the number of defects in an inspected artifact. Armed with this estimate, one can decide whether the artifact requires a reinspection to ensure that a minimal inspection effectiveness level has been attained. Little evaluative research has been performed thus far on the utility of CR models for inspections with two inspectors. Furthermore, these studies have focused on the relative error of the defect content estimates exclusively. In this paper we report on an extensive Monte Carlo simulation that evaluated six capture-recapture models for two inspectors assuming a code inspections context. In addition to relative error, we evaluate the accuracy of the reinspection decision. The latter is more congruent with the manner in which these models would be use...

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One of the recently proposed tools for controlling software inspections is capture-recapture models. These are models that can be used to estimate the number of remaining defects in a software document after an inspection. Based on this information one can decide whether to reinspect a document to ensure that it is below a prespecified defect density threshold, and that the inspection process itself has attained a minimal level of effectiveness. This line of work has also recently been extended with other techniques, such as the Detection Profile Method. In this paper we investigate an alternative approach: the use of subjective estimates of effectiveness by the inspectors for making the reinspection decision. We performed a study with 30 professional software engineers and found that the median relative error of the engineers' subjective estimates of defect content to be zero, and that the reinspection decision based on that estimate is consistently more correct than the default decision of never reinspecting. This means that subjective estimates provide a good basis for ensuring product quality and inspection process effectiveness during software inspections. Since a subjective estimation procedure can be easily integrated into existing inspection processes, it represents a good starting point for practitioners before introducing more objective decision making criteria by means of capture-recapture models or the Defect Detection Profile Method.

. This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised Inductive Logic Programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off satisfaction and novelty of the rule. The approach has been implemented in the Tertius system. The system performs an optimal best-first search, finding the k most confirmed hypotheses. It can be tuned to many different domains by setting its parameters, and it can deal either with individual-based representations as in propositional learning or with general logi...

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge

Abstract — The use of unmanned autonomous vehicles is becoming more and more significant in recent years. The fact that the vehicles are unmanned (whether autonomous or not), can lead to greater difficulties in identifying failure and anomalous states, since the operator cannot rely on its own body perceptions to identify failures. Moreover, as the autonomy of unmanned vehicles increases, it becomes more difficult for operators to monitor them closely, and this further exacerbates the difficulty of identifying anomalous states, in a timely manner. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. In this paper we propose a novel, model-free, approach for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Experiments conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) demonstrate the efficacy of the approach by detecting the vehicles deviations from nominal behavior. I.