POPEYE: A production rule-based model of multitask supervisory control (POPCORN)

Recent studies of relationships between subjective ratings of mental workload, performance, and human operator and task characteristics have indicated that these relationships are quite complex. In order to study the various relationships and place subjective mental workload within a theoretical framework, we developed a production system model for the performance component of the complex supervisory task called POPCORN. The production system model is represented by a hierarchial structure of goals and subgoals, and the information flow is controlled by a set of condition-action rules. The implementation of this production system, called POPEYE, generates computer simulated data under different task difficulty conditions which are comparable to those of human operators performing the task. This model is the performance aspect of an overall dynamic psychological model which we are developing to examine and quantify relationships between performance and psychological aspects in a complex environment

Stable Production Rule Sets are Deterministic

Production Rule Sets (PRS) are a digital event-based model for CMOS circuits; stable production rule sets are those in which in every execution, every enabled rule remains enabled until it is executed. It has been conjectured that stable production rule sets are determinstic, meaning in particular that they cannot implement arbiters, and that the sequence of values sent on any channel is independent of the execution. In this paper, we prove these facts rigorously, directly from first principles. We also propose improvements to PRS testing tools based on the resulting theory

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

Production Rule Verification for Quasi-Delay-Insensitive Circuits

[No abstract available

Learning-based constraints on schemata

Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models

Online and offline heuristics for inferring hierarchies of repetitions in sequences

Hierarchical dictionary-based compression schemes form a grammar for a text by replacing each repeated string with a production rule. While such schemes usually operate online, making a replacement as soon as repetition is detected, offline operation permits greater freedom in choosing the order of replacement. In this paper, we compare the online method with three offline heuristics for selecting the next substring to replace: longest string first, most common string first, and the string that minimized the size of the grammar locally. Surprisingly, two of the offline techniques, like the online method, run in time linear in the size of the input. We evaluate each technique on artificial and natural sequences. In general, the locally-most-compressive heuristic performs best, followed by most frequent, the online technique, and, lagging by some distance, the longest-first technique

Online and offline heuristics for inferring hierarchies of repetitions in sequences

Hierarchical dictionary-based compression schemes form a grammar for a text by replacing each repeated string with a production rule. While such schemes usually operate online, making a replacement as soon as repetition is detected, offline operation permits greater freedom in choosing the order of replacement. In this paper, we compare the online method with three offline heuristics for selecting the next substring to replace: longest string first, most common string first, and the string that minimized the size of the grammar locally. Surprisingly, two of the offline techniques, like the online method, run in time linear in the size of the input. We evaluate each technique on artificial and natural sequences. In general, the locally-most-compressive heuristic performs best, followed by most frequent, the online technique, and, lagging by some distance, the longest-first technique

A CLIPS/X-window interface

The design and implementation of an interface between the C Language Integrated Production System (CLIPS) expert system development environment and the graphic user interface development tools of the X-Window system are described. The underlying basis of the CLIPS/X-Window is a client-server model in which multiple clients can attach to a single server that interprets, executes, and returns operation results, in response to client action requests. Implemented in an AIX (UNIX) operating system environment, the interface has been successfully applied in the development of graphics interfaces for production rule cooperating agents in a knowledge-based computer aided design (CAD) system. Initial findings suggest that the client-server model is particularly well suited to a distributed parallel processing operational mode in a networked workstation environment

Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML