TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper

Combining Exploratory Learning With Structured Practice to Foster Conceptual and Procedural Fractions Knowledge

Robust domain knowledge consists of conceptual and procedural knowledge. The two types of knowledge develop together, but are fostered by different learning tasks. Exploratory tasks enable students to manipulate representations and discover the underlying concepts. Structured tasks let students practice problem-solving procedures step-by-step. Educational technology has mostly relied on providing only either task type, with a majority of learning environments focusing on structured tasks. We investigated in two quasi-experimental studies with 8-10 years old students from UK (N = 121) and 10-12 years old students from Germany (N = 151) whether a combination of both task types fosters robust knowledge more than structured tasks alone. Results confirmed this hypothesis and indicate that students learning with a combination of tasks gained more conceptual knowledge and equal procedural knowledge compared to students learning with structured tasks only. The results illustrate the efficacy of combining both task types for fostering robust fractions knowledge

Are Routines Reducible or Mere Cognitive Automatisms? Some contributions from cognitive science to help shed light on change in routines

The aim of this article is to understand permanence and changes inside organizational routines. For this purpose, it seems important to explain how individual and collective memorisation occurs, so as to grasp how knowledge can be converted into routines. Although memorisation mechanisms imply a degree of durability, our procedural and declarative knowledge, and our memorisation processes, evolve so that individuals and organisations can project themselves into the future and innovate. Some authors highlight the necessity of dreaming and forgetting (Bergson 1896); others believe that emotions play a role in our memorisation processes (Damasio 1994). These dimensions are not only important at the individual level but also in an organisational context (Lazaric and Denis 2005; Reynaud 2005; Pentland and Feldman 2005).I review the individual dimension of these memorisation processes, with the Anderson’s distinction between procedural knowledge and declarative knowledge. I discuss the notion of cognitive automatisms in order to show why routines should be investigated beyond their first literal assumption (Bargh, 1997). This leads to a clear understanding of the micro level that underpins organisational flexibility and adaptation (notably the motivational triggers). Within organisations, the memorisation mechanisms are at once similar and diverse. Indeed, organisations use their own filters and mechanisms to generate organisational coordination. Organizational memory has its own dimension as it does not merely consist of the sum of individual knowledge and must be able to survive when individuals leave. Routines depend on the organisational memory implemented and on the procedural knowledge and representations of it (individual and collective representations).Knowledge; memorisation; organizations; individuals

On the acquisition and representation of procedural knowledge

Historically knowledge acquisition has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some of some types of knowledge, little attention has been devoted to procedural knowledge. NASA personnel frequently perform tasks that are primarily procedural in nature. Previous work is reviewed in the field of knowledge acquisition and then focus on knowledge acquisition for procedural tasks with special attention devoted to the Navy's VISTA tool. The design and development is described of a system for the acquisition and representation of procedural knowledge-TARGET (Task Analysis and Rule Generation Tool). TARGET is intended as a tool that permits experts to visually describe procedural tasks and as a common medium for knowledge refinement by the expert and knowledge engineer. The system is designed to represent the acquired knowledge in the form of production rules. Systems such as TARGET have the potential to profoundly reduce the time, difficulties, and costs of developing knowledge-based systems for the performance of procedural tasks

Shallow EDSLs and Object-Oriented Programming: Beyond Simple Compositionality

Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep embeddings. Inquiry: Shallow embeddings are quite simple, but they have been criticized in the past for being quite limited in terms of modularity and reuse. In particular, it is often argued that supporting multiple DSL interpretations in shallow embeddings is difficult. Approach: This paper argues that shallow EDSLs and Object-Oriented Programming (OOP) are closely related. Gibbons and Wu already discussed the relationship between shallow EDSLs and procedural abstraction, while Cook discussed the connection between procedural abstraction and OOP. We make the transitive step in this paper by connecting shallow EDSLs directly to OOP via procedural abstraction. The knowledge about this relationship enables us to improve on implementation techniques for EDSLs. Knowledge: This paper argues that common OOP mechanisms (including inheritance, subtyping, and type-refinement) increase the modularity and reuse of shallow EDSLs when compared to classical procedural abstraction by enabling a simple way to express multiple, possibly dependent, interpretations. Grounding: We make our arguments by using Gibbons and Wu's examples, where procedural abstraction is used in Haskell to model a simple shallow EDSL. We recode that EDSL in Scala and with an improved OO-inspired Haskell encoding. We further illustrate our approach with a case study on refactoring a deep external SQL query processor to make it more modular, shallow, and embedded. Importance: This work is important for two reasons. Firstly, from an intellectual point of view, this work establishes the connection between shallow embeddings and OOP, which enables a better understanding of both concepts. Secondly, this work illustrates programming techniques that can be used to improve the modularity and reuse of shallow EDSLs

Acquiring Procedural Knowledge of a Technology Interface: Introduction to this Special Issue.

Guidelines and models for procedural instructions can be supported by three types of research. Careful analytical studies of collections of instructions can help to identify, describe, and evaluate strategies that writers and designers apply. Empirical studies measure the effects of document variables on the performance of users, thus offering evidence, contraevidence, or refinements for existing guidelines. Theoretical studies, finally, aim to describe and explain the behavior of readers of instructions. To designers and writers, they provide a deeper insight in the underlying cognitive processes that determine success or failure of their work. This special issue offers research articles in all three categories

Counting-on, trading and partitioning: effects of training and prior knowledge on performance on Base-10 tasks

Factors affecting performance on Base-10 tasks were investigated in a series of four studies with a total of 453 children aged five to seven years. Training in counting-on was found to enhance child performance on Base-10 tasks (Studies 2, 3, and 4), while prior knowledge of counting-on (Study 1), trading (Studies 1 and 3) and partitioning (Studies 1 and 4) were associated with enhanced Base-10 performance. It emerged that procedural knowledge of counting-on, trading and partitioning can lead to improvements in procedural knowledge of the Base-10 system. The findings lend support to the model of iterative development of conceptual and procedural knowledge advanced by Rittle-Johnson, Siegler and Alibali (2001)

Computer-assisted knowledge acquisition for hypermedia systems

The usage of procedural and declarative knowledge to set up the structure or 'web' of a hypermedia environment is described. An automated knowledge acquisition tool was developed that helps a knowledge engineer elicit and represent an expert's knowledge involved in performing procedural tasks. The tool represents both procedural and prerequisite, declarative knowledge that supports each activity performed by the expert. This knowledge is output and subsequently read by a hypertext scripting language to generate the link between blank, but labeled cards. Each step of the expert's activity and each piece of supporting declarative knowledge is set up as an empty node. An instructional developer can then enter detailed instructional material concerning each step and declarative knowledge into these empty nodes. Other research is also described that facilitates the translation of knowledge from one form into a form more readily useable by computerized systems