Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

I thank the commentators for their time, and generally positive remarks on the promise of the task-specific approach, in particular the generic task (GT) proposal.Johnson and Zualkernan would like a methodology for mapping domain knowledge onto one or more generic tasks so as to solve problems efficiently. This stage of problem and domain analysis in which the kind of reasoning that goes on needs to be analysed in a vocabulary of generic tasks is very important, and in our laboratory we identify this as the epistemic analysis stage. For specific classes of problems we have developed guidelines on how to perform this mapping. For example, Bylander and Smith (Bylander and Smith, 1985) describe a set of criteria and guidelines for mapping medical knowledge into CSRL-like structures for diagnostic reasoning. Similarly Brown (1984) describes criteria for mapping design knowledge into DSPL-like structures.

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Responses to “Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples” by B. Chandrasekaran

The Knowledge Engineering Review ◽

10.1017/s026988890000446x ◽

1988 ◽

Vol 3 (3) ◽

pp. 211-216

Keyword(s):

Building Blocks ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Routine Design ◽

Generic Tasks

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Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

The Knowledge Engineering Review ◽

10.1017/s0269888900004458 ◽

1988 ◽

Vol 3 (3) ◽

pp. 183-210 ◽

Cited By ~ 69

Author(s):

B. Chandrasekaran

Keyword(s):

Problem Solving ◽

Domain Knowledge ◽

Building Blocks ◽

Knowledge Based Systems ◽

Diagnostic Systems ◽

Planning Systems ◽

Knowledge Based ◽

Generic Tasks ◽

High Level ◽

And Control

AbstractThe level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

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Task Ontology-Based Human-Computer Interaction

Human Computer Interaction ◽

10.4018/978-1-87828-991-9.ch058 ◽

2009 ◽

pp. 950-960

Author(s):

Kazuhisa Seta

Keyword(s):

Problem Solving ◽

Real World ◽

Practical Knowledge ◽

Building Blocks ◽

Research Field ◽

Knowledge Based Systems ◽

Engineering Research ◽

Knowledge Based ◽

Task Ontology ◽

Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

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Task Ontology-Based Human-Computer Interaction

Encyclopedia of Human Computer Interaction ◽

10.4018/978-1-59140-562-7.ch087 ◽

2006 ◽

pp. 588-596

Author(s):

Kazuhisa Seta

Keyword(s):

Problem Solving ◽

Real World ◽

Practical Knowledge ◽

Building Blocks ◽

Research Field ◽

Knowledge Based Systems ◽

Engineering Research ◽

Knowledge Based ◽

Task Ontology ◽

Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

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Software-engineering challenges of building and deploying reusable problem solvers

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060409990047 ◽

2009 ◽

Vol 23 (4) ◽

pp. 339-356 ◽

Cited By ~ 5

Author(s):

Martin J. O'Connor ◽

Csongor Nyulas ◽

Samson Tu ◽

David L. Buckeridge ◽

Anna Okhmatovskaia ◽

...

Keyword(s):

Software Engineering ◽

Domain Knowledge ◽

Public Libraries ◽

Conceptual Modeling ◽

Building Blocks ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Application Systems ◽

Structuring Knowledge ◽

Problem Solvers

AbstractProblem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

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