Knowledge Engineering Environment: A Comprehensive Guide

A Knowledge Engineering Environment is a platform that enables the creation, management, and application of knowledge-based systems. It's a crucial tool for organizations that want to harness the power of artificial intelligence and machine learning.

These environments typically consist of a suite of tools and technologies that allow experts to design, build, and deploy knowledge-based systems. This can include expert systems, decision support systems, and other types of AI-powered applications.

The goal of a Knowledge Engineering Environment is to provide a structured and collaborative approach to knowledge management. By doing so, organizations can improve the accuracy and consistency of their knowledge-based systems, leading to better decision-making and outcomes.

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The Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems. It was developed and sold by IntelliCorp, and was first released in 1983.

KEE ran on Lisp machines, and was later ported to Lucid Common Lisp with the CLX library, an X Window System (X11) interface for Common Lisp. This version was available on several different UNIX workstations.

The KEE environment provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames. It also includes a frame-based rule system and supports non-monotonic reasoning through the concepts of worlds.

Here are some of the key features of KEE:

Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems that was first released in 1983 by IntelliCorp. It was initially developed for Lisp machines, but later ported to Lucid Common Lisp with the CLX library, an X Window System (X11) interface for Common Lisp.

KEE provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames. Frames are the core of KEE, and they can be used for both individual instances and classes.

Frames have slots and slots have facets, which can describe a slot's expected values, its working value, or its inheritance rule. Slots can have multiple values, and behavior can be implemented using a message passing model.

KEE supports non-monotonic reasoning through the concepts of worlds, which allow providing alternative slot-values of frames. Inconsistencies can be detected and analyzed through an assumption-based truth or reason maintenance system.

Here are some key features of KEE:

KEE has been used in various domains, including computer-aided design (CAD) and product lifecycle management (PLM). Its ability to integrate with other systems and provide a knowledge-based environment makes it a valuable tool for many applications.

A Knowledge Engineering Environment is a crucial component of Knowledge-Based Engineering (KBE). It's where the magic happens, and the knowledge is put to work.

Knowledge-based vs. procedural programming is a fundamental trade-off in KBE. This trade-off is reflected in the choice to use powerful knowledge-based environments or more conventional procedural and object-oriented programming environments.

In KBE, the choice of programming environment can significantly impact the system's performance and expressiveness. As Levesque demonstrated, the closer a language is to First Order Logic, the more probable that it will allow expressions that are undecidable or require exponential processing power to complete.

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Knowledge-based environments are more powerful, but also more prone to undecidability and exponential processing. Procedural and object-oriented programming environments, on the other hand, are more conventional but may lack the expressiveness of knowledge-based environments.

To illustrate the difference, consider the following table:

Note that this table is not exhaustive, but it gives you an idea of the trade-off between expressiveness and performance.

Ultimately, the choice of programming environment depends on the specific needs of the project. A Knowledge Engineering Environment that balances expressiveness and performance is ideal, but it requires careful consideration and planning.

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Agent-based models are a crucial aspect of a knowledge engineering environment. They allow for the creation and validation of conceptual agent models.

The CoMoMAS knowledge engineering toolkit, as seen in Chapter 5, enables users to create a set of conceptual agent models. This toolkit is composed of four different modules, each with a specific function.

A key feature of the CoMoMAS toolkit is its ability to transform conceptual agent models into a programming language. This is achieved through the CoMoMAS Coder module, which uses a library of functions to validate the conceptual agent models.

The CoMoMAS Constructor module builds the conceptual model set based on the knowledge delivered by the CoMoMAS Modeller module. This module covers the operations for model composition, which were defined in the local and global knowledge acquisition cycles.

The validation of conceptual agent models is realised within the simulation environment MICE, as seen in Chapter 6. The extended MICE test bed provides a graphical user interface with functions for a simplified analysis and a comprehensive evaluation of experiments.

The CoMoMAS toolkit is realised by the KADSTOOL toolkit, which is a knowledge engineering environment for the construction of expertise models following the CommonKADS approach. This highlights the importance of using established knowledge engineering environments in the development of agent-based models.

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The Wright Architecture is a key component of the Knowledge Engineering Environment. It's designed to be easily molded to any domain expertise, type of reasoning, and collection of supporting data.

KnowledgeWright is the architecture that makes this possible. It can be configured to provide a near-perfect knowledge development, test, and deployment environment for a given domain of expertise.

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The Wright Architecture is built around a standard API, called the KWI, which makes it easy for any application program to communicate with a conforming knowledgebase and reasoning engine. This API defines a dialog between a calling program and the reasoning engine.

The KWI is what makes it possible for the KnowledgeWright Workshop to test run and debug knowledgebases developed with any pre-provided or custom built KnowledgeWright Jigs. It's also used to implement the various runtime interfaces provided with KnowledgeWright.

KnowledgeWright's flexibility is one of its greatest strengths. It can be used to integrate with other sources of information, making it a powerful tool for knowledge engineering.

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KBE methodology is crucial for managing knowledge and limiting the risk associated with developing and maintaining knowledge-based applications.

The EU project MOKA proposes a methodology that focuses on structuring and formalizing knowledge, as well as linking it to implementation.

This approach helps ensure that knowledge is up-to-date and maintained effectively, reducing the risk of errors or outdated information.

General knowledge engineering methods, such as those developed for expert systems, can also be used as an alternative to MOKA.

KBE methodology is crucial for managing knowledge and limiting the risk associated with the development and maintenance of KBE applications.

The KBE development process involves identifying, capturing, structuring, formalizing, and implementing knowledge. Many KBE platforms only support the implementation step, which is not always the main bottleneck.

To address this issue, a suitable methodology is needed to manage knowledge and keep it up to date. The EU project MOKA proposes solutions that focus on structuring and formalization steps.

General knowledge engineering methods, developed for expert systems across all industries, can also be used as an alternative to MOKA. Similarly, general software development methodologies like the Rational Unified Process or Agile methods can be employed.

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KBE and PLM is a powerful combination that can significantly enhance the manufacturing process. It spans the full product lifecycle from idea generation to implementation, delivery, and disposal.

The management of the production process is a natural area of emphasis for KBE in the context of PLM. This includes configuration, trades, control, management, and optimization.

KBE supports the decision processes involved with these areas, providing automated reasoning and knowledge management services. This integration with diverse needs of lifecycle management is a significant advantage of using KBE.

By using KBE, businesses can get a more comprehensive view of their product lifecycle, covering issues such as business planning, marketing, and more.

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KBE extends and builds on the CAx domain, which includes computer-aided design of manufactured parts, software, and architecture of buildings.

CAx spans multiple domains, all sharing common issues like managing collaboration of sophisticated knowledge workers and design and re-use of complex artifacts.

KBE is analogous to Knowledge-Based Software Engineering, which extended the domain of Computer Aided Software Engineering with knowledge-based tools and technology.

The 777 Program by Boeing is an example of KBE's success in managing large-scale systems, databases, and workstations for design and analytical engineering work.

KBE allowed engineers to recompute influences to changes in the early part of the design/build stream over a weekend, enabling evaluation by downstream processes.

CAx allowed tighter tolerances to be met, and subsequent programs applied KBE in more areas, integrating KBE facilities into the CAx platform.

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The KnowledgeWright Workshop is a graphical environment where you can create, edit, and test a knowledgebase. It's a pretty cool tool that lets you see the different organizational views of your knowledgebase, which is useful for keeping track of everything.

You can run and debug a knowledgebase right from within the Workshop, which is super convenient. The test runtime even shows you a sample question dialog box, so you can see how users will interact with your knowledgebase.

The Workshop also lets you see the status and trace windows, which tell you exactly what the reasoning engine is doing as it works through your knowledgebase. This is really helpful for troubleshooting and fine-tuning your knowledgebase.

The KnowledgeWright Workshop can even generate an HTML document as a solution, which is a really handy way to present information to users. In one example, the solution is an answer to a "goal" called "main_doc" that recommends resources and products to a website visitor.

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Standardization is crucial in the Knowledge Engineering Environment, as it facilitates knowledge sharing, integration, and re-use.

Genworks GDL, a commercial product, addresses the issue of application longevity by providing a high-level declarative language kernel based on ANSI Common Lisp.

There's a trade-off between using standards and proprietary languages, with standardization offering benefits like knowledge sharing, and proprietary formats providing competitive advantages.

The Object Management Group released a KBE services RFP document in 2006, but to date, no OMG specification for KBE exists.

Gellish English is an example of a system-independent language for machine-readable ontologies in the KBE domain.

The benefits of standardization include facilitating knowledge sharing, integration, and re-use, but proprietary formats can provide competitive advantages and powerful features.

A list of areas where Knowledge Engineering is applied includes:

In Chapter 7, a case study is presented to describe a multi-agent system in terms of conceptual models. The author shows how these models are constructed and transformed into executable code.

The AIbot model, developed by B. Hayes-Roth's research group at Stanford University, is one of the examples used to illustrate this process. It's a model for adaptive intelligent systems.

The CoNomad agent model, developed within the CoMoMAS project at the INRIA/LORIA research lab, is another example used to demonstrate the transformation of conceptual models into executable code. It was developed for the control of a Nomad200 mobile robot.

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In Chapter 7, a case study is presented using conceptual models to describe a multi-agent system.

The case study illustrates how these models are transformed into executable code.

The author highlights two agent models: AIbot, developed by B. Hayes-Roth's research group at Stanford University, and CoNomad, developed by Jean-Paul Haton's research group at INRIA/LORIA during the CoMoMAS project.

Both AIbot and CoNomad were designed for controlling a Nomad200 mobile robot.

AIbot is an adaptive intelligent system model, while CoNomad is an agent model.

The chapter shows how these models are constructed and then transformed into executable code.

This process is demonstrated through two examples, providing a clear understanding of how conceptual models are used in multi-agent systems.

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This book is a valuable resource for learning about the conceptual approach to designing multi-agent systems.

The author provides a comprehensive review of the CoMoMAS methodology, which is a formalized approach for the model development process.

The book also offers a detailed description of the CoMoMAS engineering environment, making it a suitable guide for developing models using this approach.

A case study is included, providing a simple and useful way to study the conceptual modelling approach in action.

This book will be particularly useful for researchers in Artificial Intelligence, as well as practical engineers who need a detailed understanding of the CoMoMAS environment.

The book is also a great resource for students, offering a clear and concise explanation of the conceptual approach to designing multi-agent systems.

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