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Wisdom on Knowledge

Wisdom on Knowledge

Larry Lucardie

Wisdom is an advanced knowledge transmission tool that is developed and applied by Arthur Andersen to deal with three weak points in current KBS-technology.

Weak points in Knowledge-based Systems Technology

Business processes in trade and industry such as diagnosis, monitoring, engineering and planning, increasingly depend on rapid and easy processing of large amounts of complex knowledge. Knowledge-Based Systems technology is considered an essential enabling technology that will shape the pattern of computer applications in the emerging knowledge-based organisations.

There is, however, a widespread consensus in the KBS-community that a successful deployment of KBS-applications requires a more rigorous development process. Initiatives for improvements include the KADS-(II) and VITAL ESPRIT funded projects.

1. Lack of a theory of the nature of knowledge

Though considerable progress has been made in tools and methodologies, the process of transferring knowledge to computer systems for knowledge management purposes is still susceptible to major improvements. This can partly be attributed to the phenomenon that current method ologies and tools lack an underlying theory that defines the nature of knowledge. A theory of the nature of knowledge can significantly advance the process of knowledge engineering. To pave the way for such a theory, Newell introduced the knowledge level (1981): a separate computer systems level that focuses purely on knowledge. At this level, user interface and implementation aspects are irrelevant.

What did not happen yet, however, is using the knowledge level to develop a full-blown theory of the nature of knowledge, that, by explaining the fundamental nature of knowledge, provides guidelines in the knowledge modelling process and that can form the basis for the development of new and powerful methodologies, tools and languages.

1. Lack of an adequate representation formalism

A second weak point in current KBS-technology can be spotted at the level of knowledge representation languages. Business practice reveals that industrial clients impose high demands on knowledge representation formalisms. They require a representation formalism that:

- is easy understandable for laymen, computer personnel as well as computers so that knowledge represented can be discussed and validated on completeness, consistency and correctness in comprehensive ways

- has a high expressiveness so that many forms of knowledge can be expressed

- is very effective and efficient so that knowledge can be represented in a computer system with a minimal effort

- is based on mathematical logic so that knowledge can be represented in non-ambiguous ways

- is easy to adapt so that the maintenance knowledge bases is facilitated

- stimulates the structuring process

- provides simulation facilities so that the behaviour of the computer possessing knowledge can be directly shown

Analysis reveals that the representation languages of current KBS-Technology do not yet cope with all these requirements. The languages made available by the KADS(-related) research, e.g. the CML (informal language) and (ML)2 (formal language). are not easy to learn, understand and manipulate for laymen.

3. Lack of adequate tools

A third weak point is the lack of tools that provide advanced management facilities centred around a representation formalism that can cope with all the requirements above. Important here is that these facilities should be based on new graphical intuitive facilities and that they enable platform-independent and network-independent access to knowledge.

Wisdom: A New Functional Direction in KBS-Technology

To deal with the first weak point, the development of Wisdom is based on the functional theory of the nature of knowledge. In theories on the nature of knowledge concepts play an important role as classificatory and storage mechanisms. A concept has an intension and an extension. The intension of a concept is a set of constraints (or conditions) that should be satisfied by an object to belong to the class covered by the concept. The intension refers to an object-type. The extension of a concept consists of the set of objects complying with the object-type.

Knowledge-based systems perform a matching task: they all apply constraints (the object-type) to a set of real-world referents (the objects) to obtain a match. An object matches if it can be classified as an object-type. As we can see from Figure 1, the description of the object-type and the objects contains abstraction mechanisms such as generalisation, specialisation and aggregation.

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Figure 1: Matching Object-types and Objects.

There are several basic views on how to reconstruct the object-type of a concept, one of which is the functional view (Hendriks 1986; Lucardie, 1994; Reitsma; 1990; Van Der Smagt & Lucardie, 1991). Two important characteristics of the functional approach are the explicit account for goals and functional equivalence.

The goal-oriented selection principle

The reconstruction of an object-type normally leaves us with a sheer enumeration of conditions that appears intractable. A selection principle is needed. The functional approach operationalizes a selection-principle by assuming a goal or function of classification (Stepp & Michalski, 1986). An important aspect of this approach is the emphasis placed on the role of functions that should be performed by objects. These functions direct the select ion of conditions of an object-type and consequently determine the relevant attributes of objects.

Central in the functional view is the notion of functional equivalence: the phenomenon that objects are identical, fall in the same concept or are similar if they possess, even quite different, attributes to perform the same function. The functional view emphasises that an object-type of a concept is established by a goal-oriented reconstruction process wherein possibly different objects are functionally similar. Functional equivalence can be traced back to three basic mechanisms: (1) conditional relevance, (2) conceptual interaction and (3) variation limited to goal-constructed categories.

Ad 1. Conditional Relevance

The first mechanism of functional equivalence refers to the phenomenon that, under specific conditions, other attributes (descriptors) may become important for determining class membership. Thus, conditional relevance denotes the phenomenon that conditions of an object-type are not always relevant. On the contrary, their relevance is conditional upon circumstances, which need to reconstructed and incorporated in the object-type either.

Ad 2. Conceptual Interaction

Categorisations of attributes of objects may influence each other. This phenomenon is called conceptual interaction. It manifests itself in the mutual influence of the categorisations of the attributes .

Ad 3. Variation limited to goal-constructed categories

The third phenomenon contributing to functional equivalence refers to the situation that objects may have different attribute values, but that this variation is limited to, or falls within, goal-constructed categories.

To tackle the second weak point, Wisdom offers an integral knowledge representation language that consists of decision table systems, frame systems, Wislogic and Prolog in order to obtain the desired knowledge representation language that is also compatible with the functional theory

Decision table systems

Decision tables form a goal-oriented language. Variables and the relationships between variables are define d from the viewpoint of a particular goal. The goal in the car selling example (Figure 2) is to define a suitable car for a particular buyer. It appears in practice that goal-oriented modelling of knowledge most of the times leads to occurrence of conditional relevance, conceptual interaction and functional equivalence. This is shown in the example below. The table suitable car is defined from the goal suitable car.

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Figure 2: The decision table suitable car.

Decision tables can represent conditional relevance. If, for example, a car dealer is selling a car to a customer and the customer requires a sun roof if the cost of the car exceeds $20,000, the variable sun roof is a conditional relevant variable. The relevance of this variable depends on the value of the variable cost. If the cost of the car exceeds $20,000 an additional variable sun roof becomes relevant.

Decision tables can also reflect conceptual interaction. If, for example, a car dealer is selling a car to a customer and the customer requires a cost that doesn't exceed $20,000, but he is willing to raise his cost to $25,000 if the car has at least 4 doors and even to raise his cost to $30,000 if the car has 5 doors, there is conceptual interaction between on one hand the variable cost and on the other hand the variable doors. It's impossible to define here what the customer requirement for cost is without taking into account the value of the variable doors.

A decision table clearly displays conditional relevance and conceptual interaction. But it also displays another important mechanism: variation limited to goal-constructed categories.

The decision table Suitable car implies that a car of $22,000 with a sun-roof and 4 doors is equivalent to a car of $24,000 with a sun roof and 5 doors. Both are suitable cars. This doesn't mean that the cars are equivalent in all aspects, but that they are equivalent from the viewpoint of suitability for a particular buyer.

In the majority of applications, we need a decision table system: a set of at least two decision tables in which each decision table is linked to another table belonging to the same system. We can distinguish two types of links. The first type of link is established by the phenomenon that a condition subject with at least one of its alternatives of one decision table occurs as an action subject with corresponding action alternatives in another decision table. The first table is then called a head table, the second table is called a condition subtable. The second type of link is established by the phenomenon that an action subject and one of its alternatives of one decision table occurs in the same form in another table. The first table again is called a head table, the second table is now called an action subtable. Usually, the action subject in question, in this action subtable, is further specified by means of other action subjects.


Frames can be used to store a structured representation of objects that should match certain object-types. Frames form a powerful language to store structured representations of complex objects. The variable values stored in frames can be used in a goal-oriented knowledge representation language to derive goal-oriented conclusions. In this sense decision tables and frames are languages complementary to Prolog. Their mutual power is indispensable in the development of effective and high quality knowledge-based systems.


Wislogic is an additional logic language that can be used in decision table systems and frame systems as a complementary language. Wislogic offers possibilities to retrieve values present in the knowledge base and to define operations over sets.


In Wisdom can be used as a conceptual modelling language for the representation of knowledge in addition to decision tables systems, frames-systems and Wislogic. By this, a number of weaknesses of Prolog are avoided: Prolog does not provide guidelines for knowledge modelling. Prolog is just a language specifically suited to the specification of knowledge, the gap between the declarative and the procedural semantics of Prolog, it is difficult to assess the knowledge import of Prolog code.

To show how we tackled the third weak point, lack of and adequate tools, we shortly discuss three characteristics of Wisdom

Specification Facilities: The Graphical Toolbox

Wisdom is equipped with a graphical toolbox that provides a multitude of convenient graphical facilities for drawing decision tables and frames on the screen. This toolbox has a carefully designed multi-window, menu-driven mouse-oriented interface for optimal communication with users. It allows users to quickly reconstruct and modify complete decision table system and frame systems. The toolbox shows 'intelligent behaviour': it knows what a correct table or frame looks like and applies this knowledge to support the user in the drawing process. The editor also knows how to obtain a decision table or frame that occupies a minimal amount of space. This knowledge is used, when necessary, to calculate the measures of a minimal table and frame after a user's action.

Furthermore, Wisdom offers facilities to specify knowledge models through Wislogic and Prolog. This is necessary to insert knowledge that cannot easily be represented through decision tables or frames. These statements can be used while simulating specified knowledge.

Simulation facilities: the Inference engine

The inference machine of Wisdom is a flexible goal-oriented engine. It departs from a list of goal variables (variables are conditions or actions in a decision table or attributes of an object represented in a frame or other variables occurring in a field of application). The inference machine attempts to trace these goals. The inference machine of Wisdom uses a backward chaining strategy, though it is possible to influence the inference machine. The inference machine is also able to make connections with other information systems to retrieve values.

Platform independence

Wisdom knowledge bases can be consulted under several operating systems such as PowerPC, Windows and Unix. A Wisdom knowledge base is also accessible through internet.


Wisdom now is entirely developed in Prolog. We are conducting research to use Java for the user interface to increase the platform independency of Wisdom. The number of projects in various sectors of the economy in which Wisdom is being applied by Arthur Andersen is rapidly growing.


Hendriks, P. H. J. (1986). De Relationele Definitie van Begrippen. Een Relationeel Realistische Visie op het Operationaliseren en Representeren van Begrippen. Dissertation, Nijmegen.

Lucardie, G. L. (1994). Functional Object-types as a Foundation of Complex Knowledge-based Systems. Dissertation Computer Science, Technical University Eindhoven.

Newell, A. (1981). The Knowledge Level. AI Magazine, 1, 1-20.

Reitsma, R. F. (1990). Functional Classification of Space: Aspects of Site Suitability Assesment in a Decision Support Environment. Dissertation, University of Nijmegen, International Institute for Applied Systems Analysis, Laxenburg, Austria/Faculty of Policy Sciences.

Stepp, R. E., & Michalski, R. S. (1986). Conceptual Clustering of Structured Objects: A Goal-Oriented Approach. Artificial Intelligence, 28, 43-69.

Van Der Smagt, A. G. M., & Lucardie, G. L. (1991). Decision Making under not Well-Defined Conditions: From Data Processing to Logical Modelling. Journal of Economic and Social Geography, 82(4), 295-304.

Larry Lucardie

Arthur Anderson & Co

Business Consulting

Oostmaaslaan 71

Postbus 21937


Tel: 010 242 1400

Fax: 010 242 1616

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