On the decomposition of tabular knowledge systems.

Recently there has been a growing interest in the decomposition of knowledge based systems and decision tables. Much work in this area has adopted an informal approach. In this paper, we first formalize the notion of decomposition, and then we study some interesting classes of decompositions. The proposed classification can be used to formulate design goals to master the decomposition of large decision tables into smaller components. Importantly, carrying out a decomposition eliminates redundant information from the knowledge base, thereby taking away -right from the beginning- a possible source of inconsistency. This, in turn, renders subsequent verification and validation more smoothly.Knowledge; Systems;

The behavior of fuzzy implications in a fuzzy knowledge base.

More and more companies today discover the advantages of using knowledge bases for their processes and services. Recently, fuzzy set theory has also captured the attention due to good performances within control systems. Therefore, it is very appealing to combine the advantages of these two areas into a fuzzy knowledge base. However, obtaining the results of control systems in a knowleg based environment is not so straightforward. This paper will investigate one aspect of the reasoning process, namely the behavior of the implication. From the different tests performed, four main behaviors of implications can be found. First of all, there are the implications not always resulting in a convex set. A second classs - the so-called impotent implications- doesn't change the predefined set at all. A third grouping reveals always a constant value portion, that rises or falls according to the changed input. A final divsion shifts the complete set in its whole conformably the intuition.Implications; Companies; Advantages; Knowledge; Processes; Theory; Performance; Systems; Value;

Procedural Decision Support Through The Use of PRODEMO

Procedural decisions, i.e., decisions involving the application of laws, regulations, prescriptions...constitute a tremendous amount of everyday decisions made in any kind of organizations. In this paper, decision tables are put forword as a basic technique enabling the user to structure and to check procedural decisions for completeness and correctness. It is shown that the use of the interactive PRODEMO (PROcedural DEcision MOdeling) system enhances the capabilities of the technique for modeling as well as for making procedural decisions

From High-Level Task Descriptions to Executable Robot Code

For robots to be productive co-workers in the manufacturing industry, it is necessary that their human colleagues can interact with them and instruct them in a simple manner. The goal of our research is to lower the threshold for humans to instruct manipulation tasks, especially sensorcontrolled assembly. In our previous work we have presented tools for high-level task instruction, while in this paper we present how these symbolic descriptions of object manipulation are translated into executable code for our hybrid industrial robot controllers

An empirical investigation into the interpretability of data mining models based on decision trees, tables and rules

While data mining research has largely focused on developing ever more accurate predictive models, a much smaller body of research has investigated to which extent these models are actually interpretable by decision makers. Given the importance of this aspect on the model's validation, acceptance and successful application, we will discuss an experimental study in which we empirically compare the interpretability of various representation forms, viz. decision tables, decision trees, propositional rules and oblique rules, as well as explore the effect of size or complexity on their usefulness

On the decomposition of tabular knowledge systems

Recently there has been a growing interest in the decomposition of knowledge based systems and decision tables. Much work in this area has adopted an informal approach. In this paper, we first formalize the notion of decomposition, and then we study some interesting classes of decompositions. The proposed classification can be used to formulate design goals to master the decomposition of large decision tables into smaller components. Importantly, carrying out a decomposition eliminates redundant information from the knowledge base, thereby taking away -right from the beginning- a possible source of inconsistency. This, in turn, renders subsequent verification and validation more smoothly.status: publishe