Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

ON THE USE OF THE DEMPSTER SHAFER MODEL IN INFORMATION INDEXING AND RETRIEVAL APPLICATIONS

The Dempster Shafer theory of evidence concerns the elicitation and manipulation of degrees of belief rendered by multiple sources of evidence to a common set of propositions. Information indexing and retrieval applications use a variety of quantitative means - both probabilistic and quasi-probabilistic - to represent and manipulate relevance numbers and index vectors. Recently, several proposals were made to use the Dempster Shafes model as a relevance calculus in such applications. The paper provides a critical review of these proposals, pointing at several theoretical caveats and suggesting ways to resolve them. The methodology is based on expounding a canonical indexing model whose relevance measures and combination mechanisms are shown to be isomorphic to Shafer's belief functions and to Dempster's rule, respectively. Hence, the paper has two objectives: (i) to describe and resolve some caveats in the way the Dempster Shafer theory is applied to information indexing and retrieval, and (ii) to provide an intuitive interpretation of the Dempster Shafer theory, as it unfolds in the simple context of a canonical indexing model.Information Systems Working Papers Serie

MULTI-PLAYER BELIEF CALCULI: MODELS AND APPLICATIONS

In developing methods for dealing with uncertainty in reasoning systems, it is important to consider the needs of the target applications. In particular, when the source of inferential uncertainty can be tracked to distributions of expert opinions, there might be different ways to model the representation and combination of these opinions. In this paper we present the notion of multiplayer belief calculi - a framework that takes into consideration not only the 'regular' type of evidential uncertainty, but also the diversity of expert opinions when the evidence is held fixed. Using several applied examples, we show how the basic framework can be naturally extended to support different application needs and different sets of assumptions about the nature of the inference process.Information Systems Working Papers Serie

AN INTUITIVE INTERPRETATION OF THE THEORY OF EVIDENCE IN THE CONTEXT OF BIBLIOGRAPHICAL INDEXING

Models of bibliographical Indexing concern the construction of effective keyword taxonomies and the representation of relevance between document s and keywords. The theory of evidence concerns the elicitation and manipulation of degrees of belief rendered by multiple sources of evidence to a common set of propositions. The paper presents a formal framework in which adaptive taxonomies and probabilistic indexing are induced dynamically by the relevance opinions of the library's patrons. Different measures of relevance and mechanisms for combining them are presented and shown to be isomorphic to the belief functions and combination rules of the theory of evidence. The paper thus has two objectives: (i) to treat formally slippery concepts like probabilistic indexing and average relevance, and (ii) to provide an intuitive justification to the Dempster Shafer theory of evidence, using bibliographical indexing as a canonical example.Information Systems Working Papers Serie

COMPROMISE REACHING MECHANISMS IN MULTI-GROUP/MULTI-PLAYER NEGOTIATION PROCESSES

We consider a situation in which multiple decision-makers who are partitioned into two or more distinct groups are asked to recommend a uniform course of action which is drawn from a finite and explicit set of potential alternatives. We present group-level and player-level mechanisms to reach a compromise decision under such circumstances. The group-level mechanism is based on the Dempster-Shafer theory of evidence; The player-level mechanism employs a set-product operation that aggregates the individual decisions over a certain space of committees. Previous research established that the two mechanisms are isomorphic, which, in the contest of the present paper, implies that they yield the same compromise decision. However, unlike the Dempster-Shafer theory, which was criticized for lack of external validity, the set-product mechanism has plausible properties in the contest of group decision making. With that in mind, the paper seeks to (i) report about an interesting relationship between group decision research and AI methods to manage uncertainty, and (ii) build a foundation for an inter-disciplinary research that exploits this linkage.Information Systems Working Papers Serie