The Relationship between Fuzzy Reasoning and Its Temporal Characteristics for Knowledge Management

The knowledge management systems based on artificial reasoning (KMAR) tries to provide computers the capabilities of performing various intelligent tasks for which their human users resort to their knowledge and collective intelligence. There is a need for incorporating aspects of time and imprecision into knowledge management systems, considering appropriate semantic foundations. The aim of this paper is to present the FRTES, a real-time fuzzy expert system, embedded in a knowledge management system. Our expert system is a special possibilistic expert system, developed in order to focus on fuzzy knowledge.Knowledge Management, Artificial Reasoning, predictability

Challenges Facing Judges Regarding Expert Evidence in Criminal Cases

With regard to criminal cases, the focus of this Article, judges face significant challenges in ruling on the admissibility of expert testimony that do not occur in most civil cases. This Article starts by describing these challenges and then offers some suggestions about what can be done to address them

Do experts incorporate statistical model forecasts and should they?

Experts can rely on statistical model forecasts when creating their own forecasts.Usually it is not known what experts actually do. In this paper we focus on threequestions, which we try to answer given the availability of expert forecasts andmodel forecasts. First, is the expert forecast related to the model forecast andhow? Second, how is this potential relation influenced by other factors? Third,how does this relation influence forecast accuracy?We propose a new and innovative two-level Hierarchical Bayes model to answerthese questions. We apply our proposed methodology to a large data set offorecasts and realizations of SKU-level sales data from a pharmaceutical company.We find that expert forecasts can depend on model forecasts in a variety ofways. Average sales levels, sales volatility, and the forecast horizon influence thisdependence. We also demonstrate that theoretical implications of expert behavioron forecast accuracy are reflected in the empirical data.endogeneity;Bayesian analysis;expert forecasts;model forecasts;forecast adjustment

Expert Finding by Capturing Organisational Knowledge from Legacy Documents

Organisations capitalise on their best knowledge through the improvement of shared expertise which leads to a higher level of productivity and competency. The recognition of the need to foster the sharing of expertise has led to the development of expert finder systems that hold pointers to experts who posses specific knowledge in organisations. This paper discusses an approach to locating an expert through the application of information retrieval and analysis processes to an organization’s existing information resources, with specific reference to the engineering design domain. The approach taken was realised through an expert finder system framework. It enables the relationships of heterogeneous information sources with experts to be factored in modelling individuals’ expertise. These valuable relationships are typically ignored by existing expert finder systems, which only focus on how documents relate to their content. The developed framework also provides an architecture that can be easily adapted to different organisational environments. In addition, it also allows users to access the expertise recognition logic, giving them greater trust in the systems implemented using this framework. The framework were applied to real world application and evaluated within a major engineering company

The Proficiency of Experts

Expert evidence plays a crucial role in civil and criminal litigation. Changes in the rules concerning expert admissibility, following the Supreme Court\u27s Daubert ruling, strengthened judicial review of the reliability and the validity of an expert\u27s methods. Judges and scholars, however, have neglected the threshold question for expert evidence: whether a person should be qualified as an expert in the first place. Judges traditionally focus on credentials or experience when qualifying experts without regard to whether those criteria are good proxies for true expertise. We argue that credentials and experience are often poor proxies for proficiency. Qualification of an expert presumes that the witness can perform in a particular domain with a proficiency that non-experts cannot achieve, yet many experts cannot provide empirical evidence that they do in fact perform at high levels of proficiency. To demonstrate the importance ofproficiency data, we collect and analyze two decades of proficiency testing of latent fingerprint examiners. In this important domain, we found surprisingly high rates of false positive identifications for the period 1995 to 2016. These data would qualify the claims of many fingerprint examiners regarding their near infallibility, but unfortunately, judges do not seek out such information. We survey the federal and state case law and show how judges typically accept expert credentials as a proxy for proficiency in lieu of direct proof of proficiency. Indeed, judges often reject parties\u27 attempts to obtain and introduce at trial empirical data on an expert\u27s actual proficiency. We argue that any expert who purports to give falsifiable opinions can be subjected to proficiency testing and that proficiency testing is the only objective means of assessing the accuracy and reliability ofexperts who rely on subjective judgments to formulate their opinions (so-called black-box experts ). Judges should use proficiency data to make expert qualification decisions when the data is available, should demand proof of proficiency before qualifying black-box experts, and should admit at trial proficiency data for any qualified expert. We seek to revitalize the standard for qualifying experts: expertise should equal proficiency

A Focus Of Attention Algorithm For Expert Systems

This research is primarily concerned with increasing the performance of expert systems. A refined focus of attention strategy and its affect on performance are discussed. Early expert systems used a brute force approach to process the knowledge base. Each production rule in the knowledge base was evaluated each cycle. More recently, processing efficiency has been increased by focusing the attention of the inference engine on a subset of the rules by filtering for further testing, only rules that could possibly fire given the current content of the context base. Focus of attention as developed in this research increases performance over filtering systems by further narrowing the focus of attention of the inference engine, down to the subexpression level. Positive results are reported