Comparing stochastic design decision belief models : pointwise versus interval probabilities.

Decision support systems can either directly support a product designer or support an agent operating within a multi-agent system (MAS). Stochastic based decision support systems require an underlying belief model that encodes domain knowledge. The underlying supporting belief model has traditionally been a probability distribution function (PDF) which uses pointwise probabilities for all possible outcomes. This can present a challenge during the knowledge elicitation process. To overcome this, it is proposed to test the performance of a credal set belief model. Credal sets (sometimes also referred to as p-boxes) use interval probabilities rather than pointwise probabilities and therefore are more easier to elicit from domain experts. The PDF and credal set belief models are compared using a design domain MAS which is able to learn, and thereby refine, the belief model based on its experience. The outcome of the experiment illustrates that there is no significant difference between the PDF based and credal set based belief models in the performance of the MAS

Automated decision algorithms in prostate cancer

A diagnostic decision support system (DDSS) is defi ned as a methodology that provides guidance in situations involving complex decision sequences. DDSSs result in a systematic, ordered, and exhaustive evaluation of evidence and weighting of individual items of evidence as they are combined to form the basis for a fi nal decision. Most DDSSs provide a numeric measure of confi dence in the fi nal decision or diagnostic recommendation. Th e decisive advantage of DDSSs is the ability to process descriptive symbolic information, in contrast to systems limited to the handling of numerical information only for which extensive analytical procedures are already established. Most human knowledge and insight related to diagnostic and prognostic evaluation exist in symbolic form as concepts and linguistic terms, so the DDSSs have facilitated systematic evaluation of evidence to provide diagnostic and prognostic decision support. DDSSs may be implemented as inference networks (or Bayesian Belief Networks – BBNs), automated reasoning systems, case-based reasoning systems, or expert systems. In inference networks and automated reasoning systems, the emphasis is on uncertainty assessment of a given decision sequence. In case-base reasoning, the emphasis is on prognostic assessment for an individual patient. In expert systems, the emphasis is on diagnostic or prognostic assessment, by making available a comprehensive knowledge base of facts and professional experience. However, even though the emphasis is slightly diff erent in these kinds of decision support systems, much of the methodology is shared. A BBN consists of a decision node for the diagnostic alternatives and of evidence nodes for the diagnostic clues. Each clue is observed, rated, and assigned to a function with a given probability. Th e evidence is then forwarded to the decision node via a conditional probability link matrix. At the decision node, the belief in each diagnostic alternative is accumulated. A series of BBNs have already been successfully developed for prostate and non-prostate neoplasms (7-13) including comparing cancer with benign lesions and those with prostatic intraepithelial neoplasms

The Impact of Secondary School Educators’ Implementation Of Response To Intervention (RTI) On African American Female Students

Understanding the implementation of the educational policy of the Response to Intervention (RTI) and the extent to which it provides support to students with learning gaps is imperative for student’s success. However, teachers with the broad adoption of the RTI system nationwide may need the additional insight of the intervention delivery challenges, and analysis of direction and intensity. Guided by Dewey’s theory of experiential learning, which holds that educators could utilize a student’s personal experience to engage the learner, this study examined the connection of the student’s experiences facilitated by the teachers’ adoption and construction of edifying experiences to facilitate the closing of the achievement gap that exists for minority students. The researcher examined teachers’ beliefs about the effectiveness of RTI on the academic achievement of students at tier 1 and 2 of RTI. Specifically, the researcher explored teachers’ beliefs about the effectiveness of three RTI components, namely the academic abilities and performance of Students with Disabilities (SWD), Data-Based Decision Making (DBDM), and Functions of Core and Supplemental Instruction (FCSI), on the academic achievement of struggling African American Females (AAF) students. The results revealed statistically significant relationships between teachers’ belief in RTI Data-Based Decision Making scores and AAF-RTI students’ math achievement scores and between teachers’ belief in RTI Function of Core and Supplemental Instruction scores and AAF-RTI students’ math achievement scores. There was also a relationship between SWD and DBDM. This research also observed a correlation with no significance between the teachers (n = 46) belief of academic achievements of SWD and their FCSI. These results are indicative of need to create a climate supportive of experiential based learning for RTI implementation training for secondary teachers

Hybridization of Bayesian networks and belief functions to assess risk. Application to aircraft deconstruction

This paper aims to present a study on knowledge management for the disassembly of end-of-life aircraft. We propose a model using Bayesian networks to assess risk and present three approaches to integrate the belief functions standing for the representation of fuzzy and uncertain knowledge

Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature