Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems

Application of Value Focused Thinking and Fuzzy Systems to Assess System Architecture

AbstractSince a majority of resources are obligated during the design phase of a system lifecycle, critical assessment of candidate functional and system architectures is vital to identify optimal architectures before proceeding to subsequent lifecycle phases. Common challenges associated with generation and evaluation of system functional architectures include search of the expansive design space and assessment of key performance attributes that are particularly “fuzzy” and qualitative in early architecture development. Several assessment approaches have been presented in the literature to address the assessment challenge to include Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP), Value-Focused Thinking (VFT), and fuzzy logic. In this research we combine the use of value functions and fuzzy assessment to assess a functional and system architecture. There are several benefits of a methodology that combines value-focused thinking and fuzzy assessment. A distinct advantage of the methodology presented is the explicit inclusion of the customer in the assessment process through validation of the TPM value functions Involving the customer in TPM value function development and validation ensures the customer has direct input regarding the TPMs and their associated value across the range of discourse The methodology presented is flexible enough to assess architectures early in the process when things are “fuzzy” as well as later when subsystem and component performance are well defined. The methodology can also be used to analyze and assess impacts of interface changes within the system architecture. . The methodology is domain independent and can be coupled with executable models linked to scenarios. The assessment methodology is applied to the architecture for a soldier knowledge acquisition system for which the key performance attributes are affordability, performance, flexibility, updateability, and availability

AN INTERVAL TYPE 2 FUZZY EVIDENTIAL REASONING APPROACH TO PERSONNEL RECRUITMENT

Recruitment process is a procedure of selecting an ideal candidate amongst different applicants who suit the qualifications required by the given institution in the best way. Due to the multi criteria nature of the recruitment process, it involves contradictory, numerous and incommensurable criteria that are based on quantitative and qualitative measurements. Quantitative criteria evaluation are not always dependent on the judgement of the expert, they are expressed in either monetary terms or engineering measurements, meanwhile qualitative criteria evaluation depend on the subjective judgement of the decision maker, human evaluation which is often characterized with subjectivity and uncertainties in decision making. Given the uncertain, ambiguous, and vague nature of recruitment process there is need for an applicable methodology that could resolve various inherent uncertainties of human evaluation during the decision making process. This work thus proposes an interval type 2 fuzzy evidential reasoning approach to recruitment process. The approach is in three phases; in the first phase in order to capture word uncertainty an interval type 2(IT2) fuzzy set Hao and Mendel Approach (HMA) is proposed to model the qualification requirement for recruitment process. This approach will cater for both intra and inter uncertainty in decision makers’judgments and demonstrates agreements by all subjects (decision makers) for the regular overlap of subject data intervals and the manner in which data intervals are collectively classified into their respective footprint of uncertainty. In the second phase the Intervaltype 2 fuzzy Analytical hierarchical process was employed as the weighting model to determine the weight of each criterion gotten from the decision makers. In the third phase the interval type 2 fuzzy was hybridized with the ranking evidential reasoning algorithm to evaluate each applicant to determine their final score in order to choose the most ideal candidate for recruitment.The implementation tool for phase two and three is Java programming language. Application of this proposed approach in recruitment process will resolve both intra and inter uncertainty in decision maker’s judgement and give room for consistent ranking even in place of incomplete requirement

An early guidance system for a general knowledge-based aiding framework using probabilistic interventions

International audienceA common decision problem repeats a lot of time with the same kind of alternatives and the same set of criteria, but with a different decision case in each occurrence. The objective of early guidance in this kind of problem is to facilitate the selection of a subset of satisfactory alternatives for each new decision case, without asking the user any knowledge of the problem. This article proposes an early guidance system based on a model of knowledge of the common decision problem. It first presents the construction of a Bayesian network for a common decision problem to embed the knowledge in the aiding framework. Second, the concept of intervention proposed by Pearl is extended to prob-abilistic interventions for a single variable and for a set of variables. Finally the early guidance procedure is presented on the basis of the Bayesian network and using a proba-bilistic intervention to set a decision case even though it is partially observed.Un problème de décision courant se répète de nom-breuses fois, avec le même type d'alternatives et le même ensemble de critères, mais avec une situation de décision différente à chaque occurence du problème. Dans ce type de problème, le conseil en amont vise à faciliter la sélec-tion d'un sous ensemble d'alternatives satisfaisantes pour le cas de décision considéré, sans demander à l'utilisateur d'avoir des connaissances sur le problème. Cet article propose un système de conseil en amont basé sur un modèle de connaissances du problème de décision courant. Pour commencer, l'article présente la construction d'un réseau bayésien pour embarquer la connaissance dans le système. Ensuite, le concept d'intervention dans un réseau bayésien proposé par Pearl est étendu aux interventions probabilistes pour des variables simples et des ensembles de variables. Enfin, la procédure de conseil en amont pour un problème de décision courant est présentée, sur la base du modèle de connaissance et en utilisant les interventions probabilistes pour fixer l'écosystème de la personne, même lorsque le cas de décision n'est que partiellement observé