Preference learning based decision map algebra: specification and implementation

Decision Map Algebra (DMA) is a generic and context independent algebra, especially devoted to spatial multicriteria modelling. The algebra defines a set of operations which formalises spatial multicriteria modelling and analysis. The main concept in DMA is decision map, which is a planar subdivision of the study area represented as a set of non-overlapping polygonal spatial units that are assigned, using a multicriteria classification model, into an ordered set of classes. Different methods can be used in the multicriteria classification step. In this paper, the multicriteria classification step relies on the Dominance-based Rough Set Approach (DRSA), which is a preference learning method that extends the classical rough set theory to multicriteria classification. The paper first introduces a preference learning based approach to decision map construction. Then it proposes a formal specification of DMA. Finally, it briefly presents an object oriented implementation of DMA

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency

Scheduling with Fuzzy Methods

Nowadays, manufacturing industries -- driven by fierce competition and rising customer requirements -- are forced to produce a broader range of individual products of rising quality at the same (or preferably lower) cost. Meeting these demands implies an even more complex production process and thus also an appropriately increasing request to its scheduling. Aggravatingly, vagueness of scheduling parameters -- such as times and conditions -- are often inherent in the production process. In addition, the search for an optimal schedule normally leads to very difficult problems (NP-hard problems in the complexity theoretical sense), which cannot be solved effciently. With the intent to minimize these problems, the introduced heuristic method combines standard scheduling methods with fuzzy methods to get a nearly optimal schedule within an appropriate time considering vagueness adequately

Evaluating the Russian Forest Sector: Market Orientation and Its Characteristics

This paper deals with the analysis of data coming from the RUSCOMP database. The purpose of this analysis is to identify those characteristics of Russian forestry firms that are perceived to be important for a firms market orientation. The two orientations of particular interest are market-focused orientation, where the firm is responsive to its markets needs, and planned economy orientation, where the firm relies on non-market relationships. Analysis was conducted using two methods, discriminant analysis and rough sets methodology. Both methods attempt to discover relationships in data that includes observations divided into homogeneous classes described by a set of attributes. Discriminant analysis proved less successful in describing the data, with only 41% of the cases being correctly classified. Rough set analysis provided better results and when applied to a dataset described by a reduced set of the attributes, it correctly evaluated 52% of the cases. The paper describes how a reduced set of the attributes was derived and also evaluates different possible options of such a reduction. In the last stage of the evaluation, decision rules with appropriate characteristics were generated and subsequently analyzed in order to extract knowledge statements allowing for the identification of the factors that contribute to a forestry fir market orientation. In summary, the analysis indicated that market-oriented firms rely on cash-based transactions to acquire their raw materials and do not experience significant supply problems. They also export a large portion of their finished goods. They are being paid for their services, as opposed to receiving barter credits, and engage in formal arrangements. In their business dealings these firms are avoiding a reliance on relationships in favor of the market-based mechanisms. In contrast, planned economy firms often rely on barter. They experience problems with timber supply that are most likely related to cash flow problems. Their primary market is a domestic one, where it is easier to engage in informal arrangements based on relationships

A hierarchical approach to multi-project planning under uncertainty

We survey several viewpoints on the management of the planning complexity of multi-project organisations under uncertainty. A positioning framework is proposed to distinguish between different types of project-driven organisations, which is meant to aid project management in the choice between the various existing planning approaches. We discuss the current state of the art of hierarchical planning approaches both for traditional manufacturing and for project environments. We introduce a generic hierarchical project planning and control framework that serves to position planning methods for multi-project planning under uncertainty. We discuss multiple techniques for dealing with the uncertainty inherent to the different hierarchical stages in a multi-project organisation. In the last part of this paper we discuss two cases from practice and we relate these practical cases to the positioning framework that is put forward in the paper

Incorporating stakeholders’ knowledge in group decision-making

International audienc

End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

Beyond current conversational chatbots or task-oriented dialogue systems that have attracted increasing attention, we move forward to develop a dialogue system for automatic medical diagnosis that converses with patients to collect additional symptoms beyond their self-reports and automatically makes a diagnosis. Besides the challenges for conversational dialogue systems (e.g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations. Existing dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017) mostly rely on data-driven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic transitions, which integrates a relational refinement branch for encoding relations among different symptoms and symptom-disease pairs, and a knowledge-routed graph branch for topic decision-making. Extensive experiments on a public medical dialogue dataset show our KR-DS significantly beats state-of-the-art methods (by more than 8% in diagnosis accuracy). We further show the superiority of our KR-DS on a newly collected medical dialogue system dataset, which is more challenging retaining original self-reports and conversational data between patients and doctors.Comment: 8 pages, 5 figues, AAA

Grid service discovery with rough sets

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.The computational grid is evolving as a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilising grid facilities. This paper presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct non-functional properties related to Quality of Service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximise user satisfaction in service discovery. ROSSE is evaluated in terms of its accuracy and efficiency in discovery of computing services

Technology Incubators as Nodes in Knowledge Networks

It is widely accepted that new knowledge underpinned innovation and growth influences economic activities. Economic agents rely not only on their own knowledge but also knowledge from others, whether it be codified and ’transferred via ICT’ or in tacit form. Moreover, it has long been argued that the acquisition of latter type of knowledge is influenced by geographic proximity. Based on this argument, it follows that the part firms’ supply of knowledge depends on how close, in terms of physical distance, to other firms, suppliers, customers, and research institutions, they are located. They are all can be categorize as a pool of knowledge that important for the firms’ growth and innovation capacity. Today, we witness many initiatives from policy makers around the world to compete in an increasingly technology- driven global economy through the establishing of technology incubators. Technology incubators can be conceived as organizations and/or facilities to enhance high-technology firm establishment and survival. Mostly they are located near the university or research center. There are many success stories on the contribution of incubators to the regional growth. At the same time, technology incubators have been widely criticized in the academic literature when judged in terms of regional innovation and knowledge development. The critics include the relying on an outdated, linear, model of innovation, which assumes that knowledge can be transferred directly from university to firms. However, innovation is now widely recognized as a complex non-linear process involving feedback loops and the creation of synergies through a diverse range of knowledge networks. Therefore, our understanding about knowledge spillover processes connected with incubator is yet poor. Very little is known about the mechanisms of knowledge exchange and spillover initiated by incubator and their role in supporting the growth of the firm. In this study we draw on the current body of literature, mainly agglomeration theories, and use the concepts of tacit knowledge and context to understand how knowledge spillovers actually take place. Our objective is to build a conceptual framework that describes how technology incubators operate as a mediator of knowledge for their tenants. In addition, based on empirical data of high-technology start-ups at TU Delft (The Netherlands), this study tests the proposition that not only geographic proximity to the university, but also that relations with other firms, particularly customers and suppliers matters. We also consider the function of ICT in shaping the new role of technology incubators in providing knowledge support. By explicitly analyzing the knowledge spillovers and mediation role offered by technology incubators, we seek to open up the ‘black box’ of the incubation process as a source of learning and gaining knowledge resources. We conclude the paper with a few recommendations for policymaking and further research.