Hybridisation for versatile decision-making in game opponent AI

Hybridisation for versatile decision-making in game opponent A

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

Participatory Approach in Decision Making Processes for Water Resources Management in the Mediterranean Basin

This paper deals with the comparative analysis of different policy options for water resources management in three south-eastern Mediterranean countries. The applied methodology follows a participatory approach throughout its implementation and is supported by the use of three different software packages dealing with water allocation budget, water quality simulation, and Multi Criteria Analysis, respectively. The paper briefly describes the general objectives of the SMART project and then presents the three local case studies, the valuation objectives and the applied methodology - developed as a general replicable framework suitable for implementation in other decision-making processes. All the steps needed for a correct implementation are therefore described. Following the conceptualisation of the problem, the choice of the appropriate indicators as well as the calculation of their weighting and value functions are detailed. The paper concludes with the results of the Multi Criteria and the related Sensitivity Analyses performed, showing how the different policy responses under consideration can be assessed and furthermore compared through case studies thanks to their relative performances. The adopted methodology was found to be an effective operational approach for bridging scientific modelling and policy making by integrating the model outputs in a conceptual framework that can be understood and utilised by non experts, thus showing concrete potential for participatory decision making.Scientific Advice, Policy-Making, Participatory Modelling, Decision Support

Knowledge based improvement : simulation and artificial intelligence for understanding and improving decision making in an operations system

The thesis investigates the possibility of using simulation for understanding and improving the design of decision making in a real context. The approach is based on the problem of representing decision making behaviour in Discrete Event Simulation. An investigation of existing techniques led to the design of a methodology known as Knowledge Based Improvement (KBI). The KBI covers the key stages of the process of using simulation for understanding and improving the design of decision making. Using a research strategy that involves a case study in Ford, the research tests each stage of KBI. The thesis explains how simulation can be used for understanding real decision making problems and for collecting the data required for modelling individual decision making strategies. The thesis demonstrates the possibility of a simulation based knowledge elicitation in a real context and it investigates the practical difficulties involved in this process. The research tests the process of understanding decision making policies by modelling specific decision makers using Artificial Intelligence. It tests the use of simulation for assessing the decision making strategies and it shows that simulation can be used for identifying efficient strategies and for improving the design of decision making practices. The thesis reports the degree of success of the approach in relation to the data that were collected and it describes the validation checks that were undertaken. In addition, it reports the lessons learned from the application of the KBI methodology, the overall success of the approach and the main limitations that were identified during the implementation

Knowledge based improvement : simulation and artificial intelligence for understanding and improving decision making in an operations system

The thesis investigates the possibility of using simulation for understanding and improving the design of decision making in a real context. The approach is based on the problem of representing decision making behaviour in Discrete Event Simulation. An investigation of existing techniques led to the design of a methodology known as Knowledge Based Improvement (KBI). The KBI covers the key stages of the process of using simulation for understanding and improving the design of decision making. Using a research strategy that involves a case study in Ford, the research tests each stage of KBI. The thesis explains how simulation can be used for understanding real decision making problems and for collecting the data required for modelling individual decision making strategies. The thesis demonstrates the possibility of a simulation based knowledge elicitation in a real context and it investigates the practical difficulties involved in this process. The research tests the process of understanding decision making policies by modelling specific decision makers using Artificial Intelligence. It tests the use of simulation for assessing the decision making strategies and it shows that simulation can be used for identifying efficient strategies and for improving the design of decision making practices. The thesis reports the degree of success of the approach in relation to the data that were collected and it describes the validation checks that were undertaken. In addition, it reports the lessons learned from the application of the KBI methodology, the overall success of the approach and the main limitations that were identified during the implementation

Enterprise engineering using semantic technologies

Modern Enterprises are facing unprecedented challenges in every aspect of their businesses: from marketing research, invention of products, prototyping, production, sales to billing. Innovation is the key to enhancing enterprise performances and knowledge is the main driving force in creating innovation. The identification and effective management of valuable knowledge, however, remains an illusive topic. Knowledge management (KM) techniques, such as enterprise process modelling, have long been recognised for their value and practiced as part of normal business. There are plentiful of KM techniques. However, what is still lacking is a holistic KM approach that enables one to fully connect KM efforts with existing business knowledge and practices already in IT systems, such as organisational memories. To address this problem, we present an integrated three-dimensional KM approach that supports innovative semantics technologies. Its automated formal methods allow us to tap into modern business practices and capitalise on existing knowledge. It closes the knowledge management cycle with user feedback loops. Since we are making use of reliable existing knowledge and methods, new knowledge can be extracted with less effort comparing with another method where new information has to be created from scratch