Toward overcoming accidental complexity in organisational decision-making

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined

GraphFitI - A computer program for graphical chain models

Fitting a graphical chain model to a multivariate data set consists of different steps some of which being rather tedious. The paper outlines the basic features and overall architecture of the computer program GraphFitI which provides the application of a selection strategy for fitting graphical chain models and for visualising the resulting models as a graph. It additionally supports the user at the different steps of the analysis by an integrated help system

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues

Customized Learning Sequences (CLS) by Metadata.

In response to a longterm research program for a didactical ontology, this report intends to present the results and methods for representing didactical models from the ontology we developed. The question is: How can computer technology be used to support the communication of knowledge in an educational context? This question cannot be answered by psychological experiments that ignore the core of educational behaviour: the transmission of meaning (Hönigswald 1927). Therefore this article focuses on the didactical tradition. Computer technology as a medium requires a special form of knowledge organisation, which allows learners to go individually and in a reflective way through the content (Customized Learning Sequences), thus requiring teachers to produce individually navigable hypertexts. Individualization does not mean offering "pureâ€? self-directed learning, as learning presupposes instruction by others. We have to aid teachers in reorganizing knowledge to hypertexts that allows individual navigation. Supporting learners in finding their individual path is also a crucial factor.How to aid teachers and how to set up meaningful navigation aids will be discussed in four steps:\ud 1.) Theoretical considerations; 2.) First step of Web-Didactics: Decontextualisation; 3.) Second step of Web-\ud Didactics: Recontextualisation; 4.) Research. Which theoretical considerations are eternal for Web-Didactics

Exploiting Qualitative Information for Decision Support in Scenario Analysis

The development of scenario analysis (SA) to assist decision makers and stakeholders has been growing over the last few years through mainly exploiting qualitative information provided by experts. In this study, we present SA based on the use of qualitative data for strategy planning. We discuss the potential of SA as a decision-support tool, and provide a structured approach for the interpretation of SA data, and an empirical validation of expert evaluations that can help to measure the consistency of the analysis. An application to a specific case study is provided, with reference to the European organic farming business

Localist but Distributed Representations

A number of examples are given of how localist models may incorporate distributed representations, without the types of non-local interactions that often render distributed models implausible. The need to analyze the information that is encoded by these representations is also emphasized as a metatheoretical constraint on model plausibility

Construction of dynamic stochastic simulation models using knowledge-based techniques

Over the past three decades, computer-based simulation models have proven themselves to be cost-effective alternatives to the more structured deterministic methods of systems analysis. During this time, many techniques, tools and languages for constructing computer-based simulation models have been developed. More recently, advances in knowledge-based system technology have led many researchers to note the similarities between knowledge-based programming and simulation technologies and to investigate the potential application of knowledge-based programming techniques to simulation modeling. The integration of conventional simulation techniques with knowledge-based programming techniques is discussed to provide a development environment for constructing knowledge-based simulation models. A comparison of the techniques used in the construction of dynamic stochastic simulation models and those used in the construction of knowledge-based systems provides the requirements for the environment. This leads to the design and implementation of a knowledge-based simulation development environment. These techniques were used in the construction of several knowledge-based simulation models including the Advanced Launch System Model (ALSYM)