A literature review of expert problem solving using analogy

We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction

The Effectiveness of Case-Based Reasoning: An Application in Sales Promotions

This paper deals with Case-based Reasoning (CBR) as a support technology for sales promotion (SP) decisions. CBR-systems try to mimic analogical reasoning, a form of human reasoning that is likely to occur in weakly-structured problem solving, such as the design of sales promotions. In an empirical study, we find evidence that use of the CBR-system improves the quality of SP-campaign proposals. In terms of the creativity of the proposals, decision-makers who think highly divergent (i.e., who tend to generate many, and diverse ideas in response to a problem) benefit most from prolonged system usage. Creativity, in turn, is positively related to the (practical) usability of a proposal. These results suggest that the CBR-system is most effective when it is used as an idea-generation tool that reinforces the strength of divergent (creative) thinkers. A convergent thinking style, in which case the CBR-system has a compensating role, even has a negative impact on CBR-system usage. Increasing the decision-maker's personal belief in the usefulness of the system, e.g., by training or education, may help to alleviate this reluctance to use the CBR-system.marketing management support systems;sales promotions;case-based reasoning;weakly-structured decision making

The Effectiveness of Case-Based Reasoning: An Application in Sales Promotions

This paper deals with Case-based Reasoning (CBR) as a support technology for sales promotion (SP) decisions. CBR-systems try to mimic analogical reasoning, a form of human reasoning that is likely to occur in weakly-structured problem solving, such as the design of sales promotions. In an empirical study, we find evidence that use of the CBR-system improves the quality of SP-campaign proposals. In terms of the creativity of the proposals, decision-makers who think highly divergent (i.e., who tend to generate many, and diverse ideas in response to a problem) benefit most from prolonged system usage. Creativity, in turn, is positively related to the (practical) usability of a proposal. These results suggest that the CBR-system is most effective when it is used as an idea-generation tool that reinforces the strength of divergent (creative) thinkers. A convergent thinking style, in which case the CBR-system has a compensating role, even has a negative impact on CBR-system usage. Increasing the decision-maker's personal belief in the usefulness of the system, e.g., by training or education, may help to alleviate this reluctance to use the CBR-system

Social process of knowledge creation in science, The

2019 Summer.Includes bibliographical references.The Science of Team Science (SciTS) emerged as a field of study because 21st Century scientists are increasingly charged with solving complex societal and environmental challenges. This shift in the complexity of questions requires a shift in how knowledge is created. To solve the complex societal health and environmental challenges, scientific disciplines will have to work together, innovate new knowledge, and create new solutions. It is impossible for one person or one discipline to have the quantity of knowledge needed to solve these types of problems. Tackling these problems requires a team. My dissertation articles report on how knowledge is built and created on a spectrum of scientific teams from university students to long-standing teams. Collectively they answer: how is knowledge creation a social process? To answer this question, my dissertation used a mixed-methods approach that included: social network analysis, social surveys, participant observation, interviews, document analysis, and student reflections. The most important finding from my dissertation was that social relations and processes are key to knowledge creation. Historically, knowledge acquisition and creation have been thought of as individual tasks, but a growing body of literature has framed knowledge creation as a social product. This is a fundamental shift in how knowledge is created to solve complex problems. To work with scientists from other disciplines, individuals must develop personal mastery and build the necessary capacities for collaboration, collective cognitive responsibility, and knowledge building. Complex problems are solved when scientists co-evolve with teams, and individual knowledge and capacity grows alongside the ability for "team learning" Knowledge, then, is a collective product; it is not isolated or individual, but constructed and co-constructed through patterns of interactions

Management of «Systematic Innovation»: A kind of quest for the Holy Grail!

In this paper, authors propose a contribution for improving the open innovation processes. It shows the necessity to get an efficient methodology for open innovation in order to build a computer aided tool for inventive design in Process Systems Engineering (PSE). The proposed methodology will be evocated to be fully used in the context of the “revolutionary” concepts around the so-called factory for the future, also called integrated digital factory, innovative factory… As a result the main contribution of this paper is to propose a software prototype for an Open Computer Aided Innovation 2.0. By definition this open innovation relies on collaboration. This collaboration should enable a community, with a very broad spectrum of skills, to share data, information, knowledge and ideas. As a consequence, a first sub objective is to create a methodological framework that takes advantages of collaboration and collective intelligence (with its capacity to join intelligence and knowledge). Furthermore, the raise of the digital company and more particularly the breakthroughs in information technologies is a powerful enabler to extend and improve the potential of collective intelligence. The second sub objective is to propose a problem resolution process to impel creativity of expert but also to develop, validate and select innovative solutions. After dealing with the importance of Process Innovation and Problem solving investigation in PSE, the proposed approach originally based on an extension of the TRIZ theory (Russian acronym for Theory of Inventive Problem Solving), has been improved by using approach such as case-based reasoning, in order to tackle and revisit problems encountered in the PSE. A case study on biomass is used to illustrate the capabilities of the methodology and the tool

When to Explain? Model Agnostic Explanation Using a Case-based Approach and Counterfactuals

ExplainableArtificialIntelligence(XAI)systemshavegained importance with the increasing demand for understanding why and how an artificial intelligence system makes decisions. Counterfactual expla- nations, one of the rising trends of XAI, benefit from human counter- factual thinking mechanisms and aim to follow a similar way of rea- soning. In this paper, we create an eXplainable Case-Based Reasoning system using counterfactual samples with a model-agnostic approach. While CBR methodology allows us to use past experiences to create new explanations, using counterfactuals helps to increase understandability. The main idea of this paper is to generate an explanation when necessary. The proposed method is sample-centric. Thus, an adaptive explanation area is calculated for each data point in the dataset. We detect if there is any existing counterfactual of the samples to increase the coverage of the system, and we create explanation cases from detected sample- counterfactual pairs. If a query case is in the explanation area, at least one explanation case will be triggered, and a two-phase explanation will be created using a text template and a bi-directional bar graph. In this work, we will show (1) how explanation cases are created, (2) how the nature of a dataset influences the explanation area, (3) how understand- able explanations are created, and (4) how the proposed method works on open datasets

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Banking Reform in Russia: Problems and Prospects

This paper examines the state of the Russian banking sector in 2004 and assesses the most important reform initiatives of the last two years, including deposit insurance legislation, a major reform of the framework for prudential supervision, steps to increase transparency in the sector, and measures to facilitate the development of specific banking activities. The overall conclusion that emerges from this analysis is that the Russian authorities’ approach to banking reform is to be commended. The design of the reform strategy reflects an awareness of the need for a ‘good fit’ between its major elements, and the main lines of the reform address some of the principal problems of the sector. The major lacuna in the Russian bank reform strategy concerns the future of state-owned banks. Despite a long-standing official commitment to reducing the role of the state – and of the Bank of Russia in particular – in the ownership of credit institutions, there is still a need for a much more clearly defined policy in this area. The real test of Russian banking reform efforts, however, will be in implementation. The reforms challenge numerous vested interests and their successful realisation will require considerable political will as well as the development of regulatory capacities of a very high order

Supporting strategic design of workplace environments with case-based reasoning

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