Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and ...configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory.
•Representation learning is applied on each individual population of optimization process trajectory.•The Opt2Vec representations are captured through the algorithm's behavior (its populations).•The Opt2Vec representations are suitable for dynamic problem characterization.•The Opt2Vec representations are invariant to simple transformations (shifting/scaling).•The Opt2Vec representations are scalable over different problem dimensions.
Actors in business networks often struggle to integrate their resources and bridge knowledge boundaries, which makes shared understanding difficult to establish and sustain. We develop the concept of ...interfirm problem representation (IFPR) to illustrate how networks of multidisciplinary teams create shared understanding and establish collective decisions in their day-to-day negotiations and joint problem-solving. IFPR is defined as an arrangement of localized artefacts or boundary objects that are jointly created by team members, and continuously adapted to facilitate mutual agreement and shared understanding in their daily conversations. We draw evidence from the UK construction industry to illustrate how team members from different organizations and knowledge domains manage their resource dependencies by creating IFPR as a common frame of reference to guide the implementation of their shared goals. Our data collection activities involve the observation of 3 different construction project teams over a period of 11 months. During the period, a total of 43 project team meetings were attended and 32 face-to-face interviews conducted across the 3 project teams. Findings from this study advance the discussion on subjective cognition and inter-subjective representations in networks by illustrating how diverse cognitive views and knowledge boundaries of network actors are synergized through an objectified system of representation. This enables us to offer important theoretical implications related to prior research on knowledge sharing and shared cognition. Our discussion highlights how actors, engaging in a shared activity that is extended over a period of time, collectively navigate different social contingencies while utilizing IFPR as a socio-historical artefact. IFPR enables network actors to critically review past mistakes to achieve improved collaborative outcomes.
•We illustrate how teams create shared understanding through developing inter-firm problem representations (IFPR).•We draw evidence from the UK construction industry, in both pre and post contract phases of construction.•Findings from this study advance the discussion on subjective cognition and inter-subjective representations in networks.•IFPR enables network actors to critically review past mistakes to achieve improved collaborative outcomes.
A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states ...(information about the environment) and operators (to move between states). A problem such as playing a game of chess has 10120$10^{120}$ possible states, and a traveling salesperson problem with as little as 82 cities already has more than 10120$10^{120}$ different tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real‐time computation in complex problem solving.
Better understanding how humans represent and solve problems using heuristics can help design simpler algorithms and representations that can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real‐time computation in complex problem solving.
Children having better visuospatial working memory, or the capacity to store and manipulate visuospatial information, perform better in mathematics. Yet, the underlying mechanisms are not well ...understood. In this study, we proposed a pathway model which linked up visuospatial working memory and mathematical achievement through two routes: numerical magnitude representation and problem representation. The data were drawn from 541 children who were assessed in both Grades 1 and 2. Using path analysis, we found that the association between visuospatial working memory and mathematical achievement was mediated by the deployment of visuospatial processes for magnitude representation (magnitude representation pathway) as well as for problem representation in solving mathematical problems (problem representation pathway). Such findings offer an important framework for developing intervention strategies to help children with poor visuospatial working memory in learning mathematics.
•Visuospatial working memory is significantly related to math achievement.•The relation is mediated by the representations of both magnitude and problem schema.•The findings increase our understanding of the spatial-math association.
The Australian National Indigenous Reform Agreement (Closing the Gap) aims to address inequalities in various aspects of Aboriginal and Torres Strait Islander peoples’ lives. Scholars have repeatedly ...critiqued its failure to tackle structural inequalities. The agreement was revised in 2020. The current study adopts Carol Bacchi’s ‘What’s the Problem Represented to be?’ approach to critically analyse the recently revised National Agreement on Closing the Gap. This study shows that the main problematisation still concerns the unequal life outcomes between First Peoples and non-Indigenous Australians, but the major difference from the previous agreement is that such problematisation recognises the strong influence of structural inequalities. Findings indicate that Indigenous schooling is governed by the Agreement through discourses of structural change and the inherent comparative framework. This can condition the restructured relationship between First Peoples and the settler state, which makes it difficult to move beyond a comparative framework and a reductive understanding of self-determination. This study argues that the reconstructed relationships open opportunities to challenge settler colonialism in schooling and education policies. However, they should not be utilised to displace sovereignties and genuine self-determination.
Insight problems are difficult because the initially activated knowledge hinders successful solving. The crucial information needed for a solution is often so far removed that gaining access to it ...through restructuring leads to the subjective experience of “Aha!”. Although this assumption is shared by most insight theories, there is little empirical evidence for the connection between the necessity of restructuring an incorrect problem representation and the Aha! experience. Here, we demonstrate a rare case where previous knowledge facilitates the solving of insight problems but reduces the accompanying Aha! experience. Chess players were more successful than non‐chess players at solving the mutilated checkerboard insight problem, which requires retrieval of chess‐related information about the color of the squares. Their success came at a price, since they reported a diminished Aha! experience compared to controls. Chess players’ problem‐solving ability was confined to that particular problem, since they struggled to a similar degree to non‐chess players to solve another insight problem (the eight‐coin problem), which does not require chess‐related information for a solution. Here, chess players and non‐chess players experienced the same degree of insight.
In this research, our objective is to characterize the problem-solving procedures of primary and lower secondary students when they solve problems in real class conditions. To do so, we rely first on ...the concept of heuristics. As this term is very polysemic, we exploit the definition proposed by Rott (2014) to develop a coding manual and thus analyze students’ procedures. Then, we interpret the results of these analyses in a qualitative way by mobilizing the concept of semantic space (Poitrenaud, 1998). This detailed analysis of students’ procedures is made possible by collecting audiovisual data as close as possible to the students’ work using an action camera mounted on the students’ heads. We thus succeed in highlighting three different investigation profiles that we have named explorer, butterfly, and prospector. Our first results tend to show a correlation with these profiles and the success in problem-solving, yet this would need more investigation.
This article employs design ethnography to study the design process of a design science research (DSR) project conducted over eight years. The DSR project focuses on chronic wounds and how ...Information Technology (IT) might support the management of those wounds. Since this is a new and complex problem not previously addressed by IT, it requires an exploration and discovery process. As such, we found that traditional DSR methodologies were not well-suited to guiding the design process. Instead we discovered that focusing on search, and in particular, the coevolution of the problem and solution spaces, provides a much better focus for managing the DSR design process. The presentation of our findings from the ethnographic study includes a new representation for capturing the coevolving problem/solution spaces, an illustration of the search process and coevolving problem/solution spaces using the DSR project we studied, the need for changes in the purpose of DSR evaluation activities when using a search-focused design process, and how our proposed process extends and augments current DSR methodologies. Studying the DSR design process generates the knowledge that research project managers need for managing and guiding a DSR project, and contributes to our knowledge of the design process for research-oriented projects.
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