Using artificial intelligence to control fluid flow computations

Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world

The impact of visualization on flexible Bayesian reasoning

There is wide consensus that visualizations of statistical information can support Bayesian reasoning. This article focusses on the conceptual understanding of Bayesian reasoning situations and investigates whether the tree diagram or the unit square is more appropriate to support the understanding of the influence of the base rate, which is introduced as being a part of flexible Bayesian reasoning. As a statistical graph, the unit square reflects the influence of the base rate not only in a numerical but also in a geometrical way. Accordingly, in two experiments with undergraduate students (N = 148 and N = 143) the unit square outperformed the tree diagram referring to the understanding of the influence of the base rate. Our results could inform the discussion about how to visualize Bayesian situations and has practical consequences for the teaching and learning of statistics

A General Qualitative Spatio-Temporal Model Based on Intervals

Many real-world problems involve qualitative reasoning about space and/or time. Actually, it is an adequate tool for dealing with situations in which information is not sufficiently precise. However, despite its numerous applications, it is difficult for people from outside the field to incorporate the required reasoning techniques into their methods. In this paper, we present a general, easy-to-use framework that integrates and solves the reasoning process of all qualitative models based on intervals. This framework has been divided into: (1) a representation magnitude and (2) the resolution of the reasoning process. Mainly, the developed method for solving the reasoning process is based on the definition of two algorithms: the qualitative sum and the qualitative difference. In addition, here, different instances of the model as well as some practical applications of them are presented

Transforming Problem-Based Learning through Abductive Reasoning

Background: Hypothetico-deductive reasoning is the current approach for reasoning through care situations within problem-based learning (PBL). While this approach is widely used in both PBL and non-PBL curricula, abductive reasoning is recommended (as an alternative approach) due to its broader method for analyzing and explaining care situations within problem-based learning. Method: A step-by-step process rooted in abductive reasoning is proposed and demonstrated as a new way of examining and explaining care situations within problem-based learning. Results: The proposed strategy emphasizes the creation of hypotheses through phenomena detection, development of a causal model, identification of learning needs, recognition of salience, synthesis and reflection. Conclusion: Since the proposed approach has not been implemented previously, its practical implications require research attention which will contribute to the emerging field of abductive reasoning within nursing education. Résumé : Contexte : Dans l’apprentissage par problèmes (APP), le raisonnement hypothético-déductif est l’approche actuellement utilisée pour raisonner à partir de situations de soins. Or, bien que cette approche soit largement utilisée dans les programmes fondés sur l’APP et ceux qui ne le sont pas, le raisonnement abductif est recommandé (comme autre approche) puisque sa méthode d’analyse et d’explication des situations de soins au sein de l’APP est plus vaste. Méthode : Proposer et démontrer un processus étape par étape ancré dans le raisonnement abductif, comme une nouvelle manière d’analyser et d’expliquer des situations de soins dans le cadre de l’APP. Résultats : La stratégie proposée favorise la formulation d’hypothèses par la détection de phénomènes, la mise en place d’un modèle causal, l’identification des besoins d’apprentissage, la reconnaissance de la prépondérance, la synthèse et la réflexion.. Conclusion : Puisque l’approche proposée n’a pas été mise en place auparavant, ses implications pratiques nécessitent des recherches, qui contribueront au domaine émergent du raisonnement abductif dans le cadre de la formation en sciences infirmières

Human inference beyond syllogisms: an approach using external graphical representations.

Research in psychology about reasoning has often been restricted to relatively inexpressive statements involving quantifiers (e.g. syllogisms). This is limited to situations that typically do not arise in practical settings, like ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants' performance when reasoning with two notations. The first notation used topological constraints to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topo-spatial representations were more effective for inferences than topological representations alone. Reasoning with statements involving multiple quantifiers was harder than reasoning with single quantifiers in topological representations, but not in topo-spatial representations. These findings are compared to those in sentential reasoning tasks

Deductive reasoning about expressive statements using external graphical representations

Research in psychology on reasoning has often been restricted to relatively inexpressive statements involving quantifiers. This is limited to situations that typically do not arise in practical settings, such as ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants’ performance when reasoning with two notations. The first used topology to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topological- spatial representations were more effective than topological representations. Unlike topological-spatial representations, reasoning with topological representations was harder when involving multiple quantifiers and binary relations than single quantifiers and unary relations. These findings are compared to those for sentential reasoning tasks

Pass it on: towards a political economy of propensity

The paper argues that the work of Gabriel Tarde on imitation provides a fertile means of understanding how capitalism is forging a new affective technology which conforms to a logic of propensity rather than to means-end reasoning. This it does by drawing together a biological understanding of semiconscious cognition with various practical geometric arts so as to re-stage the world as a series of susceptible situations which can be ridden rather than rigidly controlled. The paper examines the advent of technologies which attend to the variable geometry of so-called animal spirits in the realm of business and then, using Tarde's work as a springboard, considers some alternative means of understanding imitative rays which have less instrumental undertones. The paper is an illustration of the way in which biology and culture have increasingly become intertwined

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology

Developing digital literacy in construction management education: a design thinking led approach

Alongside the digital innovations in AEC (Architectural, Engineering and Construction) practice, are calls for a new type of digital literacy, including a new information-based literacy informed by creativity, critical analysis and the theoretical and practical knowledge of the construction profession. This paper explores the role of design thinking and the promotion of abductive problem situations when developing digital literacies in construction education. The impacts of advanced digital modelling technologies on construction management practices and education are investigated before an examination of design thinking, the role of abductive reasoning and the rise of normative models of design thinking workflows. The paper then explores the role that design thinking can play in the development of new digital literacies in contemporary construction studies. A three-part framework for the implementation of a design thinking approach to construction is presented. The paper closes with a discussion of the importance of models of design thinking for learning and knowledge production, emphasising how construction management education can benefit from them