a b s t r a c t a r t i c l e i n f oEasy to use workbenches for Qualitative Reasoning and modelling have been virtually nonexistent. This has a limiting effect on the use of this Artificial Intelligence technology and its uptake by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models of system behaviour. The workbench employs diagrammatic representations for users to interact with model content and simulation results, and provides seamless interoperability between the different modes of use. Domain experts can use Garp3 to create conceptual models in situations where numerical information is sparse or unavailable, or when they want to formalise their conceptual understanding of how systems behave. Garp3 can be applied to stakeholder management or dissemination activities to illustrate and explain phenomena, and facilitate discussion among participants. The workbench can also be used in formal education to have learners express concepts, or interact with existing models, and support them in developing their understanding of 'how things work'. Garp3 incorporates a range of techniques from Artificial Intelligence known as knowledge-based techniques. The main goal of this paper is to present the representation and reasoning methods of these techniques as they have been developed and fine-tuned within the Garp3 workbench. The focus hereby is on the symbolic, non-numerical calculations that are required to generate the state-graph of a system's behaviour efficiently, while taking into account that users need to be able to track and understand this reasoning, both in terms of the end result and the intermediate results it delivers.

Articles AI MAGAZINE Modeling is regarded as fundamental to human cognition and scientific inquiry (Schwarz and White 2005). It helps learners express and externalize their thinking, visualize and test components of their theories, and make materials more interesting. Particularly, the importance of learners constructing conceptual interpretations of system behavior has been pointed out many times (Mettes and Roossink [1981], Elio and Sharf [1990], Ploetzner and Spada [1998], Frederiksen andWhite [2002]). Modeling environments can thus make a significant contribution to the improvement of science education.A new class of knowledge construction tools is emerging that uses logic-based (symbolic, nonnumeric) representations for expressing conceptual systems knowledge (

Do people differ in their propensity to form habits? The current study related individual differences in habitual performance on the slips-of-action task to habit formation in real life. To this end, we developed a novel key-cover procedure that controls for the amount of repetition and motivation within a naturalistic setting. Participants received a key cover for the key to their home, which after several weeks was switched with a key cover that was previously attached to a dummy key. Participants recorded effort, time, attention, and mistakes in the key-selection process. Results were in line with established properties of habits, as attention decreased in the learning phase, yet effort, time, and mistakes increased after the key-cover switch. Performance on the slips-of-action task correlated negatively with changes in attention in the real-life key-cover task. This negative correlation may reflect that flexible behavioral adjustment requires more attention in people with a relatively weak goal-directed system.Electronic supplementary materialThe online version of this article (10.3758/s13420-018-0313-6) contains supplementary material, which is available to authorized users.

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