Qualitative Reasoning

Consultation is an important feature of research and, increasingly, researchers are required to work in partnership with stakeholders to increase the impact of their work. Our aim is to demonstrate what can be learned from the scholarship on, and practice of, member checking to facilitate productive knowledge exchange. Using dialogical analysis we explore three member check interactions from three different qualitative psychology projects focusing our analysis on difficult moments between researchers and participants conceptualised here as ‘sore spots’. We identify two major genres in these sequences: participant ambivalence and participant challenge. We then consider passages that allow us to explore a more theoretical understanding of these two genres in terms of the metaphor of portraits and mirrors. Overall, we outline how implicit epistemologies and theories of subjectivity (uncomplicated, blank, and complex) may be linked to the way in which stakeholders approach research. We also provide a map with regard to the theories within which member checks can be undertaken, associated research practices in terms of a range of researcher responses to stakeholder ambivalence and challenge, and implications of these moments for knowledge exchange for qualitative research but also for psychological science as a whole. We conclude that sore spots in knowledge exchange process can be productive opportunities of transformational validity.

Keywords: member check; knowledge exchange; research impact; dialogical analysis; participant validation

One of the original motivations for research in qualitative physics was the development of intelligent tutoring systems and learning environments for physical domains and complex systems. This article demonstrates how a synergistic combination of qualitative reasoning and other AI techniques can be used to create an intelligent learning environment for students learning to analyze and design thermodynamic cycles. Pedagogically this problem is important because thermodynamic cycles express the key properties of systems which interconvert work and heat, such as power plants, propulsion systems, refrigerators, and heat pumps, and the study of thermodynamic cycles occupies a major portion of an engineering student's training in thermodynamics. This article describes CyclePad, a fully implemented articulate virtual laboratory that captures a substantial fraction of the knowledge in an introductory thermodynamics textbook and provides explanations of calculations and coaching support for students who are learning the principles of such cycles. CyclePad employs a distributed coaching model, where a combination of on-board facilities and a server-based coach accessed via email provide help for students, using a combination of teleological and case-based reasoning. CyclePad is a fielded system, in routine use in classrooms scattered all over the world. We analyze the combination of ideas that made CyclePad possible and comment on some lessons learned about the utility of various AI techniques based on our experience in fielding CyclePad.

The European Union regulatory framework on organic farming gave a new impulse to the institutionalisation of the Portuguese organic farming movement. This paper offers an analytical account based on interviews with key market and institutional actors within the organic food sector in Portugal. The qualitative analysis undertaken is underpinned by the premises of convention theory and aims at showing that this particular market is becoming polarised. This polarisation is featured by the official/pro-certification claims (where industrial and opinion conventions hold sway) and the unofficial/beyond certification claims (underpinned by green, inspirational and domestic conventions). A trend towards the 'conventionalisation' of the organic market is gradually emerging, whilst a tendency for 'movement resistance' still persists. This paper reflects upon the polarisation and polarised claims that are enmeshed in a market of disputes of contested meanings and competing conventions around organic food.

The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving.

Coupling the immersed boundary (IB) method and the lattice Boltzmann (LB) method might be a promising approach to simulate fluid-structure interaction (FSI) problems with flexible structures and moving boundaries. To investigate the possibility for future IB-LB coupled simulations of the heart flow dynamics, an IB-LB coupling scheme suitable for rapid boundary motion and large pressure gradient FSI is proposed, and the mitral valve jet flow considering the interaction of leaflets and fluid is simulated. After analyzing the respective concepts, formulae and advantages of the IB and LB methods, we first explain the coupling strategy and detailed implementation procedures, and then verify the effectiveness and second-order accuracy of the scheme by simulating a benchmark case, the relaxation of a stretched membrane immersed in fluid. After that, the diastolic filling jet flow between mitral leaflets in a simplified 2D left heart model is simulated. The model consists of the simplified transmitral passage of the heart and two curvilinear leaflets. In the simulation, the atrial and ventricular pressure histories of normal human are specified as boundary conditions, and the leaflets are treated as fibers that interact with the fluid to define their deformations and movements. The resulting opening and closing movements of the leaflets and the flow patterns of the filling jet are qualitatively reasonable and compare well with existing numerical and measured data. It is shown that this IB-LB coupling method is feasible for treating flexible boundary FSI problems with rapid boundary motion and large pressure gradient, the results of the mitral leaflet flow are valuable for understanding the transmitral FSI dynamics, and it is possible to simulate the more realistic 3D heart flow by the scheme in the future.

Foundational ontologies are axiomatic accounts of high-level domain-independent categories about the real world. They constitute toolboxes of reusable information modeling primitives for building ap- plication ontologies in specific domains. As such, they enhance seman- tic interoperability between agents by specifying descriptively adequate shared conceptualisations. The design of foundational ontologies gives rise to completely new challenges in respect of their content

Traditional Simulation solves problems executing their model on computers using numeric information but Qualitative Simulation has slightly different characteristics although it is basically a reasoning technique, which makes reasoning about physical systems, implemented using simulation’s problem solution approach. From the solution approach point of view, they also have some similar characteristics. Since both of disciplines advance on their own roads, it makes a gap between them. This paper aims to clarify the differences and similarities between two disciplines and bridge the gap in conceptual level

Common sense is on the one hand a certain set of processes of natural cognition -of speaking, reasoning, seeing, and so on. On the other hand common sense is a system of beliefs (of folk physics, folk psychology and so on). Over against both of these is the world of common sense, the world of objects to which the processes of natural cognition and the corresponding belief-contents standardly relate. What are the structures of this world? How does the scientific treatment of this world relate to traditional and contemporary metaphysics and formal ontology? Can we embrace a thesis of common-sense realism to the effect that the world of common sense exists uniquely? Or must we adopt instead a position of cultural relativism which would assign distinct worlds of common sense to each group and epoch? The present paper draws on recent work in computer science (especially in the fields of naive and qualitative physics), in perceptual and developmental psychology, and in cognitive anthropology, in order to consider in a new light these and related questions and to draw conclusions for the methodology and philosophical foundations of the cognitive sciences.

We consider the interoperability of information systems within a distributed environment, such as across statistical organisations of the Member States of the European Union. Within a logical layer between the physical storage of the data (server) and the presentation of the data to the user (client), we introduce a conceptual data model that can facilitate interoperability between a number of different data models and structures in a distributed environment. For interoperability, we discuss a micro-macrometadata model that can interface readily with data warehouses and other cube object models and can exploit the accommodation of the operational metadata necessary for statistical query processing.

Behavior simulation is an effective method to visualize the meaning of mechanical concepts and has been applied in many computer-aided education systems. These simulations are not useful, however, for a learner who has already formulated an erroneous equation. In order to help the learner to recognize and correct the error, simulating the erroneous equation is a promising method. In this paper, we propose Error-Based Simulation (EBS) which reflects an erroneous equation a learner gave in solving a mechanical problem. We have developed a prototype system which generates EBS by changing the value of "velocity" or "acceleration" of an object by using an erroneous equation. In EBS, the object behaves in a strange manner. The differences between EBS and normal simulation illustrate the error clearly. EBS, however, is not always effective for a learner to recognize and correct an error. The effect depends on how strange it behaves. Therefore, diagnosing the behavior of EBS is very important to use it adequately. In this paper, we also describe a framework for managing EBS with qualitative reasoning techniques.

The AI model-based diagnosis community has developed qualitative reasoning mechanisms for fault isolation in dynamic systems. Their emphasis has been on the fault isolation algorithms, and little attention has been paid to robust online detection and symbol generation that are essential components of a complete diagnostic solution. This paper discusses a robust diagnosis methodology for hybrid systems that combines fault detection with a combined qualitative and quantitative fault isolation scheme. We focus on fault detection, symbol generation, and parameter estimation, and illustrate the effectiveness of this method by running experiments on the fuel transfer system of aircraft. ©: 2003 IFAC

This paper presents a fuzzy qualitative representation of conventional trigonometry with the goal of bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in the domain of physical systems, especially in intelligent robotics. Fuzzy qualitative coordinates are defined by replacing a unit circle with a fuzzy qualitative circle; a Cartesian translation and orientation are defined by their normalized fuzzy partitions. Conventional trigonometric functions, rules and the extensions to triangles in Euclidean space are converted into their counterparts in fuzzy qualitative coordinates using fuzzy logic and qualitative reasoning techniques. This approach provides a promising representation transformation interface to analyze general trigonometry-related physical systems from an artificial intelligence perspective.

We present a methodology to perform Financial Analysis with as much information as available. The problem we solve is the assessment of investment projects; we are to determine whether or not an investment project is profitable. We perform that task at the qualitative, semiquantitative, and quantitative levels, depending on how much information there is at hand. The main advantage of the method presented here is that it can deal with very little information about quantities, still yielding some results. The least piece of information the system can work with is a set of Order of Magnitude Relations (omrs) among the model variables. If those omrs are sufficient to disambiguate the results of our model, we can tell whether an investment project is worthwhile or not. If all we have is a few omrs it may or may not determine the outcome of our investment project analysis. Later on, we provide the system with additional information (perhaps imprecise), and the results will be refined. The more precise the provided information is, the more accurate the results will be. If at the end, all provided variables are precise, the results are the same as performed via traditional analysis.

A central claim of computational systems biology is that, by drawing on mathematical approaches developed in the context of dynamic systems, kinetic analysis, computational theory and logic, it is possible to create powerful simulation, analysis, and reasoning tools for working biologists to decipher existing data, devise new experiments, and ultimately to understand functional properties of genomes, proteomes, cells, organs, and organisms. In this article, a novel computational tool is described that achieves many of the goals of this new discipline. The novelty of this system involves an automaton-based semantics of the temporal evolution of complex biochemical reactions starting from the representation given as a set of differential equations. The related tools also provide ability to qualitatively reason about the systems using a propositional temporal logic that can express an ordered sequence of events succinctly and unambiguously. The implementation of mathematical and computational models in the Simpathica and XSSYS systems is described briefly. Several example applications of these systems to cellular and biochemical processes are presented: the two most prominent are Leibler et al.'s repressilator (an artificial synthesized oscillatory network), and Curto-Voit-Sorribas-Cascante's purine metabolism reaction model.

We present a methodology to perform Financial Analysis with as much information as available. The problem we solve is the assessment of investment projects; we are to determine whether or not an investment project is profitable. We perform that task at the qualitative, semiquantitative, and quantitative levels, depending on how much information there is at hand. The main advantage of the method presented here is that it can deal with very little information about quantities, still yielding some results. The least piece of information the system can work with is a set of Order of Magnitude Relations (omrs) among the model variables. If those omrs are sufficient to disambiguate the results of our model, we can tell whether an investment project is worthwhile or not. If all we have is a few omrs it may or may not determine the outcome of our investment project analysis. Later on, we provide the system with additional information (perhaps imprecise), and the results will be refined. The more precise the provided information is, the more accurate the results will be. If at the end, all provided variables are precise, the results are the same as performed via traditional analysis.

The field of Qualitative Spatial Reasoning is now an active research area in its own right within AI (and also in Geographical Information Systems) having grown out of earlier work in philosophical logic and more general Qualitative Reasoning in AI. In this paper (which is an updated version of [25]) I will survey the state of the art in Qualitative Spatial Reasoning, covering representation and reasoning issues as well as pointing to some application areas.

We introduce a novel approach to the creation of Virtual Reality Art installations, which supports the design of alternative worlds, in which laws of Physics can be redefined to induce new user experiences. To implement this concept of "Alternative Reality", we have used Artificial Intelligence techniques to support the definition of the virtual environment behaviour, an approach inspired by Qualitative Reasoning systems. Besides the redefinition of physical laws, we have developed mechanisms for eliciting causal relations between events, as causality plays an important part in users' perception of virtual worlds. Our pilot installation is a CAVE TM -like system incorporating a state-of-theart computer game engine as visualisation software, which hasbeen ported to this immersive display. The event-based system underlying the game engine is used to bypass the native Physics engine and replace it with our Alternative Reality software. A first prototype has been fully implemented, the Alternative Reality modules totaling over 100,000 lines of C++ code. We present early results obtained with this approach, illustrated with examples taken from two artistic briefs, developed by digital artists associated to this research.

In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper presents Possibilistic Logic Programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on Gödel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program P by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studied.

Problems derived from the power quality aspects of a power grid are turning the monitoring and diagnosis tasks into an appealing field for electric power researchers. This interest is mainly founded on the great importance of providing highly reliable power grids, but also because of the relatively simple task (computationally speaking) required to accomplish the measurements. Despite its potential importance, efforts are mainly targeted at collecting data and confronting it with quality standards, rather than identifying problems, providing solutions or anticipating power faults. Aware of this shortcoming, this work is intended to bridge the gap that leads to self-sufficient systems, capable of anticipating and reacting to power faults, instead of a simple data gathering. This work also provides a characterization of the power quality domain, proposing a qualitative behavioral model that supports the multi-agent system in its task to anticipate and wisely react to power faults, and improve power quality.

One of the central questions of spatial reasoning research is whether the underlying processes are inherently visual or spatial. The article reports a dual-task experiment that was conducted to explore the visual and/or spatial nature of human spatial reasoning. The main tasks were inferences based on a spatial version of the interval calculus introduced by Allen (1983). The secondary tasks were presented visually or acoustically, and were either spatial or non-spatial. The results indicate that spatial reasoning is mainly based on the construction and inspection of spatial layouts, whereas no evidence of the involvement of visual representations and processes was found.

This work presents the development and implement of spray combustion modelling based on the spray size distribution moments. In this spray model, the droplet size distribution of spray is characterised by the first four moments related to number, radius, surface area and volume of droplets, respectively. The governing equations for gas phase and liquid phase employed here are solved by the finite volume method based on an Eulerian framework. These constructed equations and source terms are derived based on the moment-average quantities which are the key concept for this work. The sub-model employed for ignition and combustion is the coupling reaction rate between Arrhenius model and Eddy-Dissipation model (EDM) via a reaction progress variable. The results obtained from simulation are compared with the experimental and simulation data in the literature in order to assess the accuracy of present model. Comparing with the experimental results, present approach is capable to provide a qualitatively reasonable prediction for auto-ignition. In addition, the flame area developed during the combustion progress corresponds with the experimental data. However, the results of this model overpredict the measured flame temperature distributions. This might be due to the sub-model of turbulence/chemistry interaction employed here being based on infinitely fast chemistry assumption.

This paper describes the interface components for a system called Betty's Brain, an intelligent agent we have developed for studying the learning by teaching paradigm. Our previous studies have shown that students gain better understanding of domain knowledge when they prepare to teach others versus when they prepare to take an exam. This finding has motivated us to develop computer agents that students teach using concept map representations with a visual interface. Betty is intelligent not because she learns on her own, but because she can apply qualitative-reasoning techniques to answer questions that are directly related to what she has been taught through the concept map. We evaluate the agent's interfaces in terms of how well they support learning activities, using examples of their use by fifth grade students in an extensive study that we performed in a Nashville public school. A critical analysis of the outcome of our studies has led us to propose the next generation interfaces in a multi-agent paradigm that should be more effective in promoting constructivist learning and self-regulation in the learning by teaching framework.

This paper describes the use of qualitative reasoning mechanisms in designing computer-based teachable agents that users explicitly teach to solve problems using concept maps. Users can construct the required problem-solving knowledge structures without becoming involved in sophisticated programming activities. Once taught, the agent attempts to answer questions using qualitative reasoning schemes that are intuitive and easy to apply. Students can reflect on the agent's responses, and then revise and refine ...

Formal Ontology in Information Systems 237 AC VarziandL. Vieu (Eds.) IOS Press. 2004 Towards a Generic Foundation for Spatial Ontology John BATEMAN, Scott FARRAR The Collaborative Research Center for Spatial Cognition SFB/TR8, University of Bremen, Germany Abstract. ...

This work is part of a larger attempt to explore the nature of symbolic understanding involving graphic technology. This study describes learning advanced mathematics that occurs through constructing qualitative reasoning methods using graphic technology. Data was gathered from a precalculus class who, for a few weeks, investigated and explored asymptotic behavior of rational functions. The analysis is based on observations of group discussions and written works. Learning about asymptotes using software which serves as tool box for numerical evaluation and graphic representation amplifies epistemological complexities related to the infinity concept. Using the software to watch examples of rational functions, generated by symbolic and graphic operations between polynomial functions, enabled students to leave their own traces on the formalization of asymptoteson its definition, its symbolic structure, and its computational procedures. The discrepancy between technology as a support for visual perception but a tool that cannot support the conception of approaching infinities, makes the study of asymptotes an intriguing domain for investigating what is being manipulated with software and how.

Risk analysis is traditionally considered a critical activity for the whole software system's lifecycle. Risks are identified by considering technical aspects (e.g., failures of the system, unavailability of services, etc.) and handled by suitable countermeasures through a refined design. This, however, introduces the problem of reconsidering system requirements. In this paper, we propose a goal-oriented approach for analyzing risks during the requirements analysis phase. Risks are analyzed along with stakeholder interests, and then countermeasures are identified and introduced as part of the system's requirements. This work extends the Tropos goal modeling formal framework proposing new concepts, qualitative reasoning techniques, and methodological procedures. The approach is based on a conceptual framework composed of three main layers: assets, events, and treatments. We use ''loan origination process'' case study to illustrate the proposal, and we present and discuss experimental results obtained from the case study.

Easy to use workbenches for Qualitative Reasoning and Modelling are virtually nonexistent. This has a limiting effect on the use and update of the technology by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models. Garp3 can be used by experts to create conceptual models in situations were numerical information is sparse or unavailable. Garp3 can also be used in educational settings or dissemination activities to illustrate and educate typical features of Qualitative Reasoning.

This paper presents a proposal for the development of laboratory assignments about the design and implementation of advanced interfaces for mobile robots using speech recognition. In these assignments, the main objective is the analysis of the possibilities for using speech interfaces as a complementary system for other interaction components with a mobile robot, such as artificial vision. The paper also

We present a computational model, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision-making. Current theories of moral decision-making extend beyond pure utilitarian models by relying on contextual factors that vary with culture. MoralDM uses a natural language system to produce formal representations from psychological stimuli, to reduce tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to determine consequences and utilities when making moral judgments. We describe how MoralDM works and show that it can model psychological results and improve its performance via accumulating examples.

A combined qualitative-quantitative approach for the identification of highly cocreative technology-driven firms Milyakov, Hristo ; Tanev, Stoyan; Ruskov, Petko Citation for pulished version (APA): Milyakov, H., Tanev, S., & Ruskov, P. (2010). A combined qualitative-quantitative approach for the identification of highly co-creative technology-driven firms.

In Group Decision Making based on argumentation, decisions are made considering the diverse points of view of the different partakers in order to decide which course of action a group should follow. However, knowledge and belief are normally incomplete, contradictory, or error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of uncertain and even not precise information. On the other hand, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes, a problem that in this work will be addressed in terms of an extension to the logic programming language and based on an evaluation of the Quality-of-Information (QoI) that stems out from those extended logic programs or theories. We present a computational model to address the problem of decision making, in terms of a multitude of scenarios, also defined as logic programs or theories, where the more appropriate ones stand for the higher QoIs values.

Extra-pair paternity is widespread among avian species, and large variations in frequency occur both within and among species. These variations result from individual, ecological and phylogenetic influences that impinge upon the strategies used in searching for extra-pair copulations. We used qualitative reasoning to evaluate how the decision-making process for each sex could affect the occurrence of extra-pair paternity and fitness. We demonstrate that the quality of the female's mate influences the female's inclination to search for extra-pair copulations. Also shown is the result that high quality males produce a higher number of within-pair and extra-pair young. Since the interests of males and females converge in certain aspects, both sexes achieved an increment in fitness. The model presented in this paper suggests that the conflict of interests between the sexes is influenced by the willingness of females to engage in extra-pair copulations and by the quality of the male to which the female is mated.

We present a methodology to perform Financial Analysis with as much information as available. The problem we solve is the assessment of investment projects; we are to determine whether or not an investment project is profitable. We perform that task at the qualitative, semi-quantitative, and quantitative levels, depending on how much information there is at hand. The main advantage of

a b s t r a c t Qualitative knowledge and reasoning Conceptual knowledge and models Knowledge capture Software and workbench Easy 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.

In Greek traditional teaching a lot of significant concepts are introduced with a sequence that does not provide the students with all the necessary information required to comprehend. We consider that understanding concepts and the relations among them is greatly facilitated by the use of modelling tools, taking into account that the modelling process forces students to change their vague, imprecise ideas into explicit causal relationships. It is not uncommon to find students who are able to solve problems by using complicated relations without getting a qualitative and in-depth grip on them. Researchers have already shown that students often have a formal mathematical and physical knowledge without a qualitative understanding of basic concepts and relations." The aim of this communication is to present some of the results of our investigation into modelling activities related to kinematical concepts. For this purpose, we have used ModellingSpace, an environment that was especially designed to allow students from eleven to seventeen years old to express their ideas and gradually develop them. The ModellingSpace enables students to build their own models and offers the choice of observing directly simulations of real objects and/or all the other alternative forms of representations (tables of values, graphic representations and bar-charts). The students -in order to answer the questions- formulate hypotheses, they create models, they compare their hypotheses with the representations of their models and they modify or create other models when their hypotheses did not agree with the representations. In traditional ways of teaching, students are educated to utilize formulas as the most important strategy. Several times the students recall formulas in order to utilize them, without getting an in-depth understanding on them. Students commonly use the quantitative type of reasoning, since it is primarily used in teaching, although it may not be fully understood by them. By modeling activities, we tried to achieve the development of qualitative and semi-quantitave premise and then express magnitude relationships in a quantitative manner. It was observed that students practise semi-quantitative relations, easily can create models and make use of semi-quantitative reasoning in an accessible and comprehensible way. They developed a strategy to simplify the situation, by creating simple models containing few relationships contrarily to their original custom to create complex patterns without having fully understood their function.

Im Kurs "Intuitive Quantum Physics" werden graphische Interpretationen mathematischer Gleichungen und qualitatives Denken durch ein vereinfachtes Modell der Quantenphysik gelehrt. Unser Kurs besteht aus drei wichtigen Abschnitten: Wellenphysik, Aufbau eines Werkzeugkastens ("Toolbox") und Quantenphysik, sowie drei Schlüsselthemen: Welle-Teilchen-Dualität, die Schrödinger-Gleichung und Tunneln von Quantenteilchen. Wir unterrichten vorwiegend mit Lab-Tutorials, in denen StudentInnen in kleinen Gruppen (3 bis 4 Personen) anwendungsspezifische Arbeitsblätter durcharbeiten. In den Diskussionen werden auch Auseinandersetzungen über das "Bild der Physik", bei uns "Nature of Science" genannt, geführt. Überprüfungen haben ergeben, dass StudentInnen nicht nur die schwierigsten Konzepte des Kurses lernen können sondern auch lernen, dass die Quantenphysik begrifflich verständlich ist. Diese Einsicht ist besonders für zukünftige Lehrkräfte von Bedeutung.

■ We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive quali- tative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article then bridges to quantitative modeling, highlight- ing the benefits of qualitative reasoning-based ap- proaches in the framework of

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language -the RCC spatial logic. Below, we briefly describe the work of the group in this area.

If we allow the magnitude and angle of a complex number (expressed in polar form) to range over an interval, it describes a semicircular region, similar to a fan; these regions are what we call Complex Fans. Complex numbers are a special case of Complex Fans, where the magnitude and angle are point intervals.

We describe an implemented technique for generating event models automatically based on qualitative reasoning and a statistical analysis of video input. Using an existing tracking program which generates labelled contours for objects in every frame, the view from a fixed camera is partitioned into semantically relevant regions based on the paths followed by moving objects. The paths are indexed with temporal information so objects moving along the same path at different speeds can be distinguished. Using a notion of proximity based on the speed of the moving objects and qualitative spatial reasoning techniques, event models describing the behaviour of pairs of objects can be built, again using statistical methods. The system has been tested on a traffic domain and learns various event models expressed in the qualitative calculus which represent human observable events. The system can then be used to recognise subsequent selected event occurrences or unusual behaviours.