F. Linnebank

Garp3 - Workbench for Qualitative Modelling and Simulation

Garp3 is a domain independent, multi-platform, qualitative modelling and simulation environment. It allows modellers to articulate and refine their conceptual domain knowledge and analyse this knowledge through simulation. Garp3 has been successfully applied in Ecology and Sustainable Development (SD) and is freely available via (http://www.garp3.org). Garp3 and the NaturNet-Redime Project Ecologists in the NaturNet-Redime project (http://www.naturnet.org) are building qualitative models about sustainable development issues through several case studies. For this purpose the Garp3 workbench for building, simulating, and inspecting qualitative models was developed (Bredeweg e al, 2006). The main goals of the development was making qualitative reasoning technology usable for non-computer scientists by creating a uniform user interface, a diagrammatic visual language for representing model content, and graphical buttons to communicate the available user options and manipulations. Garp3 is implemented in SWI-Prolog (http://www.swi-prolog.org) and seamlessly integrates three previously developed software components: Garp2 for simulating models, Homer for building models, and VisiGarp for inspecting simulation results. To further support the ecologists in their modelling efforts a structured approach to modelling was developed. This framework helps modellers refine their initial ideas, represented in concept maps, into detailed conceptualisations, such as structural models, causal models, and expected model behaviour. The diagrams in the final steps of the framework are close to the actual modelling primitives used to implement qualitative models. The Sketch environment in Garp3 supports modellers with tools to create the required diagrams. These Sketches not only help modellers in creating a model, but also serve as a more general description of the final model for other users of the model. One of the main goals of the NaturNet-Redime project is to create a sustainable development curriculum that allows students to learn about specific issues through qualitative modelling and simulation. To advance this goal the case study models developed in project are being integrated into a single library of sustainability concepts. Students can run and adapt different scenarios, let Garp3 automatically gather the correct knowledge relevant to the simulation and predict the possible outcomes, and analyse the results. To support the integration of the different case study models multiple model support and copy/paste functionality have been added to Garp3. The copying functionality assures that models remain syntactically correct, avoids adding redundant knowledge, preserves existing knowledge, and merges conceptual knowledge in a semantically correct manner as much as possible. The copy functionality also allows modellers to reuse parts of

Garp3: a new workbench for qualitative reasoning and modelling

Easy to use workbenches for Qualitative Reasoning (QR) and Modelling are virtually nonexistent. This has a limiting effect on the use and uptake 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 to, discover, capture, and share conceptual knowledge on how systems behave.

A qualitative reasoning model of algal bloom in the Danube Delta Biosphere Reserve (DDBR)

Abstract This paper presents a Qualitative Reasoning model of the algal bloom phenomenon and its effects in the Danube Delta Biosphere Reserve (DDBR) in Romania. Qualitative Reasoning models represent processes and their cause–effect relationships in a flexible and conceptually rich manner and as such can be used as instruments for capturing and sharing explanations of how systems behave. The DDBR model is based on expert knowledge and captures the main factors contributing to the dynamics of this aquatic ecosystem. Focal points of the model are the relationships between temperature, water pollution from the Danube catchment area, and cyanotoxins. These factors gravely affect aquatic biota and ultimately human wellbeing. In addition to capturing domain knowledge, this paper discusses solutions for representing typical patterns in ecological systems using Qualitative Reasoning techniques.

Learning spaces as representational scaffolds for learning conceptual knowledge of system behaviour

Scaffolding is a well-known approach to bridge the gap between novice and expert capabilities in a discovery-oriented learning environment. This paper discusses a set of knowledge representations referred to as Learning Spaces (LSs) that can be used to support learners in acquiring conceptual knowledge of system behaviour. The LSs are logically self-contained, meaning that models created at a specific LS can be simulated. Working with the LSs provides scaffolding for learners in two ways. First, each LS provides a restricted set of representational primitives to express knowledge, which focus the learners knowledge construction process. Second, the logical consequences of an expression derived upon simulating, provide learners a reflective instrument for evaluating the status of their understanding, to which they can react accordingly. The work presented here is part of the DynaLearn project, which builds an Interactive Learning Environment to study a constructive approach to having learners develop a qualitative understanding of how systems behave. The work presented here thus focuses on tools to support educational research. Consequently, user-oriented evaluation of these tools is not a part of this paper.

A qualitative model of limiting factors for a salmon life cycle in the context of river rehabilitation

Qualitative Reasoning modelling has been promoted as a tool for formalising, integrating and exploring conceptual knowledge in ecological systems, such as river rehabilitation, which draw different information from multiple domains. A qualitative model was developed in Garp3 to capture and formalise knowledge about river rehabilitation and the management of an Atlantic salmon population, for use in an educational setting. The model integrated information about the ecology of the salmon life cycle, the environmental factors that may limit the survival of key life stages and their links with human activities such as agriculture, habitat rehabilitation and fishing. Whilst the compositional approach to qualitative modelling allowed simple representation of component concepts, the successful integration of these components in simulations of complex scenarios required a number of abstract representations, assumptions and technical modelling solutions to handle aspects of ambiguity and complexity, and to obtain the desired system behaviour. The final scenarios and simulations produced were able to represent river rehabilitation concepts in the context of a complete life cycle, but at this scale processing the simulations was very time consuming. Therefore, to handle this complexity an additional series of smaller demonstrator scenarios was developed that succinctly explored individual concepts within the system. This study indicates that qualitative modelling may be a valuable tool for exploring large systems, provided suitable means can be used to handle complexity and ambiguity.

The river Mesta case study: A qualitative model of dissolved oxygen in aquatic ecosystems

The dynamics of the dissolved oxygen in water bodies is the result of complex interactions involving physical and biological processes. Understanding how the balance of these influences determines the amount of oxygen available for living organisms is a key factor to interpret the water body conditions, and eventually to use dissolved oxygen as an indicator of the water quality. In this paper we present a Qualitative Reasoning model developed to improve understanding of changes in the amount of dissolved oxygen in different segments of the river Mesta in Bulgaria. Effects on dissolved oxygen result from changes in physical, chemical and biological processes induced both by natural and anthropogenic activities within the watershed. To explore the possibility of establishing a landmark value that may change according to specific conditions, we developed the concept of flexible value mapping, which dynamically captures changes in the dependencies between the landmark value and the values of other quantities as the conditions of the system change during the simulations. The paper also discusses the concept of dominance of a specific process over other competing processes affecting a quantity. With the model described here, we aim to discuss possible solutions to interesting modelling problems and to provide the community of ecological modellers support for educational activities and water resources management.

DynaLearn: architecture and approach for investigating conceptual system knowledge acquisition

DynaLearn is an Interactive Learning Environment that facilitates a constructive approach to developing a conceptual understanding of how systems work The software can be put in different interactive modes facilitating alternative learning experiences, and as such provides a toolkit for educational research.

Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

This paper presents a domain independent question generation and interaction procedure that automatically generates multiple-choice questions for conceptual models created with Qualitative Reasoning vocabulary. A Bayesian Network is deployed that captures the learning progress based on the answers provided by the learner. The likelihood of concepts being known or unknown on behalf of the learner determines the focus, and the question generator adjusts the contents of its questions accordingly. As a use case, the Quiz mode is introduced.

Knowledgeable feedback via a cast of virtual characters with different competences

DynaLearn (http://www.DynaLearn.eu) develops a cognitive artefact that engages learners in an active learning by modelling process to develop conceptual system knowledge. Learners create external representations using diagrams. The diagrams capture conceptual knowledge using the Garp3 Qualitative Reasoning (QR) formalism [2]. The expressions can be simulated, confronting learners with the logical consequences thereof. To further aid learners, DynaLearn employs a sequence of knowledge representations (Learning Spaces, LS), with increasing complexity in terms of the modelling ingredients a learner can use [1]. An online repository contains QR models created by experts/teachers and learners. The server runs semantic services [4] to generate feedback at the request of learners via the workbench. The feedback is communicated to the learner via a set of virtual characters, each having its own competence [3]. A specific feedback thus incorporates three aspects: content, character appearance, and a didactic setting (e.g. Quiz mode). In the interactive event we will demonstrate the latest achievements of the DynaLearn project. First, the 6 learning spaces for learners to work with. Second, the generation of feedback relevant to the individual needs of a learner using Semantic Web technology. Third, the verbalization of the feedback via different animated virtual characters, notably: Basic help, Critic, Recommender, Quizmaster & Teachable agent.

Acquiring conceptual knowledge about how systems behave

There is a need for software that supports learners in actively dealing with theoretical concepts by having them create models and perform concept prediction and explanation (e.g [3,4,5]) DynaLearn seeks to address this by developing a domain independent Interactive Learning Environment (ILE) based on Qualitative Reasoning (QR) [1] The QR vocabulary fits the nature of conceptual knowledge, and the explicit representation of these notions in the software provides the handles to support an automated communicative interaction that actually discusses and provides feedback at the conceptual level.