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  • Action buttons:
    Action buttons are icons in the DynaLearn interface that are used to manipulate (views of) models and simulations. Action buttons are greyed out when they cannot be used. Unlike graphical icons, the buttons correspond to actions, and not to model (or simulation) contents.
  • Agent:
    Learning spaces 4,5,6: Agents are used to model entities outside of the modelled system. Agents can have quantities influencing the rest of the system, which are sometimes called exogenous or external influences.
  • Agent Fragment:
    Learning spaces 6: Agent fragments contain an agent and may contain one or more influences. Agent fragments are used to describe the influences that agents (exogenous entities) have on the system.
  • Aggregate:
    Learning spaces 5,6: There are two kinds of aggregates, namely model fragments and scenarios. Aggregates consist of multiple model ingredients.
  • Assumption:
    Learning spaces 4,5,6: Assumptions are labels which are used to indicate that certain conditions are presumed to be true. They are often used to constrain the possible behaviour of a model. Because they describe neither structural nor behavioural aspects of a system, they belong to neither the structural building blocks nor the behavioural building blocks categories.
  • Attribute:
    Learning spaces 2,3,4,5,6: Attributes are properties of entities which remain static during simulation (i.e. they do not change). They have an associated set of attribute values, which are the possible values that the attribute can take on.
  • Attribute value:
    Learning spaces 2,3,4,5,6: Attributes values are the possible values that an attribute can have.
  • Behaviour:
    Learning spaces 2: Within the context of qualitative reasoning and modelling, the behaviour of a system means the changes of the quantity values as the result of processes with are active within the modelled system.
    Learning spaces 3,4,5,6: Within the context of qualitative reasoning and modelling, the behaviour of a system means the changes of the quantity values as the result of processes that are active within the modelled system. The behaviour of a system is visualised as a state graph.
  • Behaviour graph:See State Graph
  • Behaviour path:
    Learning spaces 3,4,5,6: A behaviour path is a sequence of successive states within a state graph. The path describes the evolution of the quantity values and ordinal relations as time progresses.
  • Behavioural building blocks:
    Learning spaces 2,3,4,5,6: Behavioural building blocks are modelling ingredients which model the behavioural aspects of a system. They can be further distinguished into features and dependencies.
  • Behavioural feature:
    Feature
  • Causal dependencies:
    Learning spaces 2,3: Causal dependencies are used to model how processes induce changes.
    Learning spaces 4,5,6: Causal dependencies are used to model how processes induce changes, either directly or indirectly. These dependencies are represented as influences (direct changes) and proportionalities (indirect changes).
  • Calculus:
    See operator
  • Causality:
    Learning spaces 2,3,4,5,6: The relationship between the causes and the effects they produce.
  • Causal dependency:
    See causality
  • Causal model:
    Learning spaces 2,3: A causal model is a diagram containing quantities and causal dependencies. It describes how the quantities are causally related.
    Learning spaces 4: A causal model is a diagram containing quantities and causal dependencies (both proportionalities and influences). It describes how the quantities are causally related.
    Learning spaces 5,6: A causal model is a diagram containing quantities and causal dependencies (proportionalities and influences). It describes how the quantities are causally related. In the context of DynaLearn the term ‘causal model’ is sometimes used to refer to the dependency diagram in the simulation environment.
  • Closed state:
    Learning spaces 3,4,5,6: A state is called closed when the successive states and transitions are generated from its pruned and merged terminations.
  • Colour coding:
    A colour code is a system for displaying information by using different colours. Throughout the DynaLearn interface colour is used as a distinguishing feature to make graphical icons represent different views and meanings, using the same icon with different colours.
  • Common sense reasoning:
    Humans perform commonsense reasoning and problem solving continuously as they go about their tasks in the physical world. These reasoning capabilities allow humans to behave successfully in new situations, achieve goals, build and use tools, and so on. In addition, all this reasoning happens without possessing formal scientific theories that cover the relevant features in a systematic, detailed and numeric way. An example of common sense reasoning might be the inference that if someone asks for a phone book it is (probably) because they want to look up a number, and make a phone call.
  • Concept:
    Learning spaces 1:
  • Concept Map:
    Learning spaces 1: A concept map is a diagram that visualises the relationships between concepts. It typically consists of nodes and arcs (connecting nodes).
  • Condition:
    Learning spaces 5,6: The conditions in a model fragment indicate what things should be true within a scenario (or state) in order for the consequences to be true. See also Conditions and consequences
  • Conditional model fragment:
    Learning spaces 5,6: A model fragment that only contains conditions.
  • Conditions and consequences:
    Learning spaces 5,6: Conditions specify what must be present or hold in order for a model fragment to apply. Consequences specify the knowledge that will be introduced when the model fragment applies. Conditional model ingredients are displayed in red, while consequences are displayed in blue. See also: Color Coding
  • Conditional relation:
    Model fragments may require other model fragments to be active in order to become active themselves. These conditional model fragments are sometimes referred to as conditional relations and can be made visible in the model fragment definitions editor.
  • Configuration:
    Configurations are used to model relations between instances of entities and agents. Configurations are sometimes referred to as structural relations.
  • Consequence:
    Learning spaces 5,6: Consequences within a model fragment specify the information which is added to a scenario or state when the conditions of the model fragment are fulfilled.
  • Context-sensitivity:
    The toolbars in DynaLearn are context-sensitive, which means that they provide visual feedback about which actions can be performed at a particular time. If an icon is greyed out, it means that the option is inactive in this context. Particularly, the selected ingredients in a workspace determine which options are available and which ones are not.
  • Correspondence:
    Correspondences are used to indicate that qualitative values of different quantity spaces occur at the same time. See also: A Value Correspondence
  • Current value:
    Learning spaces 2: The current value refers to the value of the derivative of a quantity.
    Learning spaces 3,4: The current value refers to either the magnitude or the derivative value of a quantity.
    Learning spaces 5,6: The current value refers to either the magnitude or the derivative value of a quantity in a specific situation.
  • Dependency:
    Learning spaces 2: Behavioural dependencies describe relations between behavioural features. These are the causal dependencies.
    Learning spaces 3: Behavioural dependencies describe relations between behavioural features. There are two kinds of dependencies: causal dependencies and correspondences.
    Learning spaces 4,5,6: Behavioural dependencies describe relations between behavioural features. There are three kinds of dependencies: causal dependencies, mathematical dependencies and correspondences.
  • Derivative:
    Learning spaces 3,4,5,6: The derivative indicates the direction of change of a quantity. This can be either ‘min’ (decreasing), ‘zero’ (stable), or ‘plus’ (increasing).
  • Diagram:
    Every visualisation of a part of a model or a simulation in DynaLearn is called a diagram.
  • Directed:
    A directed relation can only be processed following a single direction. The direction is typically denoted by an arrow (see also undirected).
  • DynaLearn:
    DynaLearn is a workbench for building, simulating, and inspecting qualitative models. DynaLearn is implemented in SWI-Prolog and seamlessly integrates three previously developed software components, including: Garp2 for simulating models (Linnebank, 2004), Homer for building models (Jellema, 2000), and VisiGarp for inspecting simulation results (Bouwer, 2005). Integrating these tools has led to one new tool that incorporates all of the original functionalities, and thus incorporates the advantages of each tool, but also adds interoperability and an easy to use uniform user interface. DynaLearn uses a diagrammatic approach for representing model content, and graphical buttons to communicate the available user options and manipulations. DynaLearn can be freely downloaded and used.
  • Entity:
    Learning spaces 1,2,3,4,5: Entities are the physical objects or abstract concepts that play a role within the system.
    Learning spaces 6: Entities are the physical objects or abstract concepts that play a role within the system. These entities are arranged in a subtype hierarchy.
  • Envision:
    Envision is a key term in the approach to qualitative reasoning. It refers to the notion of envisioning the behaviour of a system. The world is seen as a collection of ‘devices’. Devices consist of components, components manipulate materials (which we perceive as component behaviour), and communicate via conduits.
  • EPS file format:
    Diagrams in DynaLearn can be exported to Encapsulated PostScript (EPS) files. EPS files are resolution independent, since they store images in a vector based format instead of pixels.
  • Equation:
    Learning spaces 4,5,6: An equation is an expression consisting of quantities, operators and inequalities.
  • Equation history:
    Learning spaces 4,5,6: The equation history describes how the ordinal relations change through a sequence of states (usually a behaviour path).
  • External influence:
    See agent
  • Feature:
    Learning spaces 2,3,4,5,6: The behavioural features describe the variable aspects of the entities of a system.
  • Full quantity space correspondence:
    Learning spaces 3,4,5,6: Full Quantity Space Correspondences indicate that there is both a quantity space correspondence between the quantity spaces of the magnitudes, as well as a quantity space correspondence between the quantity spaces of the derivatives. Full correspondences can be either directed or undirected.
  • Full simulation:
    Learning spaces 3,4,5,6: A full simulation determines the complete behaviour of a system based on a scenario. During a full simulation the reasoning engine recursively finds the terminations for each state, prunes and merges these terminations, and by doing so finds all possible transitions.
  • Garp:
    Garp is the name given to the approach to qualitative reasoning developed by B. Bredeweg (published as PhD thesis by the University of Amsterdam in 1992). One of the main goals behind Garp was to unify three alternative approaches to qualitative reasoning (QPT, Envision, and QSIM), into a single framework for Expertise in Qualitative Prediction of Behaviour. Garp has been further developed since, incorporating new reasoning features and increasing usability. At this moment DynaLearn (the latest version of the software) implements an advanced workbench for building, simulating, and inspecting qualitative models.
  • Graphical icons:
    Graphical icons are icons that represent the contents of a model (the model ingredients), or simulation results. Icons are also used on action buttons.
  • Hierarchy:
    Learning spaces 6: A hierarchy is an ordering of things. In DynaLearn the most seen hierarchy is a subtype hierarchy.
  • Identity:
    Learning spaces 4,5,6: Identity relations are used to specify that two entities in different imported model fragments denote the same individual. There are two possible applications for this. Firstly, identities can be used to indicate that two entities in different imported model fragments are actually the same one. Secondly, they can be used to specialise entities in child model fragments. For example, the fish entity in a parent model fragment can be specialised to a salmon entity in the child model fragment.
  • Imported model fragment:
    Learning spaces 5,6: An imported model fragment is a condition. Multiple instances of a model fragment can be incorporated into another model fragment as seperate conditions. The model fragment that has imported model fragments can only become active when the imported model fragments are active as well.
  • Inequality:
    Learning spaces 4,5,6: Inequalities (<, <=, =, >=, >) specify an ordinal relation between two items. It represents that one item is different from (or equal to) another item. There are eleven ways to use inequalities, depending on the types of the two items that are related. Inequalities can be either directed or undirected.
  • Indirect influence:
    See Proportionality
  • Influence:
    Learning spaces 4,5,6: Influences are directed relations between two quantities, and are either positive or negative. Influences are the cause of change within a model, and are therefore said to model processes. Depending on the magnitude of the source quantity and the type of influence, the derivative of the target quantity either increases or decreases. An influence I+(Q2,Q1) causes the quantity Q2 to increase if Q1 is positive, decrease if it is negative, and remain stable when it is zero (assuming there are no other causal dependencies on Q2). For an influence I- this is just the opposite. Influences are also referred to as direct influences.
  • Ingredient:
    Ingredients are the parts that make up a structure. In DynaLearn model ingredients are all the parts that constitute a model and its simulation results.
  • Instance:
    Learning spaces 6: An instance is a specific occurence of an object of a certain type. In DynaLearn, users can create instances of the model ingredient definitions they define.
  • Interpreted state:
    Learning spaces 3,4,5,6: An interpreted state is a new state, which is generated by either applying relevant model fragments to a scenario, or by applying them to a state resulting from a transition.
  • Interval:
    Learning spaces 2: An interval is a qualitative value which describes a (possibly infinite) sequence of quantitative values. The two intervals are ‘increasing’ and ‘decreasing’.
    Learning spaces 3,4,5,6: An interval is a qualitative value which describes a (possibly infinite) sequence of quantitative values. There are never two succeeding intervals in a quantity space, since they always need to be alternated by a point value.
  • Inverse quantity space correspondence:
    Learning spaces 3,4,5,6: directed or undirected.
  • Legacy mode:
    Learning spaces 6: The mode in DynaLearn where only Legacy model:
    Learning spaces 6: DynaLearn models that are exported in legacy mode. This exports the models as a set of Prolog files which represent the model, which is the format that is used by Garp engine. DynaLearn also allows legacy models (made for previous versions of Garp) to be opened and simulated. Legacy models cannot be viewed and manipulated using the build environment.
  • Magnitude:
    Learning spaces 3,4,5,6: The magnitude indicates the current value of a quantity.
  • Mathematical dependencies:
    Learning spaces 4,5,6: Mathematical dependencies describe the mathematical relations between behavioural features.
  • Model:
    A model is an abstract representation of a system which enables users to make testable predictions about what happens to that system in different situations. One of the key differences between qualitative models and numerical models is that qualitative models only formalise the important qualitatively distinct values quantities of a system can have. Values of little importance are aggregated as intervals between these important point values.
  • Model fragment:
    Learning spaces 5,6: Model fragments describe part of the structure and behaviour of a system in a general way. They are partial models which are composed of multiple ingredients. Model fragments have the form of a rule. This means that model ingredients are incorporated as either conditions or consequences. Model fragments themselves can be reused within other model fragments as conditions, called imported model fragments. Furthermore, subclasses of model fragments can be made, which augment the parent model fragment with new ingredients. The consequence ingredients of model fragments which match the actual system situation will be added to the current scenario. In that case, the scenario fulfils the conditions specified in the model fragment (which describes a general situation). There are three different kinds of model fragments: static fragments, process fragments, and agent fragments.
  • Model ingredient:
    ingredient
  • Naming ingredients:
    In DynaLearn many model ingredients can be given a user defined name. This allows users to create models about specific domains.
  • Ordered state:
    Learning spaces 3,4,5,6: A state is ordered when transitions overruled by other transitions with higher priority have been pruned, and terminations that occur simultaneously due to correspondences have been merged.
  • Orninal relation:
    See inequality
  • Operator relation:
    Learning spaces 4,5,6: Using operator relations, more complex expressions can be created than is possible with only inequalities. They are used to calculate the sum (plus) or difference (min) between two items. Operator relations can be the target or source of an inequality relation. There are nine different ways plus/min relations can be used, depending on the type of the two items that are related.
  • OWL:
    See OWL format
  • OWL format:
    The Web Ontology Language (OWL) is a logical knowledge representation language based on RDF/XML. DynaLearn models can be exported to OWL files and uploaded to the qualitative model repository. The OWL format makes it possible for the repository to access the contents of the models to facilitate searching, which is not easily possible with the default DynaLearn files.
  • Point:
    Learning spaces 2: A point is a qualitative value which corresponds to a single quantitative value. The value ‘Zero’ of a quantity’s derivative the point value in learning space 2.
    Learning spaces 3,4,5,6: A point is a qualitative value which corresponds to a quantitative value. There are never two succeeding points in a quantity space. If there is a preceeding or succeeding value, then it must be an interval value.
  • Prediction:
    Learning spaces 3,4,5,6: A prediction is a statement or claim about likely future events or outcomes based on observation, experience or scientific reasoning.
  • Process:
    A process can be defined as a mechanism that causes changes in the properties of some object, or that causes objects to disappear or new objects to appear. The notion of processes is a key concept in the QPT approach to qualitative reasoning. Processes can also be used in DynaLearn, along with other key ideas from the field.
  • Process fragment:
    Learning spaces 6: Process fragments contain at least one influence, but no agents. These model fragments are used to describe processes which take place within the system.
  • Proportionality:
    Learning spaces 4,5,6: Proportionalities are directed relations between two quantities. They propagate the effects of a process, i.e. they set the derivative of the target quantity depending on the derivative of the source quantity. For this reason, they are also referred to as indirect influences. Like influences, proportionalities are either positive or negative. A proportionality P+(Q2, Q1) causes Q2 to increase if Q1 increases, decrease if Q1 decreases, and remain stable if Q1 remains stable (given that there are no other causal influences on Q2). For a proportionality P- this is just the opposite.
  • QSIM:
    QSIM is the software developed by B. Kuipers and co-workers. This software, and theory behind it, is documented in a book issued by MIT Press in 1994 (Qualitative Simulation, Modelling and Simulation with Incomplete Knowledge). A QSIM model is a set of constraints. These constraints are called Qualitative Differential Equations (QDE’s), the idea being that a qualitative model is essentially a rewrite of Ordinary Differential Equations (ODE’s) into QDE’s.
  • QPT:
    See qualitative_process_theory
  • Qualitative Process Theory:
    Qualitative Process Theory (QPT) is the approach to qualitative reasoning developed by K.D. Forbus in his Phd (published in the journal Artificial Intelligence in 1984). The key idea is the notion of processes. Processes are the cause of changes in the world. Causality is explicitly represented in a QPT model. QPE (Qualitative Process Engine) is the engine that implements QPT.
  • Qualitative:
    Qualitative means describing the value of a quantity using labels as distinctions instead of numerical values. For example using ‘boiling point’ instead of ‘100 degrees celcius’, ‘positive’ for all numers greater than zero, or ‘high’ as an indication of a large population.
  • Qualitative reasoning:
    Qualitative Reasoning (QR) or Qualitative Reasoning and Modelling (QRM) is an area of research within Artificial Intelligence (AI) that automates reasoning and problem solving about the (physical) world. It creates non-numerical descriptions of systems and their behaviour, preserving important behavioural properties and qualitative distinctions. Successful application areas include autonomous spacecraft support, failure analysis and on-board diagnosis of vehicle systems, automated generation of control software for photocopiers, conceptual knowledge capture in ecology, and intelligent aids for human learning.
  • Qualitative simulator:
    Learning spaces 2,3,4: The qualitative simulator is an implemented software algorithm that generates a prediction of the behaviour of a system.
    Learning spaces 5: The qualitative simulator is an implemented software algorithm that generates a prediction of the behaviour of a system. This engine takes the scenario expression as input and uses the conditional model fragments for domain knowledge and produces a state graph as result.
    Learning spaces 6: The qualitative simulator is an implemented software algorithm that generates a prediction of the behaviour of a system. This engine takes a scenario as input, uses a model fragment library for domain knowledge and produces a state graph as result.
  • Qualitative value:
    Learning spaces 2: A qualitative value is either a point (e.g. ‘Zero’ ) or an interval (e.g. ‘Negative’ or ‘Positive’). These are the values for a quantity’s derivative.
    Learning spaces 3,4,5,6: A qualitative value is either a point or interval which is one of the possible magnitudes or derivative values of a quantity. Qualitative values are organised in quantity spaces.
  • Quantitative:
    Learning spaces 2,3,4,5,6: Quantitative (numerical) means describing the value of a quantity using numbers. It is the opposite of qualitative.
  • Quantity:
    Learning spaces 2: Quantities represent changeable features of entities and agents. Each quantity is associated with a derivative that can be either decreasing (‘min’), steady (‘zero’) or increasing (‘plus’).
    Learning spaces 3,4,5,6: Quantities represent changeable features of entities and agents. Each quantity has two associated quantity spaces: a definable one for the magnitude, and the default quantity space {Min, Zero, Plus} for the derivative of the quantity.
  • Quantity space:
    Learning spaces 3,4,5,6: A quantity space specifies a range of qualitative values a quantity magnitude or derivative can have. The qualitative values in a quantity space form a total order. Each qualitative value is either a point or an interval, and within the quantity spaces these two types consecutively alternate.
  • Quantity space correspondence:
    Learning spaces 3,4,5,6: Quantity space correspondences exist between two quantity spaces, indicating that each values of one quantity space correspondes to a value of the quantity space. Like value correspondences these can be either directed or undirected.
  • Quantity value:
    Learning spaces 1,2: A quantity value is the current value of the derivative of a quantity.
    Learning spaces 3,4,5,6: A quantity value is the combination of the current value of the magnitude and the current value of the derivative of a quantity.
  • Rate variable:
    Learning spaces 2,3,4,5,6: The notion of a rate is used to refer to the quantity that represents the flow of mater, energy or information content between two [[e href=”entity”]]entities[[/a]]. For instance, there is a ‘flow of heat’ quantity whenever there are two adjacent objects that differ in temperature.
  • Refinement:
    Learning spaces 6: A refinement is a model ingredient that specialises an imported model fragment (which has to be imported in a parent model fragment) to one of its children (or further descendants). Thus, model fragments that are imported in some other model fragment can be refined to their subtypes.
  • Scenario:
    Learning spaces 6: Scenarios describe the actual state of a system, and can consist of all the ingredients which can be used as conditions in model fragments (except for imported model fragments). Scenarios are used as input for the qualitative simulator. The qualitative simulator interprets the scenario (finds applying model fragments, incorporates their consequences, and derives values) to generate one or more start states. These start states are used to generate the rest of the behavioural graph.
  • Simulation:
    Learning spaces 2: The word simulation is used to designate the process of qualitative reasoning, which is used to generate the simulation output. The simulation environment in the DynaLearn software is accessed by the simulation button.
    Learning spaces 3,4,5,6: A simulation is the process of qualitative reasoning as applied to a model. The simulation generates a simulation output. The simulation environment in the DynaLearn software offers the choice to do either a full simulation or traverse to the next reasoning step for a specific state.
  • Simulation output:
    Learning spaces 2: Simulation output refers to the derived quantity values based on the set value assignments that are given by the user.
    Learning spaces 3,4,5: Simulation output refers to the state graph generated by the qualitative reasoner as a result of simulating a qualitative model.
    Learning spaces 5: Simulation output refers to the state graph generated by the qualitative reasoner as a result of simulating a qualitative model by providing the scenario expression as input.
    Learning spaces 6: Simulation output refers to the state graph generated by the qualitative reasoner as a result of simulating a qualitative model by providing a scenario as input.
  • Simulation preferences:
    Simulation preferences allow to control specific behaviour of the simulation engine.

    The preference screen allows to select two preset preference settings:
    Default training settings and Default modelling settings.

    Overview of all preferences

  • Simulation result:
    See simulation_output
  • State:
    Learning spaces 3,4,5,6: A state describes a particular situation of a modelled system, reflecting a qualitatively unique behaviour. A state (description) is an assembly of applicable model fragments and thus contains information about the physical structure, the associated quantities and their values (in this state), and inequalities and the causal dependencies between the quantities. In a DynaLearn state graph, a state can be either: interpreted, terminated, ordered or closed. Central to Qualitative Reasoning is the way in which a system is described during a period of time in which the qualitative behaviour of the system does not change (a qualitative distinct state of behaviour). The notion of change is subtle, because numerical values of variables may change whereas from a qualitative point of view the behaviour of the system remains constant. During a heat flow, for example, the temperature of a liquid may increase, but from a qualitative point of view it is still a liquid, until another process (boiling) becomes active and the liquid becomes a gas.
  • Static fragment:
    Learning spaces 6: Static fragments are used to describe parts of the structure of the system, and the proportionalities which exist between the quantities. In static fragments all ingredients may occur except agents and influences.
  • State graph:
    Learning spaces 3,4,5,6: A state graph is a set of states, and the possible transitions between those states, which represents the behaviour of a modelled system. State graphs are generated by simulating a qualitative model.
  • State variable:
    Learning spaces 2,3,4,5,6: State variables are quantities that are most relevant to define the state of a system.
  • Structural building blocks:
    Structural modelling ingredients describe the organisation of the concepts within a system and the static features of those concepts.
  • Structural model:
    In Qualitative Reasoning the notion of a structural model is typically used to refer to the physical structure of a system, that is, a representation of the entities that constitute the system and how they are connected. In the context of DynaLearn a structural model may have two meanings. On the one hand the structural model is one of the key representations used for simulation the behaviour of a system (the notions of entity, attribute and configurations are relevant in this respect). On the other hand, the structural model is also one of the intermediate Sketch representations, which can be created in the Structural model editor.
  • Structural relations:
    See < a href="#configuration">configuration
  • Subtype hierarchy:
    Learning spaces 6: A subtype hierarchy shows the taxonomy of classes and the subtype relations which associate these classes.
  • Subtype relation:
    Learning spaces 2,3,4,5,6: A subtype relation relates a class of objects to a superclass. Instances of the class are also instances of the superclass (e.g. given that oak is a subtype of tree, instances of the class oak are also instances of the class tree).
  • Terminated state:
    Learning spaces 3,4,5,6: A state is called terminated if all the possible ways a state can end (due to changes of magnitudes or derivatives of quantities, or changes in inequalities between quantities) are identified.
  • Termination:
    Learning spaces 3,4,5,6: A termination is a possible way for a state to end. An end state can be achieved by a change in either the magnitude or derivative of a quantity, or a change in an inequality between two quantities).
  • Tooltip:
    A tooltip is a small yellow box which appears when a user hovers the mouse pointer over an action button or graphical icon without clicking on it. Text in the tooltip explains the item that is being hovered over.
  • Transitions:
    Learning spaces 3,4,5,6: A transition describes the change between two different states. The transition information describes the transition conditions which are fulfilled by the source state, and the results of the change (i.e. next state).
  • Transition history:
    Learning spaces 3,4,5,6: The transition history shows the transitions which cause the changes from one state to the other for a sequence of states.
  • Undirected:
    An undirected relation can be processed in both directions (see also directed).
  • Value:
    See value_assignment
  • Value assignment:
    Learning spaces 2: A value assignment indicates that a certain quantity should has a certain rate of change (either decreasing, steady or increasing).
    Learning spaces 3: A value assignment indicates that a certain quantity has a certain value. In a way, value assignments can be seen as abbreviations for inequalities between a quantity (or derivative) and qualitative values in its quantity space.
    Learning spaces 4,5,6: A value assignment indicates that a certain quantity has a certain value. Notice that value assignments relate to inequalities. In a way, value assignments can be seen as abbreviations for inequalities between a quantity (or derivative) and qualitative values in its quantity space.
  • Value correspondence:
    Learning spaces 3,4,5,6: Value Correspondences are relations between qualitative values of quantity spaces belonging to different quantities, and can be either directed or undirected. The former means that when value A of quantity space X corresponds to value B of quantity space Y, the simulator derives that quantity space Y has value B when quantity space X has value A. If the correspondence is undirected, it also derives that quantity space X has value A when quantity space Y has value B.
  • Value history:
    Learning spaces 3,4,5,6: The value history describes how quantity values change through a sequence of states (usually a behaviour path).