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QCM: A QP-Based Concept Map System. Morteza Dehghani and Kenneth D. Forbus. Agenda. QR and Cognitive Science Qualitative Concept Map System A Modeling Tool QP Mode Automatic Extraction of Domain Theory and Scenario files Bayesian Mode

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Qcm a qp based concept map system

QCM: A QP-Based Concept Map System

Morteza Dehghani and Kenneth D. Forbus


Agenda
Agenda

  • QR and Cognitive Science

  • Qualitative Concept Map System

    • A Modeling Tool

    • QP Mode

    • Automatic Extraction of Domain Theory and Scenario files

    • Bayesian Mode

    • Determining a Priori Probabilities using Qualitative Simulations

    • Examples

  • Related Work


Qr and cognitive science
QR and Cognitive Science

  • Qualitative representations capture the intuitive, causal aspects of many human mental models (Forbus & Gentner 1997)

    • Not handled by traditional formalisms, e.g. conditions of applicability

  • Qualitative modeling should become an important tool for cognitive science

    • Provides the formalism necessary for expressing mental models

  • A cognitive scientist friendly system is needed where

    • Qualitative representations

    • Explore mental models


Qualitative concept map system
Qualitative Concept Map System

  • Qualitative Concept Map system (QCM):

    • Provides a cognitive scientist friendly environment

    • Allows modelers:

      • Explore Qualitative models

      • Incorporate them into probabilistic models

      • Output/integrate them in formats usable in other forms of reasoning e.g. analogical reasoning

  • QCM provides a friendly interface for experimenters to enter models in two different formalisms:

    • Qualitative Process (QP) Theory (Forbus, 1984)

    • Bayesian Networks (Pearl, 1988)


Qualitative concept map system1
Qualitative Concept Map System

  • QCM uses a concept map interface (Novak & Gowin,1984)

  • QCM automatically checks for any modeling errors which violate:

    • The laws of QP theory

    • The laws of probability theory

    • providing detailed error messages

      • Eg: “Increase/Decrease can only connect a process with a rate or a parameter”

  • Types of reasoning available in QCM:

    • Limit Analysis

    • Envisionment

    • Influence Resolution

    • Probability of evidence

    • Calculation of posterior-marginal


Qualitative concept map system2
Qualitative Concept Map System

  • Graphs can be outputted in different formats:

    • GraphML (Brandes et al. 2001)

    • Predicate Calculus

    • Hugin (for Bayesian Networks)

  • QCM utilizes multiple panes to represent distinct qualitative states

    • important for capturing changes over time

  • The meta-pane allows modelers to see all the states at once

  • Vocabulary of specific processes and quantities can be easily extended

    • Expedites model creation


Qcm qp mode
QCM: QP Mode

  • QP theory as a representation language for physical phenomena includes:

    • Continuous parameters (quantities)

    • Causal relationships between them (influences)

    • Mechanisms underlying physical causality (physical processes)

  • Direct influences are modeled using I+ (≡ Increases) and I- (≡ Decreases)

    • indicate an integral connection between two parameters

  • Indirect influences are modeled by ∝Q+ (≡ Influences) and ∝Q- (≡ InfluencesOpposite)

    • indicate functional dependence between two parameters


Qcm qp mode1
QCM: QP Mode

  • Our qualitative simulator (Gizmo) has been integrated into QCM.

    • Gizmo works as the qualitative reasoning engine of QCM

  • Gizmo Mk2 is a full implementation of QP theory

  • Gizmo has been designed to be lightweight and incremental

    • to be used as a module in larger systems

  • The user has tight control over the process of qualitative simulation in Gizmo

  • Algorithms for both total and attainable envisioning are included


Automatic extraction of domain theory and scenario files
Automatic Extraction of Domain Theory and Scenario files

  • To provide support for novice modelers, the domain theory and the scenario of the model can be automatically extracted from the graph

    • a major boon to novice modelers

    • outside of computer science, experience in writing logically quantified formulae is rare

    • Being able to get results without having to first write a general domain theory helps reduce the entry barrier

  • As their models become more complex, the automatically produced models can become a starting point


Automatic extraction of domain theory and scenario files1
Automatic Extraction of Domain Theory and Scenario files

  • This extraction is performed by:

    • Going over all the nodes in the graph

    • for each node, determining the type of node it is (e.g. Entity, Process, Quantity).

    • QCM automatically obtains the required information for that type of node from the graph

    • The information is sent to Gizmo

    • If missing some required information, a detailed error message is presented to the modeler.


The food web experiment 2007
The Food Web Experiment (2007)

  • Using the QP mode of QCM to model psychological data

  • Participants were presented a scenario in nature and were asked open ended questions

    • Cultures involved:

      • Rural Menominee Native Americans, Rural European Americans

  • Examine the relationship between culture, expertise, and causal reasoning in the domain of biology

    • Qualitative Concept Maps are used for modeling and analyzing food networks

    • Analogical reasoning is used to perform automatic classification of the qualitative models


Results of the experiment
Results of the Experiment

  • Automatic classification of the models based on culture:

    • Classifying Menomonee from non-Menomonee : 64%

  • Automatic classification of the models based on the level of the expertise

    • Hunters and fishermen are considered experts within this domain

    • Classifying experts from non-experts within Menomonee models: 72.5%.

    • Classifying experts from non-experts within European American models: 52% (almost chance)

  • QCM was successful in capturing people’s metal models


Example transcript
Example Transcript

E: Do you think that the disappearance in the bears would affect other plants and animals in the forest?

S: Well, there probably be a lot more berry growth for example, because they wouldn’t be eating the berries. There probably would be a lot less maybe dead trees, because they won’t be climbing on the trees and shredding them.

E: Right, and what about animals? Do you think animals would be affected?

S: Yeah, like for example the animals that would be prey to the bear; the fish, for example, there’s a lot of fish that wouldn’t become prey. And I mentioned deer, specifically DEER, if deer are even bear food

E: How about eagles can you think of any specific way that eagles might be affected?

S: Well, eagles eat the same things that bears do, they eat fish. They might have less competition for food


A sample network
A Sample Network

Number of bears influence the eating rate

there probably be a lot more berries because they wouldn’t be eating the berries

Bears eat berries

Number of berries increase as eating rate decreases



A sample network1
A Sample Network

QCM of Fear (Khawf) as described in Jami 'al Sa'adat (18th century)



Example heat transfer domain theory
Example: Heat Transfer Domain Theory

 ;;; Processes

(defprocess heat-flow

:participants ((the-G :type G-type)

(the-F :type F-type))

:conditions ((> (temp the-G) (temp the-F)))

:quantities ((heat-flow-rate :type Rate))

:consequences ((i- (heat the-G) heat-flow-rate)

(i+ (heat the-F) heat-flow-rate)

(qprop (heat-flow-rate heat-flow)

(temp the-G))

(qprop- (heat-flow-rate heat-flow)

(temp the-F))))

;;; Quantity Functions

(defquantityfunction Rate (?thing))

(defquantityfunction heat-flow-rate (?Rate))

(defquantityfunction heat (?Amount))

(defquantityfunction Amount (?thing))

(defquantityfunction temp (?Amount))

;;; Entities

(defentity G-type

:quantities ((heat :type Amount)

(temp :type Amount))

:consequences ((qprop (temp G-type)

(heat G-type)))

:documentation "finite-thermal-physob")

(defentity F-type

:quantities ((heat :type Amount)

(temp :type Amount))

:consequences ((qprop (temp F-type)

(heat F-type)))

:documentation "finite-thermal-physob")


Bayesian mode
Bayesian Mode

  • Probabilistic models are finding wide application across the cognitive and brain sciences

    • A method for capturing and updating uncertain beliefs about the world is needed

  • Bayesian networks (Pearl, 1988): provide a succinct representation for probabilities

    • conditional probabilities can be represented and reasoned with in an efficient manner

  • Providing an interface in which both QP and Bayesian formalisms can be used in parallel can potentially be helpful for cognitive scientists.


Bayesian mode1
Bayesian Mode

  • Modelers can switch the mode of reasoning from QP to Bayesian and make probabilistic models.

  • In the Bayesian mode, modelers can perform exact inference on the network and calculate the probabilities using Recursive Conditioning (RC) (Darwiche, 2001).

  • Networks created in the Bayesian mode are saved in the Hugin format

    • The standard format for many data mining and machine learning programs


  • Determining a priori probabilities using qualitative simulations
    Determining a Priori Probabilities using Qualitative Simulations

    • One of the main obstacles in probabilistic reasoning: Finding the a priori probabilities of variables

    • One approach to overcome this obstacle is to use qualitative simulations

    • QCM uses the information available in the QP mode to calculate a priori probabilities of quantities used in the qualitative model

      • The probability distribution is defined over a set of possible worlds determined by the constraints of the qualitative model.

    • QCM can determine the probabilistic distribution of the values of parameters by model counting.

      • Can be used as a bootstrapping strategy


    Determining a priori probabilities using qualitative simulations1
    Determining a Priori Probabilities using Qualitative Simulations

    • The degree of belief in a statement over all the possible worlds determined by qualitative envisionment is calculated

      • Uniform distribution is assumed

    • For example: if (temp F) > (temp G) relationship is to be included as a node in the model:

      • QCM performs an attainable envisionment determining in how many possible worlds (temp F) β (temp G)

        • where β={<,<=,=,>=,>,?}

      • Based on this measure a probability value can be assigned to (temp F) > (temp G)

    • i.e. under the current constraints in n of m possible worlds (temp F) > (temp G)

      • the probability of (temp F) > (temp G)is n/m.


    Example
    Example Simulations


    Related work
    Related Work Simulations

    • QCM is a successor to VModel (Forbus et al. 2001)

      • VModel was developed to help middle-school students learn science.

      • Vmodel uses a subset of QP theory

      • VModel was limited to single-state reasoning

    • Similar differences hold with Betty’s Brain (Biswas et al 2001)

      • provides a domain-specific concept map environment for students

    • MOBUM (Machado & Bredeweg, 2001) and VISIGARP (Bouwer & Bredeweg 2001) which have lead to Garp3 (Bredweg et al 2006, Bredweg et al 2007).

      • Like QCM, these environments are aimed at researchers

      • their focus is on constructing models for qualitative simulation

      • QCM focuses instead on helping capture concrete, situation specific qualitative explanations of phenomena


    Related work1
    Related Work Simulations

    • Different approaches for qualitative Bayesian inference have been proposed:

      • Qualitative probabilistic networks (Wellman 1990),

      • Qualitative certainty networks (Parsons and Mamdani 1993)

      • Order of magnitude reasoning in qualitative probabilistic networks (Parsons 1995)

    • Keppens (2007a, 2007b) employs some of these methods for qualitative Bayesian evidential reasoning in the domain of crime investigation.

    • QCM integrates information available from qualitative simulations in probabilistic networks

      • whereas other approaches mostly use qualitative techniques in performing inference on Bayesian networks.


    Conclusions
    Conclusions Simulations

    • QCM provides the basic functionality needed for cognitive scientists to build, simulate and explore qualitative mental models

    • This system has been expanded in several ways since the version used in Dehghani et al. (2007):

      • It now uses Gizmo as its qualitative reasoning engine

      • Modelers can now work in a probabilistic mode

      • QCM automatically integrates qualitative information for calculating a priori probabilities of quantities

      • The interface of the system has been enhanced offering easier access to reasoning capabilities.

      • Models can now be exported in different formats facilitating collaboration between modelers.

    • QCM provides the formalism and the functionality necessary for automatic evaluation of psychological data.


    Acknowledgments
    Acknowledgments Simulations

    • This research was supported by a grant from the AFSOR.


    Our overall approach to culture research
    Our Overall Approach to Culture Research Simulations

    MoralDM: A Cognitive model of moral decision making

    EA NLU

    Interviews, surveys,& cultural stories

    collected

    Predicate calculusrepresentations ofstories, explanations

    Qualitative Concept Maps


    Our overall approach to culture research1
    Our Overall Approach to Culture Research Simulations

    Test our hypothesis by

    collecting human data

    MoralDM: A Cognitive model of moral decision making

    EA NLU

    Interviews, surveys,& cultural stories

    collected

    Predicate calculusrepresentations ofstories, explanations

    Qualitative Concept Maps


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