Qcm a qp based concept map system
This presentation is the property of its rightful owner.
Sponsored Links
1 / 29

QCM: A QP-Based Concept Map System PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

QCM: A QP-Based Concept Map System

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

QCM: A QP-Based Concept Map System

Morteza Dehghani and Kenneth D. Forbus


  • 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

  • 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 (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 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 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

  • 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 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

  • 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 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)

  • 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

  • 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

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

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 Network

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

Example: Heat Transfer

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

  • 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 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

    • 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 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.


    Related Work

    • 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 Work

    • 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.


    • 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.


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

    Our Overall Approach to Culture Research

    MoralDM: A Cognitive model of moral decision making

    EA NLU

    Interviews, surveys,& cultural stories


    Predicate calculusrepresentations ofstories, explanations

    Qualitative Concept Maps

    Our Overall Approach to Culture Research

    Test our hypothesis by

    collecting human data

    MoralDM: A Cognitive model of moral decision making

    EA NLU

    Interviews, surveys,& cultural stories


    Predicate calculusrepresentations ofstories, explanations

    Qualitative Concept Maps

  • Login