MODELER: Incorporating experiences to support model formulation—A case-based planning approach

doi:10.1016/0957-4174(93)90017-Z

Expert Systems with Applications

Volume 6, Issue 1, January–March 1993, Pages 37-56

https://doi.org/10.1016/0957-4174(93)90017-Z Get rights and content

Abstract

Model formulation is key to most mathematical modeling tactics. The enormous amount of time needed to formulate problems is well documented. In this article, we focus on the three elements of model formulation—experience, planning, and opportunism. These elements are used in the implementation of MODELER, a case-based planning system that integrates case-based reasoning methods and an opportunistic control in a blackboard data architecture. An analysis of expert verbalizations provided significant inputs to the design of MODELER.

References (50)

R. Bhaskar et al.
Problem solving in semantically rich domains: An example from engineering thermodynamics
Cognitive Science
(1977)
J.S. Brown et al.
Multiple representation of knowledge for tutorial reasoning
M.T.H. Chi et al.
Categorization and representation of physics problems by experts and novices
Cognitive Science
(1981)
B. Hayes-Roth et al.
A cognitive model of planning
Cognitive Science
(1979)
B. Hayes-Roth et al.
A blackboard architecture for control
Artificial Intelligence
(1985)
J.L. Kolodner
Towards an understanding of the role of experience in the evolution from novice to expert
International Journal of Man-Machine Studies
(1983)
F.H. Murphy et al.
An intelligent system for formulating linear programs
Decision Support Systems
(1986)
J.W. Tanaka et al.
Object categories and expertise: Is the basic level in the eye of the beholder?
Cognitive Psychology
(1991)
G. Wright et al.
Eliciting and modelling expert knowledge
Decision Support Systems
(1987)
T.M. Alexander
John Dewey's theory of art, experience, and nature: The horizons of feeling
(1987)

R.W. Blanning

Data management and model management: A relational synthesis

M. Binbasioglu

Process based reconstructive approach to model building

R.H. Bonczek et al.

A generalized decision support system using predicate calculus and network data base management

Operations Research

(1981)

J. Carbonell

Derivational analogy: A theory of reconstructive problem solving and expert acquisition

P.P. Chen

The entity-relationship model: Toward a unified view of data

ACM Transactions on Database Systems

(1976)

J. Choobineh

A data model and sublanguage for representation and formulation of linear mathematical models (Working Paper)

(1989)

G.B. Dantzig

Linear programming and extensions

(1963)

A. Dutta et al.

An artificial intelligence approach to model management in decision support systems

IEEE Computer

(1984)

K.A. Ericsson et al.

Protocol analysis: Verbal reports as data

(1984)

L.D. Erman et al.

The Hearsay-II speech understanding system: Integrating knowledge to resolve uncertainty

Computing Surveys

(1980)

K.Q. Gallagher et al.

GBB reference manual: GBB Version 1.2

A.M. Geoffrion

An introduction to structured modelling

Management Science

(1986)

A.M. Geoffrion

SML: A model definition language for structured modeling

K.J. Hammond

Opportunistic memory: Storing and recalling suspended goals

K.J. Hammond

Case-based planning: Viewing planning as a memory task

(1989)

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Citation Excerpt :
Case-based reasoning was also used in the model formulation domain. Vellore, Vinze and Sen (1993) proposed a CBR system called Modeler for model formulation which transforms a problem description into a suitable representation or form that is interpretable and executable by available tools. The authors proposed a case-based cognitive model of the planning process in an attempt to integrate experiences with planning.
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It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.
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MODELER: Incorporating experiences to support model formulation—A case-based planning approach

Abstract

Cognitive Science

Cognitive Science

Cognitive Science

Artificial Intelligence

International Journal of Man-Machine Studies

Decision Support Systems

Cognitive Psychology

Decision Support Systems

John Dewey's theory of art, experience, and nature: The horizons of feeling

Data management and model management: A relational synthesis

Process based reconstructive approach to model building

A generalized decision support system using predicate calculus and network data base management

Operations Research

Derivational analogy: A theory of reconstructive problem solving and expert acquisition

The entity-relationship model: Toward a unified view of data

ACM Transactions on Database Systems

A data model and sublanguage for representation and formulation of linear mathematical models (Working Paper)

Linear programming and extensions

An artificial intelligence approach to model management in decision support systems

IEEE Computer

Protocol analysis: Verbal reports as data

The Hearsay-II speech understanding system: Integrating knowledge to resolve uncertainty

Computing Surveys

GBB reference manual: GBB Version 1.2

An introduction to structured modelling

Management Science

SML: A model definition language for structured modeling

Opportunistic memory: Storing and recalling suspended goals

Case-based planning: Viewing planning as a memory task