Constructive Problem Solving Combining solution elements according - - PowerPoint PPT Presentation

Constructive Problem Solving

• Combining solution elements according to some constraints
• Typical constructive problem solving tasks: planning,

design, and certain kinds of diagnosis

• Planning: solution elements are actions, and solutions are

sequences of actions that achieve goals based on constraints

• f space and time.
• Design: solution elements are components, and solutions

are combinations of components to form a complex object that satisfies certain physical constraints.

• Diagnosis of multiple disorders: solution elements are

disorders, and solutions are sets of disorders which cover symptoms subject to constraints to minimize interactions among disorders.

R1/XCON

• R1: a program that configures VAX computer systems by

first checking the order is complete and then determining spatial arrangement of components.

• XCON: commercial version of R1
• In contrast to MYCIN which is hypothesis driven, R1 is

data driven

• R1 is implemented in OPS5, a forerunner of CLIPS.
• R1 needs two kinds of knowledge

– About components – About constraints

• R1 contains 10,000 rules, many of which deal with domain

specific control knowledge

R1’s Configuration Subtasks

• Check the order and rectify it, if necessary.
• Configure the CPU
• Configure the unibus modules
• Configure the paneling
• Generate a floor plan
• Do the cabling

Contexts for imposing Task Structure

• In addition to information about components and

constraints, R1’s working memory contains context symbols to determine the order of subtasks.

• Specificity Strategy:
• MEA Strategy: Stresses the first condition of a rule when

evaluating rule instantiation for possible execution.

Rule 1 If: X is a bird and X is a penguin Then: X cannot fly Rule 2 If: X is a bird Then: X can fly dominates

MOLGEN

• MOLGEN assists the geneticist in planning gene-cloning

experiments in molecular genetics. The system uses knowledge about genetics and the user’s goal to create an abstract plan and then refines it to a set of specific laboratory steps.

• MOLGEN adopts a multi-layered approach to construction

problem with a Least Commitment Strategy.

• MOLGEN uses three “planning spaces”, each with its own
• bjects and operators, which communicate with each other

via message-passing protocols.

MOLGEN’s Planning Spaces

Strategy steps

Focus Resume Guess Undo

Design steps

Check-Prediction Refine-Operator Propose-Goal

Lab steps

Sort Screen Merge Transform

INTERPRETER META-PLANNING PLANNING

Strategy space Design space Lab space

Constraint Operations in MOLGEN for Constructive Problem Solving

• Constraint Formulation creates constraints that limit the

solution space.

• Constraint propagation passes information between

subproblems which are almost independent of each other.

• Constraint satisfaction pools constraints from subproblems

as the details of the design are worked out.

Hierarchical Hypothesis and Test

• Combination of heuristic classification and constructive

problem solving

• Combining solution elements into a composite hypothesis
• Reasoning with an explicit taxonomic representation of the

hypothesis space which is usually a tree whose leaves are solution elements

• Beneficial when solution space is potentially very large
• An example of hierarchical hypothesis and test: CENTAUR

CENTAUR

• A reconstructed medical expert system from PUFF originally

implemented in EMYCIN.

• PUFF’s task is to determine presence of lung disease from

measurements of amount of gas in the lungs and the rates of flow of gases in and out of the lungs.

• CENTAUR contains a frame-like structure as representation
• f context for reasoning with production rules.

Frame Structure in CENTAUR

Name: Range of values: Important measure: Procedure: rule set Obstructive Airways Disease Superclass: Pulmonary-Disease Subtype: Bronchitis Severity: Moderately Severe Component 1 Component 2 . . . . Component N Control: meta-knowledge Fact 1 . . . . Fact M . . . . . . . . . PROTOTYPE:

Rules Embedded in Prototypes

• Prototype components represent domain specific object-

level knowledge

• Components are values of slots in the disease prototypes

but they are prototypes in their own right

• Thus prototypes are embedded in other prototypes
• Also, rules are embedded in prototypes
• Five kinds of rules in CENTAUR

– Inference rules – Triggering rules – Fact-residual rules – Refinement rules – Summary rules

Network of Prototypes in CENTAUR

CONSULTATION PULMONARY-DISEASE RESTRUCTIVE LUNG DISEASE OBSTRUCTIVE AIRWAYS DISEASE DIFFUSION DEFECT MILD MOD MOD- SEVERE SEVERE ASTHMA BRONCHITIS EMPHYSEMA SEVERITY SUBTYPE