Knowledge-based systems
Tutorial
Introduction to G2
Rozália Lakner
University of Veszprém
Department of Computer Science
Contents
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Main characteristics of G2
Main components of G2 knowledge base
Reasoning in G2
Development of knowledge base
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Main characteristics of G2
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G2 – a real-time expert system
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used for rapid prototyping and implementing expert systems
G2 possess features and properties of an expert system shell
user-friendly interfaces
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inference engine (and simulator)
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forward and backward reasoning
elements of knowledge base (items)
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well-structured natural language in a high-level
graphic-oriented environment
objects, workspaces, connections, relations, variables, parameters,
rules, procedures, functions
tools for developing knowledge base
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Main components of G2
knowledge base
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G2 - Objects
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representation of some part of the
application
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picture of each object: icon
generated manually (permanent
objects)
has a table of attributes (contains
the knowledge about the object)
object classes
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water-tank, valve, coffee-machine
attributes, icon are inherited
own specific attributes
object hierarchy
actual application objects:
instances
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Variables, parameters
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built-in object classes
represent things that have changing values
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similarities
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attributes, classes, icon, history keeping
differences
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temperature, level, …
a value of a variable may expire
a parameter always has current value (initial value)
a variable has validity interval
data-seeking sources for variable’ value
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internal data server
inference engine (backward changing)
G2 simulator
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Workspaces
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rectangular areas
can contain items (objects, connections, rules, …)
workspace-hierarchy
enabling/disabling workspaces
permanent/temporary workspaces
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Connections, relations
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connection
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relationship between objects (created manually)
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graphically links two objects together (flow-pipe, electrical wire)
represents abstract relationship (partnership, ownership)
classes of connections
objects can be referred based on connection
possible to write generic rules (any tank connected to any valve)
relation
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relationship between objects (created dynamically, „conclude” action)
classes of relations
has not graphical representation
doesn’t saved as part of a knowledge base
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Rule types 1
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If rules (common rules)
for any valve V
if the state of V = 1
then change the center stripe-color of every flow-pipe
connected to V to sky-blue
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When rules (cannot be used in reasoning)
for any container-or-vessel CV
when the value of the inventory of CV = 0
then conclude that the temperature of CV has no value
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Initial rules (invoked when KB starts or restarts)
initially for any container-or-vessel CV
if the inventory of CV > 0
then conclude that the temperature of CV = 15
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Rule types 2
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Unconditional rules (rules without condition part)
initially for any valve V
unconditionally conclude that the state of V = 0
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Whenever rules (event-controlled rules)
whenever auto-manual-state receives a value and
when the value of auto-manual-state is auto
then start auto()
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Main attributes of rules
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options (how can use the rule)
scan interval (how often to
invoke the rule)
rule priority (in case of
overloading)
depth-first backward chaining
precedence (conflict
resolution)
timeout for rule completion
(how long G2 may try to
evaluate the condition part)
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Procedures
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sequence of operations
executed by G2
like high-level procedural
languages
main part of procedures
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procedure header (name,
typed argument list, return
type)
local declarations
procedure body (begin,
sequence of procedure
statements, end)
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Reasoning in G2
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Real-time inference engine 1
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functions of inference engine (IE):
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reasons about the current state of the application
communicates with the end-user
iniciates other activity based upon what it has inferred
IE operates on the following sources of information:
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the knowledge contained in the knowledge base
simulated values
values received from sensors and other external sources
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Real-time inference engine
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abilities of IE:
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scan rules: repeatedly invoke a rule at regular time interval (scan
interval)
wakeup rules: when a variable receives a value, the inference engine
wakes up the rule that was waiting for the value of the variable
data seeking: get value from the specified data server (when the
value of the variable is expired)
chaining the rules (reasoning)
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backward chaining: IE infers the value of a variable with the help of rules
(when the value of a variable is not given by a sensor or a formula)
forward chaining: IE invoke a rule when its condition part is satisfied by
another rule
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G2 simulator
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special data server in G2
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Development of knowledgebase
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Developer interface
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graphic-oriented environment
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creating the model of the
application graphically
(schematic)
objects are represented by
icons
objects are placed in
workspaces
objects are connected
graphically
pop-up menus for objects
(attribute table, delete, change
size and colour, move, …)
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Developer interface 2
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well-structured natural language in a high-level
referring to an item:
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by name: coffee-machine
by class name: the vessel
as an instance of a class is nearest to another item on schematic:
the level-icon nearest to coffee-machine
as an instance of a class that is connected to an object by an
input or output connection: the valve connected at the output of
coffee-machine
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Developer interface 3
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interactive text editor
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text-edit workspace
inserting text from other
items or scrapbook
syntax-checking
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marking incorrect text
warning message
suggestion for correction
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Developer interface
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interactive icon editor
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graphic tool
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design icons graphically
converting into G2
grammar
layers, regions
main parts of icon editor
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icon view
buttons for creating
graphic elements
icon size display
cursor location display
layer pad and layer
display
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Developer interface 5
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tools for managing large KBs
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clone objects and statements
operate on a group of objects
inspect utility (browse KB) –
finding items easy
describe facility (informations
about item) – data server, rules
organize knowledge
hierarchically (workspaces,
subworkspaces, activate/
deactivate)
merge KBs
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Developer interface 6
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documentation in KB: free texts (only for documentation, is not part of KB)
tracing and debugging facilities
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warning messages (errors, unusual conditions)
trace messages
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current value of variable, expression (each time it receives one)
starting and finishing time of evaluating of variable, formula, rule, procedure, function
set breakpoints
highlight invoked rules
access control facilities
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restrict the choices a user has on the menus
restrict moving items, making connections, …
restrict accessing to the attribute table
restrict editing of attributes
mode of operation (specify restrictions): operator, administrator, developer, …
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User interface 1
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displays
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end-user controls
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screen items showing the value of variable, parameter, expression
control an application by the user
messages, message board
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items that display text
are used for communication
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User interface
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displays
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readout table
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chart
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value of variable in a round scale
freeform table
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value of variable in a vertical
display
dial
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plots of one or more variables
history of values change over time
meter
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variable, parameter and its value
tabular form of variable’s values
end-user controls
messages, message board
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User interface
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end-user controls
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action buttons
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radio buttons
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assign „on” or „off” value for
variable or parameter
slider
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assign a predefined value for
variable or parameter
check box
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execute an action (start, conclude,
show, …)
enter numeric value for variable or
parameter by sliding a pointer
type-in box
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enter a value for variable or
parameter from keyboard
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G2 – Aplication examples
ABB Power -- expert monitoring and diagnostics of power plant processes
Ashland Petroleum -- expert monitoring and optimization of energy systems.
Ford Motor Company -- expert control of flexible manufacturing systems.
Lafarge -- expert control of cement kilns for improved throughput, reduced
energy costs, and reduced equipment maintenance. =>25 plants
Petrobras -- expert operator advisory systems for optimizing power generation
and distribution.
Seagate Technology -- expert monitoring, diagnosis, and operator advice
improves yields of disk-drive manufacturing.
Shell Expro -- expert optimization pumps up oil field production.
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http://www.gensym.com/manufacturing/g2_success.shtml
G2/ Intelligent Objects
Knowledge modules for monitoring and operation of process equipment:
•Fired Heaters
•Compressors
•Columns
•Treaters
•Pumps
•Heat Exchangers
•Sensors
•Analyzers
•Controllers
•Tanks
•Vessels
Intelligent Objects deliver
configurable equipment
knowledge out-of-the-box, and
can be readily extended for
plant-specific requirements.
Proactive Detection of Equipment Problems - Intelligent Objects proactively monitor
equipment conditions to detect problems early and alert operators to take action - before the
problem reaches the alarm limits of a traditional process control system
Rapid Deployment - Deployment time for a first Intelligent Object is rapid - it can typically
be ready to go online within weeks for complex equipment, such as a fired heater or a
compressor, and in days for basic equipment, such instruments, vessels, heat exchangers,
or controllers.
Unit and Plant Wide Diagnostic Capability - Intelligent Objects can work together to 31
provide automated diagnosis of process problems that are impacting an entire unit or plant.
Optegrity
Optegrity is a platform from Gensym for rapidly developing and deploying abnormal
condition management applications in the process manufacturing industries
Applications built on the Optegrity platform work in real time using information from
existing control systems, data historians and databases to:
•Proactively monitor process conditions throughout a production unit or plant to
detect problems early in order to avoid or minimize disruptions
•Analyze, filter and correlate alarms to speed up operator responses
•Rapidly isolate the root cause of unit and plant wide problems to accelerate
resolution
•Guide operators through recovery to enhance safety levels while effectively
responding to problems
•Predict the impact of process disruptions so operators can prioritize actions
NeurOn-Line
Gensym's NeurOn-Line platform delivers neural network applications that improve process
performance by predicting quality and process conditions in real time. With NeurOn-Line,
engineers quickly build and deploy neural network models based on historical process data
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that capture the relationships between product quality and process conditions.