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Knowledge-based systemsTutorial
Introduction to G2
Rozália Lakner
University of Veszprém
Department of Computer Science
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Contents
Main characteristics of G2 Main components of G2 knowledge base Reasoning in G2 Development of knowledge base
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G2 – a real-time expert system
used for rapid prototyping and implementing expert systems G2 possess features and properties of an expert system shell user-friendly interfaces
well-structured natural language in a high-level graphic-oriented environment
inference engine (and simulator) forward and backward reasoning
elements of knowledge base (items) objects, workspaces, connections, relations, variables, parameters,
rules, procedures, functions
tools for developing knowledge base
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G2 - Objects representation of some part of the
application water-tank, valve, coffee-machine
picture of each object: icon generated manually (permanent
objects) has a table of attributes (contains
the knowledge about the object) object classes
attributes, icon are inherited own specific attributes
object hierarchy actual application objects: instances
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Variables, parameters
built-in object classes represent things that have changing values
temperature, level, …
similarities attributes, classes, icon, history keeping
differences 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 internal data server inference engine (backward changing) G2 simulator
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Workspaces
rectangular areas can contain items (objects, connections, rules, …) workspace-hierarchy enabling/disabling workspaces permanent/temporary workspaces
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Connections, relations
connection relationship between objects (created manually)
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 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
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
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
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
Unconditional rules (rules without condition part)initially for any valve V
unconditionally conclude that the state of V = 0
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 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 sequence of operations
executed by G2 like high-level procedural
languages main part of procedures
procedure header (name, typed argument list, return type)
local declarations procedure body (begin,
sequence of procedure statements, end)
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Real-time inference engine 1
functions of inference engine (IE): 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: 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
abilities of IE: 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)
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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Developer interface graphic-oriented environment
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
well-structured natural language in a high-level referring to an item:
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 interactive text editor
text-edit workspace inserting text from other
items or scrapbook syntax-checking
marking incorrect text warning message suggestion for correction
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Developer interface interactive icon editor
graphic tool design icons graphically converting into G2
grammar layers, regions
main parts of icon editor 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 tools for managing large KBs
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 documentation in KB: free texts (only for documentation, is not part of KB) tracing and debugging facilities
warning messages (errors, unusual conditions) trace messages
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 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 displays
screen items showing the value of variable, parameter, expression end-user controls
control an application by the user messages, message board
items that display text are used for communication
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User interface displays
readout table variable, parameter and its value
chart plots of one or more variables history of values change over time
meter value of variable in a vertical
display dial
value of variable in a round scale freeform table
tabular form of variable’s values end-user controls messages, message board
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User interface end-user controls
action buttons execute an action (start, conclude,
show, …) radio buttons
assign a predefined value for variable or parameter
check box assign „on” or „off” value for variable
or parameter slider
enter numeric value for variable or parameter by sliding a pointer
type-in box 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.
http://www.gensym.com/manufacturing/g2_success.shtml
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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 provide automated diagnosis of process problems that are impacting an entire unit or plant.
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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-LineGensym'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 that capture the relationships between product quality and process conditions.