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  1. Knowledge-based systemsTutorial Introduction to G2 Rozália Lakner University of Veszprém Department of Computer Science

  2. Contents • Main characteristics of G2 • Main components of G2 knowledge base • Reasoning in G2 • Development of knowledge base

  3. Main characteristics of G2

  4. 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

  5. Main components of G2 knowledge base

  6. 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

  7. 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

  8. Workspaces • rectangular areas • can contain items (objects, connections, rules, …) • workspace-hierarchy • enabling/disabling workspaces • permanent/temporary workspaces

  9. 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

  10. 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) initiallyfor any container-or-vessel CV if the inventory of CV > 0 then conclude that the temperature of CV = 15

  11. Rule types 2 • Unconditional rules (rules without condition part) initiallyfor 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()

  12. 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)

  13. 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)

  14. Reasoning in G2

  15. 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

  16. 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

  17. G2 simulator • special data server in G2

  18. Development of knowledge-base

  19. 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, …)

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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, …

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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.

  30. 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 that capture the relationships between product quality and process conditions.