expert systems

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expert systems

  1. 1. Application of expertsystem in roadtransportByASHISH BODHANKAR 2010B4A2594HVARUN TUMATI 2010B3AB663PBHARGAV DUTT 2010B2A2304P
  2. 2. Contents OUTLINE 1 3 EXPERT SYSTEM INTRODUCTION 2 THE DESIGN OF A RULE BASED EXPERT 3 SYSTEM DEVELOPMENT OF AN EXPERT SYSTEM 4 ADVANTAGES OF AN EXPERT SYSTEM 5 APPLICATION OF EXPERT SYSTEMS IN 6 NAVATA
  3. 3. DefinitionAn expert system is a computer system that emulates the decision making ability of a human expert.Expert system are designed to solve complex problems by reasoning about knowledge like an expert.
  4. 4. Expert System IntroductionHuman experts are able to perform at a successful level because they know a lot about their areas of expertise.An Expert System use knowledge specific to a problem domain to provide “expert quality” performance in that application area.As with skilled humans, expert systems tend to be specialists, focusing on a narrow set of problems.
  5. 5. Expert System IntroductionBecause of their heuristic, knowledge intensive nature, expert systems generally:  Support inspection of their reasoning processes.  Allow easy modification in adding and deleting skills from knowledge base.  Reason heuristically, using knowledge to get useful solutions.
  6. 6. Expert System IntroductionExpert systems are built to solve a wide range of problems in domain such as medicine, math, engineering, chemistry, geology, computer science, business, low, defense and educationThese programs address a variety of problems, the following list is a summary of general expert system problem categories:
  7. 7. Expert System IntroductionInterpretation --- forming high-level conclusions from collections of raw data.Prediction --- projecting probable consequences of given situations.Diagnosis --- determining the cause of malfunctions based on observable symptoms.
  8. 8. Expert System IntroductionDesign --- finding a configuration of system components that meets performance goals while satisfying a set of design constrains.Planning --- devising a sequence of actions that will achieve a set of goals given starting conditions and runtime constrains.
  9. 9. The Design of Rule-Based ExpertSystem• architecture of a typical expert system for a particular problem domain.
  10. 10. The Design of Rule-Based ExpertSystemThe hear of the expert system is the knowledge base, which contains the knowledge of a particular application domain.In a rule-based expert system, this knowledge is most often represented in the form of if…then…In the figure, the knowledge base contains both general and case-specific information.
  11. 11. The Design of Rule-Based ExpertSystem The inference engine applies the knowledge to the solution of actual problems. It is important to maintain this separation of the knowledge and inference engine because:  Makes it possible to represent knowledge in a more natural fashion.  Expert system builder can focus on capturing and organizing problem- solving knowledge than the details of code implementation.  Allow change to be made easily.  Allows the same control and interface software to be used in different systems.
  12. 12. Development Of An Expert SystemPhase 1: Project initialisation  Problem definition.  Needs assessment.  Evaluation of alternative solutions.  Verification that an ES approach is appropriate.  Consideration of management issues.
  13. 13. Development Of An Expert SystemComment on Phase 1:  its important to discover what problem/problems the client expects the system to solve for them, and what their real needs are. The problem may very well be that more knowledge is needed in the organisation, but there may be other, better ways to provide it.  Management issues include availability of finance, legal constraints, and finding a champion in top management.
  14. 14. Development Of An Expert SystemPhase 2: System analysis & design  Produce conceptual design  Decide development strategy  Decide sources of knowledge, and ensure co-operation  Select computer resources  Perform a feasibility study  Perform a cost-benefit analysis
  15. 15. Development Of An Expert SystemComment on Phase 2:  the conceptual design will describe the general capabilities of the intended system, and the required resources.
  16. 16. Development Of An Expert SystemPhase 3: Prototyping  Build a small prototype  Test, improve and expand it  Demonstrate and analyse feasibility  Complete the design
  17. 17. Development Of An Expert SystemComments on Phase 3:  Its important to establish the feasibility (economic, technical and operational) of the system before too much work has been done, and its easier to do this if a prototype has been built.
  18. 18. Development Of An Expert SystemPhase 4: System development  Build the knowledge base  Test, evaluate and improve the knowledge base  Plan for integration
  19. 19. Development Of An Expert SystemComments on Phase 4:  The evaluation of an expert system (in terms of validation and verification) is a particularly difficult problem.
  20. 20. Development Of An Expert SystemPhase 5: Implementation  Ensure acceptance by users  Install, demonstrate and deploy the system  Arrange orientation and training for the users  Ensure security  Provide documentation  Arrange for integration and field testing
  21. 21. Development Of An Expert SystemComments on Phase 5:  If the system is not accepted by the users, the project has largely been a waste of time.  Field testing (leading to refinement of the system) is essential, but may be quite lengthy.
  22. 22. Development Of An Expert SystemPhase 6: Post-implementation  Operation  Maintenance  Upgrading  Periodic evaluation
  23. 23. Development Of An Expert System Comments on Phase 6:  A person or group of people must be put in charge of maintenance (and, perhaps, expansion). They are responsible for correcting bugs, and updating the knowledgebase. They must therefore have some knowledge engineering skills.  The system should be evaluated, once or twice a year, in terms of its costs & benefits, its accuracy, its accessibility, and its acceptance.
  24. 24. Rule-Based Expert SystemRule based expert system represent problem-solving knowledge as if…then…It is one of the oldest techniques for representing domain knowledge in an expert system.It is also one of the most natural and widely used in practical and experimental expert system.
  25. 25. Rule-Based Expert SystemIn a goal-driven expert system, the goal expression is initially placed in working memory  The system matches rule conclusions with the goal, selecting one rule and placing its premises in the working memory.  This corresponds to a decomposition of the problems’ goal into simpler sub goals.  The process continues in the next iteration of the production system, with these premises becoming the new goals to match.
  26. 26. Advantages of a rule basedexpert system Natural knowledge representation. An expert usually explains the problem solving procedure with such expressions as this: “in such-and-such situation, I do so- and-so”. These expressions can be represented quite naturally as IF-THEN production rules. Uniform structure. Production rules have the uniform IF- THEN structure. Each rule is an independent piece of knowledge. The very syntax of production rules enables them to be self-documented.
  27. 27. Advantages of a rule basedexpert systemDealing with incomplete and uncertain knowledge. Most rule-based expert systems are capable ofrepresenting and reasoning with incomplete anduncertain knowledge.
  28. 28. A Unreal Expert System ExampleRule 1: if the engine is getting gas, and the engine will turn over, then the problem is spark plugs.Rule 2: if the engine does not turn over, and the lights do not come on then the problem is battery or cables.Rule 3: if the engine does not turn over, and the lights do come on then the problem is the starter motor.Rule 4: if there is gas in the fuel tank, and there is gas in the carburetor. then the engine is getting gas.
  29. 29. The production system at the start of a consultationin the car diagnostic example.
  30. 30. The production system at the start of a consultationin the car diagnostic example.Three rules match with this expression in working memory: rule 1, 2, and 3.If we resolve conflicts in favor of the lowest- numbered rule, then rule 1 will fire.This cause X to be bound to the value spark plugs and the premises of rule 1 to be placed in the working memory.
  31. 31. The production system after Rule 1has fired.
  32. 32. The production system after Rule 1has fired.Note that there are two premises to rule 1, both of which must be satisfied to prove the conclusion true.So now we need to find out whether  The engine is getting gas, and  The engine will turn over.We may then fire rule 4 for whether “The engine is getting gas”.
  33. 33. The system after Rule 4 has fired. Notethe stack-based approach to goalreduction.
  34. 34. The and/or graph searched in the car diagnosisexample, with the conclusion of Rule 4 matching thefirst premise of Rule 1.
  35. 35. Explanation And Transparency InGoal-driven ReasoningThe following dialogue begins with the computer asking the user about the goals present in the working memory:  Gas in fuel tank? YES  Gas in carburetor? YES  Engine will turn over? WHY
  36. 36. Explanation And Transparency InGoal-driven Reasoning In general, the two questions answered by rule-based expert system are WHY? and HOW? WHY means “why did you ask for that information”  The answer is the current rule that the production system is attempting to fire. HOW means “How did you get the result”  The answer is the sequence of rules that were used to conclude a goal.
  37. 37. Explanation And Transparency InGoal-driven Reasoning The following dialogue begins with the computer asking the user about the goals present in the working memory:  Gas in fuel tank? YES  Gas in carburetor? YES  Engine will turn over? WHYIt has been established that:1. The engine is getting gas,2. The engine will turn over, (we need to know)So that we can make the conclusion that“Then the problem is the spark plugs.”
  38. 38. Explanation And Transparency InGoal-driven ReasoningGas in fuel tank?YesGas in carburettor?YesEngine will turn over?WhyIt has been established that:1. The engine is getting gas,2. The engine will turn over,Then the problem is the spark plugs.How the engine is getting gasThis follows from rule 4:ifgas in fuel tank, andgas in carburettorthenengine is getting gas.gas in fuel tank was given by the user.gas in carburettor was given by the user .
  39. 39. Data-driven ReasoningThe previous example exhibits goal-driven search. The search was also depth-first search.Breadth-first search is more common in Data Driven reasoning.The algorithm for this category is simple: compare the contents of working memory with the conditions of each rule in the rule base according to the order of the rules.
  40. 40. Data-driven ReasoningIf a piece of information that makes up the premise of a rule is not the conclusion of some other rule,then that fact will be deemed “askable”.For example: the engine is getting gas is not askable in the premise of rule 1
  41. 41. A Unreal Expert System ExampleRule 1: if (not askable) the engine is getting gas, and the engine will turn over, then the problem is spark plugs.Rule 2: if the engine does not turn over, and the lights do not come on then the problem is battery or cables.Rule 3: if the engine does not turn over, and the lights do come on then the problem is the starter motor.Rule 4: if there is gas in the fuel tank, and there is gas in the carburettor. then the engine is getting gas.
  42. 42. Data-Driven Reasoning
  43. 43. Data-Driven ReasoningThe premise, the engine is getting gas is NOT askable, so rule 1 fails and continue to rule 2.The engine does not turn over is askable.Suppose the answer to this query is false, so “the engine will turn over” is placed in working memory.
  44. 44. The production system after evaluatingthe first premise of Rule 2, which thenfails.
  45. 45. The production system after evaluatingthe first premise of Rule 2, which thenfails.Rule 2 fails, since the first of two AND premises is false, we move to rule 3.Where rule 3 also fails.So finally, we move to rule 4.
  46. 46. The data-driven production system afterconsidering Rule 4, beginning its secondpass through the rules.
  47. 47. The data-driven production system afterconsidering Rule 4, beginning its secondpass through the rules.At this point, all the rules have been considered.With the new contents of working memory, we consider the rules in order for the second round.
  48. 48. Advantages of Expert SystemPermanence - Expert systems do not forget, but human experts may.Reproducibility - Many copies of an expert system can be made, but training new human experts is time- consuming and expensive.Completeness - An expert system can review all the transactions, a human expert can only review a sample.
  49. 49. Advantages of Expert SystemCompleteness - An expert system can review all the transactions, a human expert can only review a sample.Breadth - The knowledge of multiple human experts can be combined to give a system more breadth that a single person is likely to achieve.Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making.
  50. 50. Advantages of Expert SystemEfficiency - can increase throughput and decrease personnel costs  Although expert systems are expensive to build and maintain, they are inexpensive to operate.  Development and maintenance costs can be spread over many users.  The overall cost can be quite reasonable when compared to expensive and scarce human experts. Cost-savings: Wages - (elimination of a room full of clerks)
  51. 51. When to Use Expert SystemsDevelop an expert system if it can do any of thefollowing: Provide a high potential payoff or significantly reduce downside risk. Capture and preserve irreplaceable human expertise. Solve a problem that is not easily solved using traditional programming techniques. Develop a system more consistent than human experts.
  52. 52. When to Use Expert Systems Provide expertise needed at a number of locations at the same time or in a hostile environment that is dangerous to human health. Provide expertise that is expensive or rare. Develop a solution faster than human experts can Provide expertise needed for training and. development to share the wisdom and experience of human experts with a large number of people.
  53. 53. The Application Of Expert Systems Its applications spread in a wide range i.e. inindustrial and commercial problems etc.Diagnosis and troubleshooting of devices and system of all kindsPlanning and schedulingConfiguration of manufactured objectsFinancial decision makingKnowledge publishingProcess monitoring and control
  54. 54. Application Of Expert System InNavataExpert system has many applications at navata:i. Helpful for new recruitments.ii. Fast response in solving problems.iii. Assists in decision making.iv. Increased reliability.v. Multiple expertise.
  55. 55. Transshipment Section At NavataThe list of departments under the transshipmentsection-Loading & Unloading sectionAccounts section.Dispatch section.Invoice section.
  56. 56. www.themegallTransshipment section ery.com Loading & Unloading section Accounts Section Dispatch Section Invoice section
  57. 57. Loading & Unloading SectionGoods are loaded/unloaded in this section.Load sheets and unload sheets are prepared.The lorry driver is given an invoice and a waybill(Lorry Receipt) that he has to carry with him.This data is entered into the waybill and invoice.
  58. 58. www.themegallery.com Article damage Damage could have Damage could have been done while been done during loading/unloading transport The good will be The good will be replaced and the replaced,company hammali will be pays the price. charged.
  59. 59. www.themegallery.com Excess/shortage of articles If any two parties have same type of article then due to the mistake of hamalis excess/shortage takes place The customer produces the consignment copy and the company delivers the good to correct party
  60. 60. www.themegallery.com Delay in deliveryDue to misplacement Due to bandhs and Due to vehicle of goods riots breakdown The vehicle is The vehicle is halted and regular repaired and then process starts after the goods are the bandh delivered
  61. 61. www.themegall ery.com Misplacement of goods Short Discrepancy Good loaded inloading in LR wrong vehicleThe customer contacts The supervisor checks The company verifies the excess articles the loading sheet and the LR and contacts section and produces the good is loaded in the customerthe consignment copy the correct vehicle
  62. 62. Dispatch SectionThis section receives the waybills and receipts from the load/unload section and passes to the transshipment computer section.It receives the receipts from the drivers and monitor their work.
  63. 63. www.themegall ery.com Problems in Dispatch section Less number Less staff of vehicles LR mistake Excess kilometers Excess shift Vehicles with run by the vehicle for the repairs are due to the mistake is credited into theworking staff used personal account
  64. 64. Invoice SectionThis section receives the invoice from the lorry drivers.Invoice sheets are entered here.All the offline information regarding invoice is made online.
  65. 65. www.themegall ery.com If the reason is justifiable nothing is done Driver and theDiscrepency in the invoice agent are contacted If proper reason is not given driver/agent should pay the penalty
  66. 66. Cons of Expert SystemEvery system has it’s pros and cons, coming to the expert system : Common sense - In addition to a great deal of technical knowledge, human experts have common sense. It is not yet known how to give expert systems common sense. Creativity - Human experts can respond creatively to unusual situations, expert systems cannot.
  67. 67. Cons of Expert System Degradation - Expert systems are not good at recognizing when no answer exists or when the problem is outside their area of expertise. Sensory Experience - Human experts have available to them a wide range of sensory experience; expert systems are currently dependent on symbolic input. Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated.
  68. 68. Conclusion Expert will retire in a few years taking his expertise with him. So, the company needs to develop an expert system to diagnose the difficult problems. The system can also be used to provide training to the new recruitments
  69. 69. ConclusionIt fit the needs of the individual learner by guiding him in various prospects.Todays powerful PCs are starting to put such trainers, called ICAI (Intelligent Computer Assisted Instruction) systems, within everybodys reach.

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