The Inference Engine Week 8 – 2 nd  Lecture October 12, 2011 -21
Inferencing <ul><li>The major activity of Inference Engine (control program) is to make inferences </li></ul><ul><li>IE is...
<ul><li>Heuristic reasoning - use of  IF…THEN type of rules  </li></ul><ul><li>Focus - commonsense related to more or less...
Aspects of Human Reasoning (cont.) <ul><li>Representation - ways of organizing pieces of information </li></ul><ul><li>Ana...
Reasoning in AI <ul><li>Deductive Reasoning </li></ul><ul><li>Inductive Reasoning </li></ul><ul><li>Analogical Reasoning <...
Deductive Reasoning <ul><li>Deducing conclusions: Moving from  general to  </li></ul><ul><li>specific </li></ul><ul><li>Ap...
Deductive Reasoning (cont.) Example 1 Major Premise: All birds have wings Minor Premise: Tweety is a bird Conclusion  : Tw...
Inductive Reasoning <ul><li>Reasoning from  specific to general </li></ul><ul><li>Uses a number of established facts or pr...
<ul><li>Sufficient to start working with a system </li></ul><ul><li>Can be used to troubleshoot or study an intelligent sy...
Example Premise: I saw a bird in Zoo Negara which can fly within the interior of the cage Premise: I heard the sound of an...
Analogical Reasoning <ul><li>Deriving  conclusions by analogy </li></ul><ul><li>A process that requires an  ability to rec...
Example: Query: What is the working hours of Engineers? The computer finds the analogy between engineers and white-collar ...
Formal Reasoning <ul><li>Logical statements(e.g: predicate calculus sentences) are manipulated to arrive at a new statemen...
Metalevel Reasoning <ul><li>Reason about what one knows(e.g.:- the importance and relevance of certain facts) </li></ul><u...
Inferencing with Rules Mainly involves use of modus ponens Most of the commercial Expert Systems use rules in inferencing ...
<ul><li>Using modes ponens, the consequent(conclusion) is accepted as TRUE. This is done by firing RULE1. </li></ul><ul><l...
Generic Strategies for Inference Engine <ul><li>Generic Strategies: </li></ul><ul><ul><li>Forward Chaining </li></ul></ul>...
Forward and backward Chaining <ul><li>Every rule in the rule base can be checked to match against a given pattern </li></u...
Forward and backward Chaining: Example Consider the following scenario: Suppose you want to fly from Denver to Tokyo and t...
Backward chaining Start with all flights that arrive at Tokyo and find the city where each flight originated. Then look up...
Forward Chaining List all flights leaving Denver and mark their destination(intermediate) cities. Then look up all the fli...
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    1. 1. The Inference Engine Week 8 – 2 nd Lecture October 12, 2011 -21
    2. 2. Inferencing <ul><li>The major activity of Inference Engine (control program) is to make inferences </li></ul><ul><li>IE is a program which is the implementation of an algorithm that controls the reasoning process </li></ul><ul><li>The control program directs the search through the Knowledge base </li></ul><ul><li>It matches patterns with problem descriptions and apply relevant rules and makes inferences </li></ul>October 12, 2011 -21
    3. 3. <ul><li>Heuristic reasoning - use of IF…THEN type of rules </li></ul><ul><li>Focus - commonsense related to more or less specific goals </li></ul><ul><li>Divide and Conquer - breaking complex problems into smaller tractable sub-problems </li></ul><ul><li>Parallelism - operation of millions of neurons at the same time </li></ul>Aspects of Human Reasoning October 12, 2011 -21
    4. 4. Aspects of Human Reasoning (cont.) <ul><li>Representation - ways of organizing pieces of information </li></ul><ul><li>Analogy - being able to associate and relate concepts </li></ul><ul><li>Synergy - the whole being able to associate and relate concepts </li></ul><ul><li>Serendipity - luck(valuable discoveries by accident - learn from experience) </li></ul>October 12, 2011 -21
    5. 5. Reasoning in AI <ul><li>Deductive Reasoning </li></ul><ul><li>Inductive Reasoning </li></ul><ul><li>Analogical Reasoning </li></ul><ul><li>Formal Reasoning(Use of Logic) </li></ul><ul><li>Procedural Reasoning(e.g.:- Model-based Reasoning) </li></ul><ul><li>Metalevel Reasoning </li></ul>October 12, 2011 -21
    6. 6. Deductive Reasoning <ul><li>Deducing conclusions: Moving from general to </li></ul><ul><li>specific </li></ul><ul><li>Application of a rule that specify a general situation </li></ul><ul><li>that allows to infer a conclusion for a specific </li></ul><ul><li>situation </li></ul><ul><li>Consists of </li></ul><ul><li>- Major premise </li></ul><ul><li>- Minor premise </li></ul><ul><li>- Conclusion </li></ul><ul><li>Reasoning starts from one of the premise statements </li></ul><ul><li>and the conclusion </li></ul>October 12, 2011 -21
    7. 7. Deductive Reasoning (cont.) Example 1 Major Premise: All birds have wings Minor Premise: Tweety is a bird Conclusion : Tweety has wings Example 2 Major Premise: I do not go to work on public holidays Minor Premise: Tomorrow is Wesak day and it is a public holiday Conclusion : I will not go to work tomorrow October 12, 2011 -21
    8. 8. Inductive Reasoning <ul><li>Reasoning from specific to general </li></ul><ul><li>Uses a number of established facts or premises to draw a general conclusion </li></ul><ul><li>There is some measure of uncertainty associated with the conclusion </li></ul><ul><li>The certainty level can be increased by considering more known specific cases </li></ul><ul><li>Conclusion may get changed when new premises are added into the system. </li></ul>October 12, 2011 -21
    9. 9. <ul><li>Sufficient to start working with a system </li></ul><ul><li>Can be used to troubleshoot or study an intelligent system </li></ul><ul><li>Note:- Deductive and Inductive reasonings are used in Logic, Rule-based systems, and Frames </li></ul>Inductive Reasoning (cont.) October 12, 2011 -21
    10. 10. Example Premise: I saw a bird in Zoo Negara which can fly within the interior of the cage Premise: I heard the sound of an explosion. All the birds sitting on the nearby tree flew towards the sky Conclusion: All birds in my world can fly October 12, 2011 -21 Inductive Reasoning (cont.)
    11. 11. Analogical Reasoning <ul><li>Deriving conclusions by analogy </li></ul><ul><li>A process that requires an ability to recognize previously encountered experiences </li></ul><ul><li>Very similar to a kind of human thinking process </li></ul><ul><li>Similar to commonsense reasoning </li></ul>October 12, 2011 -21
    12. 12. Example: Query: What is the working hours of Engineers? The computer finds the analogy between engineers and white-collar employees It is known that white-collar employees work from 9 to 5. Hence by analogy, Engineers work from 9 to 5 October 12, 2011 -21 Analogical Reasoning
    13. 13. Formal Reasoning <ul><li>Logical statements(e.g: predicate calculus sentences) are manipulated to arrive at a new statement that corresponds to a conclusion </li></ul><ul><li>Prescribed rules are followed to arrive at the conclusion or to make an inference </li></ul><ul><li>e.g.:- Mathematical Logic </li></ul>October 12, 2011 -21
    14. 14. Metalevel Reasoning <ul><li>Reason about what one knows(e.g.:- the importance and relevance of certain facts) </li></ul><ul><li>Useful for developing better systems in future </li></ul><ul><li>It helps in deciding facts like which knowledge representation to use. (e.g.:- Reasoning by analogy can be more successful with semantic nets than with frames) </li></ul>October 12, 2011 -21
    15. 15. Inferencing with Rules Mainly involves use of modus ponens Most of the commercial Expert Systems use rules in inferencing uses modus ponens e.g.:- RULE 1: IF international conflict begins THEN the price of gold goes up Assume that the ES knows international conflict just started. This info. is stored in the facts(assertion) part of the knowledge base. Ie. the premise(IF) part of the rule is TRUE October 12, 2011 -21
    16. 16. <ul><li>Using modes ponens, the consequent(conclusion) is accepted as TRUE. This is done by firing RULE1. </li></ul><ul><li>This way of inferencing is very similar to the way a human make deductions. </li></ul><ul><li>The conclusion drawn is sometimes added to the KB. </li></ul><ul><li>Testing a rule premise/conclusion may be done by pattern matching. </li></ul>October 12, 2011 -21 Inferencing with Rules (cont.)
    17. 17. Generic Strategies for Inference Engine <ul><li>Generic Strategies: </li></ul><ul><ul><li>Forward Chaining </li></ul></ul><ul><ul><li>Backward Chaining </li></ul></ul>October 12, 2011 -21
    18. 18. Forward and backward Chaining <ul><li>Every rule in the rule base can be checked to match against a given pattern </li></ul><ul><li>This can be done in two directions </li></ul><ul><ul><ul><li>Backward Chaining - if the current goal is to determine the fact in the conclusion, then the process attempts to determine whether the premise clauses match the situation - a goal-driven approach </li></ul></ul></ul><ul><ul><ul><li>Forward Chaining - if premise clauses match the situation, then the process attempts to assert the conclusion - a data-driven approach </li></ul></ul></ul><ul><li>Note:- Chaining refers to the linking of rules </li></ul>October 12, 2011 -21
    19. 19. Forward and backward Chaining: Example Consider the following scenario: Suppose you want to fly from Denver to Tokyo and there are no direct flights between these two cities. Therefore, you try to find a chain of connecting flights starting from Denver and ending in Tokyo. There are two ways you can search for this chain of flights October 12, 2011 -21
    20. 20. Backward chaining Start with all flights that arrive at Tokyo and find the city where each flight originated. Then look up all the flights arriving at those cities and find where they originated. Continue this process until you find Denver. Here you are working backward from your goal(Tokyo) and hence is a goal-driven approach. October 12, 2011 -21
    21. 21. Forward Chaining List all flights leaving Denver and mark their destination(intermediate) cities. Then look up all the flights leaving these intermediate cities and find where they land; continue this process until you find Tokyo. In this case you are working forward from Denver toward your goal(Tokyo). So this is a data driven approach. October 12, 2011 -21

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