Rule Based Systems Introduction to Production System Architecture

Rule Based Systems Introduction to Production System Architecture 1

Rules and ‘Productions’ • ‘Production’ - a rule for changing grammar • Englishs regular verbs – regular_verb + (past) --> verb + ‘ed’ • Pig-latin productions – consontant string + consonant + ‘ay’ – vowel + string + ay • Is this enough? What do we have to add? • Can you reverse it? 2

Work through this production system to decode the result • z l u p p h lw llo q r v w r e • uqzv ? ? 3

Rules • Form – IF antecedent THEN conclusion – IF condition THEN action – IF antecedent THEN goal • Interpreters – Backward chaining » Trigger on conclusion/goal – Forward chaining » Trigger on antecedent/condition 4

Rules and Logic • Rules are really statement in logic – About what we believe to be true – About what should occur • Translation 1 - situations – IF may_rain THEN should_take_umbrella s. Situation s May_rain s Take_umbrella s “For all s, if s is a situation & s is a may_rain situation, then s is a take_umbrella situation. 5

Rules and Logic • Translation 2 - implied objects – IF may_rain THEN should_take_umbrella wp. Weather w Person p May_rain w Out_in p w Take_umbrella p “For all w and p, if w is a weather and w is may_rain weather and p is a person and p is out in w, then p (should) take an umbrella. ” • Translations: be careful of the words – The words are just symbols » They have no meaning except help human know what the symbols are for » Just like variable names - could all be x, y, z. . . • wp. W w P p M w O p w T p 6

Forward and Backward Chaining • Rules – r 1: IF may_rain THEN should_take_umbrella – r 2: IF cloudy THEN may_rain • Questions – “Should I take an umbrella? ” – “What should I do if it is cloudy? ” • How did you answer the questions? – Which part of the rule did you look for? (‘match’) 7

Backward Chaining • Rules – R 1: IF may_rain THEN should_take_umbrella – R 2: IF cloudy THEN may_rain • “Should I take an umbrella? ” – “Do the rules indicate I should take an umbrella? » Is there a rule about “taking umbrellas”? • R 1: goal: should_take_umbrella » How can I prove that goal? • What has to be true for r 1 to hold? – may_rain is the antecedent of r 1 » Can I prove that it may_rain? • R 2: goal: may_rain » How can I prove that goal 2 • What has to be true for r 2 to hold – cloudy is the antecedent of r 2 » How can I prove ‘cloudy’? 8

Backward Chaining Goal_2 Goal_3 rules Goal_3 Goal_4 Direction of reasoning Goal_1 Goal_2 Goal_4 Goal_5 Question 9

Backward Chaining • Rules – – R 1: IF may_rain THEN should_take_umbrella R 2: IF cloudy THEN may_rain R 3: IF may_be_intense_sun THEN should_take_umbrella R 4: IF summer AND in_tropics THEN may_be_intense_sun • “Should I take an umbrella? ” – “Do the rules indicate I should take an umbrella? » Is there are rule about “taking umbrellas”? • R 1: goal: should_take_umbrella » What is antecedent for r 1? • R 1: antecedent may_rain » Can I prove that it may_rain? • R 2: goal: may_rain » How can I prove may_rain • R 2: antecedent: cloudy » BUT NOT CLOUDY! 10

Backward Chaining: Backtracking • Rules – – R 1: IF may_rain THEN should_take_umbrella R 2: IF cloudy THEN may_rain R 3: IF may_be_intense_sun THEN should_take_umbrella R 4: IF summer AND in_tropics THEN may_be_intense_sun • “Should I take an umbrella? ” » Are there any other rules about umbrellas? • R 3: goal: should_take_umbrella » What is antecedent of R 3? • R 3: antecedent: summer AND in tropics 11

Goal_1 Goal_2 Goal_8 Goal 7 Goal_2 Goal_3 Goal_7 Goal_6 fail Goal_3 Goal_4 rules Goal_6 Goal_4 Goal_5 Question 12 Direction of reasoning Backwards Chaining with Backtracking

Backwards Chaining Systems • MYCIN – ‘The original expert system’ » Diagnosis of acute infections (Meningitis, blood infections) • Still a good example of how it works » Also used • uncertain reasoning • Explanation – ‘How’ did you prove that? – ‘Why’ are you asking me that? » Never used ‘for real’ • PROLOG – One of the two standard AI languages » A simple backwards chaining engine with backtracking 13

Backwards Chaining Engines usually written ‘backwards’ • Goal Antecedent – Umbrella may_rain – may_rain cloudy • Prolog ‘Edinburgh’ notation – umbrella : - may_rain. – may_rain : - cloudy. NB upper and lower case very important in Prolog • In Knowledge. Works – (defrule r 1 : backward <body>) » Or special functions ‘any’, ‘test’, and ‘not’ • Awkward - don’t try it first time. 14

Forward Chaining • Rules – R 1: IF may_rain THEN should_take_umbrella – R 2: IF cloudy THEN may_rain • “What should I do if it is cloudy? ” – “What do the rules indicate I should do if it is cloudy? ” » Is there a rule that applies when it is cloudy? • R 2: antecedent: cloudy » What do I conclude from that antecedent, ‘cloudy’ • R 2: conclusion: may_rain » Is there a rule that applies when it may_rain? • R 1: antecedent: may_rain » What do I conclude from that antecedent: ‘may_rain’ • R 1: conclusion: should_take_umbrella 15

Forward chaining ‘Production Systems’ – Vocabulary used differently on west and east coast of US for many years » On east coast, ‘production systems’ means forward chaining » On west coast, ‘production systems’ just means rule based systems – Usually, and in this course, ‘Production System’ means ‘forward chaining’ • Classic system is OPS 5 • Basic strategy of Knowledge. Works 16

Forward Chaining Fact_1 Fact_2 rules Fact_3 Fact_4 Direction of reasoning Fact_2 Fact_3 Fact_4 Fact_5 Action=Fact_5 17

Consider R 1: IF sky=cloudy THEN expect=rain R 2: IF expect=? X THEN weather=? X R 3: IF sky=cloudy AND temperature=freezing THEN expect=snow R 4: IF weather=rain THEN termperature=above_freezing What happens if ‘sky=cloudy’? What happens if ‘sky=cloudy and ‘temperature=freezing’? Conflict Resolution 18

Production system interpreter • Objectives: – Fire rules as the facts come in to the knowledge base – Never fire a rule unless its conditions are satisfied – Fire every rule whose conditions are satisfied • Are these objectives consistent. – Forward chaining rules sometimes called ‘demons’ » From a system called “Pandemonium” – How can they be made consistent? 19

Production System Strategy • All rules tested at each cycle • Only one rule fires at a time 20

Production System Cycle 1 2 3 4 5 6 Test all rules Put all rules satisfied into the ‘conflict set’ Choose one rule from the conflict set Fire the rule Update the dynamic database Repeat until goal reached or no more rules satisfied 21

Conflict Resolution R 1: IF sky=cloudy THEN expect=rain R 2: IF expect=? X THEN weather=? X R 3: IF sky=cloudy AND temperature=freezing THEN expect=snow R 4: IF weather=rain THEN termperature=above_freezing What happens if ‘sky=cloudy’? What happens if ‘sky=cloudy and ‘temperature=freezing’? 22

Possible Conflict Resolution Strategies • • • Specificity Priority Lexical Ordering Source file ordering Explicit rules for conflict resolution – a rule based system within a rule based system 23

Basic Production System Architecture Dynamic Memory execute tickle Rule Execution Rule Store Select (resolve conflicts) check satisfaction Conflict Set 24

Modules (Contexts) & Agendas • How to modularise RBSs • Agenda = Sequence of modules (Stack) • Module = Named Set of rules – (Rule MODULE: : rule_name IF. . . THEN. . . ) • Contexts popped off stack when finished. 25

Agenda/Modules Example • focus search stuck • defrule search: : r 1 IF hero is at ? Place-1 & ? Place-1 gives_access_to ? Place-2 THEN move hero to ? Place-2 • defrule stuck: : r 1 IF context stuck & hero is at ? Place not ? Place = goal THEN ask-user “What do I do now? ” 26

Expert Systems Configuring systems • defrule IF AND THEN AND BACKPLANE_STRUCTURE: : R 1 the_processor IS in place the_devices_to_mount INCLUDE ? Device mount ? Device BACKPLANE_STRUCTURE: : R 1 mount ? Device ? Slot_1 IS_A backplane_slot NOT lower_number_available_than ? Slot_1 ? Slot_2 ? Slot_1 is_occupied_by ? Device is_mounted. 27

The Structure of Heuristics • Trigger • Constraints • Antecedents • Actions • Conclusions • Control 28

Formalising Heuristics • Triggers – quick & sensitive • Constraints – quick & specific • Antecedents – complete and sufficient • Actions • Conclusions • Control 29

Formalising Heuristics • Triggers • Constraints • Antecedents • Actions – Things effecting the outside world • Conclusions – New facts for the dynamic data base • Control – changes to agenda 30