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Multistrategy Rule Refinement

CS 785, Fall 2001. Multistrategy Rule Refinement. Gheorghe Tecuci tecuci@cs.gmu.edu http://lalab.gmu.edu/. Learning Agents Laboratory Department of Computer Science George Mason University. Overview. The rule refinement method. Integrated modeling, learning, and solving.

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Multistrategy Rule Refinement

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  1. CS 785, Fall 2001 Multistrategy Rule Refinement Gheorghe Tecuci tecuci@cs.gmu.eduhttp://lalab.gmu.edu/ Learning Agents LaboratoryDepartment of Computer Science George Mason University

  2. Overview The rule refinement method Integrated modeling, learning, and solving Hands-on experience: Problem solving and learning Illustration of rule refinement in the COA domain Illustration of rule refinement in other domains Characterization of the PVS learning method Required reading

  3. The rule refinement method The rule refinement problem General presentation of the rule refinement method Rule refinement with a positive example Rule refinement with a negative example Characterization of the learned PVS rule

  4. The rule refinement problem GIVEN: • a plausible version space rule R; • a positive or a negative example E of the rule (i.e. a correct or an incorrect problem solving episode that has the same IF and THEN tasks as R); • a knowledge base that includes an object ontology and a set of problem solving rules; • an expert that understands why the example is positive or negative, and can answer agent’s questions. DETERMINE: • an improved rule that covers the example if it is positive, or does not cover the example if it is negative; • an extended object ontology (if needed for rule refinement).

  5. The rule refinement method The rule refinement problem General presentation of the rule refinement method Rule refinement with a positive example Rule refinement with a negative example Characterization of the learned PVS rule

  6. The rule refinement method: general presentation Let R be a plausible version space rule, U its plausible upper bound condition, L its plausible lower bound condition, and E a new example of the rule. 1. If E is covered by U but it is not covered by L then • If E is a positive example then L needs to be generalized as little as possible to cover it while remaining less general or at most as general as U. • If E is a negative example then U needs to be specialized as little as possible to no longer cover it while remaining more general than or at least as general as L. Alternatively, both bounds need to be specialized. 2. If E is covered by L then • If E is a positive example then R need not to be refined. • If E is a negative example then both U and L need to be specialized as little as possible to no longer cover this example while still covering the known positive examples of the rule. If this is not possible, then the E represents a negative exception to the rule. 3. If E is not covered by U then • If E is a positive example then it represents a positive exception to the rule. • If E is a negative example then no refinement is necessary.

  7. The rule refinement method: general presentation • If E is covered by U but it is not covered by L then • • If E is a positive example then L needs to be generalized as little as possible to cover it while remaining less general or at most as general as U. UB UB LB LB + + + + + +

  8. The rule refinement method: general presentation • If E is covered by U but it is not covered by L then • • If E is a negative example then U needs to be specialized as little as possible to no longer cover it while remaining more general than or at least as general as L. • Alternatively, both bounds need to be specialized. Strategy 1: Specialize UB by using a specialization rule (e.g. the descending the generalization hierarchy rule, or specializing a numeric interval rule). UB UB _ _ LB LB + + + +

  9. The rule refinement method: general presentation Strategy 2: Find a failure explanation EXw of why E is a wrong problem solving episode. EXw identifies the features that make E a wrong problem solving episode. The inductive hypothesis is that the correct problem solving episodes should not have these features. EXw is taken as an example of a condition that the correct problem solving episodes should not satisfy, an Except-When condition. The Except-when condition needs also to be learned, based on additional examples. Based on EXw an initial Except-When plausible version space condition is generated. UB UB LB LB + + _ + +

  10. The rule refinement method: general presentation Strategy 3: Find an additional explanation EXw for the correct problem solving episodes, which is not satisfied by the current wrong problem solving episode. Specialize both bounds of the plausible version space condition by: - adding the most general generalization of EXw, corresponding to the examples encountered so far, to the upper bound; - adding the least general generalization of EXw, corresponding to the examples encountered so far, to the lower bound. UB UB LB LB + + _ _ + +

  11. The rule refinement method: general presentation 2. If E is covered by L then • If E is a positive example then R need not to be refined. UB LB + + +

  12. The rule refinement method: general presentation - 2. If E is covered by L then • If E is a negative example then both U and L need to be specialized as little as possible to no longer cover this example while still covering the known positive examples of the rule. If this is not possible, then the E represents a negative exception to the rule. Strategy 1: Find a failure explanation EXw of why E is a wrong problem solving episode and create an Except-When a plausible version space condition, as indicated before. UB UB LB LB + + - + +

  13. The rule refinement method: general presentation 3.If E is not covered by U then • If E is a positive example then it represents a positive exception to the rule. • If E is a negative example then no refinement is necessary. - + UB UB LB LB + + + +

  14. The rule refinement method The rule refinement problem General presentation of the rule refinement method Rule refinement with a positive example Rule refinement with a negative example Characterization of the learned PVS rule

  15. Positive example covered by the upper bound Positive example that satisfies the upper bound IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 IF the task to accomplish is Identify the strategic COG candidates with respect to the industrial civilization of a force The force is Germany_1943 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 THEN accomplish the task A strategic COG relevant factor is strategic COG candidate for a force The force is Germany_1943 The strategic COG relevant factor is Industrial_capacity_of_Germany_1943 Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product Condition satisfied by positive example ?O1 IS Germany_1943 has_as_industrial_factor ?O2 ?O2 IS Industrial_capacity_of_Germany_1943 is_a_major_generator_of ?O3 ?O3 IS War_materiel_and_fuel_of_Germany_1943 less general than Plausible Lower Bound Condition ?O1 IS US_1943has_as_industrial_factor ?O2 ?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3 ?O3 IS War_materiel_and_transports_of_US_1943 explanation Germany_1943 has_as_industrial_factor Industrial_capacity_of_Germany_1943 Industrial_capacity_of_Germany_1943 is_a_major_generator_of War_materiel_and_fuel_of_Germany_1943 THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2

  16. Minimal generalization of the plausible lower bound Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product less general than (or at most as general as) New Plausible Lower Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials minimal generalization Plausible Lower Bound Condition (from rule) ?O1 IS US_1943has_as_industrial_factor ?O2 ?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3 ?O3 IS War_materiel_and_transports_of_US_1943 Condition satisfied by the positive example ?O1 IS Germany_1943 has_as_industrial_factor ?O2 ?O2 IS Industrial_capacity_of_Germany_1943 is_a_major_generator_of ?O3 ?O3 IS War_materiel_and_fuel_of_Germany_1943

  17. Generalization hierarchy of forces <object> Force Group Opposing_force Multi_state_force Single_state_force Multi_group_force Single_group_force component_state US_1943 Anglo_allies_1943 component_state Britain_1943 component_state Germany_1943 European_axis_1943 component_state Italy_1943

  18. Generalized rule IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O4 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product Plausible Lower Bound Condition ?O1 IS US_1943has_as_industrial_factor ?O2 ?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3 ?O3 IS War_materiel_and_transports_of_US_1943 Plausible Upper Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2 THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2

  19. The rule refinement method The rule refinement problem General presentation of the rule refinement method Rule refinement with a positive example Rule refinement with a negative example Characterization of the learned PVS rule

  20. A negative example covered by the upper bound Negative example that satisfies the upper bound IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 IF the task to accomplish is Identify the strategic COG candidates with respect to the industrial civilization of a force The force is Italy_1943 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 THEN accomplish the task A strategic COG relevant factor is strategic COG candidate for a force The force is Italy_1943 The strategic COG relevant factor isFarm_implement_industry_of_Italy_1943 Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product Condition satisfied by positive example ?O1 IS Italy_1943 has_as_industrial_factor ?O2 ?O2 IS Farm_implement_industry_of_Italy_1943 is_a_major_generator_of ?O3 ?O3 IS Farm_implements_of_Italy_1943 less general than Plausible Upper Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials explanation Italy_1943 has_as_industrial_factor Farm_implement_industry_of_Italy_1943 Farm_implement_industry_of_Italy_1943 is_a_major_generator_of Farm_implements_of_Italy_1943 THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2

  21. Automatic generation of plausible explanations IF Identify the strategic COG candidates with respect to the industrial civilization of Italy_1943 No! Who or what is a strategicallycritical industrial civilizationelement in Italy_1943? explanation Italy_1943 has_as_industrial_factor Farm_implement_industry_of_Italy_1943 Farm_implement_industry_of_Italy_1943 is_a_major_generator_of Farm_implements_of_Italy_1943 Industrial_capacity_of_Italy_1943 THEN Industrial_capacity_of_Italy_1943is a strategic COG candidate for Italy_1943 The agent generates a list of plausible explanations from which the expert has to select the correct one: Farm_implement_industry_of_Italy_1943 IS_NOT Industrial_capacity Farm_implements_of_Italy_1943 IS_NOT Strategically_essential_goods_or_materiel

  22. Minimal specialization of the plausible upper bound Plausible Upper Bound Condition (from rule)?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product specialization Condition satisfied by the negative example ?O1 IS Italy_1943 has_as_industrial_factor ?O2 ?O2 IS Farm_implement_industry_of_Italy_1943 is_a_major_generator_of ?O3 ?O3 IS Farm_Implements_of_Italy_1943 New Plausible Upper Bound Condition ?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materiel more general than(or at least as general as) New Plausible Lower Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materiel

  23. Fragment of the generalization hierarchy <object> Resource_or_ infrastructure_element UB Product Strategically_essential_resource_or_infrastructure_element Raw_material specialization Non-strategically_essential goods_or_services subconcept_of Strategic_raw_material Strategically_essential_goods_or_materiel LB instance_of subconcept_of Strategically_essential_ infrastructure_element War_materiel_and_transports subconcept_of + War_materiel_and_fuel + Main_airport Main_seaport Farm-implements of_Italy_1943 _ Sole_airport Sole_seaport

  24. Specialized rule IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials Plausible Upper Bound Condition?O1 IS Forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_factor is_a_major_generator_of ?O3 ?O3 IS Product Plausible Upper Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials Plausible Upper Bound Condition?O1 IS Single_state_forcehas_as_industrial_factor ?O2 ?O2 IS Industrial_capacity is_a_major_generator_of ?O3 ?O3 IS Strategically_essential_goods_or_materials THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2 THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2

  25. The rule refinement method The rule refinement problem General presentation of the rule refinement method Rule refinement with a positive example Rule refinement with a negative example Characterization of the learned PVS rule

  26. Problem solving with PVS rules PVS Condition Except-When PVS Condition Rule’s conclusion is (most likely) incorrect Rule not applicable Rule’s conclusion is plausible Rule’s conclusion is not plausible Rule’s conclusion is (most likely) correct

  27. Overview The rule refinement method Integrated modeling, learning, and solving Agent teaching: Hands-on experience Illustration of rule refinement in the COA domain Illustration of rule refinement in other domains Characterization of the PVS learning method Required reading

  28. Control of modeling, learning and solving Input Task Mixed-Initiative Problem Solving Ontology + Rules Generated Reduction Reject Reduction New Reduction Accept Reduction Solution Rule Refinement Task Refinement Modeling Rule Refinement Formalization Learning

  29. A systematic approach to agent teaching Identify the strategic COG candidates for the Sicily_1943 scenario Anglo_allies_1943 European_Axis_1943 other factors 20 other factors alliance alliance 10 individual states 1 individual states 11 Britain_1943 US_1943 Germany_1943 Italy_1943 16-19 12-15 controllingelement controllingelement 2 6 otherfactors governingelement governingelement otherfactors 9 3 civilization civilization 7 5 8 4

  30. Modeling, learning, problem solving Identify the strategic COG candidates for the Sicily_1943 scenario Which is an opposing force in the Sicily_1943 scenario? Rule_1 Anglo_allies_1943 Identify the strategic COG candidates for Anglo_allies_1943 Is Anglo_allies_1943 a single member force or a multi-member force? Anglo_allies_1943 is a multi-member force Rule_2 Identify the strategic COG candidates for the Anglo_allies_1943which is a multi-member force … Rule_1 European_Axis_1943 Identify the strategic COG candidates for European_Axis_1943 Is European_Axis_1943 a single member force or multi-member force? Rule_2 European_Axis_1943 is a multi-member force Identify the strategic COG candidates for the European_Axis _1943which is a multi-member force

  31. Overview The rule refinement method Integrated modeling, learning, and solving Hands-on experience: Problem solving and learning Illustration of rule refinement in the COA domain Illustration of rule refinement in other domains Characterization of the PVS learning method Required reading

  32. Agent teaching: hands-on experience Problem SolvingandRule Refinement

  33. Agent teaching: hands-on experience AutonomousProblem Solving

  34. Overview The rule refinement method Integrated modeling, learning, and solving Hands-on experience: Problem solving and learning Illustration of rule refinement in the COA domain Illustration of rule refinement in other domains Characterization of the PVS learning method Required reading

  35. Illustration of rule refinement in the COA domain Rule refinement with a positive example:Minimal generalization of the plausible lower bound Rule refinement with a negative example: Minimal specialization of the plausible upper bound Rule refinement with a negative example: Adding an Except-When plausible version space condition Integrated problem solving and learning

  36. A positive example covered by the upper bound Rule: R2 Positive example that satisfies the upper bound IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa COA421 Question:Is an enemy reconnaissance unit present? THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa COA421 for-unit RED-CSOP2 for-recon-action SCREEN2 Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action. Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE ?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK Plausible Upper Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK ?O4 IS ALLEGIANCE-OF-UNIT Condition satisfied by positive example ?O1 IS COA421 ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN2 ?O4 IS RED--SIDE less general than Main Condition Plausible Lower Bound ?O1 IS COA411 ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN1 ?O4 IS RED--SIDE THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3

  37. Minimal generalization of the plausible lower bound INTELLIGENCE-COLLECTION-MILTARY-TASK COA-SPECIFICATION-MICROTHEORY SUBCLASS-OF INSTANCE-OF INSTANCE-OF SCREEN-MILITARY-TASK INSTANCE-OF INSTANCE-OF COA411 COA421 SCREEN1 SCREEN2 New Plausible Lower Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN-MILITARY-TASK ?O4 IS RED--SIDE minimal generalization Plausible Lower Bound (from rule) ?O1 IS COA411 ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN1 ?O4 IS RED--SIDE Plausible Lower Bound (from example) ?O1 IS COA421 ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN2 ?O4 IS RED--SIDE

  38. Generalized rule Rule: R2 Rule: R2 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1 Question:Is an enemy reconnaissance unit present? Question:Is an enemy reconnaissance unit present? Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action. Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action. Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE ?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE ?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK Plausible Upper Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK ?O4 IS ALLEGIANCE-OF-UNIT Plausible Upper Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK ?O4 IS ALLEGIANCE-OF-UNIT Main Condition Main Condition Plausible Lower Bound ?O1 IS COA411 ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN1 ?O4 IS RED--SIDE Plausible Lower Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN-MILITARY-TASK ?O4 IS RED--SIDE THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3 THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3

  39. Illustration of rule refinement in the COA domain Rule refinement with a positive example:Minimal generalization of the plausible lower bound Rule refinement with a negative example: Minimal specialization of the plausible upper bound Rule refinement with a negative example: Adding an Except-When plausible version space condition Integrated problem solving and learning

  40. A negative example covered by the upper bound Rule: R$ASWCER-001 Negative example that satisfies the upper bound IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa COA51 Question: Is an enemy reconnaissance unit present? THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa COA51 for-unit BLUE-BATTALION1 for-recon-action SCREEN-RIGHT Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action. • Explanation: • ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE • ?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK Plausible Upper Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK ?O4 IS ALLEGIANCE-OF-UNIT Condition satisfied by positive example ?O1 IS COA51 ?O2 IS BLUE-BATTALION1 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN-RIGHT ?O4 IS BLUE-SIDE less general than Main Condition Plausible Lower Bound ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN—MILITARY-TASK ?O4 IS RED--SIDE THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3

  41. Minimal specialization of the plausible upper bound ALLEGIANCE-OF-UNIT SUBCLASS-OF specialization BLUE-SIDE RED-SIDE _ Plausible Upper Bound (from rule) ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK ?O4 IS ALLEGIANCE-OF-UNIT specialization Negative Example Specialized Plausible Upper Bound ?O1 IS COA51 ?O2 IS BLUE-BATALLION1 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN-RIGHT ?O4 IS BLUE-SIDE ?O1 IS COA-SPECIFICATION-MICROTHEORY ?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS SCREEN--MILITARY TASK ?O4 IS RED-SIDE

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