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 G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci

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Presentation on theme: " G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci"— Presentation transcript:

1  G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci CS 785, Fall 2001

2  G.Tecuci, Learning Agents Laboratory Define the problem reduction approach to problem solving. What is an instance? What is a concept? What is a positive example of a concept? What is a negative example of a concept? Give an intuitive definition of generalization. What does it mean for concept A to be more general than concept B? Indicate a simple way to prove that a concept is not more general than another concept. Given two concepts C1 and C2, from a generalization point of view, what are all the different possible relations between them? What are the basic elements in the definition of a property or a relation? Briefly define a plausible version space rule. Sample questions

3  G.Tecuci, Learning Agents Laboratory What is a generalization rule? What is a specialization rule? What is a reformulation rule? Name all the generalization rules you know. Briefly describe and illustrate with an example the “turning constants into variables” generalization rule. Define and illustrate the dropping conditions generalization rule. Define the following: a generalization of two concepts a minimally general generalization of two concepts the least general generalization of two concepts the maximally general specialization of two concepts. Define the transitivity of ISA. Define the inheritance of features (including default inheritance and multiple inheritance). Sample questions

4  G.Tecuci, Learning Agents Laboratory Briefly explain the process of reasoning with a plausible version space rule. Define the rule learning problem in Disciple. Briefly describe the rule learning method of Disciple. What is an explanation of an example? Briefly describe analogical reasoning (in general). Briefly describe analogical reasoning in Disciple. Define the rule refinement problem in Disciple. Briefly describe the rule refinement method of Disciple. What is a negative exception? What is a positive exception? Draw a picture representing a plausible version space, as well as a positive example, a negative example, a positive exception and a negative exception. Then briefly define each of these elements. Describe briefly the general architecture of the Disciple shell and the methodology for building a Disciple agent. Sample questions

5  G.Tecuci, Learning Agents Laboratory Consider the cells consisting of two bodies, each body having two attributes: - color (that may be yellow or green) and - number of nuclei (1 or 2). The relative position of the bodies is not relevant because they can move inside the cell. ((1 green) (2 yellow)) + a) Indicate ALL the possible generalizations of the following cell, and the generalization relations between them. b) Determine the number of the distinct sets of instances and the number of concept descriptions for this problem. Exercise

6  G.Tecuci, Learning Agents Laboratory c) Given the following cell descriptions ((1 green) (1 green))((1 green) (2 green))((1 yellow) (2 green)) Determine the following minimal generalizations: g(E1, E2), g(E2, E3), g(E3, E1), g(E1, E2, E3)

7  G.Tecuci, Learning Agents Laboratory The following exercises use the background knowledge consisting of this object hierarchy (semantic network) and the feature definitions from the next slide. Exercise

8  G.Tecuci, Learning Agents Laboratory namedescriptiondomainrange ISisSOMETHING OBJECTobjectTASKSOMETHING TOtoTASKSOMETHING MADE-OFmade ofSOMETHINGMATERIAL GLUESgluesADHESIVEMATERIAL STATEstateSOMETHING{solid fluid gas} TASKtaskOPERATIONTASK INTOintoOPERATIONTASK ONonTASKSOMETHING PART-OFpart ofSOMETHING Feature Definitions

9  G.Tecuci, Learning Agents Laboratory Consider the question: “Is there a part of a loudspeaker that is made of metal?” a) Which are all the answers to this question? b) Which are the reasoning operations that need to be performed in order to answer this question. c) Consider one of the answers that requires all these operations and show how the answer is found. Exercise

10  G.Tecuci, Learning Agents Laboratory Consider the following expressions: E1: ?X IS MEMBRANE E2: ?X IS MECHANICAL-CHASSIS MADE-OF ?M MADE-OF ?M ?M IS PAPER ?M IS METAL ?Z IS CONTACT-ADHESIVE ?Z IS MOWICOLL GLUES ?M GLUES ?M STATE fluid a) Find the minimally general generalizations of E1 and E2. b) Find two generalizations of E1 and E2 that are not minimally general generalizations. c) Consider one of the generalizations found at b) and demonstrate why it is a generalization of E1 and E2 but it is not a minimally general generalization. d) What would be a least general generalization of E1 and E2? Does it exist? e) Indicate a specialization of E1. Exercise

11  G.Tecuci, Learning Agents Laboratory Construct the plausible version space rule learned from them. IF the task to accomplish is ATTACH OBJECT MEMBRANE1 TO CHASSIS-ASSEMBLY1 THEN accomplish the tasks APPLY OBJECT CONTACT-ADHESIVE1 ON CHASSIS-ASSEMBLY1 PRESS OBJECT MEMBRANE1 ON CHASSIS-ASSEMBLY1 CONTACT-ADHESIVE1 IS fluid CONTACT-ADHESIVE1 GLUES PAPER and MEMBRANE1 MADE-OF PAPER CONTACT-ADHESIVE1 GLUES METAL and CHASSIS-ASSEMBLY1 MADE-OF METAL Consider the following example and its explanation: Because Exercise

12  G.Tecuci, Learning Agents Laboratory Compose an example analogous with the following one: Exercise

13  G.Tecuci, Learning Agents Laboratory Find a minimal generalization of the rule that covers the positive example. IF the task to accomplish is ATTACH OBJECT ?X TO ?Y Plausible Upper Bound IF ?XISSOMETHING MADE-OF?M ?YISSOMETHING MADE-OF?N ?ZISADHESIVE GLUES?M GLUES?N ?MISMATERIAL ?NISMATERIAL Plausible Lower Bound IF ?XISMEMBRANE1 MADE-OF?M ?YISCHASSIS-ASSEMBLY1 MADE-OF?N ?ZISCONTACT-ADHESIVE1 GLUES?M GLUES?N ?MISPAPER ?NISMETAL THEN accomplish the tasks APPLY OBJECT ?Z ON ?X PRESS OBJECT ?X ON ?Y IF the task to accomplish is ATTACH OBJECT BOLT1 TO MECHANICAL-CHASSIS1 THEN accomplish the tasks APPLY OBJECT MOWICOLL1 ON MECHANICAL-CHASSIS1 PRESS OBJECT BOLT1 ON MECHANICAL-CHASSIS1 Rule Positive Example Exercise

14  G.Tecuci, Learning Agents Laboratory Find a minimal specialization of the rule that does not cover the positive example: By using an additional explanation of the positive examples; By empirically specializing the rule. IF the task to accomplish is ATTACH OBJECT ?X TO ?Y Plausible Upper Bound IF ?XISSOMETHING MADE-OF?M ?YISSOMETHING MADE-OF?N ?ZISADHESIVE GLUES?M GLUES?N ?MISMATERIAL ?NISMATERIAL Plausible Lower Bound IF ?XISMEMBRANE1 MADE-OF?M ?YISLOUDSPEAKER-COMPONENT MADE-OF?N ?ZISLOUDSPEAKER-COMPONENT GLUES?M GLUES?N ?MISMATERIAL ?NISMETAL THEN accomplish the tasks APPLY OBJECT ?Z ON ?X PRESS OBJECT ?X ON ?Y with the positive examples (?X IS MEMBRANE1, ?Y IS CHASSIS-ASSEMBLY1, ?Z IS CONTACT-ADHESIVE1, ?M IS PAPER, ?N IS METAL) (?X IS BOLT1, ?Y IS MECHANICAL-CHASSIS1, ?Z IS MOWICOLL1, ?M IS METAL, ?N IS METAL) IF the task to accomplish is ATTACH OBJECT SCREENING-CAP1 TO LOUDSPEAKER1 THEN accomplish the tasks APPLY OBJECT SCOTCH-TAPE1 ON SCREENING-CAP1 PRESS OBJECT SCREENING-CAP1 ON LOUDSPEAKER1 Rule Negative Example Exercise

15  G.Tecuci, Learning Agents Laboratory Explain how the following questions are answered, and provide the corresponding answer(s): What is the color of membrane? What does contact-adhesive1 glue? Is there a loudspeaker component made of metal? Exercise

16  G.Tecuci, Learning Agents Laboratory The following exercises, marked S1 to S7, are based on the following semantic network from the loudspeaker manufacturing domain: Remark: Consider that each most specific concept, such as DUST or AIR-PRESS, has an instance, such as DUST1 or AIR-PRESS1. Exercises

17  G.Tecuci, Learning Agents Laboratory S1. Consider the following two expressions: E1:?XISSOFT-CLEANER REMOVES?Z ?YISMEMBRANE MADE-OF?T ?ZISWASTE-MATERIAL E2:?XISAIR-SUCKER REMOVES?Z NOT-DAMAGESPAPER ?YISMEMBRANE MADE-OFPAPER ?ZISDUST Use the generalization rules to show that E1 is more general than E2. Exercise

18  G.Tecuci, Learning Agents Laboratory S2. Determine the generalization of the following two expressions: E1:?xISentrefer MAY-HAVE?y ?yISdust ?zISair-sucker REMOVES?y E2:?xISmembrane MAY-HAVE?y ?yISsurplus-adhesive ?zISalcohol TYPEfluid REMOVES?y Exercise

19  G.Tecuci, Learning Agents Laboratory S3. Consider the following description: ?zIScleaner REMOVESsurplus-paint Determine all the possible values of ?z. Exercise

20  G.Tecuci, Learning Agents Laboratory S4. Consider the following action description: CLEANOBJECT?x OF?y WITH?z Condition ?xISentrefer MAY-HAVE?y ?yISsomething ?zIScleaner REMOVES?y Find all the possible values for the variables ?x, ?y and ?z. Indicate some of the corresponding actions. Exercise

21  G.Tecuci, Learning Agents Laboratory S5. Consider the following rule: IF the task to perform is CLEAN OBJECT ?x OF ?y Condition ?xISsomething MAY-HAVE?y ?yISsomething ?zIScleaner REMOVES?y THEN perform the task CLEAN OBJECT ?x OF ?y WITH ?z Describe how this rule is applied to solve the problem: CLEAN OBJECT entrefer1 OF dust1 Which will be the result? Remark: Consider that each most specific concept o from the object ontology has an instance o1.

22  G.Tecuci, Learning Agents Laboratory IF the task to perform is CLEAN OBJECT ?x OF ?y Condition ?xISsomething MAY-HAVE?y ?yISsomething ?zIScleaner REMOVES?y THEN perform the task CLEAN OBJECT ?x OF ?y WITH ?z Describe how this rule is applied to solve the problem: CLEAN OBJECT membrane1 OF surplus-adhesive1 Which will be the result? Remark: Consider that each most specific concept o from the object ontology has an instance o1. S6. Consider the following rule: Exercise

23  G.Tecuci, Learning Agents Laboratory IF the task to perform is CLEAN OBJECT ?x OF ?y G: plausible upper bound ?xISsomething MAY-HAVE?y ?yISsomething ?zISsomething REMOVES?y S: plausible lower bound ?xISentrefer MAY-HAVE?y ?yISdust ?zISair-sucker REMOVES?y THEN perform the task CLEAN OBJECT ?x OF ?y WITH ?z S7. Consider the following partially learned rule: Describe how Disciple generalizes this rule so as to cover the following positive example: IF the task to perform is CLEAN OBJECT membrane1 OF surplus-adhesive1 THEN perform the task CLEAN OBJECT membrane OF surplus-adhesive1 WITH alcohol1 Exercise

24  G.Tecuci, Learning Agents Laboratory Exercise Develop an object ontology that represents the following information: Puss is a calico. Herb is a tuna. Charlie is a tuna. All tunas are fishes. All calicos are cats. Cats like to eat fishes. You should define object concepts, object features and instances.

25  G.Tecuci, Learning Agents Laboratory Exercise Develop an object ontology that represents the following information: The color of Apple1 is red. The color of Apple2 is green. Apple1 is an apple. Apple2 is an apple. Apples are fruits. You should define object concepts, object features and instances.

26  G.Tecuci, Learning Agents Laboratory Exercise Develop an object ontology that represents the following information: Basketball players are tall. Muresan is a basketball player. Muresan is tall. You should define object concepts, object features and instances.

27  G.Tecuci, Learning Agents Laboratory Insert the additional knowledge that platypus lays eggs into the following object ontology: Exercise mammal cow platypus birth-mode live subclass-of Explain the result.

28  G.Tecuci, Learning Agents Laboratory Develop an object ontology that represents the following information: "Blue task force 1 penetrates Red mechanized brigade 1 with a force ratio of The recommended force ratio for a penetration is 3. A penetration is a complex military task, a military maneuver and a military attack. Use of a penetration indicates that the mission is offensive“ You should draw the ontology and should also define the features used in it (in terms of their domains and ranges). Exercise

29  G.Tecuci, Learning Agents Laboratory Develop an object ontology that represents the following information: "BLUE-TASK-FORCE1 is a blue armored and mechanized infantry battalion assigned to be main effort1. It performs two tasks, penetrate1 and clear1. It has a regular strength and has the following units under its operational control: BLUE-MECH- COMPANY1, BLUE-MECH-COMPANY2, BLUE- ARMOR-COMPANY1, BLUE-ARMOR-COMPANY2” You should draw the ontology and should also define the features used in it (in terms of their domains and ranges). Exercise

30  G.Tecuci, Learning Agents Laboratory Exercise Consider the background knowledge represented by the following generalization hierarchies: Consider also the following concept: E:?uISobject COLORyellow SHAPEcircle RADIUS 5 Indicate five different generalization rules. For each such rule determine an expression Eg which is more general than E according to that rule.

31  G.Tecuci, Learning Agents Laboratory I need to Identify and test a strategic COG candidate for Okinawa_1945 which is a major theater of war scenario US_1945 Therefore I need to Which is an opposing force in the Okinawa_1945 scenario? Identify and test a strategic COG candidate for US_1945 Is US_1945 a single-member force or a multi-member force? US_1945 is a single-member force Identify and test a strategic COG candidate for US_1945 which is a single-member force Therefore I need to Formalize the following tasks: Exercise

32  G.Tecuci, Learning Agents Laboratory Exercise US_1943 has_as_industrial_factor Industrial_capacity_of_US_1943 Identify the strategic COG candidates with respect to the industrial civilization of a force The force is US_1943 A strategic COG relevant factor is strategic COG candidate for a force The force is US_1943 The strategic COG relevant factor is Industrial_capacity_of_US_1943 IF the task to accomplish is THEN explains War_materiel_and_transports_of_US_1943 is_a_major_generator_of a)Find the analogy-based generalization of the explanations and the example. b)Find the plausible version space rule that will be learned from this example. Consider the following problem solving episode and its explanation, in the context of the background knowledge the following four slides:

33  G.Tecuci, Learning Agents Laboratory Feature definitions has_as_industrial_factor D: Force R: Industrial_factor is_a_major_generator_of D: Economic_factor R: Product The force is D: task R: Force The strategic COG relevant factor is D: task R: Force

34  G.Tecuci, Learning Agents Laboratory Economic factors Economic_factor Other_ economic_ factor Transportation_ Network_or_system Industrial_ authority Commerce_ authority Industrial_ Capacity Industrial_ Center Strategic_ Raw_ Material Transportation_ Center Information_ Network_or_system Transportation_ Factor Industrial_ factor Germany_1943 has_as_strategic_ raw_material Oil_chromium_ copper_and_bauxite_ of_Germany_1943 is_obtained_from is_critical_to_ the_production_of Balkans War_materiel_of _Germany_1943 Raw_material US_1943 is_a_major_generator_of war_materiel_and_ transports_of_ US_1943_ has_as_industrial_factor industrial_capacity_ of_US_1943 Farm_implement_industry_of_Italy_1943 Farm_implement_industry

35  G.Tecuci, Learning Agents Laboratory Opposing_force Force Single_state_forceSingle_group_forceMulti_group_forceMulti_state_force Generalization hierarchy of forces Anglo_allies_1943 European_axis_1943 US_1943 Britain_1943 Germany_1943 component_state Italy_1943 component_state Group

36  G.Tecuci, Learning Agents Laboratory Fragment of the generalization hierarchy Main_airport Main_seaport Sole_airportSole_seaport Strategically_essential_resource_ or_infrastructure_element Strategic_raw_material Strategically_essential_ goods_or_materiel War_materiel_and_transports Raw_material Strategically_essential_ infrastructure_element Resource_or_ infrastructure_element Product Non-strategically_essential goods_or_services Farm-implements of_Italy_1943 War_materiel_and_fuel Resource Farm-implements War_materiel_and_fuel_ of_Germany_1943 War_materiel_and_ transports_of_US_1943

37  G.Tecuci, Learning Agents Laboratory Exercise IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 THEN A strategic COG relevant factor is strategic COG candidate for a force The force is ?O1 The strategic COG relevant factor is ?O2 Plausible Upper Bound Condition ?O1ISForce has_as_industrial_factor ?O2 ?O2ISIndustrial_factor is_a_major_generator_of ?O3 ?O3ISProduct Plausible Lower Bound Condition ?O1ISUS_1943 has_as_industrial_factor ?O2 ?O2ISIndustrial_capacity_of_US_1943 is_a_major_generator_of ?O3 ?O3ISWar_materiel_and_transports_of_US_1943 explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 Identify the strategic COG candidates with respect to the industrial civilization of a force The force is Germany_1943 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 IF the task to accomplish is THEN accomplish the task Positive example that satisfies the upper bound 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 Minimally generalize the rule to cover the following positive example (considering the background knowledge from the previous four slides):

38  G.Tecuci, Learning Agents Laboratory Exercise IF Identify the strategic COG candidates with respect to the industrial civilization of a force The force is ?O1 Plausible Upper Bound Condition ?O1ISForce has_as_industrial_factor ?O2 ?O2ISIndustrial_factor is_a_major_generator_of ?O3 ?O3ISProduct explanation ?O1 has_as_industrial_factor ?O2 ?O2 is_a_major_generator_of ?O3 Plausible Upper Bound Condition ?O1ISSingle_state_force has_as_industrial_factor ?O2 ?O2ISIndustrial_capacity is_a_major_generator_of ?O3 ?O3IS Strategically_essential_goods_or_materials Identify the strategic COG candidates with respect to the industrial civilization of a force The force is Italy_1943 A strategic COG relevant factor is strategic COG candidate for a force The force is Italy_1943 The strategic COG relevant factor is Farm_implement_industry_of_Italy_1943 IF the task to accomplish is THEN accomplish the task Negative example that satisfies the upper bound 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 Minimally specialize the rule to no longer cover the following negative example (considering the background knowledge from the previous slides):

39  G.Tecuci, Learning Agents Laboratory Repertory grid exercises Define a repertory grid for choosing a course to enroll in. Define a repertory grid for choosing a car. Define a repertory grid for choosing a dissertation director.

40  G.Tecuci, Learning Agents Laboratory Exercise Consider the following two concepts: Indicate different generalization of them.

41  G.Tecuci, Learning Agents Laboratory Exercise Consider the following two concepts and ontology. Indicate four specializations of G1 and G2 (including a maximally general specialization).

42  G.Tecuci, Learning Agents Laboratory Develop an object ontology that represents the following information: Birds have feathers, fly and lay eggs. Albatros is a bird. Donald is a bird. Tracy is an albatros. You should define object concepts, object features and instances. Exercise

43  G.Tecuci, Learning Agents Laboratory END

44  G.Tecuci, Learning Agents Laboratory Cooperative problem solving and learning Problem solving with PVS rules Integrated problem solving and learning Demonstration

45  G.Tecuci, Learning Agents Laboratory Generalization by analogy TASK RED-CSOP1 SCREEN1 SOVEREIGN-ALLEGIANCE-OF-ORG RED--SIDE INTELLIGENCE-COLLECTION-MILTARY-TASK INSTANCE-OF Assess security wrt countering enemy reconnaissance for-coa COA411 Assess security when enemy recon is present for-coa COA411 for-unit RED-CSOP1 for-recon-action SCREEN1 IF the task to accomplish is: THEN accomplish the task: explain generalization Any value of ?O2 should be an instance of: DOMAIN(TASK)  DOMAIN(SOVEREIGN-ALLENGINCE-OF_ORG)  RANGE(FOR-UNIT) = MODERN-MILITARY-UNIT--DEPLOYABLE Any value of ?O3 should be an instance of: RANGE(TASK)  INTELLIGENCE-COLLECTION-MILITARY-TASK = INTELLIGENCE-COLLECTION-MILITARY-TASK Any value of ?O4 should be an instance of: RANGE(SOVEREIGN-ALLENGINCE-OF_ORG) = ALLEGIANCE-OF-UNIT Any value of ?O1 should be an instance of: RANGE(FOR-COA) = COA-SPECIFICATION-MICROTHEORY Knowledge-base constraints on the generalization:

46  G.Tecuci, Learning Agents Laboratory Rule: R2 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 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa?O1 Question: Is an enemy reconnaissance unit present? Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action. THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa?O1 for-unit?O2 for-recon-action?O3 Main Condition Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE ?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK 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 Positive example that satisfies the upper bound IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coaCOA421 THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coaCOA421 for-unitRED-CSOP2 for-recon-actionSCREEN2 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 A positive example covered by the upper bound

47  G.Tecuci, Learning Agents Laboratory Rule: R$ASWCER-001 IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1 Question: Is an enemy reconnaissance unit present? 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 THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3 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 Main Condition Negative example that satisfies the upper bound IF the task to accomplish is: Assess-security-wrt-countering-enemy-reconnaissance for-coa COA51 THEN accomplish the task: Assess-security-when-enemy-recon-is-present for-coa COA51 for-unit BLUE-BATTALION1 for-recon-action SCREEN-RIGHT 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 A negative example covered by the upper bound

48  G.Tecuci, Learning Agents Laboratory RED-SIDEBLUE-SIDE ALLEGIANCE-OF-UNIT SUBCLASS-OF _ specialization SCREEN1 SCREEN-MILITARY-TASK INSTANCE-OF SCREEN2 INSTANCE-OF INTELLIGENCE-COLLECTION-MILTARY-TASK SUBCLASS-OF COA411 INSTANCE-OF COA421 INSTANCE-OF COA-SPECIFICATION-MICROTHEORY


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