1.  G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu http://lalab.gmu.edu/ CS 785, Fall 2001

2.  G.Tecuci, Learning Agents Laboratory General organization of the knowledge base Representation of the object ontology Overview Reasoning with the object ontology An example: the COA object ontology More exercises Required reading 1 2 3 4 5 6

3.  G.Tecuci, Learning Agents Laboratory The structure of the knowledge base The generality of the ontology The generality of the rules 1. General organization of the knowledge base

4.  G.Tecuci, Learning Agents Laboratory The structure of the knowledge base The object ontology is a hierarchical description of the objects from the domain, specifying their properties and relationships. It includes both descriptions of types of objects (called concepts) and descriptions of specific objects (called instances). The task reduction rules specify generic problem solving steps of reducing complex problem solving tasks to simpler tasks. They are described using the objects from the ontology. Knowledge Base = Object ontology + Task reduction rules

5.  G.Tecuci, Learning Agents Laboratory A task reduction rule is an IF-THEN structure that expresses the condition C under which a task T 1 can be reduced to the simpler tasks T 1a, or to a set of simpler tasks T 11, …, T 1n. T1T1 T 1a The structure of the knowledge base (cont.) Knowledge Base = Object ontology + Task reduction rules T1T1 T 11 T 12 … T 1n C1C1 C2C2

6.  G.Tecuci, Learning Agents Laboratory The generality of the ontology An object ontology is characteristic to an entire application domain, such as military or medicine. In the military domain the object ontology will include descriptions of military units and of military equipment. These descriptions are most likely needed in almost any specific military application. Because building the object ontology is a very complex task, it makes sense to reuse these descriptions when developing a knowledge base for another military application, rather than starting from scratch.

7.  G.Tecuci, Learning Agents Laboratory The rules from the knowledge base are specific to a particular application and even to a particular SME. Consider, for instance, the agents discussed before, the agent that critiques courses of action with respect to the principles of war, and the agent that plans the repair of damaged bridges or roads. While both agents need to reason with military units and military equipment, their reasoning rules are very different, being specific not only to their particular application (critiquing vs planning), but also to the SMEs whose expertise they encode. The generality of the rules

8.  G.Tecuci, Learning Agents Laboratory Semantic network representation of the ontology Instances, concepts and generalization Object features Definition of instances and concepts 2. Representation of the object ontology Sample application: COA critiquing

9.  G.Tecuci, Learning Agents Laboratory Identifies strengths and weaknesses in a military course of action based on the principles of war and tenets of army operations. Sample application: COA critiquing

10.  G.Tecuci, Learning Agents Laboratory COA411 – the sketch Graphical depiction of a preliminary plan. It includes enough of the high level structure and maneuver aspects of the plan to show how the actions of each unit fit together to accomplish the overall purpose.

11.  G.Tecuci, Learning Agents Laboratory COA411 – the statement Explains what the units will do to accomplish the assigned mission.

12.  G.Tecuci, Learning Agents Laboratory COA411 – the statement (cont.)

13.  G.Tecuci, Learning Agents Laboratory Object ontology as a Semantic network The underlying idea of the semantic network representation is to represent the object ontology in the form of a graph in which the nodes represent objects and the arcs represent the relations between them.

14.  G.Tecuci, Learning Agents Laboratory An instance is a representation of a particular entity in the application domain. Characterization of instances and concepts BLUE-TASK-FORCE1BLUE-ARMOR-BRIGADE2 ARMORED-UNIT--MILITARY-SPECIALTY INSTANCE-OF Represents the entity called BLUE-TASK-FORCE1 Represents the set of all armored units (which includes BLUE-ARMORE-BRIGADE2 and BLUE-TASK-FORCE1 ) A concept is a representation of a set of instances.

15.  G.Tecuci, Learning Agents Laboratory Intuitive definition of generalization Generalization is a fundamental relation between concepts. Intuitively, a concept P is said to be more general than (or a generalization of) another concept Q if and only if the set of instances represented by P includes the set of instances represented by Q. MODERN-MILITARY-UNIT--DEPLOYABLE MANEUVER-UNIT-MILITARY-SPECIALTY ARMORED-UNIT-- MILITARY- SPECIALTY AVIATION-UNIT- -MILITARY-SPECIALTY INFANTRY- UNIT-- MILITARY- SPECIALTY

16.  G.Tecuci, Learning Agents Laboratory A generalization latice/hierarchy BLUE-TASK-FORCE1 MECHANIZED-INFANTRY-UNIT--MILITARY-SPECIALTY BLUE-ARMOR-BRIGADE2 MANEUVER-UNIT-MILITARY-SPECIALTYAVIATION-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTYINFANTRY-UNIT--MILITARY-SPECIALTY MODERN-MILITARY-UNIT--DEPLOYABLE MODERN-MILITARY-ORGANIZATION ORGANIZATION BLUE-MECH-BRIGADE1 BLUE-TASK-FORCE2 BLUE-TASK-FORCE3 INSTANCE-OFSUBCLASS-OF INSTANCE-OF SUBCLASS-OF

17.  G.Tecuci, Learning Agents Laboratory Object features The objects in the application domain may be described in terms of their properties and their relationships with each other. OBJECT-ACTED-ON PENETRATE1 RED-MECH-COMPANY4 FORCE-RATIO 10.6 PENETRATE1 acts on RED-MECH-COMPANY4 with a force ratio of 10.6.

18.  G.Tecuci, Learning Agents Laboratory Feature definition An object feature is itself characterized by several features which include: documentation, domain and range. The domain is the concept that represents the set of objects that could have that feature. The range is the set of possible values of the feature. BLUE-TASK-FORCE2 ASSIGNMENT SUPPORTING-EFFORT1 ASSIGNMENT DOMAIN RANGE DOCUMENTATION COA-ASSIGNMENT MODERN-MILITARY-UNIT--DEPLOYABLE "Indicates the assignment of a unit" SUBCLASS-OF

19.  G.Tecuci, Learning Agents Laboratory Feature definition: example IS-OFFENSIVE-ACTION-FOR DOMAIN RANGE ACTION "military offensive operation" PENETRATE--MILITARY-TASK IS-OFFENSIVE-ACTION-FOR {"military offensive operation”, "military offensive operation”} SUBCLASS-OF

20.  G.Tecuci, Learning Agents Laboratory Partially learned feature "military offensive operation" PENETRATE--MILITARY-TASK IS-OFFENSIVE-ACTION-FOR IS-OFFENSIVE-ACTION-FOR DOMAIN DOCUMENTATION ACTION "Indicates the context in which the action is considered as having an offensive nature” PLAUSIBLE UPPER BOUND: PLAUSIBLE LOWER BOUND: PENETRATE-MILITARY-TASK RANGE PLAUSIBLE UPPER BOUND: PLAUSIBLE LOWER BOUND: {"military offensive operation”, "military defensive operation”} {"military offensive operation”}

21.  G.Tecuci, Learning Agents Laboratory Feature hierarchy <OBJECT-FEATURE> IS-OFFENSIVE-ACTION-FOR IS-ACTION-TYPE-FOR ASSIGNMENT DOMAIN DOCUMENTATION COA-ASSIGNMENT MODERN-MILITARY-UNIT--DEPLOYABLE "Indicates the assignment of a unit" IS-SURPRISE-ACTION-FOR IS-SECURITY-ACTION-FOR UPPER BOUND: LOWER BOUND: MANEUVER-UNIT-MILITARY-SPECIALTY RANGE UPPER BOUND: LOWER BOUND: MAIN-EFFORT DOMAIN DOCUMENTATION ACTION "Indicates the context in which the action is considered as having an offensive nature” UPPER BOUND: LOWER BOUND: PENETRATE-MILITARY-TASK RANGE UPPER BOUND: LOWER BOUND: {"military offensive operation”,"military defensive operation”} {"military offensive operation”} DOMAIN RANGE ACTION {"military offensive operation”, "military defensive operation”}

22.  G.Tecuci, Learning Agents Laboratory Definition of instances and concepts When designing a knowledge base, one has to first specify some basic concepts, as well as the features that may characterize instances and concepts. Once basic concepts and features are specified, one can define new concepts and instances as logical expressions of the known concepts.

23.  G.Tecuci, Learning Agents Laboratory Basic representation unit This is a necessary definition of ‘concept k ’. It defines ‘concept k ’ as being a subconcept of ‘concept i ’ and having additional features. This means that if ‘concept i ’ represents the set C i of instances, then ‘concept k ’ represents a subset C k of C i. The elements of C k have the features ‘FEATURE 1 ’,..., ‘FEATURE n ’ with the values ‘value 1 ’,..., ‘value n ’, respectively. concept k ISAconcept i FEATURE 1 value 1... FEATURE n value n

24.  G.Tecuci, Learning Agents Laboratory Example: Concepts definition We can define a concept as being a sub-concept of known concepts and having additional features, as in the following example: PENETRATE--MILITARY-TASK INDICATES-MISSION-TYPE IS-OFFENSIVE-ACTION-FOR "military offensive operation" RECOMMENDED-FORCE-RATIO 3 HAS-SURPRISE-FORCE-RATIO 6 SUBCLASS-OF MILITARY-MANEUVER COMPLEX-MILITARY-TASK MILITARY-ATTACK PENETRATE--MILITARY-TASK is a complex military task, a military maneuver, and a military attack. It indicates that a COA that has a penetration mission is an offensive COA. In the context of an offensive operation, a penetration should be considered an offensive action. The doctrinal recommended force ratio for a penetration is 3.0. A force ratio of 6.0 should be considered a surprisingly high force ratio.

25.  G.Tecuci, Learning Agents Laboratory 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, … Example: Instance definition ECHELON-OF-UNIT OPERATIONAL-CONTROL-MILITARY-ORG SOVEREIGN-ALLEGIANCE-OF-ORG ASSIGNMENT TASK BLUE-TASK-FORCE1 TASK BATTALION--UNIT-DESIGNATION INSTANCE-OF MECHANIZED-INFANTRY-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTY BLUE--SIDE MAIN-EFFORT1 PENETRATE1 CLEAR1 REGULAR-STATUS BLUE-MECH-COMPANY1 BLUE-MECH-COMPANY2 BLUE-ARMOR-COMPANY1 BLUE-ARMOR-COMPANY2 OPERATIONAL-CONTROL-MILITARY-ORG TROOP-STRENGTH-OF-UNIT INSTANCE-OF

26.  G.Tecuci, Learning Agents Laboratory Transitivity of INSTANCE_OF and SUBCLASS_OF Inheritance Ontology matching Object expressions 3. Reasoning with the object ontology Generalization and specialization rules Types of generalizations and specializations Steps in ontology development Ontology maintenance

27.  G.Tecuci, Learning Agents Laboratory Transitivity of INSTANCE_OF and SUBCLASS_OF MECHANIZED-INFANTRY-UNIT--MILITARY-SPECIALTY MANEUVER-UNIT--MILITARY-SPECIALTY INFANTRY-UNIT--MILITARY-SPECIALTY MODERN-MILITARY-UNIT--DEPLOYABLE MODERN-MILITARY-ORGANIZATION ORGANIZATION BLUE-MECH-BRIGADE1 ORGANIZATION BLUE-MECH-BRIGADE1 INFANTRY-UNIT--MILITARY-SPECIALTY ORGANIZATION INSTANCE-OF SUBCLASS-OF

28.  G.Tecuci, Learning Agents Laboratory Inheritance OBJECT-ACTED-ON IS-TASK-OF-OPERATION TASK-HAS-PURPOSE PENETRATE1 TASK INSTANCE-OF PENETRATE--MILITARY-TASK ATTACK2 RED-MECH-COMPANY4 UNIT-ASSIGNED-TO-TASK BLUE-TASK-FORCE1 INDICATES-MISSION-TYPE IS-OFFENSIVE-ACTION-FOR "military offensive operation" FORCE-RATIO 10.6 RECOMMENDED-FORCE-RATIO 3 HAS-SURPRISE-FORCE-RATIO 6 (missing element) ASSIGNMENT MAIN-EFFORT1 MILITARY-TASK SUBCLASS-OF MILITARY-MANEUVER COMPLEX-MILITARY-TASK MILITARY-ATTACK SUBCLASS-OF SOVEREIGN-ALLEGIANCE-OF-ORG BLUE--SIDE

29.  G.Tecuci, Learning Agents Laboratory Object concept representation ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION ?N1 ?N1 IS-IN [1.. 25] The following concept represents the set of all COA specifications that contain between 1 and 25 offensive actions: One can define more complex concepts as logical expressions involving the basic concepts from the object ontology. ?O1 is a generic instance of this concept. It is a COA specification that has ?N1 offensive actions, where ?N1 is between 1 and 25.

30.  G.Tecuci, Learning Agents Laboratory Object expressions ?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3 ?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK ?O4 IS RED--SIDE The following, for instance, represents the set of deployable military units ?O2 of the red side that perform some intelligence collection military task:

31.  G.Tecuci, Learning Agents Laboratory Generalization (and specialization) rules Climbing the generalization hierarchy Dropping condition Generalizing numbers Adding alternatives Turning constants into variables

32.  G.Tecuci, Learning Agents Laboratory Generalization and specialization rules A generalization rule is a rule that transforms an expression/concept into a more general one. A specialization rule is a rule that transforms an expression/concept into a less general one. The reverse of any generalization rule is a specialization rule.

33.  G.Tecuci, Learning Agents Laboratory Turning constants into variables Generalizes an expression by replacing a constant with a variable. ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION 5 ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION ?N1 generalization specialization ?N1  5 5  ?N1 The set of COAs with 5 offensive actions. The set of COAs with any number of offensive actions.

34.  G.Tecuci, Learning Agents Laboratory Climbing the generalization hierarchy Generalizes an expression by replacing a concept with a more general one. ?O1 ISARMORED-UNIT--MILITARY-SPECIALTY ECHELON-OF-UNITBATTALION--UNIT-DESIGNATION ?O1 ISMANEUVER-UNIT--MILITARY-SPECIALTY ECHELON-OF-UNITBATTALION--UNIT-DESIGNATION generalizationspecialization ARMORED-UNIT--MILITARY-SPECIALTY  MANEUVER-UNIT--MILITARY-SPECIALTY The set of armored units at the battalion level. The set of maneuver units at the battalion level. MANEUVER-UNIT --MILITARY-SPECIALTY  ARMORED-UNIT --MILITARY-SPECIALTY MANEUVER-UNIT-MILITARY-SPECIALTYAVIATION-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTYINFANTRY-UNIT--MILITARY-SPECIALTY MODERN-MILITARY-UNIT--DEPLOYABLE SUBCLASS-OF SUBCLASS-OF SUBCLASS-OFSUBCLASS-OF MANEUVER-UNIT-MILITARY-SPECIALTYAVIATION-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTYINFANTRY-UNIT--MILITARY-SPECIALTY MODERN-MILITARY-UNIT--DEPLOYABLE SUBCLASS-OF SUBCLASS-OF SUBCLASS-OFSUBCLASS-OF

35.  G.Tecuci, Learning Agents Laboratory Dropping condition Generalizes an expression by removing a constraint from its description. ?O1 ISARMORED-UNIT-MILITARY-SPECIALTY ECHELON-OF-UNITBATTALION-UNIT-DESIGNATION ?O1 ISARMORED-UNIT-MILITARY-SPECIALTY generalizationspecialization The set of armored units at the battalion level. The set of armored units (at any level). Conversely, one can specialize an expression by adding a constraint.

36.  G.Tecuci, Learning Agents Laboratory Generalizing numbers Generalizes an expression by replacing a number with an interval, or by replacing an interval with a larger interval. ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION 5 ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION ?N1 ?N1 IS-IN [1.. 25] generalization specialization [1.. 25]  5 5  [1.. 25] The set of COAs with 5 offensive actions. The set of COAs with at least one and at most 30 of offensive actions. ?O1 IS COA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION ?N1 ?N1 IS-IN [1.. 30] generalization specialization [1.. 30]  [1.. 25] [1.. 25]  [1.. 30]

37.  G.Tecuci, Learning Agents Laboratory Adding alternatives ?O1 ISARMORED-UNIT-MILITARY-SPECIALTY ECHELON-OF-UNITBATTALION-UNIT-DESIGNATION ?O1 IS (ARMORED-UNIT-MILITARY-SPECIALTY or INFANTRY-UNIT-MILITARY-SPECIALTY) ECHELON-OF-UNITBATTALION-UNIT-DESIGNATION generalizationspecialization The set of armored units at the battalion level. The set including both armored units and infantry units at the battalion level. Generalizes an expression by replacing a concept C1 with the union (C1 U C2), which is a more general concept. Removing alternatives specializes an expression.

38.  G.Tecuci, Learning Agents Laboratory Operational definition of generalization/specialization Generalization/specialization of two concepts Least general generalization of two concepts Minimally general generalization of two concepts Types of generalizations and specializations Maximally general specialization of two concepts

39.  G.Tecuci, Learning Agents Laboratory Operational definition of generalization Non-operational definition: A concept P is said to be more general than another concept Q if and only if the set of instances represented by P includes the set of instances represented by Q. Operational definition: A concept P is said to be more general than another concept Q if and only if Q can be transformed into P by applying a sequence of generalization rules. This definition is not operational because it requires to show that each instance I from a potential infinite set Q is also in the set P.

40.  G.Tecuci, Learning Agents Laboratory Generalization of two concepts Definition: The concept Cg is a generalization of the concepts C1 and C2 if and only if Cg is more general than C1 and Cg is more general than C2. Operational definition: The concept Cg is a generalization of the concepts C1 and C2 if and only if both C1 and C2 can be transformed into Cg by applying generalization rules. MANEUVER-UNIT-MILITARY-SPECIALTY ARMORED-UNIT-- MILITARY- SPECIALTY INFANTRY- UNIT-- MILITARY- SPECIALTY MANEUVER-UNIT-MILITARY-SPECIALTY is a generalization of ARMORED-UNIT--MILITARY-SPECIALTY and INFANTRY-UNIT--MILITARY-SPECIALTY

41.  G.Tecuci, Learning Agents Laboratory Generalization of two concepts: example ?O1ISCOA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION 10 TYPEOFFENSIVE C1: ?O1ISCOA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION 5 C2: ?O1ISCOA-SPECIFICATION-MICROTHEORY TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION ?N1 ?N1IS-IN[5 … 10] C: Generalize 10 to [5.. 10] Drop “?O1 TYPE OFFENSIVE” Generalize 5 to [5.. 10]

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

43.  G.Tecuci, Learning Agents Laboratory Specialization of two concepts Definition: The concept Cs is a specialization of the concepts C1 and C2 if and only if Cs is less general than C1 and Cs is less general than C2. Operational definition: The concept Cs is a specialization of the concepts C1 and C2 if and only if both C1 and C2 can be transformed into Cs by applying specialization rules (or Cs can be transformed in both C1 and C2 by applying generalization rules). MILITARY- MANEUVER MILITARY- ATTACK PENETRATE-MILITARY-TASK is a specialization of MILITARY-MANEUVER and MILITARY-ATTACK PENETRATE- MILITARY-TASK

44.  G.Tecuci, Learning Agents Laboratory Other definitions The concept G is a minimally general generalization of A and B if and only if G is a generalization of A and B, and G is not more general than any other generalization of A and B. If there is only one minimally general generalization of two concepts A and B, then this generalization is called the least general generalization of A and B. The concept C is a maximally general specialization of two concepts A and B if and only if C is a specialization of A and B and no other specialization of A and B is more general than C.

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

46.  G.Tecuci, Learning Agents Laboratory Ontology matching Ontology matching allows one to look for instances of complex concepts in the object ontology (i.e. ask questions about the objects in the ontology). Is there a deployable military unit of the red side that performs an intelligence collection military task? Yes, RED-CSOP1 is a deployable military unit of the red side that performs SCREEN1 which is an intelligence collection military task? Example:

47.  G.Tecuci, Learning Agents Laboratory RED-CSOP1 TASK ?O2 ?O3 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 INTELLIGENCE-COLLECTION-MILTARY-TASK IS MODERN-MILITARY-UNIT--DEPLOYABLE Ontology matching: example RED--SIDE IS TASK SOVEREIGN-ALLEGIANCE-OF-ORG INTELLIGENCE-COLLECTION-MILTARY-TASK MODERN-MILITARY-UNIT--DEPLOYABLE RED--SIDE SCREEN1 SCREEN-MILITARY-TASK INSTANCE-OF SUBCLASS-OF MECHANIZED-INFANTRY-MORTAR-UNIT—MILITARY-SPECIALTY MECHANIZED-INFANTRY-UNIT—MILITARY-SPECIALTY INFANTRY-UNIT—MILITARY-SPECIALTY MANEUVER-UNIT—MILITARY-SPECIALTY INSTANCE-OF SUBCLASS-OF IS Is there a deployable military unit of the red side that performs an intelligence collection military task? Yes, RED-CSOP1 is a deployable military unit of the red side that performs SCREEN1 which is an intelligence collection military task?

48.  G.Tecuci, Learning Agents Laboratory IF the task to accomplish is: ASSESS-SECURITY-WRT-COUNTERING-ENEMY-RECONNAISSANCE FOR-COA ?O1 Then accomplish the task: ASSESS-SECURITY-WHEN-ENEMY-RECON-IS-PRESENT FOR-COA ?O1 FOR-UNIT ?O2 FOR-RECON-ACTION ?O3 Condition: ?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 RED--SIDE Rules as ontology-based representations of PSS A rule is an ontology-based representation of a problem solving step. ?O1 is a COA and ?O2 is a deployable military unit of ?O4 (which is the red side) and performs the task ?O3 (which is an intelligence collection military task) Condition: This rule will be applicable only if the current ontology contains an instance of the complex concept (?O1 ?O2 ?O3 ?O4) represented by the condition.

49.  G.Tecuci, Learning Agents Laboratory Ontology maintenance Maintaining the consistency of the object ontology is a complex knowledge engineering activity because the object and feature definitions interact in complex ways. Example: Deleting an object concept requires the updating of all the knowledge base elements that refer to it (e.g. the rules that contain it in their conditions; the features that contain it in their ranges or domains; the concepts that inherit its features).

50.  G.Tecuci, Learning Agents Laboratory Initial StateModified State f domain A f 7 C Af A C can no longer have the feature f because it is no longer in the domain of f B A f 7 C B Potential consequence of editing operations: Illustration

51.  G.Tecuci, Learning Agents Laboratory B AD Initial State Modified State C B A D C added superconcept resulted redundancy Potential consequence of editing operations: Illustration

52.  G.Tecuci, Learning Agents Laboratory Steps in ontology development 1.Define the basic concepts, and their organization into a hierarchical structure (the generalization hierarchy). 2.Define the generic object features, using the previously defined concepts to specify their domains and ranges. 3.Extend the object ontology with new concepts and features. 4.Repeat steps 1,2,3 until the ontology is judged to be complete enough.

53.  G.Tecuci, Learning Agents Laboratory Steps in ontology development: illustration 1. Define basic object concepts 2. Use the basic object concepts to define generic object features 3. Use object and feature definitions to define new objects and their features.

54.  G.Tecuci, Learning Agents Laboratory Representation of the input description of a COA Overall organization of the COA object ontology Ontology of military events Ontology of military organizations 4. An example: the COA object ontology Representation of the structure of the COA

55.  G.Tecuci, Learning Agents Laboratory SOVEREIGN-ALLEGIANCE-OF-ORG ASSIGNMENT TASK BLUE-TASK-FORCE1 TASK BLUE--SIDE MAIN-EFFORT1 PENETRATE1 CLEAR1 IS-TASK-OF-OPERATION ATTACK2 Example: BLUE-TASK-FORCE1, a balanced task force (MAIN-EFFORT1) attacks to penetrate RED-MECH-COMPANY4, then clears RED-TANK-COMPANY2 … OBJECT-ACTED-ON RED-MECH-COMPANY4 OBJECT-ACTED-ON RED-TANK-COMPANY2 Representation of the input description of a COA The information from the COA sketch and COA statement is translated into descriptions in the object ontology.

56.  G.Tecuci, Learning Agents Laboratory ACTION MILITARY-PURPOSE MILITARY-EVENT MODERN-MILITARY-ORGANIZATION ORGANIZATION MILITARY-OPERATIONMILITARY-TASK PURPOSE MILITARY-EQUIPMENT EQUIPMENT GEOGRAPHICAL-REGION COA-SPECIFICATION-MICROTHERY PLAN Overall organization of the COA object ontology SUBCLASS-OF

57.  G.Tecuci, Learning Agents Laboratory BLUE-TASK-FORCE1 MECHANIZED-INFANTRY-UNIT--MILITARY-SPECIALTY BLUE-ARMOR-BRIGADE2 MANEUVER-UNIT-MILITARY-SPECIALTYAVIATION-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTYINFANTRY-UNIT--MILITARY-SPECIALTY MODERN-MILITARY-UNIT--DEPLOYABLE MODERN-MILITARY-ORGANIZATION BLUE-MECH-BRIGADE1 BLUE-TASK-FORCE2 BLUE-TASK-FORCE3 Ontology of military organizations INSTANCE-OFSUBCLASS-OF

58.  G.Tecuci, Learning Agents Laboratory ECHELON-OF-UNIT OPERATIONAL-CONTROL-MILITARY-ORG SOVEREIGN-ALLEGIANCE-OF-ORG ASSIGNMENT TASK BLUE-TASK-FORCE1 TASK BATALLION--UNIT-DESIGNATION INSTANCE-OF MECHANIZED-INFANTRY-UNIT--MILITARY-SPECIALTY ARMORED-UNIT--MILITARY-SPECIALTY BLUE--SIDE MAIN-EFFORT1 PENETRATE1 CLEAR1 REGULAR-STATUS BLUE-MECH-COMPANY1 BLUE-MECH-COMPANY2 BLUE-ARMOR-COMPANY1 BLUE-ARMOR-COMPANY2 OPERATIONAL-CONTROL-MILITARY-ORG TROOP-STRENGTH-OF-UNIT INSTANCE-OF COA411 Representation of a specific organization

59.  G.Tecuci, Learning Agents Laboratory AREA-DEFENSE--MILITARY-OPERATION MILITARY-MANEUVER MILITARY-EVENT MILITARY-OPERATIONMILITARY-TASK DEFEND1DEFEND2 SCREEN--MILITARY-TASK SCREEN1 PENETRATE--MILITARY-MANEUVER PENETRATE1PENETRATE2 Ontology of military events INSTANCE-OFSUBCLASS-OF

60.  G.Tecuci, Learning Agents Laboratory OBJECT-ACTED-ON IS-TASK-OF-OPERATION TASK-HAS-PURPOSE DESTROY1 TASK INSTANCE-OF DESTROY--MILITARY-TASK PREVENT-MILITARY-PURPOSE3 RED-CSOP1 UNIT-ASSIGNED-TO-TASK BLUE-TASK-FORCE2 INDICATES-MISSION-TYPE "military offensive operation" FORCE-RATIO 20 RECOMMENDED-FORCE-RATIO 2.5 (missing element) ASSIGNMENT SUPPORTING-EFFORT1 MILITARY-TASK "military offensive operation" IS-OFFENSIVE-ACTION-FOR SUBCLASS-OF COA-411 Representation of military tasks

61.  G.Tecuci, Learning Agents Laboratory IS-SECURITY-ACTION-FOR "military offensive operation" IS-SECURITY-ACTION-FOR "military defensive operation" IS-INDEPENDENT-ACTION-FOR SCREEN--MILITARY-TASK "military offensive operation" IS-INDEPENDENT-ACTION-FOR "military defensive operation" IS-ECONOMY-OF-FORCE-ACTION-FOR "military offensive operation" "military defensive operation" IS-ECONOMY-OF-FORCE-ACTION-FOR Representation of military tasks SCREEN1 INSTANCE-OF MILITARY-TASK SUBCLASS-OF

62.  G.Tecuci, Learning Agents Laboratory COA- SPECIFICATION- MICROTHERY COA411 BLUE-BRIGADE-OP BLUE-BRIGADE-TASK TOTAL-NBR-ACTIONS TOTAL-NBR-SECURITY-ACTIONS-FOR-MISSION TOTAL-NBR-SURPRISE-ACTIONS-FOR-MISSION TOTAL-NBR-DECEPTION-ACTIONS-FOR-MISSION COA-MAIN-EFFORT-OFFENSIVE-ACTION-FOR-MISSION TOTAL-NBR-SUPPORTING-EFFORTS-OFFENSIVE-ACTIONS-FOR-MISSION MISSION-LEVEL-TASK-OF-COA OPERATION-OF-COA COA-SECURITY-ACTION-FOR-MISSION TOTAL-NBR-MAIN-EFFORT-OFFENSIVE-ACTIONS-FOR-MISSION COA-SUPPORTING-EFFORT-OFFENSIVE-ACTION-FOR-MISSION TOTAL-NBR-INDEPENDENT-ACTIONS-FOR-MISSION END-STATE-OF-COA TOTAL-NBR-OFFENSIVE-ACTIONS-FOR-MISSION PLAN-TO-ACHIEVE1 PENETRATE1 DESTROY1 DESTROY2 SECURE1 FIX1 FIX2 COA-SUPPORTING-EFFORT-OFFENSIVE-ACTION-FOR-MISSION COA-SECURITY-ACTION-FOR-MISSION INSTANCE-OF 3 0 0 0 11 6 1 4 COA411 HAS-DECISIVE-POINT DECISIVE-POINT1 Representation of the structure of the COA

63.  G.Tecuci, Learning Agents Laboratory MILITARY-OFFENSIVE-OP CLOSE-UNIT-IN-MISSION RESERVE-UNIT-IN-MISSION REAR-UNIT-IN-MISSION DEEP-OPERATION-TASK FIRE-OPERATION-TASK BLUE-BRIGADE-OP CLOSE-UNIT-IN-MISSION SECURITY-UNIT-IN-MISSION BLUE-TASK-FORCE1 BLUE-TASK-FORCE2 BLUE-MECH-BATALION1 SECURITY-UNIT-IN-MISSION BLUE-MECH-COMPANY8 BLUE-MECH-PLT1 DESTROY3 SUPRESS1 INSTANCE-OF COA-411 Representation of the main operation of COA411

64.  G.Tecuci, Learning Agents Laboratory IS-TASK-OF-OPERATION TASK-HAS-PURPOSE OBJECT-ACTED-ON SUB-TASKS--MILTARY BLUE-BRIGADE-TASK INDICATES-MISSION-TYPE IS-OFFENSIVE-ACTION-FOR SUB-TASKS--MILTARY DESTROY1 PENETRATE1 CLEAR1 FIX2 CLEAR3 FIX1 CLEAR2 SECURE1 SUPRESS1 DESTROY2 DESTROY3 PENETRATE--MILITARY-TASK RED-MECH-REGIMENT2 BLUE-BRIGADE-OP ENABLE-MILITARY-PURPOSE1 "military offensive operation" COA-411 INSTANCE-OF Representation of the main task of COA411

65.  G.Tecuci, Learning Agents Laboratory Exercises 5. Exercises

66.  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

67.  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.

68.  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.

69.  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.

70.  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.

71.  G.Tecuci, Learning Agents Laboratory Consider the question: “Is there a part of a loudspeaker that is made of metal?” In the context of the following object ontology. 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

72.  G.Tecuci, Learning Agents Laboratory Consider the ontology from the previous slide and 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

73.  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 10.6. 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

74.  G.Tecuci, Learning Agents Laboratory Exercise Consider the background knowledge represented by the following generalization hierarchies and theorem: 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.

75.  G.Tecuci, Learning Agents Laboratory 6. Required reading G. Tecuci, Building Intelligent Agents, Academic Press, 1998, pp. 33-78 (required). Boicu M., Tecuci G., Stanescu B., Balan G.C. and Popovici E., Ontologies and the Knowledge Acquisition Bottleneck, in Proceedings of IJCAI-2001 Workshop on Ontologies and Information Sharing, Seattle, Washington, August 2001. http://lalab.gmu.edu/publications/data/2001/ontbottleneck.pdf (required)