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

2.  G.Tecuci, Learning Agents Laboratory Overview Learning agent shells Limits of the classical knowledge engineering approaches Advanced approaches to KB and agent development Recommended reading

3.  G.Tecuci, Learning Agents Laboratory How are agents built A knowledge engineer attempts to understand how a subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base. The expert analyzes the solutions generated by the agent (and often the knowledge base itself) to identify errors, and the knowledge engineer corrects the knowledge base.

4.  G.Tecuci, Learning Agents Laboratory Why it is hard to build agents The knowledge engineer has to become a kind of subject matter expert in order to properly understand expert’s problem solving knowledge. This takes time and effort. Experts express their knowledge informally, using natural language, visual representations and common sense, often omitting essential details that are considered obvious. This form of knowledge is very different from the one in which knowledge has to be represented in the knowledge base (which is formal, precise, and complete). This modeling and representation of knowledge is long, painful and inefficient.

5.  G.Tecuci, Learning Agents Laboratory Limited ability to reuse previously developed knowledgeThe knowledge acquisition bottleneckThe knowledge maintenance bottleneckThe scalability of the agent building processFinding the right balance between using general tools and developing domain specific modules Portability of the tools and of the developed agents Limiting factors in developing intelligent agents

6.  G.Tecuci, Learning Agents Laboratory Advanced approaches to KB and agent development Limited ability to reuse previously developed knowledge Problem: Ontology reuse (import, merge, export, OKBC protocol, CYC) Solution: Example: Ontologies of military units and equipment developed for a particular military planning agent could be reused by a course of action critiquing agent or other military agent.

7.  G.Tecuci, Learning Agents Laboratory The knowledge acquisition bottleneck Problem: Automation of knowledge acquisition through machine learning Solution: Example: A subject matter expert teaching an agent through examples and explanations, similarly to how the expert would teach an apprentice. Advanced approaches to KB and agent development

8.  G.Tecuci, Learning Agents Laboratory The knowledge maintenance bottleneck Problem: Use of machine learning methods by the agent, to continuously update its knowledge base in response to changes in the application domain or in the requirements of the application. Solution: Example: A subject matter expert providing feedback to the agent and guiding it to update its knowledge base. Remark: Currently, software maintenance is four times more expensive that software development. With learning agents that are directly taught by humans, there is no longer a distinction between building the agent and maintaining it. Advanced approaches to KB and agent development

9.  G.Tecuci, Learning Agents Laboratory Finding the right balance between using general tools and developing domain specific modules Problem: Customizable learning agent shell. It is applicable to a wide variety of application domains. Requires limited customization. Solution: Example: Disciple learning agent shell Advanced approaches to KB and agent development

10.  G.Tecuci, Learning Agents Laboratory Learning agent shells Sample Disciple problem solving agent The concept of learning agent shell The Disciple learning agent shell Illustration of the Disciple approach to agent development

11.  G.Tecuci, Learning Agents Laboratory Expert system shell Problem Solving Engine Expert System Shell Empty Knowledge Base An expert system is a system that can help solve complex, real-world problems, in specific scientific, engineering, medical specialties, etc., by using large bodies of domain knowledge (facts, and procedures) gleaned from human experts, that have proven useful for solving typical problems in their domain. An expert system shell is a system that consists of an inference engine for a certain class of tasks (like planning, design, diagnosis, monitoring, prediction, interpretation, etc.) and supports representation formalisms in which a knowledge base can be encoded. If the inference engine of is adequate for a certain expert task, then the process of building the expert system is reduced to the building of the knowledge base.

12.  G.Tecuci, Learning Agents Laboratory Learning agent shell: definition A learning agent shell is a tool for building agents. It contains a general problem solving engine, a learning engine and an empty knowledge base structured into an object ontology and a set of rules. Building an agent for a specific application consists in customizing the shell for that application and in developing the knowledge base. The learning engine facilitates the building of the knowledge base by subject matter experts and knowledge engineers. Interface Problem Solving Learning Ontology + Rules

13.  G.Tecuci, Learning Agents Laboratory The Disciple learning agent shell: - can use imported ontological knowledge; - solves problems through task reduction; - can be taught directly by subject matter experts to become a knowledge-based assistant. Mixed-initiative reasoning between the expert that has the knowledge to be formalized and the agent that knows how to formalize it. Disciple learning agent shell The expert teaches the agent to perform various tasks in a way that resembles how the expert would teach a person. The agent learns from the expert, building, verifying and improving its knowledge base Interface Problem Solving Learning Ontology + Rules

14.  G.Tecuci, Learning Agents Laboratory In this approach, called Disciple, the complex knowledge engineering activities, traditionally performed by a knowledge engineer with assistance from a subject matter expert, are replaced with equivalent ones performed by the subject matter expert and a learning agent, through mixed-initiative reasoning, and with very limited assistance from the knowledge engineer. Define domain model Create ontology Define rules Verify and update rules KE SME Traditionally KE Agent SMEAgent SME Specify instances Learn ontological elements Import and create initial ontology Agent Learn rules SME Agent Define and explain examples SME AgentSMEAgent Critique examples Refine rules Explain critiques SME Agent Extend domain model SME KE Define initial model With Disciple Main idea of the Disciple mixed-initiative approach

15.  G.Tecuci, Learning Agents Laboratory 1. Automating the domain modeling process that consists of making explicit, at an informal level, the way the expert solves problems. 4. Learning complex problem solving rules directly from a subject matter expert. 5. Learning object concepts that extend the generic ontology directly from a subject matter expert. 2. Building the initial generic object ontology through import from external repositories and direct elicitation from a subject matter expert. 3. Populating the generic object ontology with instances and relationships that describe a specific situation or scenario. What are the main technical challenges

16.  G.Tecuci, Learning Agents Laboratory 1.Develop a general approach to domain modeling that allows a subject matter expert to express the way he or she performs a task based on the task reduction paradigm. 2.Structure the knowledge base into an object ontology that can be imported/reused and a set of problem solving rules that can be learned from a subject matter expert. 3.Develop methods to import/reuse ontological knowledge from previously developed knowledge bases or repositories. 4.Develop a learnable knowledge representation that can express partially learned knowledge at various levels of formalization. 5.Develop multistrategy learning methods that synergistically integrate several learning strategies. 6.Develop methods for integrated teaching and learning where the SME helps the agent to learn, and the agent helps the SME to teach it. 7.Use of plausible reasoning to hypothesize solutions based on incomplete and partially incorrect knowledge. How are these challenges addressed

17.  G.Tecuci, Learning Agents Laboratory General architecture of Disciple-RKF Instances Solving Rules Teaching, Learning and Problem Solving Intelligent User Interface Autonomous Problem Solving Modeling Knowledge Base Management Ontology Import Ontology Instances Solving Problem Rules Mixed_Initiative Problem Solving Natural Language Generation Ontology Editors and Browsers Rule Learning Rule Refinement Scenario Elicitation Task Learning

18.  G.Tecuci, Learning Agents Laboratory Sample Disciple problem solving agent Illustration of the problem solving process: Rule-based task reduction Agent task: Identification of strategic center of gravity candidates The structure of the knowledge base: Object ontology + task reduction rules

19.  G.Tecuci, Learning Agents Laboratory Agent task Identify strategic Center of Gravity (CoG) candidates for a military scenario. Input A description of a military scenario, such as the World War II invasion of Sicily by the Anglo allies, in 1943. Output Strategic CoG candidates for each opposing force and its members (e.g. Anglo allies, US, Britain). The agent will also provide a detailed description of its lines of reasoning. Sample agent: Disciple-RKF/CoG

20.  G.Tecuci, Learning Agents Laboratory The center of gravity of an entity (state, alliance, coalition, or group) is the foundation of capability, the hub of all power and movement, upon which everything depends, the point against which all the energies should be directed. Carl Von Clausewitz, “On War,” 1832. If a combatant eliminates or influences the enemy’s strategic center of gravity, then the enemy will lose control of its power and resources and will eventually fall to defeat. If the combatant fails to adequately protect his own strategic center of gravity, he invites disaster. The Center of Gravity (CoG) concept

21.  G.Tecuci, Learning Agents Laboratory Strategic COG candidates identified by the agent The will of the Anglo_allies_1943 is a strategic COG candidate for Anglo_allies_1943 which is an equal partner alliance The cooperation between the subgroups of Allied_forces_operations_Husky which conduct “combined and joint operations” is a strategic COG candidate for Anglo_allies_1943 President_Roosevelt is a strategic COG candidate for US_1943 which is a representative democracy and a member of Anglo_allies_1943 The “will of the people” of US_1943 is a strategic COG candidate for US_1943 which is a representative democracy and a member of Anglo_allies_1943 Industrial_capacity_of_US_1943 is a strategic COG candidate for US_1943 which is a member of Anglo_allies_1943 Army_of_Germany_1943 is a strategic COG candidate for for Germany_1943 which is a member of European_Axis_1943 … Identify the strategic COG candidates for the Sicily_1943 scenario

22.  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 tasks to simpler tasks. They are described using the objects from the ontology. Knowledge Base = Object ontology + Task reduction rules

23.  G.Tecuci, Learning Agents Laboratory has_as_primary_ force_element ScenarioStrategic COG relevant factor Force Force goal has as opposing force Sicily_1943 “WWII Allied invasion of Sicily in 1943” brief_description Opposing_force Single_state_force Single_group_force Multi_group_force Multi_state_force Anglo_allies_1943 US_1943 Britain_1943 component_ state Group Allied_forces_operation_Husky type_of_operations has_as_subgroup US_7 th _Army_ (Force_343) “combined and joint operations” has_as_subgroup Br_8 th _Army_ (Force_545) has_as_subgroup Western_Naval_TF has_as_subgroup Eastern_Naval_TF has_as_subgroup US_9 th _Air_Force has_as_subgroup Northwest_Africa_Air_Force instance_of subconcept_of instance_of Fragment of the Disciple-CoG ontology Multi_state_alliance Equal_partners_ multi_state_ alliance subconcept_of Resource_or_ infrastructure_ element Strategically_ essential_ goods_or_ materiel War_materiel_ and_transports Product War_materiel_ and_transports _of_US_1943 Economic_ factor Industrial_ capacity Industrial_ factor has_as_industrial_factor industrial_ capacity_ of_ US_1943 is_a_major_ generator_of instance_of subconcept_of … … … … …

24.  G.Tecuci, Learning Agents Laboratory Sample task reduction rule IF Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is ?O1 The member is ?O2 THEN A strategic COG candidate for a member of a force has been identified The force is ?O1 The member is ?O2 The strategic COG candidate is ?O3 Condition ?O1ISForce ?O2ISForce has_as_industrial_factor ?O3 ?O3ISIndustrial_factor is_a_major_generator_of ?O4 ?O4ISStrategically_essential_goods_or_materiel IF Identify the strategic COG candidates with respect to the industrial civilization of ?O2 which is a member of ?O1 Question Who or what is a strategically critical industrial civilization element in ?O2 ? Answer ?O3 THEN ?O3 is a strategic COG candidate for ?O2 which is a member of ?O1 INFORMAL STRUCTURE OF THE RULE FORMAL STRUCTURE OF THE RULE A rule is an ontology-based representation of an elementary problem solving process.

25.  G.Tecuci, Learning Agents Laboratory Illustration of rule-based task reduction Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is Anglo_allies_1943 The member is US_1943 IF the task is Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is ?O1 The member is ?O2 THEN A strategic COG candidate for a member of a force has been identified The force is ?O1 The member is ?O2 The strategic COG candidate is ?O3 Condition ?O1ISForce ?O2ISForce has_as_industrial_factor ?O3 ?O3ISIndustrial_factor is_a_major_generator_of ?O4 ?O4ISStrategically_essential_goods_or_materiel ?O1  Anglo_allies_1943 ?O2  US_1943 A strategic COG candidate for a member of a force has been identified The force is Anglo_allies_1943 The member is US_1943 The strategic COG candidate is Industrial_capacity_of_US_1943 ?O3  Industrial_capacity_of_US_1943 ?O1  Anglo_allies_1943 ?O2  US_1943 US_1943 has_as_industrial_factor Industrial_capacity_of_US_1943 War_materiel_and_ transports_of_US_1943 is_a_major_generator_of Anglo_allies_1943 Strategically_essential_ goods_or_materiel Industrial_factor Force ?O3 = ?O4 = ?O1 = ?O2 =

26.  G.Tecuci, Learning Agents Laboratory Rule-based CoG candidates identification Rule_1 Rule_2 Identify the strategic COG candidates for a member of a force with respect to its civilization The force is Anglo_allies_1943 The member is US_1943 Who or what is a strategically critical industrial civilization element in US_1943? Industrial_capacity_of_US_1943 A strategic COG candidate for a member of a force has been identified The force is Anglo_allies_1943 The member is US_1943 The strategic COG candidate is Industrial_capacity_of_US_1943 Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is Anglo_allies_1943 The member is US_1943 US_1943 is an industrial civilization At what level is the civilization of US_1943 organized?

27.  G.Tecuci, Learning Agents Laboratory The Disciple approach to agent development Development of the scenario elicitation module Modeling the problem solving process as task reduction Ontology specification, import and development Agent teaching

28.  G.Tecuci, Learning Agents Laboratory Modeling the problem solving process as task reduction The knowledge engineer and the SME have to represent naturally (in English) the SME’s problem solving process as a sequence of task reduction and composition steps. A complex task is solved by: successively reducing it to simpler tasks, finding the solutions of the simplest tasks, successively composing these solutions until the solution to the initial task is developed. S 1 S 11 S 1n S 111 S 11m T 11m T 111 T 1n T 11 T1T1 … …

29.  G.Tecuci, Learning Agents Laboratory Identification through task reduction Identification means recognizing an entity as being a certain thing. Example: Identification of the strategic center of gravity candidates in military conflicts.

30.  G.Tecuci, Learning Agents Laboratory Identify the strategic COG candidates for the Okinawa_1945 scenario US_1945 Which is an opposing force in the Okinawa_1945 scenario? Is US_1945 a single member force or a multi-member force? US_1945 is a single-member force I consider strategic COG candidates with respect to the type of operations being conducted by this force I need to Identify the strategic COG candidates for US_1945 Therefore I need to Identify the strategic COG candidates for US_1945 which is a single-member force Therefore I need to … Identify the strategic COG candidates with respect to the type of operations being conducted by the US_1945, a single member force Therefore I need to Modeling CoG identification through task reduction What types of strategic COG candidates should I consider for this single_member force?

31.  G.Tecuci, Learning Agents Laboratory Ontology specification, import and development Development of the object ontology Modeling-based ontology specification Import of ontological knowledge from knowledge repositories

32.  G.Tecuci, Learning Agents Laboratory Identify the strategic COG candidates for the Okinawa_1945 scenario US_1945 Identify the strategic COG candidates for US_1945 Which is an opposing force in the Okinawa_1945 scenario? Is US_1945 a single member force or a multi-member force? US_1945 is a single-member force Identify the strategic COG candidates for US_1945 which is a single-member force What types of strategic COG candidates should I consider for this single_member force? scenario strategic COG candidate force opposing force single member force multi member force Okinawa 1945 US 1945 Subject Matter Expert Knowledge Engineer Ontology specification Modeling-based ontology specification

33.  G.Tecuci, Learning Agents Laboratory Mixed initiative Ontology Import DISCIPLE ONTOLOGY INTERMEDIATE DISCIPLE ONTOLOGY Mixed-initiative ontology retrieval ONTOLINGUA-KBCYC-KB … Specialized Ontology Retrieval … CYC Ontology Retrieval OKBC Ontology Retrieval Intermediate OKBC File Intermediate CYC File … Automatic Ontology Translation Translation Engine OKBC Rule Library CYC Rule Library Subject Matter Expert Knowledge Engineer Ontology import facilitates the development of the initial ontology by reusing knowledge from the external repositories. Object ontology import

34.  G.Tecuci, Learning Agents Laboratory Ontology Browsing and Editing Several specialized tool for browsing and editing the ontology facilitate the development of the initial object ontology by the knowledge engineer. Instances Solving Rules Ontology Tools Knowledge Base Management Ontology Instances Solving Problem Rules Scenario Elicitation Object Hierarchy Browsers Feature Hierarchy Browsers Association Browser Object Viewer Feature Viewer Object Editor Feature Editor Ontology development using Disciple tools

35.  G.Tecuci, Learning Agents Laboratory ScenarioStrategic COG relevant factor Force Force goal Opposing_force Single_state_force Single_group_force Multi_group_force Multi_state_forceGroup subconcept_of Fragment of the generic CoG ontology Multi_state_alliance Equal_partners_ multi_state_ alliance subconcept_of Resource_or_ infrastructure_ element Strategically_ essential_ goods_or_ materiel War_materiel_ and_transports Product Economic_ factor Industrial_ capacity Industrial_ factor subconcept_of … … … … … The initial ontology contains a hierarchical description of the types of objects from the CoG domain.

36.  G.Tecuci, Learning Agents Laboratory Development of the scenario elicitation module Populating the object ontology through scripts execution Elicitation scripts Scenario elicitation interface The scenario elicitation module

37.  G.Tecuci, Learning Agents Laboratory SCENARIO ELICITATION ENGINE SCENARIO DESCRIPTION GENERIC OBJECT ONTOLOGY ELICITATION SCRIPTS Natural language The SME is guided by Disciple to describe the relevant aspects of a strategic environment. Disciple populates a generic object ontology that represents the scenario. The scenario elicitation module Guides the expert to describe a scenario or situation and creates a formal description of it consisting of instances of the generic object ontology.

38.  G.Tecuci, Learning Agents Laboratory subconcept_of Scenario Script type: Elicit properties of an instance of Scenario: Script calls: Elicit the feature brief_description for Elicit the feature description for Elicit the feature has_as_opposing_force for elicitation_script Script type: Elicit instances of Scenario Controls: Question: “Provide a name for the scenario to be analyzed:” Answer variable: Default value: “new-scenario” Control type: single-line Ontology actions: instance-of Scenario Script calls: Elicit the properties of the instance elicitation_script Sample elicitation scripts

39.  G.Tecuci, Learning Agents Laboratory Scenario elicitation interface

40.  G.Tecuci, Learning Agents Laboratory Scenario Force Opposing_force subconcept-of instance-of Okinawa Japan-1945 Has_as_opposing_force instance-of US-1945 Has_as_opposing_force instance-of Script type: Elicit the feature Has_as_opposing_force for an instance Controls: Question: Name the opposing forces in Answer variable: Control type: multiple-line, height 4 Ontology actions: instance-of Opposing_force Has_as_opposing_force Script calls: Elicit properties of the instance in new window Execution of the elicitation scripts

41.  G.Tecuci, Learning Agents Laboratory Agent teaching requires completion of all these phases by the subject matter experts, with limited assistance from knowledge engineers. Scenario specification Domain modeling Task learning Rule learning Rule refinement Problem solving Teaching of the Disciple agent by the SME

42.  G.Tecuci, Learning Agents Laboratory Scenario Elicitation Demo Scenario Elicitation - Demo

43.  G.Tecuci, Learning Agents Laboratory has_as_primary_ force_element ScenarioStrategic COG relevant factor Force Force goal has as opposing force Sicily_1943 “WWII Allied invasion of Sicily in 1943” brief_description Opposing_force Single_state_force Single_group_force Multi_group_force Multi_state_force Anglo_allies_1943 US_1943 Britain_1943 component_ state Group Allied_forces_operation_Husky type_of_operations has_as_subgroup US_7 th _Army_ (Force_343) “combined and joint operations” has_as_subgroup Br_8 th _Army_ (Force_545) has_as_subgroup Western_Naval_TF has_as_subgroup Eastern_Naval_TF has_as_subgroup US_9 th _Air_Force has_as_subgroup Northwest_Africa_Air_Force instance_of subconcept_of instance_of Fragment of the Disciple-CoG ontology Multi_state_alliance Equal_partners_ multi_state_ alliance subconcept_of Resource_or_ infrastructure_ element Strategically_ essential_ goods_or_ materiel War_materiel_ and_transports Product War_materiel_ and_transports _of_US_1943 Economic_ factor Industrial_ capacity Industrial_ factor has_as_industrial_factor industrial_ capacity_ of_ US_1943 is_a_major_ generator_of instance_of subconcept_of … … … … …

44.  G.Tecuci, Learning Agents Laboratory Scenario specification Domain modeling Task formalization Rule learning Rule refinement Problem solving Domain Modeling by SME is the most uncertain R&D issue. Teaching of the Disciple agent by the SME

45.  G.Tecuci, Learning Agents Laboratory Modeling problem solving as task reduction Identify the strategic COG candidates with respect to the civilization of US_1943 which is a member of Anglo_allies_1943 At what level is the civilization of US_1943 organized? US_1943 is an industrial civilization Who or what is a strategically critical industrial civilization element in US_1943? Industrial_capacity_of_US_1943 Identify the strategic COG candidates with respect to the industrial civilization of US_1943 which is a member of Anglo_allies_1943 Industrial_capacity_of_US_1943 is a strategic COG candidate for US_1943 which is a member of Anglo_allies_1943 I need to Therefore I need to Therefore I conclude that

46.  G.Tecuci, Learning Agents Laboratory The modeling interface

47.  G.Tecuci, Learning Agents Laboratory Domain Modeling Demo Domain Modeling Tool - Demo

48.  G.Tecuci, Learning Agents Laboratory Scenario specification Domain modeling Task learning Rule learning Rule refinement Problem solving Teaching of the Disciple agent by the SME

49.  G.Tecuci, Learning Agents Laboratory Formalization of the tasks Identify the strategic COG candidates for a member of a force with respect to its civilization The force is Anglo_allies_1943 The member is US_1943 Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is Anglo_allies_1943 The member is US_1943 Identify the strategic COG candidates with respect to the civilization of US_1943 which is a member of Anglo_allies_1943 At what level is the civilization of US_1943 organized? US_1943 is an industrial civilization Identify the strategic COG candidates with respect to the industrial civilization of US_1943 which is a member of Anglo_allies_1943 I need to Therefore I need to Who or what is a strategically critical industrial civilization element in US_1943? Industrial_capacity_of_US_1943 Industrial_capacity_of_US_1943 is a strategic COG candidate for US_1943 which is a member of Anglo_allies_1943 Therefore I conclude that A strategic COG candidate for a member of a force has been identified The force is Anglo_allies_1943 The member is US_1943 The strategic COG candidate is Industrial_capacity_of_US_1943

50.  G.Tecuci, Learning Agents Laboratory Task learning Identify the strategic COG candidates for a member of a force with respect to its civilization The force is Anglo_allies_1943 The member is US_1943 Plausible upper condition ?O1 IS Force ?O2 IS Force Plausible lower bound condition ?O1 IS Anglo_allies_1943 ?O2 IS US_1943 Force Opposing_force Single_state_force Multi_state_force Anglo_allies_1943 US_1943 Britain_1943 component_ state instance_of subconcept_of instance_of Multi_state_alliance Equal_partners_ multi_state_ alliance subconcept_of Identify the strategic COG candidates for a member of a force with respect to its civilization The force is ?O1 The member is ?O2 FORMAL STRUCTURE OF THE TASK Identify the strategic COG candidates with respect to the civilization of ?O2 which is a member of ?O1 Identify the strategic COG candidates with respect to the civilization of US_1943 which is a member of Anglo_allies_1943 INFORMAL STRUCTURE OF THE TASK

51.  G.Tecuci, Learning Agents Laboratory Task Formalization Demo Task Formalization - Demo

52.  G.Tecuci, Learning Agents Laboratory Scenario specification Domain modeling Task formalization Rule learning Rule refinement Problem solving Rule learning is a very difficult barrier in agent development by an SME. Teaching of the Disciple agent by the SME

53.  G.Tecuci, Learning Agents Laboratory Identify the strategic COG candidates with respect to the industrial civilization of a state which is a member of a force The force is Anglo_allies_1943 The member is US_1943 A strategic COG candidate for a member of a force has been identified The force is Anglo_allies_1943 The member is US_1943 The strategic COG candidate is Industrial_capacity_of_US_1943 Explanation: US_1943 has_as_industrial_factor Industrial_capacity_of_US_1943 Industrial_capacity_of_US_1943 is_a_major_generator_of War_materiel_and_transports_of_US_1943 War_materiel_and_transports_of_US_1943 IS Strategically_essential_goods_or_materiel Natural LanguageLogic Who or what is a strategically critical industrial civilization element in US_1943? Industrial_capacity_of_US_1943 Industrial_capacity_of_US_1943 is a strategic COG candidate for US_1943 which is a member of Anglo_allies_1943 Identify the strategic COG candidates with respect to the industrial civilization of US_1943 which is a member of Anglo_allies_1943 Language to logic translation

54.  G.Tecuci, Learning Agents Laboratory Example of a task reduction step PVS Rule analogy PLB PUB Knowledge Base Incomplete justification Analogy and Hint Guided Explanation Analogy and Hint Guided Explanation Analogy-based Generalization Analogy-based Generalization The rule learning method

55.  G.Tecuci, Learning Agents Laboratory IF Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is ?O1 The member is ?O2 THEN A strategic COG candidate for a member of a force has been identified The force is ?O1 The member is ?O2 The strategic COG candidate is ?O3 Plausible Upper Bound Condition ?O1ISForce ?O2ISForce has_as_industrial_factor ?O3 ?O3ISIndustrial_factor is_a_major_generator_of ?O4 ?O4ISProduct Plausible Lower Bound Condition ?O1ISAnglo_allies_1943 ?O2ISUS_1943 has_as_industrial_factor ?O3 ?O3ISIndustrial_capacity_of_US_1943 is_a_major_generator_of ?O4 ?O4ISWar_materiel_and_transports_of_US_1943 Justification ?O2 has_as_industrial_factor ?O3 ?O3 is_a_major_generator_of ?O4 Learned plausible version space rule IF Identify the strategic COG candidates with respect to the industrial civilization of ?O2 which is a member of ?O1 Question Who or what is a strategically critical industrial civilization element in ?O2 ? Answer ?O3 THEN ?O3 is a strategic COG candidate for ?O2 which is a member of ?O1 INFORMAL STRUCTURE OF THE RULE FORMAL STRUCTURE OF THE RULE

56.  G.Tecuci, Learning Agents Laboratory Rule Learning Demo Rule Learning - Demo

57.  G.Tecuci, Learning Agents Laboratory Scenario specification Domain modeling Task formalization Rule learning Rule refinement Problem solving Rule refinement is another very difficult barrier in agent development by an SME. Teaching of the Disciple agent by the SME

58.  G.Tecuci, Learning Agents Laboratory Knowledge Base Failure explanation PVS Rule Example of task reductions generated by the agent Incorrect example Correct example Learning from Explanations Learning from Explanations Learning by Analogy And Experimentation Learning by Analogy And Experimentation Learning from Examples The rule refinement method

59.  G.Tecuci, Learning Agents Laboratory Identify the strategic COG candidates for the Sicily_1943 scenario Anglo_allies_1943 Identify the strategic COG candidates for Anglo_allies_1943 Which is an opposing force in the Sicily_1943 scenario? Modeling, learning, problem solving Is Anglo_allies_1943 a single member force or a multi-member force? Anglo_allies_1943 is a multi-member force Identify the strategic COG candidates for the Anglo_allies_1943 which is a multi-member force … Rule_1 European_Axis_1943 Identify the strategic COG candidates for European_Axis_1943 Rule_2 Is European_Axis_1943 a single member force or multi-member force? European_Axis_1943 is a multi-member force Identify the strategic COG candidates for the European_Axis _1943 which is a multi-member force

60.  G.Tecuci, Learning Agents Laboratory Task and rule refinement Identify the strategic COG candidates for a member of a force with respect to its civilization The force is Anglo_allies_1943 The member is Britain_1943 Britain_1943 is an industrial civilization Who or what is a strategically critical industrial civilization element in Britain_1943? Industrial_capacity_of_Britain_1943 Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is Anglo_allies_1943 The member is Britain_1943 A strategic COG candidate for a member of a force has been identified The force is Anglo_allies_1943 The member is Britain_1943 The strategic COG candidate is Industrial_capacity_of_Britain_1943 I need to Therefore I need to Therefore I conclude that At what level is the civilization of Britain_1943 organized? Task Refinement Rule Refinement

61.  G.Tecuci, Learning Agents Laboratory Refined rule IF Identify the strategic COG candidates for a member of a force with respect to its industrial civilization The force is ?O1 The member is ?O2 THEN A strategic COG candidate for a member of a force has been identified The force is ?O1 The member is ?O2 The strategic COG candidate is ?O3 Plausible Upper Bound Condition ?O1ISForce ?O2ISForce has_as_industrial_factor ?O3 ?O3ISIndustrial_factor is_a_major_generator_of ?O4 ?O4ISProduct Justification ?O2 has_as_industrial_factor ?O3 ?O3 is_a_major_generator_of ?O4 Plausible Upper Bound Condition ?O1ISAnglo_allies_1943 ?O2ISSingle_state_force has_as_industrial_factor ?O3 ?O3ISIndustrial_capacity is_a_major_generator_of ?O4 ?O4ISWar_materiel_and_transports Single_state_force US_1943Britain_1943 instance_of Industrial_capacity Industrial_capacity_ of_ US_1943 instance_of Industrial_capacity_ of_ Britain_1943 War_materiel_and_transports War_materiel_ and_transports_ of_US_1943 instance_of War_materiel_ and_transports_ of_Britain_1943 instance_of

62.  G.Tecuci, Learning Agents Laboratory input task KNOWLEDGE BASE RULE LEARNING RULE REFINEMENT MIXED-INITIATIVE PROBLEM SOLVER MODELING AND RULE/TASKS REFINEMENT provide new reduction reject incorrect reduction accept correct reduction generalized rule and tasks specialized rule new rule expert reduction task reduction rules object ontology Mixed-initiative pb. solving, teaching and learning and tasks FORMALIZATION

63.  G.Tecuci, Learning Agents Laboratory Rule Refinement Demo Rule Refinement - Demo

64.  G.Tecuci, Learning Agents Laboratory Scenario specification Domain modeling Task formalization Rule learning Rule refinement Problem solving Teaching of the Disciple agent by the SME

65.  G.Tecuci, Learning Agents Laboratory Strategic COG candidates identified by the agent The will of the Anglo_allies_1943 is a strategic COG candidate for Anglo_allies_1943, an equal partner alliance The cooperation between the subgroups of Allied_forces_operations_Husky, which conduct “combined and joint operations” is a strategic COG candidate for Anglo_allies_1943 President_Roosevelt is a strategic COG candidate for US_1943, a representative democracy and a member of Anglo_allies_1943 The “will of the people” of US_1943 is a strategic COG candidate for US_1943, a representative democracy and a member of Anglo_allies_1943 Industrial_capacity_of_US_1943 is a strategic COG candidate for US_1943, a member of Anglo_allies_1943 Army_of_Germany_1943 is a strategic COG candidate for for Germany_1943, a member of European_Axis_1943 … Identify the strategic COG candidates for the Sicily_1943 scenario

66.  G.Tecuci, Learning Agents Laboratory Problem Solving Demo Problem Solving - Demo

67.  G.Tecuci, Learning Agents Laboratory Present and future research Evolution of user-computer interaction Present research problem Long term research vision

68.  G.Tecuci, Learning Agents Laboratory Elaborate a theory, methodology and system for the development of knowledge bases and agents by subject matter experts, with limited assistance from knowledge engineers. Intelligent Agent Knowledge Base Present research problem

69.  G.Tecuci, Learning Agents Laboratory This research aims at changing the way future knowledge-based agents will be built, from being programmed by computer scientists and knowledge engineers, to being taught by subject matter experts and typical computer users. Develop a capability that will allow subject matter experts and typical computer users to build and maintain knowledge bases and agents, as easily as they use personal computers for text processing. Long term research vision

70.  G.Tecuci, Learning Agents Laboratory Mainframe Computers Software systems developed and used by computer experts Personal Computers Software systems developed by computer experts and used by persons that are not computer experts Learning Agents Software systems developed and used by persons that are not computer experts Evolution of User-Computer Interaction

71.  G.Tecuci, Learning Agents Laboratory Required reading G. Tecuci, Building Intelligent Agents, Academic Press, 1998, pp. 13-33. Boicu M., Tecuci G., Stanescu B., Marcu D., Cascaval C., Automatic Knowledge Acquisition from Subject Matter Experts, in Proceedings of the 2001 International IEEE Conference on Tools with Artificial Intelligence, Dallas, Texas, November 7-9, 2001. http://lalab.gmu.edu/publications/data/2001/Tools-AI.pdf