 G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University George Tecuci IT/CS 803, Spring 2002

 G.Tecuci, Learning Agents Laboratory Overview Overview of the Disciple approach to agent development Modeling an expert’s problem solving process How to choose a PhD advisor: Brainstorm of the modeling process Assignment Course objectives and Class introduction

 G.Tecuci, Learning Agents Laboratory Course objectives Writing a publishable research paper, by each student, focusing on one aspect of the agent development process. Hands-on experience with designing and developing an end-to- end instructable agent. In-depth evaluation of the Disciple agent-development process: - Strengths and weaknesses; - Lessons learned; - Future research directions. Writing a detailed research report (by the entire class) on how to choose a PhD advisor (both general advice and description of the developed agent). Making the agent publicly available. Development of a comprehensive evaluation methodology. Publishing a joint paper (by the entire class) on how to choose a PhD advisor.

 G.Tecuci, Learning Agents Laboratory Overview of the agent development Overview of the Disciple approach to agent development Modeling an expert’s problem solving process How to choose a PhD advisor: Brainstorm of the modeling process Assignment Course objectives and Class introduction

 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

 G.Tecuci, Learning Agents Laboratory

Modeling an expert’s problem solving process Overview of the Disciple approach to agent development Modeling an expert’s problem solving process How to choose a PhD advisor: Brainstorm of the modeling process Assignment Course objectives and Class introduction

 G.Tecuci, Learning Agents Laboratory Illustration of modeling: identification and testing General guidelines for the modeling process Specific guidelines for the modeling process Modeling based on the task reduction paradigm

 G.Tecuci, Learning Agents Laboratory The general task reduction paradigm A complex problem solving task is performed 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 obtained. S 1 S 11 S 1n S 111 S 11m T 11m T 111 T 1n T 11 T1T1 … …

 G.Tecuci, Learning Agents Laboratory Question-answering based task reduction S 1 S 11a S 1n S 11b1 S 11bm T 11bm T 11b1 T 1n T 11a … … T1T1 Q1Q1 S 11b T 11b A 1n S 11 A 11 … … A 11b1 A 11bm S 11b Q 11b Let T1 be the problem solving task to be performed. Finding a solution is an iterative process where, at each step, we consider some relevant information that leads us to reduce the current task to a simpler task or to several simpler tasks. The question Q associated with the current task identifies the type of information to be considered. The answer A identifies that piece of information and leads us to the reduction of the current task.

 G.Tecuci, Learning Agents Laboratory Modeling the planning process S 1 S 11a S 1n S 11b1 S 11bm T 11bm T 11b1 T 1n T 11a … … T1T1 Q1Q1 S 11b T 11b A 1n S 11 A 11a … … A 11b1 A 11bm S 11b Q 11b T 1 is a general action/task that accomplishes the goal. Ask a question about the current situation to determine alternative ways of performing this action. If the answer of Q 1 is A 11, then to perform T 1 one could perform T 11. When the action to perform (e.g. A 11 ) is completely defined, break it down into sub- actions (e.g. T 11a and T 11b ). In this case the question and the answer summarize the solution, or could simply be absent. Continue this reduction process until the you obtain elementary actions. S 11 T 11 A 11 Q 11a

 G.Tecuci, Learning Agents Laboratory Modeling the planning process (cont.) S 1 S 11a S 1n S 11b1 S 11bm T 11bm T 11b1 T 1n T 11a … … T1T1 Q1Q1 S 11b T 11b A 1n S 11 A 11a … … A 11b1 A 11bm S 11b Q 11b Follow the tree from bottom to top to compose the elementary actions into plans, as illustrated bellow: S 11b is the union of S 11b1 … S 11bm This leads to alternative plans. S 11 is the set of plans obtained by composing the sub-plans from S 11a and the sub-plans from S 11b. S 11 T 11 A 11 Q 11a

 G.Tecuci, Learning Agents Laboratory S 11 Modeling the critiquing process To assess a course of action with respect to a specific principle or tenet one needs a certain amount of information about that course of action, information related to that principle or tenet. This information is obtained by asking a series of questions. The answer to each question allows one to reduce the current assessment task to a more specific and simpler one. This process continues until one has enough information to recognize a weakness or a strength. S 1 S 1n S 11a1 S 11am T 1n T 11a … … T1T1 Q1Q1 A 1n A 11 … A 11a1 A 11am Q 11a Each leaf is a solution (a weakness or a strength). The solution corresponding to an intermediate node is the union of the solutions of its immediate children.

 G.Tecuci, Learning Agents Laboratory Modeling the identification process S 11 To identify a center of gravity candidate for a given scenario (e.g. Sicily_1943) one needs a certain amount of information which is obtained by asking a series of questions. The answer to each question allows one to reduce the current identification task to a more specific and simpler one. This process continues until one has enough information about an entity in the scenario to identify it as a center of gravity candidate. S 1 S 1n S 11a1 S 11am T 1n T 11a … … T1T1 Q1Q1 A 1n A 11 … A 11a1 A 11am Q 11a Each leaf of the tree is a solution (a COG candidate). The solution corresponding to an intermediate node is the union of the solutions of its immediate children.

 G.Tecuci, Learning Agents Laboratory Illustration of modeling: identification and testing General guidelines for the modeling process Specific guidelines for the modeling process Modeling based on the task reduction paradigm

 G.Tecuci, Learning Agents Laboratory Identify and test a strategic COG candidate for the Okinawa_1945 scenario Okinawa_1945 is a major theater of war scenario Identify and test a strategic COG candidate for Okinawa_1945 which is a major theater of war scenario What kind of scenario is Okinawa_1945? I need to Therefore I need to Which is an opposing force in the Okinawa_1945 scenario? Japan_1945 Is Japan_1945 a single-member force or a multi-member force? Japan_1945 is a single-member force Identify and test a strategic COG candidate for Japan_1945 which is a single-member force Therefore I need to Identify and test a strategic COG candidate for Japan_1945 continues US_1945 Therefore I need to

 G.Tecuci, Learning Agents Laboratory I need to Identify and test a strategic COG candidate for Japan_1945 which is a single-member force What type of strategic COG candidate should I consider for a single-member force? Identify and test a strategic COG candidate with respect to the government of Japan_1945 I consider a strategic COG candidate with respect to the controlling elements outside of the government of Japan_1945 I consider a strategic COG candidate with respect to the civilization of Japan_1945 I consider a strategic COG candidate with respect to other sources of moral or physical strength, power and resistance of Japan_1945 I consider a strategic COG candidate with respect to the government of Japan_1945 continues Therefore I need to I consider a strategic COG candidate with respect to the armed forces of Japan_1945

 G.Tecuci, Learning Agents Laboratory I need to Identify and test a strategic COG candidate with respect to the government of Japan_1945 Identify and test a strategic COG candidate with respect to the feudal king-god government of Japan_1945 What type of governing body controls Japan_1945? Japan_1945 has a feudal god-king government Therefore I need to Who is the feudal god-king of Japan_1945? Emperor_Hirohito Therefore I need to Test whether Emperor_Hirohito is a viable strategic COG candidate with respect to the government of Japan_1945 Identify Emperor_Hirohito as a strategic COG candidate with respect to the government of Japan_1945 continues

 G.Tecuci, Learning Agents Laboratory I need to Test whether Emperor_Hirohito can impose Japan_1945 to accept the unconditional_surrender_of_Japan What is the strategic goal of US_1945? Unconditional_surrender_of_Japan Therefore I need to Test whether Emperor_Hirohito is a viable strategic COG candidate with respect to the government of Japan_1945 Does Emperor_Hirohito have the power to cause the people_of_Japan_1945 to accept unconditional_surrender_of_Japan? Yes, because he is seen as divine by the people_of_Japan_1945 and his will is actually their will Test whether Emperor_Hirohito, who can impose his will on the people_of_Japan_1945, can impose Japan_1945 to accept the unconditional_surrender_of_Japan Therefore I need to continues

 G.Tecuci, Learning Agents Laboratory Does Emperor_Hirohito have the power to cause the government_of_Japan_1945 to accept unconditional_surrender_of_Japan? Yes, because Emperor_Hirohito is the head of the government_of_Japan_1945 Emperor_Hirohito is a strategic COG candidate that cannot be eliminated Therefore Test whether Emperor_Hirohito, who can impose his will on the people_of_Japan_1945 and on the military_of_Japan_1945, can impose Japan_1945 to accept the unconditional_surrender_of_Japan Does Emperor_Hirohito have the power to cause the military_of_Japan_1945 to accept unconditional_surrender_of_Japan? Yes, because Emperor_Hirohito is the god-king of Japan_1945 and the commander in chief of the military_of_Japan_1945 Therefore I need to I need to Test whether Emperor_Hirohito, who can impose his will on the people_of_Japan_1945, can impose Japan_1945 to accept the unconditional_surrender_of_Japan

 G.Tecuci, Learning Agents Laboratory Sample modeling for US_1945 I need to Identify and test a strategic COG candidate with respect to the civilization of US_1945 At what level is the civilization of US_1945 organized? US_1945 is an industrial civilization Who or what is a strategically critical industrial civilization element in US_1945? Industrial_capacity_of_US_1945 because it is a major generator of war_materiel_and_transports_of_US_1945 Identify and test a strategic COG candidates with respect to the industrial civilization of US_1945 Therefore I need to Test whether industrial_capacity_of_US_1945 is a viable strategic COG candidate with respect to the civilization of US_1945 continues Identify industrial_capacity_of_US_1945 is a strategic COG candidate with respect to the civilization of US_1945 …

 G.Tecuci, Learning Agents Laboratory Would the reduction in the quantity of newly produced war_materiel_and_transports_of_US_1945 have a deteriorating effect on US_1945? No, because US_1945 has a surplus of war_materiel_and_transports_of_US_1945 Therefore Industrial_capacity_of_US_1945 is a strategic COG candidate that can be eliminated Test whether the reduction in the quantity of newly produced war_materiel_and_transports_of_US_1945 by the industrial_capacity_of_US_1945 would cause US_1945 to accept US_giving_honorable_end_of_hostilities_to_Japan Test whether reducing industrial_capacity_of_US_1945 would cause US_1945 to accept US_giving_honorable_end_of_hostilities_to_Japan What is the main effect of the reduction of the industrial_capacity_of_US_1945? The reduction in the quantity of newly produced war_materiel_and_transports_of_US_1945 Therefore I need to I need to Test whether industrial_capacity_of_US_1945 is a viable strategic COG candidate with respect to the civilization of US_1945 What is the strategic goal of Japan_1945? US_giving_honorable_end_of_hostilities_to_Japan Therefore I need to

 G.Tecuci, Learning Agents Laboratory Illustration of modeling: identification and testing General guidelines for the modeling process Specific guidelines for the modeling process Modeling based on the task reduction paradigm

 G.Tecuci, Learning Agents Laboratory General guidelines Partition the domain into classes of problems. Select representative problems for each class. Model one class at a time. Model one example solution at a time. Organize the top level part of the problem solving tree to identify the class of the problem.

 G.Tecuci, Learning Agents Laboratory Partition the domain into classes of problems Workaround damage Workaround damaged tunnels Workaround damaged bridges Workaround damaged roads Workaround damaged bridges with fording Workaround damaged bridges with fixed bridges Workaround damaged bridges with floating bridges Workaround damaged bridges with rafts Workaround Domain

 G.Tecuci, Learning Agents Laboratory Partition the domain into classes of problems Each principle and tenet leads to a different class of critiquing task. COA Domain

 G.Tecuci, Learning Agents Laboratory Partition the domain into classes of problems Type of scenario Major theater war Peace keeping Drug/law enforcement Counter insurgency Counter terrorism CoG Domain

 G.Tecuci, Learning Agents Laboratory Illustration of modeling: identification and testing General guidelines for the modeling process Specific guidelines for the modeling process Modeling based on the task reduction paradigm

 G.Tecuci, Learning Agents Laboratory 1.Identify the problem to be solved, then form a task name by writing a clear, thorough, natural language sentence describing that problem. 2.Follow each task or sub-task with a single, concise, question relevant to solving the named task. - Ask small, incremental questions that are likely to have a single category of answer (but not necessarily a single answer). This usually means ask “who”, or “what”, or “where”, or “what kind of”, or “is this or that” etc., not complex questions such as “who and what”, or “what and where”, 3.Follow each question with one or more answers to that question. - Express answers as complete sentences, restating key elements of the question in the answer. - Even well formed, simple questions are likely to generate multiple answers. Select the answer that corresponds to the example solution being modeled and continue down that branch. Go back and explore possible branches in a solution tree when you are ready to model a new example solution. Specific guidelines for the modeling process

 G.Tecuci, Learning Agents Laboratory 4.Evaluate the complexity of each question and its answers. When a question leads to apparently overly complex answers, especially answers that contain an “and” condition, rephrase the question in a simpler, more incremental manner leading to simpler answers. 5.For each answer, form a new sub-task, or several sub-tasks, or a solution corresponding to that answer, by writing a clear, thorough, natural language sentence describing the new sub-tasks or solution. - To the extent that it is practical, incorporate key relevant phrases and elements of preceding task names in sub-task names to portray the expert’s chain of problem solving thought and the accumulation of relevant knowledge. - If the answer has led to several sub-tasks, then model their solutions in a depth-first order. Specific guidelines for the modeling process (cont.)

 G.Tecuci, Learning Agents Laboratory 6.After completing a solution tree for an example solution, revisit the potential branches of that tree to model additional example solutions within that category of solutions, reusing existing model components to the greatest extent possible. 7.Utilize the tools and learning ability of Disciple to the greatest extent possible to minimize the amount of modeling required. 8.Only completely model solutions that are unique in their entirety. Entirely unique solutions will be rare. Specific guidelines for the modeling process (cont.)

 G.Tecuci, Learning Agents Laboratory How to choose a PhD advisor: Overview of the Disciple approach to agent development Modeling an expert’s problem solving process How to choose a PhD advisor: Brainstorm of the modeling process Assignment Course objectives and Class introduction

 G.Tecuci, Learning Agents Laboratory Identify a potential PhD advisor for Bob Artificial Intelligence Identify a potential PhD advisor for Bob in the area of Artificial Intelligence What is a research area of interest for Bob? Preliminary modeling I need to Therefore I need to John Doe Determine whether John Doe is an appropriate PhD advisor for Bob Who is a professor who is an expert in Artificial Intelligence? Therefore I need to continues Dan Smith Information security … …

 G.Tecuci, Learning Agents Laboratory Preliminary modeling Determine whether John Doe is an appropriate PhD advisor for Bob Yes because John Doe is a tenured faculty Identify John Doe as a potential PhD Advisor for Bob Is John Doe likely to stay on the faculty for the duration of Bob’s dissertation? Yes because X has a very good record is very likely to get tenure No because X is likely to retire in the near future Therefore I need to Other possible answers No because X is a visiting faculty that will leave next year Assess whether John Doe is an appropriate PhD Advisor for Bob …

 G.Tecuci, Learning Agents Laboratory Professional reputation (1,11,14,15,24,25), general personality and work compatibility (21,22 + 8, 10, 23), learning experience (12,14,19,20,24), responsiveness to the student (2, 3, 5, 6, 10,26), support (15, 16, 17, 18, 19), quality of results (9, 12, 13, 20,24,25) Which are the main criteria to consider in judging John Doe? I need to Assess John Doe with respect to his professional reputation Assess John Doe with respect to his general personality and work compatibility Assess John Doe with respect to the expected learning experience Assess John Doe with respect to his responsiveness to students Assess John Doe with respect to the support offered to students Assess John Doe with respect to the expected quality of student’s work Therefore I need to Assess whether John Doe is an appropriate PhD Advisor for Bob

 G.Tecuci, Learning Agents Laboratory Assignment: Understanding the expertise domain Perform literature search on the subject of choosing a PhD advisor and prepare a short presentation for the class. Due date: February 4 th, 2002