Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Lecture 18 of 42 Knowledge Representation Continued: KE, Inheritance, & Representing Events over Time Discussion: Structure Elicitation, Event Calculus William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: Course web site: Instructor home page: Reading for Next Class: Section 10.4 – 10.9, p. 341 – 362, Russell & Norvig 2 nd edition IM: Event calculus: Protégé-OWL tutorials:

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Lecture Outline Reading for Next Class: Sections 10.4 – 10.9 (p. 341 – 362), R&N 2 e Last Class: Knowledge Engineering (KE), Protocol Analysis, Fluents  Ontology engineering: defining classes/concepts, slots  Concept elicitation techniques  Unstructured  Structured  Protocol analysis (“thinking aloud”) Today: Frames, Semantic Nets, Inheritance; Event & Fluent Calculi  Structure elicitation  Computational information and knowledge management (CIKM)  Representing time, events  Situation calculus  Event calculus  Fluent calculus  Brief tutorial: OWL ontologies in Protégé ( Coming Week: CIKM, Logical KR Concluded; Classical Planning

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Acknowledgements © 2004 H. Knublauch TopQuadrant, Inc. (formerly University of Manchester) © 2005 M. Hauskrecht University of Pittsburgh CS 2740 Knowledge Representation Milos Hauskrecht Associate Professor of Computer Science University of Pittsburgh Holger Knublauch Vice President, TopQuadrant previously Research Fellow, Stanford Medical Informatics & Univ. of Manchester © 2005 N. Noy & S. Tu Stanford Center for Biomedical Informatics Research Samson Tu Senior Research Scientist BMIR Natasha Noy Senior Research Scientist BMIR © 2001 G. Tecuci George Mason University Georghe Tecuci Professor of Computer Science Director, Learning Agents Center George Mason University

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Universe of Decision Problems Recursive Enumerable Languages (RE) Recursive Languages (REC) Decision Problems: Review Co-RE (RE C ) L H : Halting problem L D : Diagonal problem Semi-decidable duals: α  L VALID iff ¬α  L SAT C Undecidable duals α  L VALID C iff ¬α  L SAT α ⊢ RES  ? α Y ✓✗ N

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence “Concept” and “Class” are used synonymously Class: concept in the domain  wines  wineries  red wines Collection of elements with similar properties Instances of classes  Particular glass of California wine Adapted from slides © 2005 N. Noy & S. Tu Stanford Center for Biomedical Informatics Research Concepts/Classes: Review Middle level Top level Bottom level

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Slots in class definition C  Describe attributes of instances of C  Describe relationships to other instances  e.g., each wine will have color, sugar content, producer, etc. Property constraints (facets): describe/limit possible values for slot Adapted from slides © 2005 N. Noy & S. Tu Stanford Center for Biomedical Informatics Research Slots/Attributes/Relations: Review Slots & facets for Concept/Class Wine

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Tabs partition different work areas Buttons and widgets for manipulating slots Area for manipulating the class hierarchy Protégé – Default Interface: Review Adapted from slides © 2005 N. Noy & S. Tu Stanford Center for Biomedical Informatics Research Downloads, primer, documentation:

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Advanced approaches to KB and agent development Elicitation based on the personal construct theory A scenario for manual knowledge acquisition Elicitation of expert’s conception of a domain Knowledge acquisition for role-limiting methods Knowledge Engineering: Review © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence 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. © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ How Agents Are Built: Review

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Defining problem to solve and system to be built: requirements specification Choosing or building an agent building tool: Inference engine and representation formalism Development of the object ontology Development of problem solving rules or methods Refinement of the knowledge base Feedback loops among all phases Understanding the expertise domain Adapted from slide © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Agent Development Process: Review

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence (based primarily on Gammack, 1987) 1.Concept elicitation: methods (elicit concepts of domain, i.e. agreed-upon vocabulary) 2.Structure elicitation: card-sort method (elicit some structure for concepts) 3.Structure representation (formally represent structure in semantic network) 4.Transformation of representation (transform representation to be used for some desired purpose) © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Elicitation Methodology: Review

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Structure Elicitation: Card-Sort Method The Card-Sort Method (elicit the hierarchical organization of the concepts) Type the concepts on small individual index cards. Ask the expert to group together the related concepts into as many small groups as possible. Ask the expert to label each of the groups. Ask the expert to combine the groups into slightly larger groups, and to label them. The result will be a hierarchical organization of the concepts

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slide © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Card-Sort Method: Illustration Satchwell Time Switch Programmer Thermostat Set Point Rotary Control Knob Gas Control Valve Solenoid Electrical System Electrical Supply Electrical Contact Fuse Pump Motorized Valve Electric Time Controls Thermostat Gas Control Electrical Supply Electrical Components Mechanical Components ControlElectricity Part of the hierarchy of concepts from the card-sort method

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Strengths gives clusters of concepts and hierarchical organization splits large domains into manageable sub-areas easy to do and widely applicable Weaknesses incomplete and unguided strict hierarchy is usually too restrictive Card-Sort Method: Properties Adapted from slide © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Structure Representation [1]: Definition © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Represents the acquired concepts into a semantic network and acquires additional structural knowledge: Ask the expert to sort the concepts by considering each concept C as a reference, and identifying those related to it. Ask the expert to order the concepts related to C along a scale from 0 to 100, marked at the side of a table. The values are read off the scale and entered in a data matrix. Generate a network from the matrix, where the nodes are the concepts and the weighted links represent proximities. For each pair of concepts identified as related, ask the expert what that relationship is.

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slide © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Structure Representation [2]: Illustration

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slide © 2001 G. Tecuci, George Mason University CS 785 Knowledge Acquisition and Problem-Solvinghttp://lalab.gmu.edu/cs785/ Structure Representation [3]: Properties Strengths gives information on the domain structure in the form of a network shows which links are likely to be meaningful organizes the elicitation of semantic relationships Weaknesses results depend on various parameter settings requires more time from the expert combinatorial explosion limits its applicability

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Hierarchy and Taxonomy © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Graphical Representation of Inheritance © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Inheritance Networks [1]: Trees with Strict Inheritance Based on slide © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Inheritance Networks [2]: Lattices with Strict Inheritance Based on slide © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Inheritance Networks [3]: Defeasible Inheritance Based on slide © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Problems with Shortest Path Based on slide © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Formal: Inheritance Hierarchy © 2005 M. Hauskrecht, Univ. of PittsburghCS 2740 Knowledge Representation

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Protégé API (Classes, properties, individuals, etc.) Protégé GUI (Tabs, Widgets, Menus) DB Storage Protégé Core System Protégé OWL API (Logical class defn’s, restrictions, etc.) Protégé OWL GUI (Expression Editor, Conditions Widget, etc.) OWL File Storage Jena API (Parsing, Reasoning) OWL Plugin OWL Extension APIs (SWRL, OWL-S, etc.) OWL GUI Plugins (SWRL Editors, ezOWL, OWLViz, Wizards, etc.) OWL Plug-in Architecture Adapted from slide © 2004 H. Knublauch (formerly University of Manchester)

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence OWL Metadata (Individuals) OWL Metadata (Individuals) OWL Metadata (Individuals) OWL Metadata (Individuals) Tourism Ontology Web Services Destination AccomodationActivity © 2004 H. Knublauch (formerly University of Manchester) Tourism Semantic Web

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Adapted from material © 2003 – 2004 S. Russell & P. Norvig. Situation calculus Figure 10.2 p. 329 R&N 2 e Actions, Situations, Time & Events [1]: Situation Calculus Revisited Axioms: Truth of Predicate P  Fully specify situations where P true   biconditional ( , iff) Original Predicates  Describe state of world  Each augmented with situation argument s

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Actions, Situations, Time & Events [2]: Event Calculus Domain-Independent Axioms Domain-Dependent Axioms Still Need to Solve Frame Problem (by Circumscription) Figure © 2003 S. Russell & P. Norvig. Event calculus Figure 10.3 p. 336 R&N 2 e

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Actions, Situations, Time & Events [3]: Fluent Calculus Fluent: Condition (Predicate) That Can Change Over Time (e.g., On) Fluent Calculus: Variant of Situation Calculus  Defaults  ∘ (concatenation) of fluents with state Figure © 2003 S. Russell & P. Norvig. State fluents Figure 10.6 p. 340 R&N 2 e Washington Adams Jefferson

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence CIKM: Review Information Management  Data acquisition: instrumentation, collection, polling, elicitation  Data and information integration: combining multiple sources  May be heterogeneous (different in quality, format, rate, etc.)  Underlying formats, properties may correspond to different ontologies  Ontology mappings (functions to convert between ontologies) needed  Data transformation: preparation for reasoning, learning  Preprocessing  Cleaning  Includes knowledge capture: assimilation from various sources Knowledge Management  Term used most often in business administration, management science  Related to IM, but capability and process-centered  Focus on learning and KA, organization theory, decision theory  Discussion, apprenticeship, forums, libraries, training/mentoring  Modern theory: KBs, Expert Systems, Decision Support Systems

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Terminology Knowledge Engineering (KE): Process of KR Design, Acquisition  Knowledge  What agents possess (epistemology) that lets them reason  Basis for rational cognition, action  Knowledge gain (acquisition, learning): improvement in problem solving  Knowledge level (vs. symbol level): level at which agents reason  Semantic network: inheritance and membership/containment relationships  Knowledge elicitation: KA/KE process from human domain experts  Protocol analysis: preparing, conducting, interpreting interview  Less formal methods: subjective estimation & probabilities Fluents: Conditions (Predicates) That Can Change over Time  Classes, nominals (objects / class instances): spatial, temporal extent  Fluent calculus: situation calculus with defaults, ∘ (concatenation) Computational Information and Knowledge Management (CIKM)  Data/info integration & transformation: collecting, preparing data  Includes knowledge capture: assimilation from various sources

Computing & Information Sciences Kansas State University Lecture 18 of 42 CIS 530 / 730 Artificial Intelligence Summary Points Last Class: Knowledge Engineering, Elicitation, Knowledge Rep.  Elicitation  Techniques: unstructured, structured, “think aloud” (protocol analysis)  Stages: concept (last time), structure (today)  Knowledge acquisition (KA)  Information management, knowledge management defined  KR: situation calculus and successor state axioms; fluents, intervals Today: KE, Ontologies Concluded; CIKM; Event and Fluent Calculi  Structure elicitation  From semantic networks to ontologies  Information management  Knowledge management  Event calculus  Fluent calculus Next Class: Defaults, Defeasible Reasoning; Planning Preview Coming Week: Planning (Section IV)