PPT-Knowledge Representation and Reasoning

1 Copyright IKS Consortium Representing and organizing knowledge The natural way of expressing and representing knowledge is the language On the Web this takes the form of text In order to make machines able to elaborate knowledge it has to be encoded in some structured form. Introduction. Domain specific knowledg. e is needed to solve some problems.. Knowledge base – representation.. Inference techniques. Use to prove facts.. Use to answer queries. Knowledge Representation Schemes. Kurt Hungerford. CSCI 8110. Bottom Line, Up Front. The Knowledge Machine is a knowledge representation and reasoning system that allows users to store concepts and relationships and then perform inferences on the knowledge base.. The Edwin Smith papyrus. Title:. Instructions for treating a fracture of the cheekbone.. Symptoms:. If you examine a man with a fracture of the cheekbone, you will find a salient and red fluxion, bordering the wound.. Anne Watson. AMET 2010. How would you. slice a regular hexagon into 5 equal parts?. It’s not what you know, but how you know it - how your knowledge and experience is structured - what is variable?. Introduction to Artificial Intelligence Lecture 13: Knowledge Representation & Reasoning II. 1. Resolution. And yet Another Example:. Resolving P  Q  R with P  W  Q  R. Ashker Ibne Mujib. Andrew Reinders. 1. The Classical . Model. . (Also called . possible-worlds. model). There are a number of possible worlds (states of affairs). Some of these possible worlds may be indistinguishable to an agent from the true world.. Humans use their common sense all the time. what is it?. can we instill it in our AI programs?. if not, what are the consequences for AI?. We might think of common sense reasoning as the knowledge accumulated through experience that gives us the ability to. Part II. Description Logic . & . Introduction to Protégé. Jan Pettersen Nytun. 1. The Semantic . Web. Knowledge Representation Part II, JPN, UiA. 2. ". The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“. Humans use their common sense all the time. what is it?. can we instill it in our AI programs?. if not, what are the consequences for AI?. We might think of common sense reasoning as the knowledge accumulated through experience that gives us the ability to. Chitta. . Baral. Arizona State University. Apologies. For not being here physically on this special occasion. My early history with SMS. My . Ph.D. during 1987-1991. Finding the “right” semantics of negation in logic programming was an important topic . Bart Selman. selman@cs.cornell.edu. Logical Agents --- . Intro Knowledge Representation. & Boolean Satisfiability (SAT) encodings. R&N: Chapter 7. A Model-Based Agent. Requires: Knowledge and Reasoning. Bart Selman. selman@cs.cornell.edu. Module: Knowledge, Reasoning, and Planning. Part 1. Logical Agents. R&N: Chapter 7. A Model-Based Agent. Requires: Knowledge and Reasoning. Knowledge and Reasoning . Concepts, Category, Networks, and Schemas. Concepts- . an idea about something that provides means of understanding the world. Organization of Declarative Knowledge. Category – . Provers. Originally Presented by. Peter Lucas. Department of Computer Science, Utrecht University. Presented . by. Sarbartha. . Sengupta. (10305903). Megha. Jain (10305028. ). Anjali . Singhal. (10305919).