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. 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.. . Learning. for. . Word, Sense, Phrase, Document and Knowledge. Natural . Language Processing . Lab. , Tsinghua . University. Yu Zhao. , Xinxiong Chen, Yankai Lin, Yang Liu. Zhiyuan Liu. , Maosong Sun. Argument. Monty Python – Argument Clinic video. Monty Python. Premises + Conclusion = Argument. Argument – a group of statements including one or more premises and a conclusion. Premise – a statement in an argument that provides reason or support for the conclusion. 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. 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. Lecture Outline. Inductive Reasoning. Generalizations. Cause and Effect. Analogy. Deductive Reasoning. Syllogism. Enthymeme. Inductive Reasoning. Inductive Reasoning: Review. The process of citing a number of specific examples or . 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. Mimi . Opkins. CECS 100. Fall 2011. Problem Solving. Logic. – The science of correct reasoning.. Reasoning. – The drawing of inferences or conclusions from known or assumed facts.. When solving a problem, one must understand the question, gather all pertinent facts, analyze the problem i.e. compare with previous problems (note similarities and differences), perhaps use pictures or formulas to solve the problem.. 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 . - Charles Sanders Peirce. On the Radar. Researching the Persuasive Speech Assignment. Due Wednesday on . WebCT. (by 11:59 p.m.). Exam Two. This Friday in Lecture. Study Guide on Course Website. Workshops for the Persuasive Speech. Ernest Davis. Army Research Lab. January 18, 2018. 4 challenges. Reasoning beyond simulation. Integrating high-level planning with robotics*. Physical reasoning in language understanding*. Science and commonsense physical 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).