Subject Artificial Intelligence Topic Knowledge Representation Prepared By

Subject : Artificial Intelligence Topic : Knowledge Representation Prepared By. Prof. Syed Rehan Anjuman College Of Engg. & Tech. Department Of Computer Science

Syllabus (Artificial Intelligence – 6 th Sem CSE � Introduction: What is AI? History & Applications, Artificial intelligence as representation & Search, Production system, Basics of problem solving: problem representation paradigms, defining problem as a state space representation, Characteristics. � Search Techniques: Uninformed Search techniques, Informed Heuristic Based Search, Generate and test, Hill-climbing, Best-First Search, Problem Reduction, and Constraint Satisfaction. � Knowledge representation: Knowledge representation Issues: First order logic, Predicate Logic, Structured Knowledge Representation: Backward Chaining , Resolution , Semantic Nets, Frames, and Scripts, Ontology. � Uncertainty: Handing uncertain knowledge, rational decisions, basics of probability, axioms of probability, Baye’s Rule and conditional independence , Bayesian networks , Exact and Approximate inference in Bayesian Networks, Fuzzy Logic. � Learning: What is learning? , Knowledge and learning, Learning in Problem Solving, Learning from example, learning probabilistic models, Formal Learning Theory

Course Outcome � Explain the concept behind problem representation paradigms & its characteristics, production system and defining problem as a state space representation. � Analyse various AI search algorithms (uninformed, heuristic, constraint satisfaction, best-first search, problem reduction � Explain the fundamentals of knowledge representation (logic-based, frame-based, semantic nets), inference and theorem proving , Know how to build simple knowledgebased systems. � Demonstrate working knowledge of reasoning in the presence of incomplete and/or uncertain information by applying Bayesian Networks and Fuzzy Logic. � Apply learning in problem solving , learning probabilistic models.

Contents: �Knowledge Representation Issues �Semantic Networks � FRAMES � Scripts

Knowledge Representation Issues � It becomes clear that particular knowledge representation models allow for more specific more powerful problem solving mechanisms that operate on them. � Examine specific techniques that can be used for representing & manipulating knowledge within programs. � Representation & Mapping � Facts : - truths in some relevant world � These are things we want to represent. � Representations of facts in some chosen formalism. � Things we are actually manipulating. Structuring these entities is as two levels. � The knowledge level, at which facts concluding each agents. Factsbehavior & Internal current goals are described. Representations English understanding English Representations generation English

Approaches to knowledge Representation. �Representational adequacy the ability to represent all of the kinds of knowledge that are needed in that domain. �Inferential Adequacy: - the ability to manipulate the representation structures in such a way as to derive new structures corresponding to new knowledge inferred from ol. �Inferential Efficiency: - the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanism in the most promising directions. �Acquisitioned Efficiency: - the ability to acquire new information easily. The simplest case involves direct insertion by a person of new

Semantic Networks �Semantic Network is a structure for representing knowledge as a pattern of interconnected nodes and arcs. It is also defined as a graphical representation of knowledge. �The objects under consideration serves as nodes & the relationships with another node give the arcs. �Nodes represent �Entities, Attributes, States or Events Arcs in the network give the relationship between the nodes & Labels on the arc specify what type of relationship actually exists.

�Example Scooter is a Two - wheeler Motor – bike is a has Brakes Moving – vehicles Engine has Electrical system Fuel - system �Is a & instance relations. Mammal is a Has part Person Uniform color Blue Nose instance Pee-wee Reese Brooklyn Dodgers team

FRAMES � FRAMES : - means of representing common sense knowledge. Knowledge is organized into small packets called “Frames”. All frames of a given situation constitute the system. � A frame can be defined as a structure that has slots for various objects & a collection of frames consist of expectation for a given situation. � Frame are used to represent two types of knowledge viz. declarative/factual and procedural, declarative & procedural Frames: Namemerely : Computer Centre Nameabout of the frameobjects is � A frame that contains description call a declarative type/factual situational frame. A/c Stationary cupboard Computer Dumb terminals Printer Slots in the frame

� Frames which have procedural knowledge embedded in it are called action procedure frames. The action frame has the following slots. � Actor slot which holds information @ who is performing the activity. � Source Slot hold information from where the action has to begin. � Destination slot holds information about the place where action has to end. Name : Cleaningthe ict of carburetor � Task slot This generates necessary sub frames Actor required to perform the operation. Expert Object Source Destination Scooter Task 1 Remove Carburetor Scooter Task 2 Task 3 Clean Fix Carburetor Nozzl e

Scripts � A mechanisms for representing knowledge about common sequences of events. � A script is a structure that describes a stereotyped sequence of events in a particular content consist of slots contains values/default values. � Components of a script � Entry conditions – conditions before the events described in the script can occur. � Result – conditions that will in general be true after the events described in the script have occurred. � Props - slots representing objects that are involved in the event described in the script. � Roles – Slots representing people who are envolved in the events described in the script. � Track – The specific variation on a more general pattern that is represented by this particular script. � Scenes – The actual sequences of events that occur.

�Pseudo form of a restaurant script Script : Going to a restaurant Scene 1 : Entering the restaurant. Props : Food Enters the restaurant. Tables scans the tables chooses the best one. Menu decides to sit there. Money goes there. Roles : Owner occupies the seat. Customer Waiter Cashier Entry conditions Scene 2: Ordering the food. Customer is hungry Customer asks for menu. Customer has money Waiter brings it. Owner has food. Customer glances it. Chooses what to eat. Orders that item. Results : Scene 3 : Customer is hungry. Eating the food. Owner has more money. Waiter brings the food. Customer has less money. Customer eats it. Owner has less food.

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