This document discusses knowledge representation in artificial intelligence. It begins by discussing what AI is and some of the underlying assumptions of AI techniques. It then discusses how knowledge representation captures generalizations, is understood by people, can be modified, and is useful in different situations. It provides examples of knowledge representation using tic-tac-toe and magic squares. It also discusses representing facts, reasoning, the frame problem, predicate logic, and approaches to knowledge representation.
1. The document discusses predicate calculus and knowledge representation. It provides examples of forward chaining, backward chaining, and resolution to perform inference in predicate calculus.
2. It also discusses representing knowledge as semantic graphs and in the UNL format. An example knowledge representation of "Ram is reading the newspaper" is shown.
3. The document then presents examples of using predicate calculus to represent and solve problems, including a problem about members of a himalayan club and their preferences to infer if there is a mountain climber who is not a skier. Resolution refutation is applied to solve this problem.
The document discusses search algorithms and uninformed graph search. It begins with an introduction to search problems and how they can be modeled as state space problems. A state space problem is defined as consisting of a set of states, starting state, goal states, and a set of actions that transform one state into another. An example railroad switching problem is provided. Graph representations of state space problems are discussed. The document then covers uninformed graph search algorithms like depth-first search and breadth-first search. It also introduces the concept of weighted state space problems where weights are assigned to edges.
The document discusses knowledge representation using propositional logic and predicate logic. It begins by explaining the syntax and semantics of propositional logic for representing problems as logical theorems to prove. Predicate logic is then introduced as being more versatile than propositional logic for representing knowledge, as it allows quantifiers and relations between objects. Examples are provided to demonstrate how predicate logic can formally represent statements involving universal and existential quantification.
Here is the first class in the subject of search. Here, we start with the basics:
1.- BFS
2.- DFS
3.- Iterative Deepening
Together with several of the theorems that explain several of the complexities for these algorithms.
Andres
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
The document discusses various concepts in predicate logic including:
1. Universal and existential quantification allow representing statements like "for all" or "there exists".
2. Syntax of first-order logic includes constants, variables, functions, predicates, and quantifiers.
3. A predicate is satisfiable if true for some values, valid if true for all values, and unsatisfiable if false for all values.
4. Negating quantifiers flips the quantifier and negates the predicate. Free variables can be substituted while bound variables cannot. Restrictions filter domains.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
1. The document discusses predicate calculus and knowledge representation. It provides examples of forward chaining, backward chaining, and resolution to perform inference in predicate calculus.
2. It also discusses representing knowledge as semantic graphs and in the UNL format. An example knowledge representation of "Ram is reading the newspaper" is shown.
3. The document then presents examples of using predicate calculus to represent and solve problems, including a problem about members of a himalayan club and their preferences to infer if there is a mountain climber who is not a skier. Resolution refutation is applied to solve this problem.
The document discusses search algorithms and uninformed graph search. It begins with an introduction to search problems and how they can be modeled as state space problems. A state space problem is defined as consisting of a set of states, starting state, goal states, and a set of actions that transform one state into another. An example railroad switching problem is provided. Graph representations of state space problems are discussed. The document then covers uninformed graph search algorithms like depth-first search and breadth-first search. It also introduces the concept of weighted state space problems where weights are assigned to edges.
The document discusses knowledge representation using propositional logic and predicate logic. It begins by explaining the syntax and semantics of propositional logic for representing problems as logical theorems to prove. Predicate logic is then introduced as being more versatile than propositional logic for representing knowledge, as it allows quantifiers and relations between objects. Examples are provided to demonstrate how predicate logic can formally represent statements involving universal and existential quantification.
Here is the first class in the subject of search. Here, we start with the basics:
1.- BFS
2.- DFS
3.- Iterative Deepening
Together with several of the theorems that explain several of the complexities for these algorithms.
Andres
This document provides an overview of predicate logic and various techniques for representing knowledge and drawing inferences using predicate logic, including:
- Representing facts as logical statements using predicates, variables, and quantifiers.
- Distinguishing between propositional logic and predicate logic and their abilities to represent objects and relationships.
- Techniques like resolution and Skolem functions that allow inferring new statements from existing ones in a logical and systematic way.
- How computable functions and predicates allow representing relationships that have infinitely many instances, like greater-than, in a computable way.
The document discusses these topics at a high-level and provides examples to illustrate key concepts in predicate logic and automated reasoning.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
The document discusses various concepts in predicate logic including:
1. Universal and existential quantification allow representing statements like "for all" or "there exists".
2. Syntax of first-order logic includes constants, variables, functions, predicates, and quantifiers.
3. A predicate is satisfiable if true for some values, valid if true for all values, and unsatisfiable if false for all values.
4. Negating quantifiers flips the quantifier and negates the predicate. Free variables can be substituted while bound variables cannot. Restrictions filter domains.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
The document provides an overview of theory of automata and formal languages. It discusses key concepts such as formal languages, grammars, automata, Chomsky hierarchy, and the relationships between grammars, languages and machines. The Chomsky hierarchy classifies formal languages based on the type of grammar that generates them and the corresponding automata that recognizes them. The document also provides mathematical background on topics like Boolean logic, truth tables, first-order logic, interpretations and models.
This document provides an overview of logical agents and knowledge representation. It discusses knowledge-based agents and the Wumpus world example. It introduces propositional logic and various inference techniques like forward chaining, backward chaining, and resolution that can be used for automated reasoning. It also discusses concepts like entailment, models, validity, and satisfiability. Finally, it discusses how these logical concepts can be applied to build an agent that reasons about the Wumpus world using propositional logic.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This file contains the slides for the topic "Representing Knowledge using Rules". The slides focus mainly on difference b/w declarative and procedural knowledge, and forward & backward reasoning. In addition, the role of control knowledge in problem solving is depicted.
The document discusses logic agents and logical reasoning. It provides background on logic, including syntax, semantics, models, and inference rules. It then discusses how logic can be used to represent knowledge in knowledge-based agents and systems. The agents use a knowledge base and inference engine, where the inference engine derives new knowledge by applying inference rules to the knowledge base.
This document provides an introduction to logic, including propositional logic and predicate calculus. It defines key concepts such as logical values, propositions, operators, truth tables, logical expressions, worlds, models, inference rules, quantification, and definitions. Propositional logic manipulates true and false values using operators like AND and OR. Predicate calculus extends this to allow predicates, constants, functions, and quantification over variables. Inference involves applying rules to derive new statements, but the search space grows too large to feasibly perform by hand.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
L03 ai - knowledge representation using logicManjula V
The document discusses knowledge representation using predicate logic. It begins by reviewing propositional logic and its semantics using truth tables. It then introduces predicate logic, which can represent properties and relations using predicates with arguments. It discusses representing knowledge in predicate logic using quantifiers, predicates, and variables. It also covers inferencing in predicate logic using techniques like forward chaining, backward chaining, and resolution. An example problem is presented to illustrate representing a problem and solving it using resolution refutation in predicate logic.
The document provides an overview of artificial intelligence and knowledge-based systems. It discusses definitions of intelligence and AI, as well as knowledge representation schemes like logical, procedural, semantic network, and frame-based representations. The key components of a knowledge-based system are described as the knowledge base, which represents problem domain knowledge, and the inference engine, which uses reasoning techniques to solve problems. Ideal features of knowledge-based systems include efficient problem-solving using knowledge, heuristics, and eliminating unproductive solutions.
This document introduces some basic concepts in propositional logic. It defines propositional logic as the study of how simple propositions combine to form more complex propositions. It discusses statements as descriptions that can be true or false, and provides examples. It also introduces logical connectives like negation, conjunction, disjunction, implication and biconditional, and shows how they combine atomic propositions into compound propositions. Truth tables are provided to illustrate the truth values of compound propositions formed with different connectives.
The document discusses various topics in artificial intelligence including the Turing test, knowledge representation using semantic networks and search trees, expert systems, neural networks, natural language processing, robotics, and ethical issues. It provides examples and explanations of each topic to demonstrate key concepts in AI such as how knowledge is represented, how expert systems make inferences, how neural networks are trained, and challenges with natural language comprehension. The chapter aims to distinguish problems humans solve best from those computers solve best and define important AI terms and techniques.
This document provides an overview of algebra and mathematical logic. It discusses:
1) The history of algebra, from its origins in Arabic mathematics to its modern conception as the study of algebraic structures.
2) The key concepts in elementary algebra, including solving different types of equations.
3) Important terms and concepts in mathematical logic like statements, proofs, quantifiers, and methods of proof including direct proof, proof by contradiction, and proof by induction.
4) How modern abstract algebra studies algebraic structures in a broad sense.
The pure heuristic search algorithm maintains an open list of generated nodes that have not been expanded and a closed list of nodes that have. It begins with the initial state on the open list and at each cycle expands the node with the minimum heuristic value, generating its children and placing them on the open list in heuristic order. This continues until a goal state is expanded. Heuristic search sacrifices completeness for efficiency by using heuristics to guide the search towards the goal. Examples given include the 15-puzzle, maze navigation, and the missionaries and cannibals river crossing problem.
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
Genetic algorithms are a search method that uses principles of natural selection and evolution to find optimal solutions to problems. They work by generating an initial population of random solutions, then selecting the fittest to breed a new generation by combining traits and mutating genes. This process is repeated until a satisfactory solution emerges. The eight queens problem can be solved using a genetic algorithm approach by representing board configurations as individuals, evaluating their fitness based on non-attacking queens, breeding the fittest to produce new configurations, and repeating until a solution is found with all queens non-attacking. Semantic networks represent knowledge through nodes and relationships like IS-A and HAS to model how concepts are related, allowing inference of new facts by traversing links
Sean Holden (University of Cambridge) - Proving Theorems_ Still A Major Test ...Codiax
This document discusses applying machine learning techniques to automated theorem proving and formal proof checking. It begins by providing background on logic-based theorem proving and efforts to formally prove mathematical theorems. It then discusses using machine learning to help guide automated theorem provers by selecting optimal heuristics and recommending useful lemmas. The document concludes by noting the challenges of developing mathematical languages that are both natural for humans and amenable to formal verification.
The document discusses predicate logic and how it can be used to represent facts and relationships between entities. Predicate logic builds upon propositional logic by adding predicates and quantifiers. Key points include:
- Predicate logic uses predicates and quantifiers like "all" and "some" to make statements about relationships between entities.
- Common predicates include things like "man(Marcus)" to represent that Marcus is a man. Quantifiers like "∀x" are used to represent statements about all or some entities.
- Examples are given to show how facts about a person named Marcus can be represented using predicates and quantifiers in predicate logic.
- Additional concepts like computable functions and predicates, resolution, and
This document summarizes Robert Fry's presentation on computation and design of autonomous intelligent systems. It outlines a computational theory of intelligence based on defining questions and answers within a system. Key points include:
- Intelligent systems acquire information to make decisions to achieve goals.
- Questions are defined as sets of possible answers. Boolean algebra is used to represent questions and assertions.
- Probability and entropy theories are derived from this logical framework.
- A simple protozoan system is used to illustrate how a system maps information to decisions.
- Neural computation is modeled using this theory, with neurons posing questions and making optimal decisions.
- Hebbian learning allows neural systems to adapt optimally via dual-matching.
This document provides an overview of automated theorem proving. It discusses:
1) The history and background of automated theorem proving, from Hobbes and Leibniz proposing algorithmic logic to modern computer-based approaches.
2) The theoretical limitations of automated reasoning due to results like Godel's incompleteness theorems, but also practical applications like verifying mathematics and computer systems.
3) How automated reasoning involves expressing statements formally and then manipulating those expressions algorithmically, as anticipated by Leibniz centuries ago.
Introduction to logic and prolog - Part 1Sabu Francis
The document provides an introduction to logic and Prolog programming. It discusses:
1) Alan Turing's invention of the modern computer to solve complex problems like decoding encrypted messages. This established the concept of algorithms being carried out through linear instruction processing.
2) Prolog programming focuses solely on logic and removes concerns about procedural elements like instruction pointers. It allows programmers to focus only on the problem's logic.
3) Logic is a tool for reasoning that uses concepts like true, false, if-then statements, and, or, etc. It helps clarify reasoning but cannot validate conclusions on its own if premises are flawed.
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
The document provides an overview of theory of automata and formal languages. It discusses key concepts such as formal languages, grammars, automata, Chomsky hierarchy, and the relationships between grammars, languages and machines. The Chomsky hierarchy classifies formal languages based on the type of grammar that generates them and the corresponding automata that recognizes them. The document also provides mathematical background on topics like Boolean logic, truth tables, first-order logic, interpretations and models.
This document provides an overview of logical agents and knowledge representation. It discusses knowledge-based agents and the Wumpus world example. It introduces propositional logic and various inference techniques like forward chaining, backward chaining, and resolution that can be used for automated reasoning. It also discusses concepts like entailment, models, validity, and satisfiability. Finally, it discusses how these logical concepts can be applied to build an agent that reasons about the Wumpus world using propositional logic.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This file contains the slides for the topic "Representing Knowledge using Rules". The slides focus mainly on difference b/w declarative and procedural knowledge, and forward & backward reasoning. In addition, the role of control knowledge in problem solving is depicted.
The document discusses logic agents and logical reasoning. It provides background on logic, including syntax, semantics, models, and inference rules. It then discusses how logic can be used to represent knowledge in knowledge-based agents and systems. The agents use a knowledge base and inference engine, where the inference engine derives new knowledge by applying inference rules to the knowledge base.
This document provides an introduction to logic, including propositional logic and predicate calculus. It defines key concepts such as logical values, propositions, operators, truth tables, logical expressions, worlds, models, inference rules, quantification, and definitions. Propositional logic manipulates true and false values using operators like AND and OR. Predicate calculus extends this to allow predicates, constants, functions, and quantification over variables. Inference involves applying rules to derive new statements, but the search space grows too large to feasibly perform by hand.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
L03 ai - knowledge representation using logicManjula V
The document discusses knowledge representation using predicate logic. It begins by reviewing propositional logic and its semantics using truth tables. It then introduces predicate logic, which can represent properties and relations using predicates with arguments. It discusses representing knowledge in predicate logic using quantifiers, predicates, and variables. It also covers inferencing in predicate logic using techniques like forward chaining, backward chaining, and resolution. An example problem is presented to illustrate representing a problem and solving it using resolution refutation in predicate logic.
The document provides an overview of artificial intelligence and knowledge-based systems. It discusses definitions of intelligence and AI, as well as knowledge representation schemes like logical, procedural, semantic network, and frame-based representations. The key components of a knowledge-based system are described as the knowledge base, which represents problem domain knowledge, and the inference engine, which uses reasoning techniques to solve problems. Ideal features of knowledge-based systems include efficient problem-solving using knowledge, heuristics, and eliminating unproductive solutions.
This document introduces some basic concepts in propositional logic. It defines propositional logic as the study of how simple propositions combine to form more complex propositions. It discusses statements as descriptions that can be true or false, and provides examples. It also introduces logical connectives like negation, conjunction, disjunction, implication and biconditional, and shows how they combine atomic propositions into compound propositions. Truth tables are provided to illustrate the truth values of compound propositions formed with different connectives.
The document discusses various topics in artificial intelligence including the Turing test, knowledge representation using semantic networks and search trees, expert systems, neural networks, natural language processing, robotics, and ethical issues. It provides examples and explanations of each topic to demonstrate key concepts in AI such as how knowledge is represented, how expert systems make inferences, how neural networks are trained, and challenges with natural language comprehension. The chapter aims to distinguish problems humans solve best from those computers solve best and define important AI terms and techniques.
This document provides an overview of algebra and mathematical logic. It discusses:
1) The history of algebra, from its origins in Arabic mathematics to its modern conception as the study of algebraic structures.
2) The key concepts in elementary algebra, including solving different types of equations.
3) Important terms and concepts in mathematical logic like statements, proofs, quantifiers, and methods of proof including direct proof, proof by contradiction, and proof by induction.
4) How modern abstract algebra studies algebraic structures in a broad sense.
The pure heuristic search algorithm maintains an open list of generated nodes that have not been expanded and a closed list of nodes that have. It begins with the initial state on the open list and at each cycle expands the node with the minimum heuristic value, generating its children and placing them on the open list in heuristic order. This continues until a goal state is expanded. Heuristic search sacrifices completeness for efficiency by using heuristics to guide the search towards the goal. Examples given include the 15-puzzle, maze navigation, and the missionaries and cannibals river crossing problem.
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
Genetic algorithms are a search method that uses principles of natural selection and evolution to find optimal solutions to problems. They work by generating an initial population of random solutions, then selecting the fittest to breed a new generation by combining traits and mutating genes. This process is repeated until a satisfactory solution emerges. The eight queens problem can be solved using a genetic algorithm approach by representing board configurations as individuals, evaluating their fitness based on non-attacking queens, breeding the fittest to produce new configurations, and repeating until a solution is found with all queens non-attacking. Semantic networks represent knowledge through nodes and relationships like IS-A and HAS to model how concepts are related, allowing inference of new facts by traversing links
Sean Holden (University of Cambridge) - Proving Theorems_ Still A Major Test ...Codiax
This document discusses applying machine learning techniques to automated theorem proving and formal proof checking. It begins by providing background on logic-based theorem proving and efforts to formally prove mathematical theorems. It then discusses using machine learning to help guide automated theorem provers by selecting optimal heuristics and recommending useful lemmas. The document concludes by noting the challenges of developing mathematical languages that are both natural for humans and amenable to formal verification.
The document discusses predicate logic and how it can be used to represent facts and relationships between entities. Predicate logic builds upon propositional logic by adding predicates and quantifiers. Key points include:
- Predicate logic uses predicates and quantifiers like "all" and "some" to make statements about relationships between entities.
- Common predicates include things like "man(Marcus)" to represent that Marcus is a man. Quantifiers like "∀x" are used to represent statements about all or some entities.
- Examples are given to show how facts about a person named Marcus can be represented using predicates and quantifiers in predicate logic.
- Additional concepts like computable functions and predicates, resolution, and
This document summarizes Robert Fry's presentation on computation and design of autonomous intelligent systems. It outlines a computational theory of intelligence based on defining questions and answers within a system. Key points include:
- Intelligent systems acquire information to make decisions to achieve goals.
- Questions are defined as sets of possible answers. Boolean algebra is used to represent questions and assertions.
- Probability and entropy theories are derived from this logical framework.
- A simple protozoan system is used to illustrate how a system maps information to decisions.
- Neural computation is modeled using this theory, with neurons posing questions and making optimal decisions.
- Hebbian learning allows neural systems to adapt optimally via dual-matching.
This document provides an overview of automated theorem proving. It discusses:
1) The history and background of automated theorem proving, from Hobbes and Leibniz proposing algorithmic logic to modern computer-based approaches.
2) The theoretical limitations of automated reasoning due to results like Godel's incompleteness theorems, but also practical applications like verifying mathematics and computer systems.
3) How automated reasoning involves expressing statements formally and then manipulating those expressions algorithmically, as anticipated by Leibniz centuries ago.
Introduction to logic and prolog - Part 1Sabu Francis
The document provides an introduction to logic and Prolog programming. It discusses:
1) Alan Turing's invention of the modern computer to solve complex problems like decoding encrypted messages. This established the concept of algorithms being carried out through linear instruction processing.
2) Prolog programming focuses solely on logic and removes concerns about procedural elements like instruction pointers. It allows programmers to focus only on the problem's logic.
3) Logic is a tool for reasoning that uses concepts like true, false, if-then statements, and, or, etc. It helps clarify reasoning but cannot validate conclusions on its own if premises are flawed.
The document discusses logic programming and propositional logic. It covers topics like:
- Logic is the study of valid reasoning and determining what conclusions follow from a set of premises.
- Propositional logic represents logical statements using variables and logical connectives. It deals with propositions that can be either true or false.
- Predicate logic extends propositional logic by allowing reasoning about objects and relationships using variables, predicates, functions and quantifiers.
- Logic programming languages like Prolog are based on predicate logic and allow defining facts and rules to represent relationships between objects. Prolog can be used to infer new facts by applying resolution and unification on queries against the defined facts and rules.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
The document discusses knowledge representation and reasoning in artificial intelligence. It covers the following key points in 3 sentences:
Intelligent agents should have the capacity for perceiving, representing knowledge, reasoning about what they know, and acting. Knowledge representation involves representing an understanding of the world, while reasoning involves inferring implications of what is known. Logic provides a way to represent and reason about knowledge through specifying a logical language with syntax, semantics, and inference rules.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses key concepts in logic programming like predicates, clauses, resolution, and backward chaining. It also describes the basic syntax and execution model of Prolog, including how it uses unification, backtracking, and trace to evaluate queries against a knowledge base of facts and rules.
This document provides an overview and syllabus for CS 446: Machine Learning, taught by Gerald DeJong. Key details include:
- The course will use Mitchell's Machine Learning textbook
- Important dates: midterm on Oct 4, final on Dec 12
- Homeworks and projects will be submitted in class, with late penalties of 20% per day up to 3 days late
- Topics covered will include decision trees, linear threshold units, probabilistic representations, reinforcement learning, clustering, and more based on student interest
This document provides an overview and syllabus for CS 446: Machine Learning, taught by Gerald DeJong. Key details include:
- The course will use Mitchell's Machine Learning textbook
- Important dates: midterm on Oct 4, final on Dec 12
- Homework and projects will be submitted in class, with late penalties of 20% per day up to 3 days late
- Topics covered will include decision trees, linear threshold units, probabilistic representations, reinforcement learning, clustering, and more based on student interest
Frequentist inference only seems easy By John MountChester Chen
This is part of Alpine ML Talk Series:
The talk is called “Frequentist inference only seems easy” and is about the theory of simple statistical inference (based on material from this article http://www.win-vector.com/blog/2014/07/frequenstist-inference-only-seems-easy/ ). The talk includes some simple dice games (I bring dice!) that really break the rote methods commonly taught as statistics. This is actually a good thing, as it gives you time and permission to work out how common statistical methods are properly derived from basic principles. This takes a little math (which I develop in the talk), but it changes some statistics from "do this" to "here is why you calculate like this.” It should appeal to people interested in the statistical and machine learning parts of data science.
The document provides information on various topics related to artificial intelligence including:
- Examples of intelligence such as solving puzzles, performing complex math problems quickly, and following rules.
- Definitions of AI from early researchers such as John McCarthy who coined the term, and descriptions of AI as the study of intelligent behavior in machines.
- Key areas of AI research and applications such as game playing, reasoning, learning, robotics, and machine learning.
- Approaches to problem solving in AI like state space search, knowledge representation, and using heuristics to guide searches.
This document provides an introduction to machine learning and inductive inference. It discusses what machine learning is, common learning tasks like concept learning and function learning, different data representations, and example applications such as knowledge discovery and building adaptive systems. The course will cover generalizing from specific examples to broader concepts through inductive inference and different learning approaches.
This document provides an overview of propositional logic and introduces first-order logic. It defines key concepts in propositional logic like logical connectives, truth tables, validity, and soundness. Examples are given of representing propositions and inferences. The limitations of propositional logic are discussed. First-order logic is then introduced as adding objects, properties, relations, functions, variables and quantifiers to increase expressiveness over propositional logic. Terms, atoms, and well-formed formulas are defined for first-order logic.
The document discusses different methods of representing knowledge in artificial intelligence systems, including formal logic, production rules, and structured objects like semantic networks and frames. It provides examples of representing statements in propositional and predicate calculus, and how logic-based languages like Prolog can be used for knowledge representation and reasoning. Semantic networks are introduced as a way to organize knowledge representation in a graph-like structure similar to how human memory works.
The axiomatic power of Kolmogorov complexity lbienven
1. The document discusses random axioms and probabilistic proofs in Peano arithmetic. It describes a proof strategy where one could randomly select an integer n that satisfies some formula φ and add it as a new axiom.
2. While this intuition of probabilistic proofs makes sense, it is not really useful since any statement provable with sufficiently high probability is already provable in PA. However, probabilistic proofs can be exponentially more concise than deterministic proofs.
3. The document also discusses Kolmogorov complexity and how statements about it relate to the provability of PA. It can be shown that if C(x) is less than some value, PA will prove it, but PA will never prove a
Is it important to explain a theorem? A case study in UML and ALCQIAlexandre Rademaker
The document discusses conceptual modeling from a logical point of view. It outlines the main steps of conceptual modeling as observing the world, determining relevance, choosing terminology, writing axioms, and verifying correctness. It notes that steps 1-2 can use informal notations like UML but are essentially an "art". Step 5 of verification demands significant knowledge of the model. The document also discusses using logic to explain theorems proven from an ontology, providing examples of proofs using tableaux and sequent calculus that the ontology implies a disjunction.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
1. What is AI ?????????????
• The AI problems (eg.Chess etc.)
• The underlying assumption
• What is AI technique ?
- Intelligence requires knowledge
Knowledge properties:-
.. It is voluminous
.. It is hard to characterize accurately
.. It is constantly changing
.. Organized way & usage -differ
AI Technique representation
• knowledge captures generalization
• To be Understood by people who provide it
• Easily to be modified to correct errors
• To be useful in great many situations
• To be useful under narrow range of possibilities that must be considered
Tic- tac – toe
• To play Tic-Tac-toe
• (magic square )
• Complexity
• Generalized usage
• Clarity of their knowledge
• Extensibility of their approach
2. Programs
• Board
• Nine element vector representing the board positions such that no number is to be
repeated
• Row sum = column sum = diagonal sum
• Algorithm
• Comments
Knowledge representation
Facts….> internal ……………..Reasoning
Representation.. programs
English ……………………………….english
Understanding………………..generation
English
Representation
Mapping between facts & representations
· Knowledge level
· Symbol level
English : spot is a dog
Logic : dog(spot)
For every x : dog(x) ..> hastail(x)
Then hastail(spot)
3. Representation of Facts
Initial …..desired real …..final
Facts reasoning facts
! ^
* ! ! *
V !
Forward Backward
representation representation
mapping mapping
operation
of programs
Internal ….………………..> Internal
Representation representation
Of initial of final
Facts facts
Approaches to knowledge representation
· Representation adequacy
· Inferential adequacy
· Inferential efficiency
· Acquisitional efficiency
4. Knowledge
1.Inheritable
2. inferential
3. Procedural
Simple relational knowledge
Player ht wt bats/throws
Hank 6-0 180 right/right
Wille 5-10 176 right/left
Babe 6-2 215 left/left
Wilman 6-3 205 left/right
Representing sets of objects
Eg. Set of SUN’s planets on which people live is { earth}
{ x : sun-planet(x) ^ human-inhabited(x) }
Finding the Right Structures as Needed
· How to perform an initial selection of the most appropriate structures ?
· How to fill in appropriate details from the current situation ?
· How to find a better structure?
· What to do if none is available ?
· When to create and remember a new structure ?
5. The Frame problem
Similarity net……
Chair->Table->Desk->Sideboard
Chair -> Table :: too big, no back
Table-> desk :: drawers
Desk-> sideboard :: no knee room
Using predicate logic
Simple facts in prepositional logic
It is raining
RAINING
It is sunny
SUNNY
It is windy
WINDY
If it is raining , then it is not sunny
RAINING NOT SUNNY
Eg. Socrates is a man
Plato is a man
Then represent as follows :-
MAN(SOCRATES)
MAN(PLATO)
6. Some Facts
1.Marcus was a man.
2. Marcus was Pompeian.
3. All Pompeians were Romans.
4. Caesar was a ruler.
5. All Romans were either loyal to Caesar or hated him.
6. Everyone is loyal to someone.
7. People only to try assassinate rulers they are not loyal to.
8. Marcus tried to assassinate Caesar .
Now write the Predicate logic :-
1. man(Marcus)
2. Pompeian(Marcus)
3. for all x such that Pompeian(x) -> Roman(x)
4. ruler(Caser)
5. for all x such that Roman(x)
->loyalto(x,Caesar) or hate(x,Caesar)
6. for all x, there exists y such that loyalto(x,y)
7. for all x such that for all y person(x) and ruler(y) and tryassassinate(x,y) ->not
loyalto(x,y)
8. tryassassinate(Marcus,Casear)
7. Conclusion:-
Was Marcus loyal to Caesar ?
From 7 & 8 we prove that Marcus was not royal to Caesar.
Notroyal(marcus,Caesar)
Symbolic Reasoning under uncertainty
Introduction to Nonmonotonic Reasoning
· To reason with incomplete information
· At any time , a statement is believed to be true, believed to be false, or not believed to be
either
Whereas in Statistical reasoning, the representation allows some kind of numerical measure of
certainty ( rather than simple true or false )
Eg. Consider a case :
1.That Abbott did not commit the crime
2. That Babbitt did not
3. That Abbott or Babbitt or Cabot did
4. That Cabot did not
Properties of conventional reasoning system
· It is complete with respect to the domain of interest
· It is consistent
· The facts can be added when they are available
8. If these properties are missing then conventional logic based reasoning systems will not work.
· At that time Nonmonotonic reasoning systems are designed to solve the problems.
Logics for Nonmonotonic reasoning
1.Define the set of possible words for the given facts
2. Provide a way that we believe in some models rather than others
3. Provide the basis for the practical implementation of this kind of reasoning
4. Correspond to our intuitions about how this kind of reasoning works
To use nonmonotonic reasoning, Default reasoning is necessary
Default reasoning :
To draw conclusions based on what most likely to be true
Nonmonotonic Logic(NML)
Consider the formula
For all x,y : Related(x,y) and
M Getalong(x,y) -> WillDefend(x,y)
This means that
For all x,y , if x and y are related and if x gets along with y with everything else that is believed ,
then conclude that x will defend y
Here M is called modal operator
In the original formulation of NML , the semantics of the modal operator M which is self-
referential , were unclear.
9. Default Logic(DL)
Reiter’s Default logic has the inference rule has
A : B
C
This means that
“ if A is provable and it is consistent to assume B then conclude C “
Abduction
Standard logic performs deduction
Given two axioms,
For all x, : A(x) -> B(x)
A( C )
Then we can conclude B(C) using deduction.
What about reverse ?
For example
Suppose the axiom as
For all x : Measles(x)->Spots(x)
This means that Measles implies having spots.
Supposing we notice spots, we conclude Measles on the reverse.
But that conclusion is not licensed by the standard rule of logic and it may be wrong too many a
times.
But it may be a best Guess !
This specific form is called abductive reasoning.
Inheritance
10. One common use of nonmonotonic reasoning is as a basis for inheriting attribute values from a
prototype description of a class to the individual entities that belong to the class.
Minimalist reasoning
Methods for saying very specific and highly useful class of things that are generally true.
Closed world assumption (CWA)
A simple kind of minimalist reasoning is suggested by CWA
Eg. A(Joe) or B(Joe)
This is a single statement
The resulting extended knowledge base is
A(Joe) or B(joe)
Not A(Joe)
Not B(joe)
Is inconsistent.
Again consider
Single(John)
Single(Mary)
The CWA will yield
Not single(jane)
Now consider the predicate married in place of single.
Not Married(John)
Not Married(Mary)
The CWA will yield
Not Married(Jane)
Implementation issues
11. In real systems follow the points:-
1.How to derive exactly those nonmonotonic conclusions that are relevant to solve the
problems.
2. How to increment our knowledge base as problem progresses.
3. In nonmonotonic reasoning systems, more than one interpretation is possible .
How to manage these ?
4. These theories are not computationally effective and so none is effective.
Augmenting a problem solver
1.Reason forward from what is known
2. Reason backward to find out whether some expression P is true.
Truth maintenance systems
Justification based truth maintenance systems
Logic based truth maintenance systems
Statistical reasoning
Probability
· Tossing a coin and getting head or tail
· P(H)= 1/2 P(T)=1/2
· Rolling a die
· P(any No.to occur)=1/6
· P(Hi / E) = Probability that hypothesis Hi is true given evidence E
· P(E / Hi) = the probability that we will observe evidence E given that hypothesis
Hi is true
12. · P(Hi) = the a priori probability that hypothesis Hi is true in the absence of any
specific evidence.
· K= No of possible hypothesis
· Then Baye’s theorem states that
· P(Hi / E) = P(E/Hi) . P(Hi)
· ----------------
· sum(P(E/Hi).P(Hi))
· PROSPECTOR is AI system to locate any mineral from the earth using the above
theorem (1979 Duda et.al.)
Example : Ball problems
There are two boxes A & B
A has 4 Red balls 3 White balls
B has 5 Red balls 4 White balls
Q1. What is P(A/R) ?
Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
nextQ.A red ball from A is put in B
Q1. What is P(A/R) ?
Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
nextQ.A white ball of A is put in B
Q1. What is P(A/R) ?
13. Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
Similarly reverse problem can be thought of.
Q.There are two boxes A & B
A has 5 Red balls 3 White balls
B has 3 Red balls 5 White balls
A ball of A is put in B
Then What is P(A/R) ?
What is P(A/W) ?
After transferring the ball,
It can be either R or W.
Mutually exclusive case.
What is P(B/R) ?
What is P(B/W) ?
Thus, Baye’s theorem helps this type of problem-solving as a statistical reasoning.
Applied intelligent system in oil & gas industry
Case study
There are 5 wild cat areas of oil & gas as follows:-
Area 1 : 3 gas and 2 oil wells
Area 2 : 1 gas and 2 oil wells
14. Area 3 : 4 gas and 2 oil wells
Area 4 : 2 gas and 4 oil wells
Area 5 : 5 gas and 3 oil wells
Apply Baye’s theorem and discuss
The following :-
P(G) ? P(O) ?
P(G OR O) ?
P(G AND O) ?
Certainty Factors & Rule-based systems
MYCIN system
It is a kind of rule based expert system.
- performs the task normally done by human expert 1
- It represents most of its diagnostic knowledge as a set of rules.
A typical rule
If 1. the strain of the organism is gram-positive and
2.the morphology of the organism is coccus and
3. the growth confirmation of the organism is clumps,
then there is suggestive evidence that the identity of the organism is staphylococcus.
This is the form in which the rules are stated to the user.
Uncertain Rules
Now combine uncertain rules and apply the properties :-
1. Since the order in which evidence is collected is arbitrary, the combining functions should be
commutative and associative.
15. 2. Until certainty is reached , additional combining evidence should increase MB and (similarly
for disconfirming evidence and MD) where MB means a measure of belief ( between 0 and 1)
and MD is a measure of disbelief (between 0 and 1)
3. If uncertain influences are chained together then the result should be less certain than either of
the inferences alone.
Game playing
Games provide good domain to explore machine intelligence :
1. Providing a structured task in which it is easy to measure success or failure
2. They need not large amount of knowledge.
The first is true but not the second !
Why ?
Consider chess game. An average player needs minimum of 50 moves ! so in order to complete
the game we have to examine 100 to the power of 35, if average branching factor is 35 !
Thus huge amount of knowledge is necessary.
The MINIMAX search
One ply-search
A
B C D
8 3 -2
Apply Minimum ply
Then A(-2)
Apply maximum ply
Then A(8)
Two ply search
16. A
B C D
E F G H I J K
9 -6 0 0 -2 -4 -3
APPLY MINIMIZING AND MAXIMIZING AND GET THE BACK-UP VALUES
Apply Minimizing ply ,
We find that
B(-6) , C(-2), D(-4)
Then apply Maximizing ply,
We get A(-2)
Application :
In the case of profit center concept, maximizing ply and in the case of expenses, minimizing ply
are applied as applied intelligent systems.
This is called Minimax Search Procedure.
· This procedure is very simple
· Performance is improved significantly with a few refinements.
How ?
Consider the example :
Alpha Cutoff
17. A Maximize
B C
D E F G Minimize
3 5 -5
Answer
A(>3) Maximize
B(3) C(< -5)
D E F G Minimize
3 5 -5
Similarly when we apply Minimizing ply, then maximizing ply, then minimizing ply, and then
maximizing ply, it is called Alpha and Beta Cutoffs.
There are other varieties of modifications to minimax procedure to improve its performance.
Class work :
Create some example for Alpha and Beta procedure .
Natural language processing
Language – for communication about the world.
Language processing – two taks
1. Processing written text, using lexical , syntactic and semantic knowledge of the
language
2. Processing spoken language, using all the information needed + additional
knowledge to handle any confusions that arise in speech.
18. Problem
Some dogs are outside =>
1.Some dogs are on the lawn
2.Three dogs are on the lawn
3.Rover, Tripp, Spot are on the lawn.
I called Lynda to ask her to the movies. She said she would love to go. =>
1. She was home when I called.
2. She answered the phone.
3. I actually asked her.
Good side
1. Language allows speakers to be as vague or as precise as they like.
2. It also allows speakers to leave out things they believe their hearers already know.
Problem
The same expression means different things in different context.
1. Where’s the water ?(In chemistry lab, it must be pure)
2. Where’s the water ? (when you are thirsty ,it must be potable)
3. Where’s the water ? (dealing with a leaky roof )
Good side
Language lets us communicate about an infinite world using a finite number of symbols.
The problem (lot of ways of saying)
Mary was born on Oct.11.
19. Mary’s birthday falls on Oct.11.
Good side:
when you know a lot ,facts imply each other.
Thus language is used as agents who know a lot.
Steps in the process
1. Morphological Analysis
- Individual words are analysed into components.
- Punctuations are separated from words.
‘print’,’file’ can all function more than one syntactic category.
2. Syntactic Analysis
Linear sequences of words are transformed into structures that show how the words relate to
each other.
Some word sequences may be rejected if they violate the rules of the language grammer.
Eg. “Boy the go to store”
3. Semantic Analysis
The structure created by the syntactic analyzer are assigned meanings. In other words, a mapping
is made between syntactic structures and objects in the task domain. If no such mapping is
possible then such structures will be rejected.
Eg. “colorless green ideas sleep furiously” will be rejected.
A knowledge base fragment
User068
Instance: user
20. Login-name: susan-black
F1
Instance: File-struct
Name: stuff
Extension: .init
Owner: user073
In-director: /wsmith/
4. Discourse Integration
The meaning of individual sentence may depend on the sentences that precede it and may
influence the meaning of sentences that follow . For eg. ‘it’,
‘John wanted it ‘
5. Pragmatic Analysis
The structure representing what was said is reinterpreted to find out what was actually meant.
Eg. ‘ Do you know what time is ?’ is reinterpreted as a request to be told time.
Representing the indended meaning
Meaning
Instance: commanding
Agent: user068
Performer: this-system
Object: p27
Instance: printing
Agent: this-system
Object: F1
21. Expert Systems (ES)
Expert system is a prototype computerized system to behave like an expert in a particular filed of
specialization, like medical, oil & gas industry etc.
Example :
1. MYCIN is an expert system in medical field.
2. PROSPECTOR is an expert system in Oil & Gas industry.
Expert system is classified as
ES = KA + KB + IA + PD
WHERE
ES :: Expert system
KA :: Knowledge Acquisition
KB :: Knowledge Base
IA :: Inferential Analysis
PD :: Prototype Development
Discuss : Medical system under above classification.
Merits of Expert System :
· Acts like an expert when real expert in a particular filed of specialization is not
available.
· Time saving.
· Cost – effective.
· Most Modern.
· Faster solution provider.
· No need of appointment of real expert.
· Multiple solutions are possible.
22. · Multiple consultations are possible.
· Case – study oriented.
· Easy to use.
Demerits
· Successful 25-30 % only world wide.
· Real Experts do not offer their expertise knowledge to develop the system for fear
of loosing importance personally.
· If not updated periodically, wrong conclusions are possible.
· Due to multiple solutions, some times confusions are possible.
· Awareness in the minds of people is lacking and thereby belief is not there much.
· A lot of difficulty in Knowledge Acquisition.
· Time consuming job – development of ES.
IMPORTANT:
Entering knowledge , maintaining the knowledge base consistency , ensuring knowledge
base completeness are the key factors of success of expert systems.
MOLE (Eshelman 1988) is a knowledge acquisition system dealing with diagnosing
diseases.
SALT (Marcus 1989) is a program to provide mechanism for extracting knowledge from
an expert.
META-DENDRAL (Mitchell 1978) is the first program to use learning techniques to
construct rules for an expert system automatically.
Conclusions:
23. · Expert systems offer their power in a domain specific knowledge rather than a
single powerful technique
· In successful systems, the needed knowledge is about a particular area and is well-
defined.
· An expert system is built normally with the aid of one or more experts.
· Transfer of knowledge takes place gradually after many interactions.
· The amount of knowledge is depending on a task.
· The choice of control structure depends on specific characteristics of the system.
· It is possible to extract non-domain specific parts from existing expert systems and use
them to build a new expert system in new domains.
What is learning ?
Often AI – machines are blamed that they cannot be called intelligent until they are able to do
new things and adapt to new situations rather than simply doing things as directed !
Knowledge Acquisition itself includes many different activities.
- Simple storing of computed information or rote learning , is the basic learning activity.
Rote learning
A
B C D
E F G H I J K
- Rote learning of this sort is very simple.
- It does not appear to involve any sophisticated problem solving capabilities .
24. - ORGANISED storage information
- generalization
Learning by taking advice
A computer program might make use of the advice by adjusting its static evaluation function to
include factor-based on the number of center squares attacked by its own pieces , in Chess.
Learning in problem solving
- Learning by parameter adjustment
- Learning with macro - operators
- Learning by Chunking : is a process similar to macro-operators
(memory & problem solving )
Learning from examples : Induction
- class discussions
Decision trees
- class discussions
Explanation based learning
- class discussions
Discovery
- class discussions
Formal Learning theory
- class discussions
Neural Nets
First efforts in machine learning – tried to mimic animal learning at a neural level !
25. - This is quite different from symbolic manipulation methods
- Neural network models are based on computational “brain metaphor”
- A number of other learning techniques make use of metaphor based on evolution.
- Learning algorithms inspired by evolution are called genetic algorithms.
Learning in Neural Networks
Perceptrons
- An invention of Rosenblatt(1962), one of the earliest nueral network models.
- A perceptron models a neuron by taking weighted sum of its inputs and sending the output ‘1’
if the sum is greater than some adjustable threshold value or otherwise it send ‘0’.
Let the inputs be (x1,x2,……xn) and the weights be (w1,w2,…..wn)
Consider g(x) = sum(wi.xi)
o(x) = 1 if g(x) > 0
= 0 if g(x) < 0
Early notion of intelligent system built from Trainable perceptrons
A linearly separable pattern classification problem fig. 18.9 page 496.
Applications of neural networks
- Pattern recognizers and associative memories
- Pattern transformers
- Dynamic inferencers
- Most of the people use the first category in industrial applications.
- Some are using the second category too.
26. - Third one is still in primitive stage.
Turbo prolog programming
It is a trade-mark of Borland international Inc.
- The name ‘prolog’ is taken from ‘programming in logic’
- Originally developed in 1972 by
Alain Colmerauer & P.Roussel,
University of Marseiles,France.
Starting Turbo prolog
c>PROLOG
Then the user-friendly editor appears as follows:-
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
----Dialog-----
--------Message---------
--------Trace----------
27. Use first letter of Option or select with -> or <-
Create a Sample program
1.Select Edit mode & enter program
2. Enter text or corrections as necessary
3. Save the program to disk
4. Compile the program
5. Execute the program
Similar to Turbo pascal
SAMPLE TEST PROGRAM
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
/* SAMPLE TEST PROGRAM */
predicates
likes(symbol , symbol)
clauses
likes(frank,sue).
----Dialog-----
28. likes(Harold,ruth).
--------Message---------
--------Trace----------
F1:Help F3:Search F4:Subst F5:Copy F6:Move F7:del F8:ExtEdit F9:ExtCopy F10:End
Starting to execute the program
To compile and execute the
program exit the editor using Esc
or F10. Then select the Run option
from the main menu.
If NO errors, then see the prompt
in dialog box as follows:-
If error, turbo prolog will return
to the ditor.
Then compile again till ‘No error’
29. SAMPLE TEST PROGRAM
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
/* SAMPLE TEST PROGRAM */
predicates
likes(symbol , symbol)
clauses
likes(frank,sue).
likes(Harold,ruth).
----Dialog-----
Goal :-
--------Message---------
--------Trace----------
F1:Help F3:Search F4:Subst F5:Copy F6:Move F7:del F8:ExtEdit F9:ExtCopy F10:End
Goal : likes(Harold,ruth)<-! (return key press )
Turbo Prolog will reply to you as
True
Goal : ( wait for next goal )
Expressing Facts
(English to (Turbo)T. Prolog)
The right speaker is dead (English)
Is(right_speaker,dead). (T.prolog)
30. Fact Relation
has_a(bill, computer). has_a
is_a(collie,dog). is_a
likes(sue,chocolate). Likes
Predicates (examples)
employee(bill)
eligible(mary)
marital_status(joyce,married)
For example, you can express the fact that mary is married and is eligible for employment as
eligible(mary) and marital-status(mary,married).
Facts expressed as prolog clauses
English: Bill is an employee.
Prolog: employee(Bill).
English: Bob is married to Mary.
Prolog: married_to(Bob,Mary).
English:The speaker is defective.
Prolog: defective_speaker.
English: Tom is a student.
Prolog: student(Tom).
Relationship of clauses, predicates, relations and objects.
31. Clauses
|
|__________________|
Facts Rules
|________________|
Predicates Periods(.)
|____________|
Relations Arguments
|
|_______________|
Objects Variables
Example : Not for medical use !
domains
disease,indication=symbol
predicates
symptom(disease,indication)
clauses
symptom(chicken_pox,high_fever). symptom(chicken_pox,chills). symptom(flu,chills).
symptom(cold,mild_body_ache). symptom(flu,severe_body_ache).
symptom(cold,runny_nose). symptom(flu,runny_nose). symptom(flu,moderate_cough).
Goal : symptom(cold,runny_nose) <-!
Response from prolog :
True
32. Goal : symptom(cold,headache) <-!
Response from prolog :
False
Using rules to solve the problems
Here another example to use
rules to solve the problems,
using hypothesis- concepts
Example : Medical Diagnostic System
domains
disease,indication,name=symbol
predicates
hypothesis(name,disease)
symptom(disease,indication)
clauses
symptom(charlie,fever). symptom(charlie,rash). symptom(charlie,headache).
symptom(charlie,runny_nose).
Hypotheis(patient,measles):-
symptom(charlie,fever), symptom(charlie,rash), symptom(charlie,headache),
symptom(charlie,runny_nose),
symptom(charlie,cough).
Hypotheis(patient,german_measles):-
symptom(charlie,fever), symptom(charlie,rash), symptom(charlie,headache),
symptom(charlie,runny_nose).
Hypotheis(patient,flu):-