Artificial Intelligence CS3811- PDF slides and Helping material. Here you can download PDF slides and handouts helping material Undergraduate course offered in different universities like UOS, PU, VU, GCUF etc.
Artificial Intelligence Introduction :
I read in my basic course of computer science that fifth generation consists on artificial intelligence (AI) technology. Teacher explained very amazing things about AI, Like computer can think with it, can speak to us, and computer can reason and get knowledge. Formally, Artificial intelligence is sometimes called machine intelligence, is intelligence shown by a machine. It can be defined as that an area of research by which any device can perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Artificial Intelligence CS-3811 Course Outline (UOS)
- Introduction: What is AI, Foundations of AI, History of AI. Intelligent Agents: Agents and Environments, The Nature of Environments, The Structure of Agents [TB: Ch. 1, 2]
- Problem Solving by Searching: Problem Solving Agents, Searching for Solutions, Uninformed Search Strategies.
- Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening, Depth-first Search, Comparison of Uninformed Search Strategies. [TB: Ch. 3]
- Informed Search and Exploration: Informed (Heuristic) Search Strategies: Greedy Best-first Search, A* Search, Heuristic Functions, Local Search Algorithms and Optimization Problems. [TB: Ch. 4]
- Constraint Satisfaction Problems: Backtracking Search for CSPs, Local Search for CSPs. Adversarial Search: Games, Minimax Algorithm, Alpha-Beta Pruning. [TB: Ch. 5, 6]
- Reasoning and Knowledge Representation: Introductions to Reasoning and Knowledge Representation, Propositional Logic, First Order Logic: Syntax and Semantics of First-Order Logic, Knowledge Engineering in First-Order Logic, [TB: Ch. 7, 8]
- Inference in First-Order Logic: Inference rules for quantifiers, A first-order inference rule, Unification, Forward Chaining, Backward Chaining, A backward chaining algorithm, Logic programming, The resolution inference rule [TB: Ch. 9]
- Introduction to Prolog Programming
- Reasoning Systems for Categories, Semantic Nets and Description logics, Reasoning with Default Information: Open and closed worlds, Negation as failure and stable model semantic. Truth Maintenance Systems [TB: Ch. 10]
- Reasoning with Uncertainty & Probabilistic Reasoning: Acting Under Uncertainty, Bayes’ Rule and Its Use, [TB: Ch 13]
- Representing Knowledge in an Uncertain Domain, The Semantics of Bayesian Networks. [TB: Ch. 14]
- Learning from Observations: Forms of Learning, Inductive Learning, Learning Decision Trees [TB: Ch. 18]
- Knowledge in Learning, Explanation-Based Learning, Inductive Logic Programming. [TB: 19]
- Statistical Learning, Neural Networks [TB: Ch. 20]
CS-3811 Artificial Intelligence Recommended book
- Artificial Intelligence: A Modern Approach, by Russell and Norvig, Prentice Hall. 2ndEdition. ISBN-10: 0137903952 (Text Book)
- Artificial Intelligence: A Systems Approach by M. Tim Jones, Jones and Bartlett Publishers, Inc; 1stEdition (December 26, 2008). ISBN-10: 0763773379 (Reference Book)
- Artificial Intelligence in the 21st Century by Stephen Lucci, Danny Kopec, Mercury Learning and Information (May 18, 2012). ISBN-10: 1936420236 (Reference Book)
Chap 7 What is Reasoning and Knowledge Representation
Colloquially, the term “artificial intelligence” is used to describe machines that copycat(mimic) “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving“. Following are the topics covered in Today’s Lecture slides.
- what is Artificial intelligence?
- AI Cycle and What is reasoning and Knowledge representation?
- What is propositional logic?
- What are facts and rules?
- What is first order logic
- What syntax and semantic network?
- What is Perception, and Knowledge acquisition?
- How Automated reasoning works?
- What is inference in artificial intelligence?
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Chapter 8 :First order Logic and Knowledge Engineering
In last Lecture we learned about propositional logic which lacks the expressive power to concisely describe an environment with many objects. We can adopt the foundation of propositional logic—a declarative, compositional semantics that is context-independent and unambiguous—and build a more expressive logic on that foundation, borrowing representational ideas from natural language while avoiding its drawbacks.
In today lecture and slides we will learn First order logic and Knowledge Engineering. We will discuss its SYNTAX AND SEMANTICS. In the end we will discuss about what is knowledge engineering and process of knowledge engineering.
Natural languages consists of nouns, verbs and other parts of speech. Noun phrases that refer to objects in formal languages. Verb phrases that refer to relations among objects and sometimes these Relations are functions—relations in which there is only one “value” for a given “input.” Click to Image below to Download lecture slides
Chapter 9 : Reasoning and Inference
After knowledge representation, Lets look at mechanisms to reasoning. Reasoning is the process of deriving logical conclusions from given facts. Durkin defines reasoning as ‘the process of working with knowledge, facts and problem solving strategies to draw conclusions’. Followings are the types of reasoning
- deductive reasoning
- inductive reasoning
- abductive reasoning
- common-sense reasoning
- Analogical reasoning
- non monotonic reasoning
A process of deriving new information from known information. In the domain of AI, the component of the system that performs inference is called an inference engine. We will look at inference within the framework of ‘logic’, which we introduced earlier. We can use proof system : –Begin with initial premises of the proof (or knowledge base) –Use rules, i.e. apply rules to the known information –Add new statements, based on the rules that match
Forward and backward chaining PDF slides. click image below to download PDF slides.
Chapter 12 Reasoning System with Categories
So far we learned about knowledge representation. How to create these representations, concentrating on general concepts—such as Events, Time, Physical Objects, and Beliefs— that occur in many different domains. ONTOLOGICAL ENGINEERING is a way to representing everything in the world is and overwhelming task. For example, we will define what it means to be a physical object, and the details of different types of objects—robots, televisions, books, or whatever—can be filled in later. (OOP)
Categories are the primary building blocks of large-scale knowledge representation schemes. There are two closely related families of systems: semantic networks provide graphical aids for visualizing a knowledge base and efficient algorithms for inferring properties
of an object on the basis of its category membership Description logics provide a formal language for constructing and combining category definitions and efficient algorithms for deciding subset and superset relationships between categories.
Following topics are covered in these PDF slides
- Semantic Networks
- Open and closed worlds
- Description logic
- Default logic
- Types of Truth maintenance System (JTMS)
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UOS-BSIT- Introduction to PROLOG- An Artificial Intelligence Language
Prolog is a logic programming language associated with artificial intelligence and computational linguistics. Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program logic is expressed in terms of relations, represented as facts and rules. A computation is initiated by running a query over these relations
Data Types in Prolog
Prolog’s single data type is the term. T
Terms are either atoms, numbers, variables or compound terms.
An atom is a general-purpose name with no inherent meaning. Examples of atoms include x, red, ‘Taco’, and ‘some atom’.
Numbers can be floats or integers. ISO standard compatible Prolog systems can check the Prolog flag “bounded”. Most of the major Prolog systems support arbitrary length integer numbers.
Variables are denoted by a string consisting of letters, numbers and underscore characters, and beginning with an upper-case letter or underscore. Variables closely resemble variables in logic in that they are placeholders for arbitrary terms. A compound term is composed of an atom called a “functor” and a number of “arguments”, which are again terms. Compound terms are ordinarily written as a functor followed by a comma-separated list of argument terms, which is contained in parentheses.
Special cases of compound terms:
A List is an ordered collection of terms. It is denoted by square brackets with the terms separated by commas or in the case of the empty list, []. For example, [1,2,3] or [red,green,blue].
Strings: A sequence of characters surrounded by quotes is equivalent to either a list of (numeric) character codes, a list of characters (atoms of length 1), or an atom depending on the value of the Prolog flag double_quotes. For example, “to be, or not to be”.
ISO Prolog provides the atom/1, number/1, integer/1, and float/1 predicates for type-checking.[16]
Chapter 2 and Chapter 2 Agent and problem solving with searching algorithms
•An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. •An agent’s choice of action at any given instant can depend on the entire percept sequence observed to date, but not on anything it hasn’t perceived •We use the term percept to refer to the agent’s perceptual inputs at any given instant. An agent’s percept sequence is the complete history of everything the agent has ever perceived
•An agent’s
behavior is described by the agent function that maps any
given percept sequence to an action.
•The agent function for an artificial agent will be
implemented by an agent
program. It is
important to keep these two ideas distinct.
•The agent function
is an abstract mathematical description;
•the agent program
is a concrete implementation,
running
within some physical system.
•Intelligent
agents are supposed to maximize their performance measure by
achieving a goal and aim at
satisfying it.
•Problem formulation is the process
of deciding what actions and states to consider, about a given a goal.
•Lets us
consider a very common problem of rout finding in a city to which an agent do
not know.
Problems solving agents and searching
•The process of looking for a sequence of actions that reaches the goal is called search. •A search algorithm takes a problem as input and returns a solution in the form of an action sequence. •Once a solution is found, the actions it recommends can be carried out. This is called the execution phase. •An agent that carries out its plans with its eyes closed, so to speak, must be quite certain of what is going on. this an open-loop system, because ignoring the precepts.
