CPSC 322 Introduction to Artificial Intelligence September 15, 2004.

Slides:



Advertisements
Similar presentations
1 Knowledge Representation Introduction KR and Logic.
Advertisements

ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
Artificial Intelligence in the 21 st Century S. Lucci / D. Kopec Chapter 5: Logic in Artificial Intelligence 1.
Computer Science CPSC 322 Lecture 25 Top Down Proof Procedure (Ch 5.2.2)
Logic Use mathematical deduction to derive new knowledge.
Logic CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
CPSC 322, Lecture 23Slide 1 Logic: TD as search, Datalog (variables) Computer Science cpsc322, Lecture 23 (Textbook Chpt 5.2 & some basic concepts from.
CPSC 322, Lecture 19Slide 1 Propositional Logic Intro, Syntax Computer Science cpsc322, Lecture 19 (Textbook Chpt ) February, 23, 2009.
CPSC 322 Introduction to Artificial Intelligence September 13, 2004.
Constraint Logic Programming Ryan Kinworthy. Overview Introduction Logic Programming LP as a constraint programming language Constraint Logic Programming.
CPSC 322 Introduction to Artificial Intelligence September 20, 2004.
CS 561, Sessions Knowledge and reasoning – second part Knowledge representation Logic and representation Propositional (Boolean) logic Normal forms.
CPSC 322, Lecture 23Slide 1 Logic: TD as search, Datalog (variables) Computer Science cpsc322, Lecture 23 (Textbook Chpt 5.2 & some basic concepts from.
CS 460, Sessions Knowledge and reasoning – second part Knowledge representation Logic and representation Propositional (Boolean) logic Normal forms.
Logical Agents Chapter 7. Why Do We Need Logic? Problem-solving agents were very inflexible: hard code every possible state. Search is almost always exponential.
Knowledge in intelligent systems So far, we’ve used relatively specialized, naïve agents. How can we build agents that incorporate knowledge and a memory?
Let remember from the previous lesson what is Knowledge representation
CPSC 322 Introduction to Artificial Intelligence September 10, 2004.
Some Thoughts to Consider 6 What is the difference between Artificial Intelligence and Computer Science? What is the difference between Artificial Intelligence.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions.
Knowledge Interchange Format Michael Gruninger National Institute of Standards and Technology
1 Knowledge Based Systems (CM0377) Lecture 4 (Last modified 5th February 2001)
Fall 98 Introduction to Artificial Intelligence LECTURE 7: Knowledge Representation and Logic Motivation Knowledge bases and inferences Logic as a representation.
Formal Models in AGI Research Pei Wang Temple University Philadelphia, USA.
Logic Programming Module 2AIT202 Website Lecturer: Dave Sharp Room: AG15
CMPF144 FUNDAMENTALS OF COMPUTING THEORY Module 5: Classical Logic.
1 Logical Agents CS 171/271 (Chapter 7) Some text and images in these slides were drawn from Russel & Norvig’s published material.
Logical Agents Logic Propositional Logic Summary
Slide 1 Propositional Definite Clause Logic: Syntax, Semantics and Bottom-up Proofs Jim Little UBC CS 322 – CSP October 20, 2014.
CPSC 322, Lecture 23Slide 1 Logic: TD as search, Datalog (variables) Computer Science cpsc322, Lecture 23 (Textbook Chpt 5.2 & some basic concepts from.
1 Logical Agents CS 171/271 (Chapter 7) Some text and images in these slides were drawn from Russel & Norvig’s published material.
Logical Agents Chapter 7. Outline Knowledge-based agents Logic in general Propositional (Boolean) logic Equivalence, validity, satisfiability.
Ch. 13 Ch. 131 jcmt CSE 3302 Programming Languages CSE3302 Programming Languages (notes?) Dr. Carter Tiernan.
CS6133 Software Specification and Verification
Introduction to Prolog. Outline What is Prolog? Prolog basics Prolog Demo Syntax: –Atoms and Variables –Complex Terms –Facts & Queries –Rules Examples.
CPSC 322, Lecture 19Slide 1 (finish Planning) Propositional Logic Intro, Syntax Computer Science cpsc322, Lecture 19 (Textbook Chpt – 5.2) Oct,
1 CSC384: Intro to Artificial Intelligence Lecture 5.  Knowledge Representation.
Computer Science CPSC 322 Lecture 22 Logical Consequences, Proof Procedures (Ch 5.2.2)
Logic Programming (LP) expresses code using of a restricted form of symbolic logic. LP programs are declarative, rather than algorithmic. An LP program.
11 Artificial Intelligence CS 165A Thursday, October 25, 2007  Knowledge and reasoning (Ch 7) Propositional logic 1.
1 Knowledge Based Systems (CM0377) Lecture 6 (last modified 20th February 2002)
Slide 1 Propositional Logic Intro, Syntax Jim Little UBC CS 322 – CSP October 17, 2014 Textbook § – 5.2.
Logic: Proof procedures, soundness and correctness CPSC 322 – Logic 2 Textbook §5.2 March 7, 2011.
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.
First-Order Logic Semantics Reading: Chapter 8, , FOL Syntax and Semantics read: FOL Knowledge Engineering read: FOL.
Some Thoughts to Consider 5 Take a look at some of the sophisticated toys being offered in stores, in catalogs, or in Sunday newspaper ads. Which ones.
Artificial Intelligence Logical Agents Chapter 7.
By P. S. Suryateja Asst. Professor, CSE Vishnu Institute of Technology
ece 720 intelligent web: ontology and beyond
CS 4700: Foundations of Artificial Intelligence
Knowledge and reasoning – second part
CS 4700: Foundations of Artificial Intelligence
EA C461 – Artificial Intelligence Logical Agent
Learning and Knowledge Acquisition
Logical Agents Chapter 7.
Logic Use mathematical deduction to derive new knowledge.
Logic: Top-down proof procedure and Datalog
KNOWLEDGE REPRESENTATION
Artificial Intelligence: Logic agents
CPSC 322 Introduction to Artificial Intelligence
Knowledge and reasoning – second part
Back to “Serious” Topics…
Logic: Domain Modeling /Proofs + Computer Science cpsc322, Lecture 22
Logics for Data and Knowledge Representation
Knowledge Representation I (Propositional Logic)
Chapter 2: Prolog (Introduction and Basic Concepts)
Representations & Reasoning Systems (RRS) (2.2)
Logical Agents Prof. Dr. Widodo Budiharto 2018
Presentation transcript:

CPSC 322 Introduction to Artificial Intelligence September 15, 2004

Highlights from last time 1. A peek at what’s inside the intelligent agent

The Intelligent Agent as Black Box Prior knowledge Past Experience Goals and Values Observations Actions Reasoning and Representation System (RRS)

Reasoning and Representation System A language for communication with the computer A way to assign meaning to the language Procedures to compute answers to problems given as input in the language

Highlights from last time 1. A peek at what’s inside the intelligent agent 2. Five steps to building an intelligent agent

Five Simple Steps to World Domination (or how to build the black box) 1.Begin with a task domain that you want to characterize 2.Distinguish the things you want to talk about in the domain (the ontology) 3.Use symbols in the computer to represent objects and relations in the domain. (Symbols denote objects...they’re not really the objects.) 4.Tell the computer the knowledge about the domain. 5.Ask the RRS a question which prompts the RRS to reason with its knowledge to solve a problem, produce answers, or generate actions

Highlights from last time 1. A peek at what’s inside the intelligent agent 2. Five steps to building an intelligent agent 3. We built an intelligent agent

The Diagnostic Assistant Domain

Our first intelligent agent* cilog: tell on(l2) <- hot(w4). cilog: tell hot(w4) <- closed(s3) & hot(w3). cilog: tell hot(w3) <- closed(cb1) & hot(outside_power). cilog: tell closed(cb1). cilog: tell hot(outside_power). cilog: tell closed(s3). cilog: ask on(l2). Answer: on(l2). *OK, it has less intelligence than a plant, but everybody has to start somewhere.

Let’s go back to our three-part Reasoning and Representation System A language for communication with the computer formal language Grammar defines legal symbols and how they can be put together in sentences Sentences express knowledge about domain The language is all the sentences that can be created from the grammar A knowledge base is a set of sentences from the language

Let’s go back to our three-part Reasoning and Representation System A way to assign meaning to the language semantics Specifies the meaning of sentences in the language A commitment to how the symbols in the language relate to the task domain The semantic commitment is yours -- it’s in your head, not in the computer

Let’s go back to our three-part Reasoning and Representation System Procedures to compute answers to problems given as input in the language reasoning theory or proof procedure A (possibly nondeterministic) specification of how an answer can be derived from the knowledge base Often a set of inference rules for creating new knowledge from the knowledge base......or a (nondeterministic) specification of how an answer is computed

Two New Words nondeterministic: exhibiting nondeterminism nondeterminism: a property of a computation which may have more than one result. How to implement nondeterminism: depth-first search with backtracking explore all possible solutions in parallel find an oracle

Two New Words inference: (1) The act or process of deriving logical conclusions from premises known or assumed to be true. (2) The act of reasoning from facutal knowledge or evidence. Three general classes of inference: deductive inference inductive inference abductive inference

An Implementation of a Reasoning and Representation System Consists of: A language parser that maps legal sentences of the formal language to some internal form stored as data structures A reasoning procedure that combines a reasoning theory with a search strategy. The search strategy is a commitment to how to resolve the nondeterminism. Note that this is all independent of semantics. This is just symbol manipulation by following a set of rules.

Simplifying Assumptions for our first Reasoning and Representation System An agent’s knowledge can be usefully described in terms of individuals and relations among individuals. An agent’s knowledge base consists of definite and positive statements. (i.e., nothing vague, no negation) The environment is static. (i.e., nothing changes) There are only a finite number of individuals of interest in the domain Some of these assumptions will be relaxed as we go on.

Syntax for CILOG(Datalog) A variable is a word that starts with an uppercase letter. X, Y, Kurt, The_bald_guy, Q42 A constant is a word starting with a lowercase letter or it can be all digits (a numeral). x, y, kurt, daughter, happy, q42, 493 A predicate symbol is a word starting with a lowercase letter. x, y, kurt, daughter, happy, q42 A term is either a variable or a constant.

Syntax for CILOG(Datalog) An atomic symbol (or atom) is of the form p or p(t 1,..., t n ) where p is a predicate symbol and each t i is a term. happy, teaches(kurt, cs322), between(s3, l2, cb1), mother(elizabeth, X) A body is of the form a 1 &... & a m (or a 1 ^... ^ a m ) where each a i is an atom. A definite clause is either an atom, a, called a fact, or of the form a <- b, called a rule, where a, the head, is an atom and b is a body. The <- is read as “if”.

Syntax for CILOG(Datalog) A query is of the form ask b (or ?b) where b is a body. An expression is either a term, an atom, a definite clause, or a query. A knowledge base is a set of definite clauses.

Your ordinary everyday family tree William Harry Peter Zara Beatrice Eugenie Louise Diana x Charles Anne x Mark Andrew x Sarah Edward x Sophie Elizabeth x Philip Margaret George x Mum Adapted from Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig Updated from

mother charles married diana elizabeth female(elizabeth) parent(elizabeth,charles) mother(X,Y) <- female(X) & parent(X,Y)