We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byMyles Sims
Modified about 1 year ago
Copyright © 2002 Cycorp Logical Aspects of Inference Incompleteness in Searching Incompleteness from Resource Bounds and Continuable Searches Inference Features in Cyc Efficiency through Heuristics Inference in Cyc
Copyright © 2002 Cycorp The Search Tree Query/root Possible Answers: Branches/ Child nodes “Who does Hamlet know and love?”
Copyright © 2002 Cycorp Justifying the Answers Query Goal! Logical Deduction with the Most General Unifier
Copyright © 2002 Cycorp Justifying the Answers (cont.’d) Goal Node “true” “yes, I was able to prove it true” Bindings values assigned to variables in the unification process only includes values that were used in successful proofs included in the justification
Copyright © 2002 Cycorp Incompleteness in Searching Any sufficiently complicated logical system is inherently incomplete. “Incompleteness of logic” “Halting problem”
Copyright © 2002 Cycorp The Halting Problem millions of facts + tens of thousands of rules infinite possibilities
Copyright © 2002 Cycorp Depth-first Search Start at the top Expand as you go down A Goal! Need more information
Copyright © 2002 Cycorp Depth-first Search Traversal Start here
Copyright © 2002 Cycorp Depth-first Search Traversal ?
Copyright © 2002 Cycorp Depth-first Search Traversal ? ?
Copyright © 2002 Cycorp Depth-first Search Traversal ? *? GOAL!
Copyright © 2002 Cycorp Depth-first Search Traversal ? ? *?
Copyright © 2002 Cycorp Depth-first: advantage and disadvantage + Algorithmically efficient - No end
Copyright © 2002 Cycorp A Depth-first Rabbit Hole: “Cyc-ic Friends” “Who are all of the elected leaders of countries north of the equator?”
Copyright © 2002 Cycorp A Depth-first Rabbit Hole: “Cyc-ic Friends” * ** = ? The Halting Problem caused Cyc to keep trying, even when the logic (to us) was completely nonsensical….
Copyright © 2002 Cycorp A Depth-first Rabbit Hole: “Cyc-ic Friends” How did it get so far off? Steps were logical Steps involved default-true rules usually true in isolation need more logic when so many are chained together
Copyright © 2002 Cycorp The Halting Problem: a Trade-off The Halting Problem: Should I just try and do a little more work to try and prove this? Can I prove that I shouldn’t even be bothering to try and do this proof? Maybe you’re missing a key constraint....
Copyright © 2002 Cycorp The Halting Problem: a Trade-off Trying to prove that I shouldn’t even be bothering to try and do this proof…. Proof by contradiction Can I prove that this couldn’t possibly be true? Work harder or work smarter?
Copyright © 2002 Cycorp Breadth-first Search: advantage and disadvantage 0-step 1-step 2-step n-step + Deal with simplest proofs first - Infinite fan-out is common
Copyright © 2002 Cycorp Breadth-first Search: another disadvantage There isn’t enough space to store all of the child nodes that you haven’t looked at yet.
Copyright © 2002 Cycorp We Need a Better Search Can’t use depth-first - might get stuck in a rabbit hole Can’t use breadth-first - space of possible solutions may be too big Query Goal!
Copyright © 2002 Cycorp Summary The Search Tree Incompleteness of Logic The Halting Problem –Infinite Possibilities –Work harder or work smarter? Depth-first Searches –Rabbit-holes Breadth-first Searches –Infinite fan-out –Infinite space required to store possible solutions
Copyright © 2002 Cycorp Inference in Cyc Logical Aspects of Inference Incompleteness in Searching Incompleteness from Resource Bounds and Continuable Searches.
Copyright © 2002 Cycorp Logical Aspects of Inference Incompleteness in Searching Incompleteness from Resource Bounds and Continuable Searches Inference.
CPSC 322, Lecture 9Slide 1 Search: Advanced Topics Computer Science cpsc322, Lecture 9 (Textbook Chpt 3.6) January, 23, 2009.
1 Solving problems by searching Chapter 3. Depth First Search Expand deepest unexpanded node The root is examined first; then the left child of the root;
CPSC 322, Lecture 9Slide 1 Search: Advanced Topics Computer Science cpsc322, Lecture 9 (Textbook Chpt 3.6) January, 22, 2010.
Intelligent Agents What is the basic framework we use to construct intelligent programs?
24-Jun-15 Pruning. 2 Exponential growth How many leaves are there in a complete binary tree of depth N? This is easy to demonstrate: Count “going left”
Optimization Problems Greg Stitt ECE Department University of Florida.
Automated Reasoning Early AI explored how to automated several reasoning tasks – these were solved by what we might call weak problem solving methods as.
Solving Problems by Searching Currently at Chapter 3 in the book Will finish today/Monday, Chapter 4 next.
Copyright ©2015 Creating Inspired Learning. A justification is not just the answer to a problem but also includes how you can prove that you have made.
Introduction to Mixed Integer Linear Programming.
Problem Solving Agents A problem solving agent is one which decides what actions and states to consider in completing a goal Examples: Finding the shortest.
Busby, Dodge, Fleming, and Negrusa. Backtracking Algorithm Is used to solve problems for which a sequence of objects is to be selected from a set such.
Introduction to search Chapter 3. Why study search? §Search is a basis for all AI l search proposed as the basis of intelligence l all learning algorithms,
Inference in FOL Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 9 Spring 2004.
Problem Solving and Search Andrea Danyluk September 11, 2013.
Introduction to search Chapter 3. Why study search? §Search is a basis for all AI l search proposed as the basis of intelligence l inference l all learning.
Properties of breadth-first search Complete? Yes (if b is finite) Time? 1+b+b 2 +b 3 +… +b d + b(b d -1) = O(b d+1 ) Space? O(b d+1 ) (keeps every node.
Slide 1 Search: Advanced Topics Jim Little UBC CS 322 – Search 5 September 22, 2014 Textbook § 3.6.
How We’re Going to Solve the AI Problem Pedro Domingos Dept. Computer Science & Eng. University of Washington.
MAE 552 – Heuristic Optimization Lecture 27 April 3, 2002 Topic:Branch and Bound and Tree Search.
CHAPTERS 7, 8 Oliver Schulte Logical Inference: Through Proof to Truth.
1 Best-First Search: Agendas An agenda is a list of tasks a system could perform. Associated with each task there are usually two things: a list of reasons.
Cooperating Intelligent Systems Informed search Chapter 4, AIMA 2 nd ed Chapter 3, AIMA 3 rd ed.
Cooperating Intelligent Systems Informed search Chapter 4, AIMA.
1 Chapter 4 Search Methodologies. 2 Chapter 4 Contents l Brute force search l Depth-first search l Breadth-first search l Properties of search methods.
Basic Problem Solving Search strategy Problem can be solved by searching for a solution. An attempt is to transform initial state of a problem into some.
Ch. 13 Ch. 131 jcmt CSE 3302 Programming Languages CSE3302 Programming Languages (notes?) Dr. Carter Tiernan.
SLD-resolution Introduction Most general unifiers SLD-resolution Soundness Completeness.
Logic Programming CSC 358/ Outline Pattern matching Unification Logic programming.
Programming Languages Third Edition Chapter 4 Logic Programming.
Copyright R. Weber Search in Problem Solving Search in Problem Solving INFO 629 Dr. R. Weber.
Informed search algorithms Chapter 4. Outline Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search.
Explanation-Based Learning (EBL) One definition: Learning general problem-solving techniques by observing and analyzing human solutions to specific problems.
Best-first search is a search algorithm which explores a graph by expanding the most promising node chosen according to a specified rule.
Chapter 14 An Overview of Query Optimization. Copyright © 2005 Pearson Addison-Wesley. All rights reserved Figure 14.1 Typical architecture for.
CS 5751 Machine Learning Chapter 11 Explanation-Based Learning 1 Explanation-Based Learning (EBL) One definition: Learning general problem- solving techniques.
Informed Search Methods Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 4 Spring 2004.
Constraint Logic Programming Ryan Kinworthy. Overview Introduction Logic Programming LP as a constraint programming language Constraint Logic Programming.
Copyright R. Weber Search in Problem Solving ISYS 370 Dr. R. Weber.
Recursive Back Tracking & Dynamic Programming Lecture 7.
1 CO Games Development 1 Week 6 Introduction To Pathfinding + Crash and Turn + Breadth-first Search Gareth Bellaby.
1 Using Search in Problem Solving Part I. 2 Search and AI Introduction Introduction Search Space, Search Trees Search Space, Search Trees Search in Graphs.
CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling1 Chapter 8: Organizational Structures and Retrieval Algorithms This chapter deals with how to find.
Computer Science CPSC 322 Lecture 9 (Ch , 3.7.6) Slide 1.
Empirical Explorations with The Logical Theory Machine: A Case Study in Heuristics by Allen Newell, J. C. Shaw, & H. A. Simon by Allen Newell, J. C. Shaw,
Binary Tree Implementation. Binary Search Trees (BST) Nodes in Left subtree has smaller values Nodes in right subtree has bigger values.
Review: Tree search Initialize the frontier using the starting state While the frontier is not empty – Choose a frontier node to expand according to search.
CPSC 322, Lecture 5Slide 1 Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 13, 2013.
© 2017 SlidePlayer.com Inc. All rights reserved.