Andrew Courter Texas Tech University CS5331.  PKS Why PKS? STRIPS The Databases Inference Algorithm Extended Features  PKS Examples  Conclusion and.

Slides:



Advertisements
Similar presentations
REVIEW : Planning To make your thinking more concrete, use a real problem to ground your discussion. –Develop a plan for a person who is getting out of.
Advertisements

AE1APS Algorithmic Problem Solving John Drake. The island of Knights and Knaves is a fictional island to test peoples ability to reason logically. There.
Situation Calculus for Action Descriptions We talked about STRIPS representations for actions. Another common representation is called the Situation Calculus.
Methods of Proof Chapter 7, second half.. Proof methods Proof methods divide into (roughly) two kinds: Application of inference rules: Legitimate (sound)
Inferences The Reasoning Power of Expert Systems.
Session for Project NExT Fellows Mathfest, 2013 Portland, OR Presenter: Carol S. Schumacher Kenyon College.
Planning and Scheduling. 2 USC INFORMATION SCIENCES INSTITUTE Some background Many planning problems have a time-dependent component –  actions happen.
Learning Objectives Explain similarities and differences among algorithms, programs, and heuristic solutions List the five essential properties of an algorithm.
Chapter 10 Algorithmic Thinking. Copyright © 2013 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Learning Objectives List the five essential.
1 Chapter 16 Planning Methods. 2 Chapter 16 Contents (1) l STRIPS l STRIPS Implementation l Partial Order Planning l The Principle of Least Commitment.
Chapter 12: Expert Systems Design Examples
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering CSCE 580 Artificial Intelligence Ch.5 [P]: Propositions and Inference Sections.
Calculating Spectral Coefficients for Walsh Transform using Butterflies Marek Perkowski September 21, 2005.
Proof System HY-566. Proof layer Next layer of SW is logic and proof layers. – allow the user to state any logical principles, – computer can to infer.
Knowledge in intelligent systems So far, we’ve used relatively specialized, naïve agents. How can we build agents that incorporate knowledge and a memory?
CS 106 Introduction to Computer Science I 03 / 28 / 2008 Instructor: Michael Eckmann.
Planning with Incomplete, Unbounded Information May 20, 2003 Tal Shaked.
Rules of Exponents In this lesson, you will be able to simplify expressions involving zero and negative exponents.
While Loops and Do Loops. Suppose you wanted to repeat the same code over and over again? System.out.println(“text”); System.out.println(“text”); System.out.println(“text”);
Python Programming, 2/e1 Python Programming: An Introduction to Computer Science Chapter 3 Computing with Numbers.
Chapter Seven Advanced Shell Programming. 2 Lesson A Developing a Fully Featured Program.
Problem Solving Methods. Class Objectives Learn to apply the problem solving process Learn techniques for error-free problem solving.
Session for Project NExT Fellows Mathfest, 2013 Portland, OR Presenter: Carol S. Schumacher Kenyon College.
GCSE History Paper 1 walkthrough Question 1A - What is this message of this cartoon? 7 marks Question 1B - Explain why something happened? 8 marks Contents.
Lecture 5 Knights and Knaves.. Administration Show hand in form. Show plagiarism form. Any problems with coursework? Google knight and knaves and look.
Lesson Starter Look at the specifications for electronic balances. How do the instruments vary in precision? Discuss using a beaker to measure volume versus.
Chapter 1: Lecture Notes What Is an Argument? (and What is Not?)
08/10/ Iteration Loops For … To … Next. 208/10/2015 Learning Objectives Define a program loop. State when a loop will end. State when the For.
Tonga Institute of Higher Education Design and Analysis of Algorithms IT 254 Lecture 8: Complexity Theory.
CSC-682 Cryptography & Computer Security Sound and Precise Analysis of Web Applications for Injection Vulnerabilities Pompi Rotaru Based on an article.
Section 3.1: Proof Strategy Now that we have a fair amount of experience with proofs, we will start to prove more difficult theorems. Our experience so.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. A Concise Introduction to MATLAB ® William J. Palm III.
Making a Claim Grounds for Claim Evaluation Beyond Brainstorm.
110/19/2015CS360 AI & Robotics AI Application Areas  Neural Networks and Genetic Algorithms  These model the structure of neurons in the brain  Humans.
5.3 Geometric Introduction to the Simplex Method The geometric method of the previous section is limited in that it is only useful for problems involving.
© ETH Zürich Eric Lo ETH Zurich a joint work with Carsten Binnig (U of Heidelberg), Donald Kossmann (ETH Zurich), Tamer Ozsu (U of Waterloo) and Peter.
# 1# 1 Error Messages, VLookup, Practical Tips What use is VLookup? How do you error check in Excel? CS 105 Spring 2010.
PROBLEM SOLVING WITH LOOPS Chapter 7. Concept of Repetition Structure Logic It is a computer task, that is used for Repeating a series of instructions.
Understanding Rubrics What is a rubric? A scoring tool that lists the criteria for a piece of work, or “what counts” (e.g., purpose, organization, detail,
Devina DesaiF r a m e P r o b l e m What is a Frame Problem Environment for an agent is not static Identifying which things remain static in changing word.
Soft Computing Lecture 19 Part 2 Hybrid Intelligent Systems.
“The perfect project plan is possible if one first documents a list of all the unknowns.” Bill Langley.
Uncertainty Management in Rule-based Expert Systems
ITEC 380 Organization of programming languages Lecture 8 – Prolog.
LDK R Logics for Data and Knowledge Representation Propositional Logic: Reasoning First version by Alessandro Agostini and Fausto Giunchiglia Second version.
Modeling Speech Acts and Joint Intentions in Modal Markov Logic Henry Kautz University of Washington.
Learn to identify and evaluate expressions. WARM-UP Please answer the questions below showing all your work! = – 189 = 3.46 x 847=
Boolean Data Lesson CS1313 Fall Boolean Data Outline 1.Boolean Data Outline 2.Data Types 3.C Boolean Data Type: char or int 4.C Built-In Boolean.
An infrastructure for context-awareness based on first order logic Ubiquitous Software Lab Oh Min Kyoung
Logic UNIT 1.
1 Knowledge Based Systems (CM0377) Introductory lecture (Last revised 28th January 2002)
Solving an equation with one unknown From arithmetic to algebra Modifying equations in algebra.
2.6 Problem solving strategies. Problem-solving skills are essential to success in physics. the ability to apply broad physical principles, usually represented.
Chapter 4 Test Design Techniques MNN1063 System Testing and Evaluation.
UNIT 7 MONITORING AND EVALUATION  Monitoring and evaluation is the process of examining progress against institution’s goals or plan.  The term SM &
Chapter 2 © Houghton Mifflin Harcourt Publishing Company Accuracy and Precision Accuracy refers to the closeness of measurements to the correct or accepted.
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.
Arrays and Loops. Learning Objectives By the end of this lecture, you should be able to: – Understand what a loop is – Appreciate the need for loops and.
REU 2007-ParSat: A Parallel SAT Solver Christopher Earl, Mentor: Dr. Hao Zheng Department of Computer Science & Engineering Introduction Results and Conclusions.
SLAM Techniques -Venkata satya jayanth Vuddagiri 1.
1 Solving Problems with Methods Questions. 2 Problem solving is a process similar to working your way through a maze. But what are these “steps” and what.
Copyright Pearson Prentice Hall
LESSON 11 – WHILE LOOPS UNIT 5 – 1/10/17.
Do Now 10/31/11 In your notebook on a new page, complete the activity on page 193 in the textbook. Fill in the 2 tables in the “Investigate” section.
Coding Concepts (Basics)
Knowledge and reasoning – second part
Test Drop Rules: If not:
Sampling Distributions
Review of Previous Lesson
Habib Ullah qamar Mscs(se)
Presentation transcript:

Andrew Courter Texas Tech University CS5331

 PKS Why PKS? STRIPS The Databases Inference Algorithm Extended Features  PKS Examples  Conclusion and Future Work  Questions CS5331

 Planning with Knowledge and Sensing System  Goal: To come up with natural plans with an incomplete set of knowledge  Implement new features in PKS that will be able to solve a wider range of problems CS5331

 (Stanford Research Institute Problem Solver) is the framework that PKS is based on  The known world is represented in a database and actions are represented as updates to the database  PKS uses multiple databases and stores knowledge instead of the state of the world CS5331

 The first database is like a STRIPS database(containing ground literals) except that both positive and negative facts are allowed  The second database is used for plan time reasoning about sensing actions  The third database stores information about function values that will be known at execution time CS5331

 The fourth database contains disjunctive knowledge  If one ground literal is found to be true the rest of the literals become false or if all but one literals are false the remaining one is true CS5331

 Examines database contents to determine if an actions preconditions hold true  Also determines what the effects of an action should be activated and whether or not a plan achieved its goal  Four different rules are used to determine conclusions about the effects CS5331

 The PKS has a complete knowledge of action effects and non-effects  The agent’s actions are the only source of change in the world CS5331

 Knowing that a final conclusion relates to the initial state and all other states  Numeric expressions used in evaluating numbers(evaluated down to a number at plan time)  Finite valued functions in the exclusive-or knowledge database CS5331

 The PKS system was fine tuned and can handle a wider range of planning problems using the new inference algorithm  In the problems a know-whether state was achieved CS5331

 Develop extensions to handle unknown numeric quantities  Current system is unable to treat unknown file sizes in a general way in the Unix example CS5331

 On page 2, they discuss the Kv section how, can you guarantee the value will become known? Do you have an example? Why can’t they make the numeric evaluation work with numbers not known at run time? Can you explain the painted door problem, I am confused?  What happens if the algorithm cannot deduce a plan given the current inputs? Does it stop or does it try the plan that considered a "best fit"? What is STRIPS? CS5331

 Does the PKS guarantee optimal solutions (plans)?  When a human does not know exactly how something works or to do, they try something that they think of right on the spot, do you think this can ever be accomplished with these techniques?  Can you give us some examples to explain the application of PKS? CS5331