'Blocks world' presentation slideshows

LECTURE 4: PRACTICAL REASONING

982 views • 73 slides

Planning

Planning. Chapter 11.1-11.3. Some material adopted from notes by Andreas Geyer-Schulz and Chuck Dyer. Overview. What is planning? Approaches to planning GPS / STRIPS Situation calculus formalism [revisited] Partial-order planning. A. C. B. A. B. C. Blocks World Planning.

760 views • 66 slides

Artificial Intelligence Planning System

Artificial Intelligence Planning System. L. Manevitz. Components of a Planning System. Chose the best rule to apply next based on the best available heuristic information. Apply the chosen rule to compute the new problem state that arises from its application.

793 views • 61 slides

Block II, Unit III, Symbolic AI in the world

Block II, Unit III, Symbolic AI in the world. This unit has four main sections Planning Robots Learning adaptation and heuristics Uncertainty. Block II, Unit III, Symbolic AI in the world. Planning Planning might appear to be just another form of problem solving.

600 views • 51 slides

Planning

Planning. Chapter 11.1-11.3. Some material adopted from notes by Andreas Geyer-Schulz and Chuck Dyer. A. C. B. A. B. C. Blocks World Planning. Blocks world. The blocks world is a micro-world consisting of a table, a set of blocks and a robot hand

851 views • 48 slides

Technology for automated assessment: The World-Wide-Mind

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DNF to CNF

DNF to CNF. (a ^ b) V (b ^ c) V (a ^ c) ? (a V b) ^ (b V c) ^ (a V c) Proof: (a ^ b) V (b ^ c) V (a ^ c) = [(a^b) V (b^c)] V (a^c) = [((a^b) V b) ^ ((a^b) V c)] V (a^c) = [(a V b) ^ b ^ (c V a) ^ (b V c)] V (a^c) = [((a V b) ^ b ^ (c V a) ^ (b V c)) V a] ^

1.45k views • 41 slides

Chapter 1: Constraints

Chapter 1: Constraints. What are they, what do they do and what can I use them for. Constraints. What are constraints? Modelling problems Constraint solving Tree constraints Other constraint domains Properties of constraint solving Topic for later: constraints and state. Constraints.

524 views • 37 slides

Lirong Xia

Planning. Lirong Xia. Tuesday , April 22, 2014. Last time. The kernel trick Neuron network Clustering (k-means). Kernelized Perceptron. If we had a black box ( kernel ) which told us the dot product of two examples x and y: Could work entirely with the dual representation

541 views • 37 slides

Planning as model checking, (OBDDs)

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CPS 170: Artificial Intelligence cs.duke/courses/spring09/cps170/ Planning

Instructor: Vincent Conitzer. CPS 170: Artificial Intelligence http://www.cs.duke.edu/courses/spring09/cps170/ Planning. Planning. We studied how to take actions in the world (search) We studied how to represent objects, relations, etc. (logic) Now we will combine the two!.

406 views • 29 slides

CPS 170: Artificial Intelligence cs.duke/courses/spring09/cps170/ Planning

Instructor: Vincent Conitzer. CPS 170: Artificial Intelligence http://www.cs.duke.edu/courses/spring09/cps170/ Planning. Planning. We studied how to take actions in the world (search) We studied how to represent objects, relations, etc. (logic) Now we will combine the two!.

372 views • 29 slides

Lecture 26 of 42

Lecture 26 of 42. Conditional, Continuous, and Multi-Agent Planning Discussion: Probability Refresher. Wednesday. 24 October 2007 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3

445 views • 28 slides

Pertemuan 17 Planning

Pertemuan 17 Planning. Matakuliah : T0264/Intelijensia Semu Tahun : Juli 2006 Versi : 2/1. Learning Outcomes. Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : << TIK-99 >> << TIK-99>>. Outline Materi. Materi 1 Materi 2 Materi 3 Materi 4 Materi 5. 13.1. Overview.

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Progress Toward Implementing Conceptual Graph Processes

Progress Toward Implementing Conceptual Graph Processes. Coursework Masters Thesis University of South Australia School of Computer and Information Science September 2000 Student: David Benn Supervisor: Dan Corbett. Plan. The Idea Motivations for Processes Motivations for pCG

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Lecture 22 of 42

Lecture 22 of 42. Partial-Order Planning Discussion: Exam 1 Review. Monday, 16 October 2006 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3 Course web site: http://www.kddresearch.org/Courses/Fall-2006/CIS730

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Planning in FOL Systems

329 views • 24 slides

6.870 Object Recognition and Scene Understanding

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Problem Solving in Common Lisp

Problem Solving in Common Lisp. Common Lisp J.E. Spragg May 1997. The Problem Space Model. People seem to handle adequately the notion of physical space. It seems natural to extend our notions of physical space and use it as a topological metaphor for problems and problem solving.

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Are we comparing Dana & Fahiem or SHOP and TLPlan? (A Critique of Knowledge-based Planning Track at ICP)

155 views • 6 slides