'Evaluation function' presentation slideshows

Announcements

Announcements. Assignments: P0: Python & Autograder Tutorial Due Thu 9/5, 10 pm HW2 (written) Due Tue 9/10, 10 pm No slip days. Up to 24 hours late, 50 % penalty P1: Search & Games Due Thu 9/12, 10 pm Recommended to work in pairs Submit to Gradescope early and often.

1.01k views • 72 slides

Artificial Intelligence

Artificial Intelligence. Rehearsal Lesson. Solving Problems with Search Algorithms. Input: a problem P . Preprocessing: Define states and a state space Define Operators Define a start state and goal set of states. Processing:

658 views • 59 slides

Adversarial Search and Game Playing

Adversarial Search and Game Playing CS 271: Fall 2007 Instructor: Padhraic Smyth CSPs: phase transitions in problem space Hogg and Williams, AI Journal, 1994 Average time constraints/variables Graph coloring by human subjects From Kearns, Suri, Montfort, Science, 2006

891 views • 51 slides

580 views • 51 slides

Search Strategies

Search Strategies. CPS4801. Uninformed Search Strategies. Uninformed search strategies use only the information available in the problem definition Breadth-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening search. Breadth-first search.

790 views • 51 slides

G5BAIM Artificial Intelligence Methods

G5BAIM Artificial Intelligence Methods. Genetic Algorithms. Graham Kendall. G5BAIM Genetic Algorithms. Charles Darwin 1809 - 1882. "A man who dares to waste an hour of life has not discovered the value of life". Genetic Algorithms. Based on survival of the fittest

615 views • 45 slides

594 views • 37 slides

Notes

526 views • 34 slides

26 June 2014 Geneva

Evaluation Office Annual Evaluation Report 2014. 26 June 2014 Geneva. A reminder: the purpose of evaluation at UNFPA is three-fold. Corporate Evaluations. Programme-level Evaluations. THEMATIC EVALUATIONS Conducted by the Evaluation Office. Corporate Evaluations (2012-2013).

465 views • 34 slides

Heuristic Search

473 views • 24 slides

Game Playing

440 views • 23 slides

Reinforcement Learning

Reinforcement Learning . Control learning Control polices that choose optimal actions Q learning Convergence. Control Learning. Consider learning to choose actions, e.g., Robot learning to dock on battery charger Learning to choose actions to optimize factory output

496 views • 22 slides

Tree Searching Strategies

Tree Searching Strategies. The procedure of solving many problems may be represented by trees. Therefore the solving of these problems becomes a tree searching problem. Satisfiability problem . Tree Representation of Eight Assignments. .

319 views • 21 slides

The independent evaluation office of the united nations development programme reflections on contribution to transpare

The independent evaluation office of the united nations development programme reflections on contribution to transparency, learning and accountability . Indran A. Naidoo Director. June 2013. Setting the stage. Demand from Members states and management.

366 views • 20 slides

Proposed Organisation of Evaluation of the Romanian NSRF and Operational Programmes, 2007-2013

Proposed Organisation of Evaluation of the Romanian NSRF and Operational Programmes, 2007-2013. Niall McCann, Technical Assistance Project for Programming, Monitoring and Evaluation, MACSF. Basis for proposed model?. The Evaluation Standards, to be approved by the EWG

420 views • 18 slides

Ant Colony Optimization for Cell Routing in VLSI Chips

Ant Colony Optimization for Cell Routing in VLSI Chips. Shashank Hegde, Santosh Kelgeri. Design Flow Overview. Idea/Specifications. High Level Language Description. Synthesis & Placement. Routing. Chip Signoff. Manufacturing/Testing/Packaging. Design Flow Overview.

349 views • 18 slides

UNICEF Plan for Global Evaluations

472 views • 17 slides

Game-Playing

Game-Playing. Read Chapter 6 Adversarial Search. State-Space Model Modified. States the same Operators depend on whose turn Goal: As before: win or win amount Search: somewhat different. Game Types. Two-person games vs multi-person chess vs monopoly vs poker

271 views • 17 slides

538 views • 16 slides

Reinforcement Learning in Games

Reinforcement Learning in Games. Colin Cherry colinc@cs Oct 29/01. Outline. Reinforcement Learning & TD Learning TD-Gammon TDLeaf Chinook Conclusion. The ideas behind Reinforcement Learning. Two broad categories for learning: Supervised Unsupervised (Our concern)

509 views • 15 slides