'Search algorithms' diaporamas de présentation

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Last time: search strategies

Last time: search strategies. Uninformed: Use only information available in the problem formulation Breadth-first Uniform-cost Depth-first Depth-limited Iterative deepening Informed: Use heuristics to guide the search Best first:

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Uninformed Search (AIMA, Sections 3.3-3.4)

Introduction to Artificial Intelligence. Uninformed Search (AIMA, Sections 3.3-3.4). Alan M Frisch, Dimitar Kazakov kazakov@cs.york.ac.uk Department of Computer Science University of York. Outline for this Topic (2 lectures). What is search? What is uninformed search?

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blog.lib.umn/torre107/si/pics/superficialintelligence2.jpg

http://blog.lib.umn.edu/torre107/si/pics/superficialintelligence2.jpg. Uninformed Search. CS311 David Kauchak Spring 2013. Adapted from notes from : Sara Owsley Sood , Eric Eaton. Administrative. Send me videos! Written problems will be posted today

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CSM6120 Introduction to Intelligent Systems

CSM6120 Introduction to Intelligent Systems. Defining the problem + Uninformed search. Groups!. Topics: Philosophical issues Neural Networks Genetic Algorithms Bayesian Networks Knowledge Representation (semantic networks, fuzzy sets, rough sets, etc)

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Minimal Preference Elicitation in Combinatorial Auctions

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Machine Learning and Review

Machine Learning and Review Reading: C. 18 Bayesian Approach Each observed training example can incrementally decrease or increase probability of hypothesis instead of eliminate an hypothesis Prior knowledge can be combined with observed data to determine hypothesis

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Machine Learning and Review

Machine Learning and Review. Reading: C. 18. Bayesian Approach. Each observed training example can incrementally decrease or increase probability of hypothesis instead of eliminate an hypothesis Prior knowledge can be combined with observed data to determine hypothesis

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Innovative Analytics: Quantifying the Business Value of Relationships on the Social Web

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Lessons from a Decade of Adaptive Compilation

Lessons from a Decade of Adaptive Compilation. This lecture differs from the others given this semester in that it is explicitly a history of the work on adaptive compilation done at Rice. Keith D. Cooper Department of Computer Science Rice University Houston, Texas.

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Methods of Proof

Methods of Proof. Chapter 7, second half. Proof methods. Proof methods divide into (roughly) two kinds: Application of inference rules: Legitimate (sound) generation of new sentences from old. Resolution Forward & Backward chaining Model checking Searching through truth assignments.

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Notes 8: Predicate logic and inference

Notes 8: Predicate logic and inference. ICS 270a Spring 2003. Outline. New ontology objects,relations,properties,functions. New Syntax Constants, predicates,properties,functions New semantics meaning of new syntax Inference rules for Predicate Logic (FOL) Resolution

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Monte-Carlo Search Algorithms

Monte-Carlo Search Algorithms. Daniel Bjorge and John Schaeffer. Problem. Where it needs to be recognized. Important stuff. Solution. Best early prediction?. Which is faster?. Search Algorithms. Best ? parameter?. Comparing Search Algorithms. Option 1: Computer Go.

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From traditional to e-learning: case studies

From traditional to e-learning: case studies. Katerina Zdravkova Faculty of Natural Sciences and Mathematics Institute of Informatics e-mail: keti@ii.edu.mk. Contents. Introduction Kick-off event: NCP in ODL Distance Learning at Institute of Informatics Computer Applications

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Data Integration for the Relational Web

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Modeling and Analysis of Random Walk Search Algorithms in P2P Networks

Modeling and Analysis of Random Walk Search Algorithms in P2P Networks. Nabhendra Bisnik, Alhussein Abouzeid ECSE, Rensselaer Polytechnic Institute. Contributions. Analytical expressions for performance metrics of random walk search in decentralized P2P networks

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CS 173: Discrete Mathematical Structures

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3.5 Informed (Heuristic) Searches

3.5 Informed (Heuristic) Searches. This section show how an informed search strategy can find solution more efficiently than uninformed strategy. Best-first search, Hill climbing, Beam search, A*, IDA*, RBFS, SMA* New terms Heuristics Optimal solution Informedness Hill climbing problems

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Implementation of Search Algorithms

Implementation of Search Algorithms. Function GENERAL-SEARCH ( problem, queing-fn) returns a solution or failure queue ? MAKE-QUEUE (MAKE-NODE(INITIAL-STATE[ problem ])) loop do if queue is empty, then return failure node ? Remove-Front( queue )

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Validation of realistically simulated non-stationary data. Soma Mukherjee

Validation of realistically simulated non-stationary data. Soma Mukherjee Centre for Gravitational Wave Astronomy University of Texas at Brownsville. GWDAW9, Annecy, France, 17/12/04 1. Why model the data ?.

190 views • 10 slides