'Branching factor' diaporamas de présentation

State-space tree for tic-tac-toe (subset).

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Strong Method Problem Solving

8. Strong Method Problem Solving. 8.0 Introduction 8.1 Overview of Expert System Technology 8.2 Rule-Based Expert Systems 8.3 Model-Based, Case Based, and Hybrid Systems. 8.4 Planning 8.5 Epilogue and References 8.6 Exercises.

1.06k views • 81 slides

Strong Method Problem Solving

8. Strong Method Problem Solving. 8.0 Introduction 8.1 Overview of Expert System Technology 8.2 Rule-Based Expert Systems 8.3 Model-Based, Case Based, and Hybrid Systems. 8.4 Planning 8.5 Epilogue and References 8.6 Exercises.

1.04k views • 81 slides

Strong Method Problem Solving

8. Strong Method Problem Solving. 8.0 Introduction 8.1 Overview of Expert System Technology 8.2 Rule-Based Expert Systems 8.3 Model-Based, Case Based, and Hybrid Systems. 8.4 Planning 8.5 Epilogue and References 8.6 Exercises.

815 views • 81 slides

Problem Solving

Problem Solving. Topics. Problem Solving Searching Methods Game Playing. Introduction. Problem solving is mostly based on searching.

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Efficient Symmetric-key Private Authentication

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Adversarial Search; Heuristics in Games

Adversarial Search; Heuristics in Games. Foundations of Artificial Intelligence. Games and AI. Why games? Games like Chess or Go are compact settings that mimic the uncertainty of interacting with the natural world involving other rational agents

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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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Introduction to Artificial Intelligence LECTURE 3 : Uninformed Search

Introduction to Artificial Intelligence LECTURE 3 : Uninformed Search. Problem solving by search: definitions Graph representation Graph properties and search issues Uninformed search methods

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CPS 570: Artificial Intelligence Search

Instructor: Vincent Conitzer. CPS 570: Artificial Intelligence Search. Search. We have some actions that can change the state of the world Change induced by an action perfectly predictable Try to come up with a sequence of actions that will lead us to a goal state

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Game Reformulation

General Game Playing Lecture 7. Game Reformulation. Michael Genesereth Spring 2012. Metagaming. Metagaming is match-independent game processing, i.e. game processing that is done independent of any particular opponent or any particular state.

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G51I AI Introduction to AI

G51I AI Introduction to AI. Heuristic Searches. Instructor: Ho Sooi Hock. Outline. Defining Heuristic Search g(n), h(n) Best-First Search Greedy vs. A* Implementation Admissibility and Monotonicity Heuristics Relaxed Problems Informedness Effective Branching Factor.

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Dana S. Nau CMSC 722, AI Planning University of Maryland, Fall 2004

Lecture slides for Automated Planning: Theory and Practice. Chapter 4 State-Space Planning. Dana S. Nau CMSC 722, AI Planning University of Maryland, Fall 2004. Motivation. Nearly all planning procedures are search procedures Different planning procedures have different search spaces

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ITCS 3153 Artificial Intelligence

ITCS 3153 Artificial Intelligence. Lecture 5 Informed Searches. Informed Searches. We are informed (in some way) about future states and future paths We use this information to make better decisions about which of many potential paths to pursue. A* Search. Combine two costs

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The n queens problem

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The n queens problem

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Depth Increment in IDA*

Depth Increment in IDA*. Ling Zhao Dept. of Computing Science University of Alberta July 4, 2003. Outline. Introduction Examples Modifications on IDA* Analytical Model Experimental results Work to do Conclusions. Introduction.

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Computer Go : A Go player

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Evaluating Evaluation Metrics for Ontology-Based Applications

Evaluating Evaluation Metrics for Ontology-Based Applications. Diana Maynard, Wim Peters, Yaoyong Li University of Sheffield, UK. Motivation Traditionally, Precision and Recall are used to evaluate IE systems, which gives a binary score for each entity found.

144 views • 1 slides