'Branching factor' presentation slideshows

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

1.92k views • 146 slides

1.42k views • 92 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.

1.08k 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.

1.07k 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.

829 views • 81 slides

Problem Solving

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

1.3k views • 73 slides

Efficient Symmetric-key Private Authentication

704 views • 59 slides

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

627 views • 50 slides

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

619 views • 44 slides

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

939 views • 42 slides

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

486 views • 41 slides

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.

537 views • 38 slides

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.

549 views • 29 slides

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

387 views • 25 slides

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

426 views • 18 slides

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.

244 views • 15 slides

Computer Go : A Go player

219 views • 10 slides

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.

166 views • 1 slides