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        <description>T-622-ARTI. Spring 2013 - Final Projects

Project Topics and Groups
PresentationStudentsTopic08.04.Jan Straka  Character and word recognition from printed text 08.04.Bjarni Benediktsson, Gunnar Jörgen Viggósson, Víðir Orri Reynisson  Personalized news feed using natural language processing and learning</description>
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        <description>Lab 1 - Agents

Problem Description

Implement the control program for a vacuum cleaner agent.
The environment is a rectangular grid of cells which may contain dirt or an obstacle. The agent is located somewhere in this grid and facing in one of the four directions: north, south, east or west.
The agent can execute the following actions:</description>
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        <description>Lab 2 - Formulating Search Problems

Problem Description

Consider an agent for playing Connect-4 on a c x r board (c columns and r rows).

Tasks

1. Formulate the search problem, that is,

	*  find a representation (data structure) for states
	*  find a representation (data structure) for actions</description>
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        <description>Lab 3 - Hashing States

Many environments have the property that there are several paths that lead to the same state.

Tasks

	*  Which of the search algorithms that we covered in class benefit from detecting repeated states? For each algorithm give a short explanation how detecting repeated states makes it better.</description>
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        <description>Lab 4 - Propositional Logic / Inference

Problem Description

(Based on “The Adventure of Silver Blaze,” an original Sherlock Holmes mystery by Arthur Conan Doyle)

A prize-winning racehorse named Silver Blaze has been stolen from a stable, and a bookmaker named Fitzroy Simpson has been arrested as the prime suspect by good old Inspector Gregory. Sherlock Holmes, however, after ample use of his magnifying glass and some of the strongest black tobacco this side of the Atlantic, finds the true thi…</description>
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        <description>Lab 5: Bayesian Networks

In this session we will look at some basic Bayesian Networks and you will solve two problems using a Bayesian Network simulator.
You can work together in groups. Hand in the results in MySchool.

Material

Here are two different Bayesian Network simulations that you can use:</description>
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        <description>Learning Decision Trees

Material:

	*  [java project]
	*  javadoc for the framework

The zip archive contains three data sets and Java code to learn a decision tree from those data sets. Your task is to check how well the learning algorithm performs on the given data sets.

Tasks:

	*  Change the existing code, so that it prints the necessary data for a learning curve. That is, change the code such that it learns trees for increasing numbers of training examples and test each of the trees using…</description>
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        <description>Lab 7: Particle Filter

In this session we will look at how to use particle filtering for localization of a robot.

Material

Download the following file: 

It includes:

	*  Source code for a simple robot simulator
	*  A skeleton for a particle filter algorithm (ParticleFilter.java)</description>
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        <description>T-622-ARTI, Introduction to Artificial Intelligence, Spring 2013

Basic Info

	*  Instructor:  Stephan Schiffel (stephans+ru.is)
	*  Office:  3rd floor, Venus, 695-2998
	*  Lectures:  Mondays 12:20-13:55 (M104), Fridays 12:20-13:05 (M124) 
	*  Labs:  Group 1: Thursdays 13:10-14:45 (M106), Group 2: Thursdays 17:25-19:00 (M110), you can pick whichever suits you best</description>
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        <description>Programming Assignment 1 - Search

Use the forum, if you have any questions or problems with the assignment. Start early, so you still have time to ask in case of problems!

Problem Description

Find a good plan for the vacuum cleaner agent.
The Environment is still the same as in</description>
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        <description>Programming Assignment 2 - Adversarial Search

Use the forum or email me, if you have any questions or problems with the assignment. Start early, so you still have time to ask in case of problems!

Problem Description

Implement an agent that is able to play</description>
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