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Carleton College

Carleton College is an independent non-sectarian, coeducational, liberal arts college in Northfield, Minnesota, USA. The college currently enrolls 1,958 undergraduate students, and employs 198 full-time faculty members. Robert A. Oden is the current President. In 2009 U.S. News and World Report ranked Carleton College the 8th best liberal arts college in the United States.

More information: http://en.wikipedia.org/wiki/Carleton_College

Carleton Motion Capture (CMC) Matt Kracum, Will Levine, Coryn Pavelsky, Kohei Shinkawa Jack Goldfeather

Carleton Motion Capture (CMC) Matt Kracum, Will Levine, Coryn Pavelsky, Kohei Shinkawa Jack Goldfeather
Informed Search Methods

Informed Search Methods

How can we make use of other knowledge about the problem to improve searching strategy? Map example: Heuristic: Expand those nodes closest in “as the crow flies” distance to goal 8-puzzle: Heuristic: Expand those nodes with the most tiles in place
Three kinds of learning

Three kinds of learning

Supervised learning Learning some mapping from inputs to outputs Unsupervised learning Given “data”, what kinds of patterns can you find? Reinforcement learning Learn from positive negative reinforcement
Artificial Neural Networks

Artificial Neural Networks

k-Nearest Neighbor Decision Tree Neural Network Training Data Test Data Classification Artificial Neural Networks are (among other things) another technique for supervised learning
Reinforcement Learning

Reinforcement Learning

Game playing: So far, we have told the agent the value of a given board position. How can agent learn which positions are important? Play whole bunch of games, and receive reward at end (+ or -) How to determine utility of states that aren’t ending states?
Machine Learning

Machine Learning

Definition 1 “The subfield of AI concerned with programs that learn from experience” Russell / Norvig, AIMA Definition 2 “the application of induction algorithms, which is one step in the knowledge discovery process.” Machine Learning definition in glossary from Machine Learning at http://robotics.stanford.edu/~ronnyk/glossary.html

Uncertainty Logical approach problem: we do not always know complete truth about the environment Example: Leave(t) = leave for airport t minutes before flight Query: ?
Plans for Today

Plans for Today

Chapter 2: Intelligent Agents (until break) Lisp: Some questions that came up in lab Resume intelligent agents after Lisp issues
Problem Solving Agents

Problem Solving Agents

A problem solving agent is one which decides what actions and states to consider in completing a goal Examples: Finding the shortest path from one city to another 8-puzzle

Game Playing

Game Playing

Perfect decisions Heuristically based decisions Pruning search trees Games involving chance
First-Order Logic: Better choice for Wumpus World

First-Order Logic: Better choice for Wumpus World

Propositional logic represents facts First-order logic gives us Objects Relations: how objects relate to each other Functions: return value for given input
Problem Solving Agents

Problem Solving Agents

A problem solving agent is one which decides what actions and states to consider in completing a goal Examples: Finding the shortest path from one city to another 8-puzzle Discussion on prereqs (algorithms vs data structures vs none etc) This material similar to algorithms (but with AI spin) – goal is to get through it to get to more AI stuff in chap 4
Iterative Improvement Algorithms

Iterative Improvement Algorithms

For some problems, path to solution is irrelevant: just want solution Start with initial state, and change it iteratively to improve it (find a “best”, or “minimum” value Examples: Finding the optimal way of assigning dates within n people (match.com problem) Traveling salesperson problem Knapsack problem

Uncertainty Logical approach problem: we do not always know complete truth about the environment Example: Leave(t) = leave for airport t minutes before flight Query: ?
Planning

Planning

Many thanks to Robin Burke, University of Chicago, for many of these ideas: http://people.cs.uchicago.edu/~burke/cs250/lectures/lecture1107.html Planning is a special case of reasoning We want to achieve some state of the world Typical example is robotics
Three kinds of learning

Three kinds of learning

Supervised learning Learning some mapping from inputs to outputs Unsupervised learning Given “data”, what kinds of patterns can you find? Reinforcement learning Learn from positive negative reinforcement
Random Administrivia

Random Administrivia

In CMC 306 on Monday for LISP lab Take attendance At end of class, go over syllabus and describe class structure
Informed Search Methods

Informed Search Methods

How can we improve searching strategy by using intelligence? Map example: Heuristic: Expand those nodes closest in “as the crow flies” distance to goal 8-puzzle: Heuristic: Expand those nodes with the most tiles in place Intelligence lies in choice of heuristic
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