CS 561a: Introduction to Artificial Intelligence

CS 561: Artificial Intelligence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CS 561: Artificial Intelligence ,[object Object],[object Object],[object Object]

Practical issues ,[object Object],[object Object],[object Object]

Administrative Issues ,[object Object],[object Object],[object Object],[object Object],[object Object]

Why study AI? Search engines Labor Science Medicine/ Diagnosis Appliances What else?

Honda Humanoid Robot Walk Turn Stairs http://world.honda.com/robot/

Natural Language Question Answering http://www. ai . mit . edu /projects/ infolab / http://aimovie.warnerbros.com

Acting Humanly: The Turing Test ,[object Object],[object Object],[object Object],[object Object],[object Object]

What tasks require AI? ,[object Object],[object Object]

What tasks require AI? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acting Humanly: The Full Turing Test ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What would a computer need to pass the Turing test? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Thinking Humanly: Cognitive Science ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Thinking Rationally: Laws of Thought ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acting Rationally: The Rational Agent ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

How to achieve AI? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Branches of AI ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

AI State of the art ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Course Overview sensors effectors Agent

Course Overview (cont.) ,[object Object],[object Object],[object Object],How can we solve complex problems? 3 l 5 l 9 l Using these 3 buckets, measure 7 liters of water. Traveling salesperson problem

Course Overview (cont.) ,[object Object],[object Object],[object Object],[object Object],tic-tac-toe

Course Overview (cont.) ,[object Object],[object Object],Towards intelligent agents wumpus world

Course Overview (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Course Overview (cont.) ,[object Object],[object Object],Kahn & Mcleod, 2000 An ontology for the sports domain

Course Overview (cont.) ,[object Object],[object Object],Example of backward chaining

Course Overview (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Semantic network used in an insight generator (Duke university)

Course Overview (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Center of largest area Center of gravity

Course Overview (cont.) ,[object Object],[object Object]

Course Overview (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CLIPS expert system shell

Course Overview (cont.) ,[object Object],[object Object],[object Object],[email_address]

A driving example: Beobots ,[object Object]

Beowulf + robot = “ Beobot”

A driving example: Beobots ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The basic components of vision Attention Localized Object Recognition Scene Layout & Gist Riesenhuber & Poggio, Nat Neurosci, 1999 Original Downscaled Segmented +

Beowulf + Robot = “ Beobot”

Main challenge: extract the “minimal subscene” (i.e., small number of objects and actions) that is relevant to present behavior from the noisy attentional scanpaths. Achieve representation for it that is robust and stable against noise, world motion, and egomotion.

Prototype Stripped-down version of proposed general system, for simplified goal: drive around USC olympic track, avoiding obstacles Operates at 30fps on quad-CPU Beobot; Layout & saliency very robust; Object recognition often confused by background clutter.

Major issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The task-relevance map Scalar topographic map, with higher values at more relevant locations

More formally: how do we do it? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outlook ,[object Object],[object Object],[object Object]

CS 561a: Introduction to Artificial Intelligence

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CS 561a: Introduction to Artificial Intelligence