Syllabus change

	We are no longer going to cover robotics, vision and natural language as advanced topics in optional lectures.
	Instead, we will substitute a lecture on natural language processing for the planning lecture.
	Please see the revised syllabus for more details.
	Problems?  See me.  I encourage you to give me feedback.

Introduction to
Advanced AI Topics

	Vision
	Natural Language Processing
	Robotics

Homework #2

	We are not yet ready to hand out Homework #2.  We will probably have it ready for you by next week.
	The second homework is on constraint satisfaction problems
		You can either do it as an individual or as two students in a group.
		If you’re interested in doing the team assignment, you should find a partner either by talking to people in class or by using the IVLE forum.

Advanced Topics Overview

	Agents have sensors and actuators
	Sensors:
		Seeing (visual input) Þ Image Processing and Computer Vision
		Hearing (audio input) Þ Natural Language Processing
	Actuators:
		Moving and manipulating Þ Robotics

Computer Vision

	Perception

Definition: versus graphics

	Graphics
		Have world model W
		Generate the sensory stimulus from the model S = f(W)
	Vision
		Generate the model from the sensors: W = f-1(S)
		To think about: f() doesn’t have a proper inverse.  Why?

Ambiguity in sensory input

	Girls playing with dollhouses
	Or giants playing with people?

Definition: versus image processing

	Image Processing
		A transformation of data to other data
		e.g., smoothing
	Computer Vision
		Reduction in data to a (more useful) abstraction
		e.g., digit / face recognition

Applications

	Surveillance – can we detect objects or people as they move around our field of vision?
	Handwriting recognition – from handwritten addresses to barcodes
	Content based Image Retrieval – query for images using without any text features.  “Show me similar pictures”
	Automated Driving – speaks for itself

Natural Language Processing

	Communication

Definition of NLP

	Examines communication in human languages.
		Theoretical and practical aspects.
		Similar to vision, has production and understanding affects
			Understanding: speech / text to meaning
			Generation: meaning to speech / text
		Both processes have inherent ambiguity

Not so great newspaper headlines

	Squad helps dog bite victim.
	Helicopter powered by human flies
	Portable toilet bombed; police have nothing to go on.
	British left waffles on Falkland Islands.
	Teacher strikes idle kids.

Sample Applications

	Restaurant Query converts English queries into SQL.
	MS Dictation converts speech into text
	Babelfish translates Web pages to different languages
	Summarizing multiple news articles from the web

Robotics

	Planning in the real world environment

Getting around

	Effectors
		Sensors on effectors? Is the output noisy?
		Low-level: need to build higher-level abstractions

Problems

	Localization – where am I?
		Mobile robots but also robotic arms
	Mapping – what does my environment look like?
	Moving – how do I get from here to my goal? What type of plan do I have execute?

Applications of robotics

	Robotic Flight – robotic helicopter, unmanned piloting
	Path planning for exploration
	Rock climbing, perhaps difficult even for some of us

Summary

	All three areas deal with search:
		Vision: search for most likely world w given input sensor s
		Natural Language Processing: given an input utterance / text i, find most likely meaning m
		Robotics:
			Localization: given unknown input configuration / location, determine configuration.
			Planning: given goal g and state s output plan p to reach g from s

Summary

	All three areas use heuristics :
		Vision: trihedral structure
		Natural Language Processing: grammars of language, most frequent meanings
		Robotics: decomposition of problems into cells, maximizing distance between obstacles
	Many of these heuristics involve probability, which we will return to at the end of the semester.