From: engelson-sean@CS.YALE.EDU (Sean Philip Engelson)
Subject: Artificial Intelligence at Yale
Organization: Yale AI Mobile Robotics Project
Date: Thu, 1 Oct 1992 20:22:09 GMT



   Artificial Intelligence at the Yale Computer Science Department

Artificial Intelligence is the study of computational models of mind.  At
Yale, there are a wide variety of topics studied, including knowledge
representation, inference, planning, learning, vision, robotics, and neural
networks.

The term ``artificial intelligence'' is somewhat misleading, because the
focus of research in the field is often on more mundane activities, such as
simple visual perception, than the word ``intelligence'' would suggest.
The field has learned over the years that the effortlessness of a skill
such as vision is deceptive, that in fact the brain does a great deal of
hard labor behind the scenes to allow us to see without conscious effort.
It will take us years to duplicate the skills that nature evolved over
eons.

AI uses many of the same techniques as other areas of Computer Science
application, from numerical optimization to symbolic indexing.  The key to
solving any problem is always the algorithm and its analysis.  The goal is
always to characterize precisely a set of problems and demonstrate an
algorithm that solves them with reasonable efficiency.  But, at least at
its current state of development, AI is of necessity more exploratory than
other areas.  We are often forced to define a problem at the same time that
we try to solve it.  It often happens that we don't know how to analyze the
performance of an algorithm with existing tools, but we believe that its
average-case performance is much better than its worst-case performance,
and this belief must be backed up with experiments.  Sometimes a piece of
research is valuable even though all that it accomplishes is the discovery
of a dimension of knowledge representation that we as yet do not know how
to incorporate into algorithms at all.

In general we think it is a mistake for AI research to focus on central
mental function and ignore input and output.  In the long run, machines
will not be treated as intelligent unless they can perceive and manipulate
the objects around them.  Real perception and action impose stubborn
constraints on thinking.  Sophisticated robot planning is wasted if the
robot crashes into the wall while trying to generate a predicate-calculus
description of the world in front of it.  So our planning research focuses
on models of execution and replanning in realistic, changing worlds, and
not so much on provably correct plans.

Here is a partial list of the projects we are now working on:

Map learning for mobile robots:
	How can a robot get back to a place it's been before?  To
	answer this question, you need to define what a place is.  We
	are pursuing a model in which a place is a perceptually
	distinctive point, reachable from other places by fairly
	robust but not perfect operations.  As the robot wanders
	around its world, it builds up a graph of places and the
	metrical relationships among them. It is able to correct
	errors in this graph by merging two places that are really the
	same, and splitting a place that really corresponds to two
	similar places.

Perception and decision making:
	The information that an autonomous system has available is
	often incomplete and may contain errors.   In order to carry
	out its activities, the system must use sensors to gather
	information.  Of course, a central problem area is the
	development of methods for interpreting and using sensor
	information.   However, these methods must match the
	capabilities of the system.  Consequently  research also
	focuses on optimizing the use of sensing equipment and
	computer resources to gather and process information for a
	particular task.

Transformational reactive planning:
	A plan is a program an agent gives to itself in response to
	problems it encounters.  In changing worlds, the plan must
	contain rules for reacting to problems, but in addition there
	must be a planning module with a more long-range outlook that
	can revise the plan before execution is completed.  Our
	planner revises the plan by using information gained by
	mentally simulating, or ``projecting,'' it to see how
	execution will go.

Engineering problem solving:
	Scientists and engineers model, analyze, design, and explain
	complicated physical systems using reasoning that is often
	heuristic, qualitative, and graphical in addition to formal
	mathematical arguments.  Understanding this ability requires a
	deep understanding of the techniques in simplifying
	mathematical models, finding approximate solutions, and
	deriving consequences from mathematical models by mixed
	symbolic, numerical, and geometric computations. Current
	research involves the automatic analysis of certain classes of
	fluid dynamics problem.  Our goal is to produce computer
	programs that not only serve as high-level assistants to
	scientists and engineers but also smart enough to carry out
	independent research in fluid dynamics and write research
	papers of publishable quality.  

Neural networks for vision:
	One way to approach problems in computer vision is to
	construct an approximate mathematical model of the image
	generation process and then, given an image, to ``invert''
	this process by finding the most likely explanation for the
	image.  The resulting comptutions involve image processing,
	symbolic reasoning and knowledge representation, and may be
	performed by neural networks that settle into an optimal
	interpretation of each image.  Over time such a neural network
	may also be able to improve its interpretations by learning
	more about the image generation process.  
-- 
Sean Philip (Shlomo) Engelson		
Yale Department of Computer Science	
Box 2158 Yale Station			
New Haven, CT 06520			
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