INTELLIGENT SYSTEMS QUALIFIER
Fall 2008
Each IS student has two specialty areas. Answer 2 of the 3 questions in each of your specialty area as
well as 2 of the 3 Core questions below.
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All IS students must answer 2 of the 3 Core questions:
Core:
#1
In popular culture, great detectives typically are viewed as being great at deductive inferences. Consider,
for example, Sherlock Holmes, Arthur Conan Doyle's fictional detective. According to Wikipedia, "Holmes
stories often begin with a bravura display of his talent for "deduction" and "He also holds back his chain
of reasoning, not revealing it or giving only cryptic hints and surprising results, until the very end, when
he can explain all of his deductions at once."
However, some AI researchers have argued that that great detectives being especially good at deduction
is a fallacy, that the real talent of Sherlock Holmes lies in making abductive inferences. Take a look at a
short story by Doyle involving Holmes, such as "A Scandal in Bohemia"
(http://221bakerstreet.org/adventures/scandal_in_bohemia.txt).
Analyze some of the inferences made by Holmes and other characters in the story such as Dr. Watson,
Holmes' friend, who often seems surprised by Holmes' inferences. What kind of inferencing, deduction,
abduction or induction, distinguishes Holmes from Dr. Watson?
#2
We teach simulated annealing in the Introduction to Artificial Intelligence course, and then we discuss
how many of the commonly used algorithms incorporate a similar approach at some point in their
process. Suppose you are preparing a end-of-term review on the subject for the introductory course.
Explain the similarity between simulated annealing and
a) Hidden Markov models (HMMs)
b) Genetic Algorithms
c) Min-conflicts (iterative improvement) approaches to the n-queens problem
d) Hill climbing with random restarts
e) Neural nets
#3
Categorization vs Recognition
Object recognition is by now a relatively well studied problem in computer vision. Categorization on the
other hand is still a bleeding edge research challenge. What is the fundamental difference between
recognition and categorization? There are quite a few approaches to categorization. Mention at least 3
fundamentally different approaches to categorization and describe the pros and cons for each approach.
If you have to do space categorization how would you approach the problem?
If you had to do the same for categorization of furniture would you choose the same approach? Why /
why not?
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If one of your two areas is Cognitive Science, answer 2 of the 3 questions below:
Cognitive Science:
#1
Let us suppose that cognitive science faculty at a major research university decide to launch a few new
research initiatives. The criteria for selecting a research program to initiate are: (a) it should address a
real, large, complex and important problem, (b) existing theories of cognition should provide at least a
good starting point for addressing the problem, (c) progress in addressing the problem should be
measurable, (d) the research program should engage several research perspectives (though not all need
be from cognitive science), and (e) even partial success of the program should be expected to result in
new models of some aspect of cognition in general.
Make a case that the specific research project you are working on is suited for elevation to such a
cognitive science research initiative.
#2
One of the unusual and intriguing papers at the International Conference on Design Computing and
Cognition recently held at Georgia Tech was by Uday Athavankar, a cognitive scientist from India. In his
presentation, Athavankar described an experiment in which young but experienced architectural
designers were first taken to a large hall in an empty urban building, then blindfolded, and finally asked
to design a facility in the empty hall such as an office cafeteria. The blindfolded designers then generated
their designs, talking aloud about their design decisions, hypothesizing the creation of various rooms and
other spaces, putting a door here, a wall with a window there, etc.
The main findings from the experiment were that (i) the designs created by the blindfolded designers
were of a quality as good as similar designs generated under normal conditions, (ii) after the experiment,
when the blindfolds were removed, the designers could reproduce their designs with a very high degree
of fidelity, (iii) during the experiments, the blindfolded designers physically walked through the building
space as they went about designing hypothetical rooms, putting a door here, a wall with a window there,
and (iv) when the blindfolded designers wanted to retract an earlier design decision, they walked
backwards, following the various hypothetical walls and doors they had designed as if the walls were
present physically.
a) Why would any cognitive scientist want to conduct such an experiment? What are the research issues
here? What might be the hypotheses?
b) Much research on design computing has focused on providing designers with external media that go
beyond the usual paper and pencil, such as graphics, visualization and virtual reality tools. Does the
above experiment, with its focus on internal representations, negate the value of external media?
Explain.
c) What do the results of the experiment indicate about the internal representations of the designers? Try
to be as specific as possible.
#3
Some would say that the goal of AI should be to achieve human level performance on "intelligent" tasks.
Yet human performance often is lacking. Give examples where a computer may already provide betterthan-human performance at
a) "understanding" an image
b) "understanding" a video
c) "parsing" an audio stream
Hint: your examples should not be based on situations where the computer simply has different sensors
than a human (e.g. seeing in infrared or hearing at higher frequencies) but where a human's perception
(as opposed to sensation) is fooled or otherwise inferior to what a computer can do.
Even where a human's performance is inferior to that of a computer, some researchers would claim it is
still important to make computer systems that emulate the performance of a human.
d) Give at least two reasons for this belief.
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If one of your two areas is Knowledge Representation Reasoning, and Natural Language, answer 2 of the
3 questions below:
Knowledge Representation Reasoning, and Natural Language:
#1
In the introduction to his recent book "The Emotion Machine" Marvin Minsky writes (see
http://web.media.mit.edu/~minsky/ Introduction.html):
"So naturally, psychologists tried to imitate physicists - by searching for compact sets of laws to explain
what happens inside our brains. However, this book will argue that this quest will fail because no simple
such set of laws exists ...
Once we recognize that our brains contain such complicated machinery, this suggests that we need to do
the opposite of what those physicists did: instead of searching for simple explanations, we need to find
more complicated ways to explain our most familiar mental events."
(a) Give an example of an universal principle (or method) from knowledge-based AI.
(b) Give an example of multiple, specialized abilities (or agencies) cooperating to address a complex
problem.
(c) Given these examples, do you agree or disagree with Minsky?
#2
"Explainable AI" is the notion that there are certain domains in which a user needs to understand the
process or representations of knowledge used by a computer system. For example, in a medical diagnosis
system, the user might want to know how the system came up with the diagnosis and where uncertainty
played a role. In other situations, users may need to be able to inspect the knowledge of an agent or
exert system to determine its correctness (this is sometimes also referred to as "scrutability"). One could
imagine two ways of achieving explainability and scrutability. First, one could create data structures,
process definitions, etc. that are human-readable.
Second, one could use data structures and process definitions that are not human-readable but provide
the ability for the intelligent system to translate those structures into natural language.
a) Discuss the pros and cons of the two approaches described above.
b) Describe the types of knowledge that would be required by each approach above and beyond the
domain-specific knowledge already required by the intelligent system (e.g. domain knowledge for a
medical diagnosis system is medical knowledge and diagnosis procedures). You may want to use
examples from your own research work.
#3
Analogy is often cited as a critical component of creativity. Analogical reasoning systems can determine if
two knowledge structures are analogous and find mappings between concepts within those data
structures. For example, the Strucuture-Mapping Engine (SME) can determine that the "Karla the Hawk"
story is analogous to the "Country of Zerdia" story. However, SME and other analogy finding systems
could not produce the "Zerdia" story given just the "Karla" story. Suppose we want to build a
hypothetical system that can produce story analogs.
a) We want to use SME as a component in our hypothetical system. But we need to find an analogy
between something other than the source (e.g. "Karla") story and target (e.g. "Zerdia") story. What
additional knowledge would our hypothetical system need about source and target? (Hint: think about
the deep structural differences between the world of "Karla" and the world of "Zerdia").
b) An analogy in SME and similar systems is a mapping between concepts/symbols. Describe a process
in which the non-story analogy from part a) is used to produce an analog to the source story.
c) Is our hypothetical system creative? Provide an argument supporting your belief.
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If one of your two areas is Robotics, answer 2 of the 3 questions below:
Robotics
#1
The navigator: We are considering the design of a system for navigation in an indoor office building. As
part of the system there is a need for a localization and mapping system. You have the option of using
sensors such as sonar, RFID, laser scanner and vision. What are the principal advantages and
disadvantages associated with each sensor for mapping and localization? Would it make a difference if
you had to solve the same problem outdoors? Is so, how?
You have to choose a representation for map. What are good map representations? And that are the
trade-offs between these?
Which estimation method would you choose for the approach to localization and mapping and how does
this impact your overall solution
#2
Developmental robotics takes the stance that true intelligence can only be achieved by an agent being
raised within its environment. The robot begins with only a limited amount of innate knowledge and then
actively acquires more as it matures.
a) How can this method help facilitate a strong robot-environment relationship that might not be
available otherwise?
b) What difficulties are there in starting with a near tabula rasa for a robotic system?
c) How might you design a robotic system that learns like a human toddler, if given the ability to move
and perceive similar to a two year old child. How can it recognize salient objects in the environment and
ignore others given an innate set of goals? Ground this discussion in terms of learning what is safe and
useful in the environment for a robot developmentally learning to play a game, perhaps an extremely
simple form of Robocup. Here the goals are at first just to be able to acquire the basic skills needed, then
play with others, and then as it matures to win. What underlying learning algorithms can assist in the
development of the next robot Pele?
#3
Most robots are made of rigid metal and plastic. Yet, if we are to make robot nurses, such devices could
easily hurt their patients.
Another approach is to make pliant robots, where the manipulators/arms/etc. are made of rubber-like
materials. Suppose we were to design such a pliant robot for the sole purpose of moving from bed to
bed in a hospital ward and rolling patients from their back to their side and back again every so many
hours to avoid bed sores.
a) Sketch an image of an appropriate robot.
b) What benefits would the pliant robot have over a rigid robot?
c) What difficulties will we encounter in planning a patient roll, and how can we address these
difficulties?
d) What difficulties will we encounter in actually rolling the patient, and how do we address them?
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If one of your two areas is Perception, answer 2 of the 3 questions below:
Perception:
#1
You are given a 2ft x 2ft x 1in board of wood and 3 cameras. The goal is to move this board in an
environment and determine the POSE of the board as it moves in the environment.
a) Do you need 3 cameras to do this Pose Estimation? What method would you use? Specify what else
would you need and why?
b) If you do use all the three cameras, why (i.e. what would be the advantage)? How would you place
them? In a triangle? 2 on one side, 1 on the other side? Justify your reasoning.
#2
a) Explain the difference between PCA and ICA.
b) Both are in wide use for classification purposes. Why?
c) Which would be a good method for face recognition? Why?
d) Which one for classification based on motion? Why?
#3
Particle Filters: Explain how the core algorithm in particle filters is really importance sampling. In
particular,
a) Give formulas for the the proposal density and target density, and what are the importance weights?
As an example, use target tracking of a spherical 3D object in a 2D image as an example.
b) Explain what happens if we increase the number of objects from one to two or more? How will the
quality of the estimated state evolve as a function of the number of particles and the number of targets?
Please make sure to explain this in terms of importance sampling.
c) Finally, if we use a 1000 particles, how many targets do you expect to be able to track? Explain your
reasoning listing all factors that would go into your answer.
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If one of your two areas is Machine Learning, answer 2 of the 3 questions below:
Machine Learning
#1
You recently landed a job at General Motors (GM). They hired you for your Machine Learning expertise
because they are designing a new robot for their factory floor that will work alongside humans in the car
assembly process.
Currently robots on the factory floor do not learn or adapt, but this new generation of GM robots will start
with a basis set of behaviors needed for assembly, and will be able to be tasked by people to help with
various aspects of the assembly process.
The basis set of behaviors includes: pickup<object>, putdown<object,location>,
fasten<object1,object2>; You can assume that the robot has the ability to perceive and locate all of the
objects in the workspace, and has the ability to manipulate all of the objects. The goal of the learning
algorithm is to learn an arbitrary assembly task, where an assembly will result in a particular
configuration of objects in the workspace. For example, an assembly
task may involve fastening four objects together in a particular
order. A human partner is available to help the robot learn the
assembly task. The particular input that will come from the human is up to you to define.
a) Describe how you would solve this problem with a:
Unsupervised learning algorithm
Supervised learning algorithm
Semi-supervised learning algorithm
Reinforcement learning algorithm
(CHOOSE ONLY TWO OF THE FOUR)
Describe in detail the state, action, input, output, reward, etc., and in particular point out any
assumptions you have to make. The assembly task is quite generic. As you describe each approach,
characterize details you are assuming about the assembly task and the space of different assembly tasks
that the robot will be able to learn. Be sure to mention specific implementation details where you have
them (e.g., are there techniques you would use to make a particular algorithm you've described faster?)
b) Finally, you are asked to make a presentation to your boss summarizing your recommendations. Give
the pros/cons of each of the two algorithms you have described and argue for the approach you would
recommend be used for this project.
#2
Machine learning algorithms have traditionally had difficulty scaling to large problems. In classification
and traditional supervised learning this problem arises with data that exist in very high dimensional
spaces or when there are many data points for computing, for example, estimates of conditional
densities. In reinforcement learning the problem of scale often arises when there are many, many states.
Typical approaches to addressing such problems in RL include function approximation and problem
decomposition.
a) Compare and contrast these two approaches. What are their strengths and weaknesses? Can they
work well together? How do these approaches relate to state abstraction?
b) What are the differences between hierarchical and modular reinforcement learning? Are there any
theoretical or practical limits to either of these approaches?
#3
David Stork of the Open Mind Initiative once said that all of the real algorithms in pattern recognition
have already been discovered, and all the research papers now are just variants of the same base
principles. In this question you will be asked to both defend this premise (part a) and refute it (part c).
a) If you were to defend this premise, name at least four of these base algorithms/principles from which
all other algorithms in machine learning and pattern recognition can be derived (hint: think of all the
unsupervised variants, supervised variants, etc.).
b) For each of these algorithms, list at least two example domains where they are applied.
c) The best survey papers (i.e. your answers to part a and b) help the reader discover new directions of
research from what has NOT been reported to date. Propose a new problem/direction in machine
learning where the pursuit of the research should result in fundamentally new algorithms or new variants
of the algorithms you describe above.
Explain why your problem requires a new approach to what is in the machine learning literature.
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If one of your two areas is Planning and Search, answer 2 of the 3 questions below:
Planning and Search:
#1
Compare Classical Planning with Markov Decision Process (MDP) Planning
a) Is there any advantage to Classical Planning (logic-based) as compared to MDPs? List any advantages
you find.
b) Describe a planning domain where the advantage of classical planning is apparent.
c) Show this advantage by representing your domain as both a logical domain and an MDP.
d) Prove that the classical planner has better performance.
e) When do MDPs have an advantage (in general, not just this domain)?
List at least two advantages and explain.
f) Can we get the best of both worlds? How?
#2
Motion Planning with Many Degrees of Freedom:
You are asked to create a motion planner for a robot with many degrees of freedom such as a Humanoid
robot or Mobile Manipulator that avoids obstacles.
a) What representation would you use for your state space?
b) What algorithm would you use?
c) Give at least two representations and compare them with regard to completeness, optimality and
efficiency.
d) Give at least two algorithms and compare them with regard to analogous metrics.
e) Choose a representation and algorithm that you think would be most effective. Suppose we add an
additional constraint that your robot has to maintain balance while it avoids obstacles. How would you
incorporate this constraint into your planner? Would it still have all the positive qualities you described
earlier?
#3
In the first half of the last decade, partial-order planners dominated planning research. More recently,
state-space heuristic search (HSP-R, UNPOP) and constraint-satisfaction (graphplan, SATPLAN)
approaches have done better in planning competitions. However, partial-order planning has not been
abandoned.
a) Describe the circumstances and domains under which partial-order planners will dominate other
approaches.
b) Some believe that traditional metrics such as speed, ratio of problems solved, and plan length ignore
critical properties of planners. What other criteria could be considered? What features of partial-order
planning algorithms, and partial-order plan representations might be considered advantageous over other
approaches when considering these new criteria?