Friday, January 14, 2011

Teaching evaluations

[To ASU 101 Fall 10, Rao's section students]

 Happy new year to you all. Hope you had a relaxing break; I enjoyed mine in Greece ;)

I  received the results of the college teaching evaluations that you folks filled in and enjoyed reading them. 

To the five of you who took time to fill these, thanks (..and for the other 13--especially the freshmen-- I would encourage you to consider taking part in future evaluations). 

It is my somewhat quixotic custom to allow access to the evaluations to the class students for a limited time. It might give you a feel as to how your individual views stacked up with the rest of the class. In keeping with it, here are links to the full evaluations--warts and all--in case you are interested:

If you have any other things you need to get off your chest regarding the course, feel free to let me know.  Otherwise, this will hopefully be the last communication on this mailing list. As I said, you are welcome to stop by  for advice all through your time at ASU. 

Subbarao Kambhampati

Friday, December 17, 2010

Happy winter solstice..

Dear all

 I hope all of you--especially the freshmen--survived the semester ;-). Please feel free to drop me a note and
let me know how things went for you and what you are doing next semester. 

 In the meantime, enjoy the glorious arizona winter weather (yesterday and today excluded of course), and feel free to
email/drop-in etc if you need any CS-related advice.


Friday, October 22, 2010

(Final) Blog questions on AI and Intelligent Agent Design..

1. Consider the game of chess. If you are developing a chess playing agent, is the environment in question accessible? deterministic? static?  There is a variant of Chess called "kriegspiel". Find out what it is and how its environment differs from that of chess (in our three dimensions). 

 2. Is it possible for an environment to be fully observable for one agent and only partially observable for another one?

3. Is it possible that an agent can model the same  environment either as (a) partially observable but with deterministic actions or (b) fully observable but with stochastic actions? Philosophically, what does this tell us about the nature of "randomness ? (bonus points: see if you can make a connection to the way we understand eclipses now vs. the way there were understood by our ancestors). 

4. Suppose you are trying to design an  agent which has goals of achievement (i.e., it is judged based on whether or not it ended up in a state where the "goal" holds). Would you want the agent to be given "hard goals" (i.e., goals that must be satisfied) or "soft goals" (i.e., goals which, if satisfied, will give the agent a reward; but if skipped won't stop the agent from experiencing the rewards it gets from other goals).  Focus on which is a harder "computational" problem.
(bonus: see if you can make a connection between this question and your high-school life vs. your university life..)

5. An environment is called "ergodic" if an agent in that environment can reach any state from any other state.   Can you think of examples of ergodic vs. non-ergodic environments? Which are easier for an agent? (in particular, think of an agent which doesn't want to "think too much" and prefers to do some random action and see what happens. How does such an agent fare in an ergodic vs. non-ergodic environment?)

6. We talked about the fact that the agent needs to have a "model" of the environment--where the model tells it what are properties of a state in the environment, and how the environment "evolves" both when left to itself and when you do an action on it.   A model is considered "complete" if it is fully faithful to the real environment. Do you think it is reasonable to expect models to have (a) complete models (b) no models or (c) partial models?    Suppose an agent has a partial model, and according to its model, it should pick some action A at this point. Should it *always* pick A or should it once in a while "live a little" and pick something other than A?    The question here has something to do with a fundamental tradeoff called "Exploration vs. Exploitation" tradeoff. See if you can relate this to the question of  when you should make the decision about which area of computer science you should specialize in. 


(reply to this mail) Attendance assessment


 As you know there were 10 meetings for ASU 101. Please respond to this mail and let me know

1. how many classes you missed in total

2. which classes, if any, you missed with prior notification to me

Please send this information just to me (*NOT* on the class blog)

(if you need to jog your memory as to what happened in which class, here is the list: )


*Required*: Acquired Wisdom Assignment (to be completed on the Blog)


 Here is the last *required* assignment for ASU101. Please enter your answer to the following question
as a comment on the blog:

  List 5 things (pieces of advice and/or technical ideas) that you took away from this course


Your answers will be a sort of the interactive summary of what actually happened in this class (and will be linked as 
"acquired wisdom" from the class page).


ps: There will be another mail from me with a set of blog questions on the intelligent agents. That one is not required but
recommended (like all the other blog questions were). 

Thursday, October 21, 2010

Please come to tomorrow's class with questions...


 As I said, tomorrow is the last officially scheduled meeting for ASU101. Although I have the "intelligent agent design" 
as the scheduled topic, I am happy to convert the class into a general discussion session for your questions. So, please
come prepared with questions if you have any. If you have nothing, then I will do the scheduled topic.