Thinking Cap topic 1:

[Subbarao Kambhampati <rao@asu.edu>]

[[Folks:
 By now I hope you all had enough time to get yourself signed up
to the class blog. As I said, participation is "required" in this
class. Participation involves
doing assigned readings, being attentive in the class, and most importantly, taking part in
the class blog discussions.

While any discussions you want to do on the blog are fine, I will occasionally throw in topics I want you to discuss. For historical reasons, we will call them thinking-cap topics.  Here is the first discussion topic  for your edification.  

As for the quantity vs. quality of your comments, I suggest you go by the Woody Allen quote below for guidance.. ;-)
  --Rao ]]


Here are some of the things that I would like to see discussion/comments from the class. Please add your thoughts as comments to this blog post. Also, please check any comments already posted to see if your viewpoint is already expressed (remember--this is not a graded homework, but rather a discussion forum). 


1. Explain what you understand by the assertion in the class that often
it is not the hardest environments but rather the medium-hard ones that
give most challenges to the agent designer (e.g. stochastic is harder
in this sense than non-deterministic; multi-agent is harder than 
much-too-many-agents; partially accessible/observable is harder than
full non-observable).

2. We said that accessibility of the environment can be connected to the limitations of sensing in that what is accessible to one agent may well be inaccessible/partially accessible to another. Can you actually think of cases where partial accessibility of the environment has nothing to do with sensor limitations of the agent?


3.
Optimality--given that most "human agents" are anything but provably
optimal, does it make sense for us to focus on optimality of our agent
algorithms? Also, if you have more than one optimality objective (
e.g., cost of travel and time of travel), what should be the goal of an
algorithm that aims to get "optimal" solutions? 

4. Prior
Knowledge--does it make sense to consider agent architectures where
prior knowledge and representing and reasoning with it play such
central roles (in particular, wouldn't it be enough to just say that everything important is already encoded in the percept sequence)? Also, is it easy to compare the "amount" of knowledge
that different agents start with? 

5. Environment vs. Agent
complexity--One big issue in agent design is that an agent may have
very strong limitations on its memory and computational resources. A
desirable property of an agent architecture should be that we can
instantiate it for any <agent, enviornment> pair, no matter how
complex the enviornment and how simplistic the agent. Comment on
whether or not whether or not this property holds for the architectures
we saw. Also, check out "Simon's Ant" on the web and see why it is
related to this question. 


6. Anything else from the first two classes that you want to hold-forth on. 

Rao


----------------

  "The
question is have I learned anything about life. Only that human being
are divided into mind and body. The mind embraces all the nobler
aspirations, like poetry and philosophy, but the body has all the fun.
The important thing, I think, is not to be bitter... if it turns out
that there IS a God, I don't think that He's evil. I think that the
worst you can say about Him is that basically He's an underachiever.
After all, there are worse things in life than death. If you've ever
spent an evening with an insurance salesman, you know what I'm talking
about. The key is, to not think of death as an end, but as more of a
very effective way to cut down on your expenses. Regarding
love, heh, what can you say? It's not the quantity of your sexual
relations that counts. It's the quality. On the other hand if the
quantity drops below once every eight months, I would definitely look
into it. Well, that's about it for me folks. Goodbye. "
                ---Boris in Love & Death (1975 http://us.imdb.com/title/tt0073312/ )