Easy-to-discuss RISKS: Technostress the fact that some ppl aren't comfortable w/technology so dealing w/it might be stressful/overwhelming. Multitaskingappears to be doing multiple things at the same time, but really just rapidly switching among tas
Study Aid 2
Horn Form (restricted)
KB = conjunction of Horn clauses
For example, C (B A) (C D B)
Modus Ponens (for Horn Form): complete for Horn KBs
1n)/
(1,n
Can be used with forward chaining or backward chaining.
These algorithms are very natural and ru
Study Aid 1
Typically we can calculate a probability for each possible deal.
Seems just like having one big dice roll at the beginning of the game.
Compute the minimax value of each action in each deal, then choose the action
with highest expected value o
Lecture Notes 4
Extensive form, a generalization of the game tree
Normal form, a matrix representation of payoffs for different actions
If both players go left (from their perspective) they will pass each other and
each realize payoff of 1.
Prisoners dile
Lecture Notes 3
Each driver chooses Straight with probability 0.1
Consider payoffs to Driver1 while keeping Driver2s strategy fixed
The payoff of Driver1 choosing Straight is (1/10)(10) + (9/10)1 = 1/10
The payoff of Driver1 choosing Swerve is (1/10)(1) +
Lecture Notes 2
Doubles solvable depth
A simple example of the value of reasoning about which computations are
relevant (a form of metareasoning)
Unfortunately, ? 35? ^50 is still impossible.
Resource limits
Standard approach:
Use Cutoff-Test instead of T
Lecture Notes 1
CSP: find variable assignment that satisfy a set of constraints
Backtracking search: consider only a single variable
assignment in depth-first search (leverage commutativity)
Improving backtracking
Variable choice: Minimum remaining values
In Class Assignment 2
Events are described using propositions:
W = Cloudy W = Snowy
D cfw_(5,6), (6,5)
R = True
30 S 50
Notation: P(A) is the probability of the set of world states in which
proposition A holds
P(X = x), or P(x) for short, is the probabili
In Class Assignment 1
Frequentism
Probabilities are relative frequencies
For example, if we toss a coin many times, P(heads) is the
proportion of the time the coin will come up heads
But what if were dealing with events that only happen once?
E.g., wha
HW 5
1. Atomic event: a complete specification of the state of the world, or a
complete assignment of domain values to all random variables
Atomic events are mutually exclusive and exhaustive
E.g., if the world consists of only two Boolean variables Cavi
Feb 23, 2011
We Live in Public (Day 2)
2nd Half of Film
*Started film at 59:00*
Fame + Alienation
People are willing to do stupid, horrible things for fame and Josh is a prime example of this
We see that living in public is starting to eat at Josh and T
Group 7 Notes
February, 21 2011
We Live In Public (Day 1)
Josh Harris is the creator of Jupiter. He developed chat capabilities and sold to prodigy
Jupiter went public and the .com world developed. Became a millionaire in the early
90s through the Interne
Anonymity: a lost art?
2/16/2011
Anonymity:
o Anonymous speech has historically been a widely held value to be a right in
American culture - dates back to pamphleteering during American Revolution
o When U.S became a free colony, maintained level of prot
February 14, 2011
Group 1: eHarmony
eHarmony is an online dating website that uses relationship science to match single men
and women for long-term relationships.
According to eHarmony, 5% of married couples in the United States had met online
through eHa
February 9, 2011
REVIEW: The online self and construction of online persona
Occurs on an individual level and as a community
MUD: simple text-based virtual environment, one moves through environment via avatar
(Turkle) Effects of selfhood on having a vari
A History of the Internet
pre-1957, computers could only work on a single task
o Batch Processing: executing a series of jobs on a computer without
manuel intervention
Affter 1957, time sharing, or concurrent use of one computer by multiple users,
became
Study Aid 3
Knowledge-based agents
Knowledge base (KB) = set of sentences in a formal language
Declarative approach to building an agent (or other system):
Tell it what it needs to know
Then it can Ask itself what to do - answers should follow from the K