The Scientific Community Game (SCG) for CS 4800 (Algorithms and Data)

We want to give algorithm learners some control over their learning experiences. Choose your game (hypothesis) in an area where you want to refine your skills. Adults strive for control over their learning experiences.

With SCG I create a learning environment that provides opportunities for collaboration with other algorithm learners. What learning objectives are covered by playing the game? (1) recognizing incorrect algorithms (2) finding inputs that show why the algorithm is incorrect (3) recognizing algorithms with a wrong performance analysis (4) finding inputs that show why the algorithm analysis is incorrect (5) solving algorithm design problems and predicting the resource consumption of the algorithms. (6) describing algorithms at various levels of abstraction (7) distinguishing true algorithmic statements from false algorithmic statements

While the game creates a productive learning environment, only your active engagement with the topic area can realize the skills described in the above learning objectives.

Short High-Level Definition

SCG is a two-player game where each player, called Scholar, proposes and opposes hypotheses about algorithms.

Students form teams of two, each one acting as Scholar.

Choose a hypothesis with your partner scholar. Scholar 1 will first propose the hypothesis and Scholar 2 must oppose it. Then the roles change: Scholar 2 will propose the same hypothesis and Scholar 1 opposes.

Hypothesis selection takes two forms:

1. You choose from one of the hypotheses that I put into the hypothesis market. http://www.ccs.neu.edu/home/lieber/courses/algorithms/cs4800/sp10/homeworks/hypotheses-market/

2. You come up with your own hypotheses using material that we covered in class.

Note that the hypothesis should not be refutable; but this is not so important for this version of the game because you play in both roles, as proposer and opposer.

Opposition engages two scholars in an opposition protocol which has a success or failure outcome. The opposition protocol for a hypothesis involves the scholars providing problems legal by the hypothesis and solving them to substantiate the hypothesis.

The game will often end in a tie when Scholar 1 and 2 sucessfully defend or when they both successfully refute. For the opposition role: When Scholar 2 successfully refutes, but Scholar 1 does not then Scholar 2 wins. In other words: For the proposition role, When Scholar 2 successfully supports the hypothesis, but Scholar 1 does not then Scholar 2 wins. In summary, when the outcome of both rounds is the same, we have a draw. If the outcome is different, one of them wins: In the proposition role the supporter and in the opposition role the refuter.

At the end of the game, the scholars get together and propose an improved hypothesis which is "close" to the given hypothesis. Ideally it should be a theorem.

Game Rules

Scholars mutually agree on Hypothesis, Problem and Solution languages to be used. You may use a formal notation defined by a grammar (see Theory of Computation) or an informal notation.

The scholars, through mutual agreement, play the role of the administrator which makes sure the game rules are followed.

Game steps to follow:

Choose hypothesis H
Scholar 1 proposes H
Scholar 2 opposes H
  Scholar 1 provides problem pp that is valid wrt the hypothesis H$
  Check pp
  Scholar 2 provides solution ss to problem pp $
    (depending on the hypothesis, the Scholar 1 and Scholar 2 are exchanged)
    The solution step may involve Scholar 1.
  Check ss
  Check outcome: Hypothesis.refuted(pp,ss)

Reverse roles of Scholar 1 and Scholar 2

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Game in a nutshell:
hypothesis, problem, solution
hypothesis = claim about problems and solutions
opposition protocol

Hypothesis Design

Shapes of hypotheses:

mathematical hypotheses
E: exists a problem
A: for all solutions
(EA)

A: for all problems
E: exists solution
(AE)

algorithmic hypotheses
E: exists an algorithm
A: for all inputs

Mathematical hypotheses:

Flow networks: G=(V,E), with each edge a capacity c(e),
non-negative number
single source node s in V
single target node t in V

definition of flow with capacity and conversation constraints.

based on (7.9)
for all network flow problems G=(V,E) such that f is an s-t-flow
  such that there is no s-t path in the residual graph G[f]
there exists an s-t cut (A*, B*) in G for which v(f) = c(A*, B*).

Algorithmic hypotheses:

C = sum [e out of s] c(e).
m = |E|.

based on (7.10)
There exists an algorithm FF 
for all network flow problems nfi FF(nfi) computes a maximum flow
in time O(mC).

based on (7.11)
There is an algorithm that computes
for all network flow problems G with maximum flow f
 an s-t cut of minimum capacity in time O(m).

Mathematical hypothesis:

based on (7.12)
For all flow networks G,
there exists a flow f and a cut (A,B), so that v(f) = c(A,B).