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  Id's source
  archives (includes Wolf 3D, DOOM, and Quake ED)<!-- <li<a href="bounce.html?http://www.ladder.org/ddr/"Descent Developers Resource</a (includes <a href="bounce.html?http://www.descent2.com/download/ddn/sources/descent1/d1srcpc.exe"sources for Descent</a) -->
  

  
  
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  CrystalSpace Open Source
  portable 3D game engine

  
  
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  Jet3D Open source Windows 3D game
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  Mine
  Sweeper (DOS version)

  
  
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  PC
  Moria

  
  
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  Tetris
  

  
  
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  Boulder
  Dash

  
  
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  Boggle

  
  
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  Angband

  
  
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  MyChess chess viewer and client server code

The following links point to a set of tutorials on many aspects of
statistical data mining, including the foundations of probability, the
foundations of statistical data analysis, and most of the classic machine
learning and data mining algorithms.

These include classification algorithms such as decision trees, neural nets,
Bayesian classifiers, Support Vector Machines and cased-based (aka
non-parametric) learning. They include regression algorithms such as
multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and
neural nets. And they include other data mining operations such as clustering
(mixture models, k-means and hierarchical), Bayesian networks and Reinforcement
Learning.

Tutorial Slides by Andrew
Moore

Introduction to algorithms for computer game playing. We describe the
assumptions about two-player zero-sum discrete finite deterministic games of
perfect information. We also practice saying that noun-phrase in a single
breath. After the recovery teams have done their job we talk about solving such
games with minimax and then alpha-beta search. We also discuss the dynamic
programming approach, used most commonly for end-games. We also debate the
theory and practice of heuristic evaluation functions in games.

Download Tutorial Slides (PDF format)

For planar objects, i.e., lying in the plane, the convex hull may be
easily visualized by imagining an elastic band stretched open to encompass the
given object; when released, it will assume the shape of the required convex
hull.

It may seem natural to generalise this picture to higher dimensions by
imagining the objects enveloped in a sort of idealised unpressurised elastic
membrane or balloon under tension. However, the equilibrium (minimum-energy)
surface in this case may not be the convex hull — parts of the resulting surface
may have negative curvature, like
a saddle surface (see
article about minimal
surfaces for examples). For the case of points in 3-dimensional space, if a
rigid wire is first placed between each pair of points, then the balloon will
spring back under tension to take the form of the convex hull of the points.^[citation

In computing, a Las Vegas
algorithm is a randomized algorithm that always gives correct results; that is, it
always produces the correct result or it informs about the failure. In other
words, a Las
Vegas algorithm does not gamble with the verity of the result; it only
gambles with the resources used for the computation. A simple example is
randomized quicksort, where the
pivot is chosen randomly, but the result is always sorted. The usual definition
of a Las Vegas algorithm includes the restriction that the expected run
time always be finite, when the expectation is carried out over the space of
random information, or entropy, used in the algorithm.

The name comes from the fact that in Las Vegas, "the house always wins".^{[citation needed]} Las Vegas
algorithms can be used in situations where the number of possible solutions is
relatively limited, and where verifying the correctness of a candidate solution
is relatively easy while actually calculating the solution is complex.

These devices measure the gas composition of your breath, which is a very
accurate way of measuring calorie burn. By wearing this type of device and the
Fitbit at the same time, equations/algorithms can be developed that attempt to
accurately convert the raw data collected by the Fitbit into the calorie numbers
reported by the “truth” device.

Developing these algorithms take a lot of experimentation and test data.
Sometimes the algorithms you develop work very well in one case but
completely fail in another. For instance, your algorithm might be really
accurate for slow walking but starts to fall apart during running. A
lot of our research is finding algorithms that work reasonably well across a lot
of different scenarios.

Evolutionary techniques mostly involve metaheuristic optimization algorithms such as:



• evolutionary algorithms (comprising genetic algorithms,
  evolutionary programming, evolution
  strategy, genetic programming and learning classifier systems)
  
• swarm
  intelligence (comprising ant colony optimization and particle swarm optimization)
  
• self-organization such as self-organizing
  maps, growing
  neural gas, competitive
  learning demo applet
  
• differential evolution
  
• artificial life
  (also see digital
  organism)
  
• cultural
  algorithms
  
• harmony search
  algorithm
  
• artificial immune systems
  
• Learnable Evolution Model