Alife

1
Alife Robotics

• Changes in the definition of cognition and
  intelligence to highlight adaptability
• Ground symbols in perception or eliminate them
  altogether

2
ALife

• Computing?Biology
• Use computational techniques to inform biological
  theory
• E.g., model evolution of biological species given
  specific constraints

• Biology ?Computing
• Use adaptive, evolutionary ideas of biology to
  inform computing
• E.g., create design solutions via genetic
  algorithms

3
Emergent behaviors

• Individuals have simple behaviors which combine
  in interesting ways.
• Flock of birds in V

4
Graphics

• Due to the complexity involved in rendering
  realistic-looking biological forms and group
  behavior, some animators are turning to alife
  techniques.

e.g. GVU
5
Genetic algorithms

• The Travelling Salesman/ Shortest Path Problem
  Given a set of n cities what is the shortest
  path to visit all of these cities once, after
  which you return to the start city?

• 1. Computer constructs a set of random paths.
  This is the first "generation" of the
  "population" of possible paths.
• 2. For each of the paths in the population,
  calculate the length of the paths.
• 3. Select the best paths from the population, and
  create a new population based on these selected
  paths.
• asexual reproduction Randomly mutate some of
  the properties of the paths in population by
  switching the order in which the cities are
  visited.
• sexual reproduction combine good paths with
  each other by randomly crossing-over the paths of
  the solutions.

From http//news.alife.org/index.php?page1stalife
talkcontext1stalifetalk
6
Game of Life

• Most famous CA
• Created by John Conway in 1970
• first implemented with stones on a GO board
• ON square is alive
• OFF square is dead
• If a square is off, it turns on if exactly three
  of its neighbors are on.
• If a square is on, it stays on if exactly two or
  three neighbors are on otherwise it turns off
• (due to overcrowding when more squares are on
  due to loneliness when fewer are on).

7
Cellular automata principles

• Parallelism
• Cell update/development happens simultaneously
• Locality
• Cells are only affected by nearby cells
• Homogeneity
• Same rules apply to all cells

8
Cellular automata

• Depending on rules used, CAs usually drift to
  stable states or chaos.
• Demonstrate complex patterns from simple
  principles.

9
CA uses

• Modelling fluctuations in electric power grid
• CAPOW by Rudy Rucker