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## COM3542 Nature-Motivated Calculation Simulated Life and Cell Automata

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**COM3542Nature-Inspired ComputationArtificial Life and**Cellular Automata**Today’s Plan**• Introduction to Artificial Life • Cellular Automata • Cells • States • State transition rules • Neighbourhoods • Running a CA • Stopping Criteria • Workshop/Demo**Artificial Life (ALife)**• To this point, we’ve used nature as the inspiration for algorithms • Genetic algorithms – evolution • Ant colony algorithms – ant colonies • Particle swarm optimisation – flocking/swarming behaviours • And we will look at artificial immune systems, based on the human immune system • Artificial life is somewhat different • Computer systems simulating life**Artificial Life II**Organism • Artificial life is about better understanding what it is to be alive. • Biology is primarily reductionist – an explanation of a behaviour or phenomenon at one level can be explained by further investigation at the level below (see left) • This is a reasonable top-down approach. • Artificial life takes a bottom-up approach. Organs Tissues Cells Organelles Molecules**Artificial Life III**• Study into Alife is conducted primarily at 3 levels • Wetware – using bits from biology (e.g. RNA, DNA) to investigate evolution • Software (what we have been/will be dealing with) – simulating biological systems • Hardware – for instance, robotics. • And with 2 distinct philosophies • Strong ALife – life is not just restricted to a carbon-based chemical process. Life can be ‘created’ in silico. • Weak ALife – computer simulations are just that, simulations and investigations of life**Artificial Life IV**• In fact, all the techniques we’ve seen so far can be considered Artificial Life in so much as: • Genetic algorithms are simulating or actually doing evolution • Ant colony algorithms are simulating the real behaviour of ants • Particle swarm algorithms are simulating the real behaviour of flocks • What if we consider strong Alife? • Actual evolution, ants and flocks? • Almost certainly not, but what about a ‘life’ Turing Test?**Artificial Life V**• We will be looking today at a software-based technique – cellular automata. • One of the original Alife techniques, cellular automata embodies the bottom-up approach • It is involved with the emergent behaviour of collections of simple elements • Similar to the ‘emergent’ behaviour seen in swarm intelligence • These automata are mainly used for the simulation of biological systems, although they can be used for optimisation**Cellular Automata Introduction**• Cellular Automata originally devised in the late 1940s by Stan Ulam (a mathematician) and John von Neumann. • Originally devised as a method of representing a stylised universe, with rules (e.g. laws of thermodynamics) acting over the entire universe. • Have subsequently been used for a wide variety of purposes in simulating systems from chemistry and physics • CAs have started to be used in bioinformatics and other areas • Consist of a grid or lattice of ‘cells’**Cellular Automata**Cell • An automaton consists of a grid/lattice of cells each of which can be in a (normally small and finite) number of states • The figure shows a 5x5 automaton where each cell can be in a filled or empty state. State = empty/off/0 State = filled/on/1**Cellular Automata II**• An automaton can be • 1-D (i.e. just a line of cells) • 2-D (as we have already seen) • 3-D+ there is no theoretical limit to the number of dimensions • Also, automata are often toroidal (cells ‘wrap around’ to the other side)**Execution**• The CA ‘runs’ by changing the states of the cells by the state transition rules (next slide). • These state transition rules depend on the state of the cell and it’s neighbours • Every cell in the automaton has it’s rules applied before the automaton is updated • Each timestep the automaton can be seen as a system configuration for that particular snapshot in time. T=1 Apply rules T=2**State Transition Rules**• The states of an automaton change over time in discrete timesteps • The state of each cell is modified in parallel at each timestep according to the state transition rules • These determine the new states of each of the cells in the next timestep from the states of that cells neighbours For (int i=0 to CellCount) { Cell[i].State[t+1] = STR(Cell[i].Neighbour.State[t] }**Neighbourhoods**• Neighbourhoods are important as mechanisms for controlling the execution of the CA • Neighbourhoods determine the extent of the interaction between cells in the grid • Two popular neighbourhoods are:**Conway’s Life**• Conways “Game of Life” is the most often cited CA. The rules used are: • If a cell is off (state 0) and exactly three of its neighbours are on (state 1) then that cell becomes on (state 1) in the next timestep, otherwise it remains off. • If a cell is on and either two or three of its neighbours are then on the next timestep, that cell remains on, otherwise it is turned off. • Even a simple set of rules like this can have unexpected results.**Conway’s Game of Life**• Probably the most famous cellular automaton • Is “nature-inspired” • The rules are meant to represent life itself • A dead cell will come to life (be born) if 3 of it’s neighbours are alive • Alive cells must not be overcrowded (more than 3 alive neighbours) or lonely (less than 2 alive neighbours) otherwise they will die.**Cellular Automata**• Important properties which make a CA a CA: • Localism • States are updated based on the properties of the neighbourhood • Parallelism • The state of every cell is updated in parallel • Homogeneity • The same set of rules is applied across the automaton • These properties distinguish cellular automata from other types of automata or algorithm**Types of Cellular Automata**• It is not possible to predict, in advance, what behaviour will be displayed by the CA given a set of rules. • There are a number of possible states into which a CA can descend into • Wolfram proposed a classification scheme based on these criteria: • Evolution leads to a homogeneous state. • Evolution leads to a set of separated simple stable or periodic structures. • Evolution leads to a chaotic pattern. • Evolution leads to complex localized structures, sometimes long-lived.**Next Time**• Applications of cellular automata