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CS 484 – Artificial Intelligence 1

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• Lab 4 due today, November 8• Homework 8 due Tuesday, November 13• ½ to 1 page description of final project due

Thursday, November 15• Current Events

• Kevin - now• Christian - Tuesday

• Research Paper due Tuesday, November 20

Artificial Life

Lecture 14


What is life?

• What are the defining features of life?• self-reproduction• ability to evolve by Darwinian natural selection• response to stimuli• ability to die• growth or expansion

• Not all living things obey these rules, and some things that are not alive do.

• Defining life is very difficult!


Emergent Behavior

• The idea that complex behavior emerges from simple rules.

• Is seen in systems such as CYC, but is particularly prevalent in systems based on evolutionary methods, such as genetic algorithms.

• Example: • Boids – simulations of birds given very simple rules

about how to fly.• Automatically flew in such a way as to avoid large obstacles,

without being taught explicitly how to do so.

• http://www.red3d.com/cwr/boids/


Finite State Automata

• FSA: a machine with afinite number of states.

• The FSA is given inputs, which result in transitions between states.

• Some states are accepting, meaning the FSA is saying “Yes”.

• Other states are rejecting.• In this example there are two possible input

characters – a and b, and two states, 1 and 2.• It will finish in state 1 (the accepting state) if the

input has an even number of a’s.


Conway’s Life (1)• A two dimensional cellular automaton.• A two dimensional grid of cells, each of

which can be alive or dead.• A set of rules determines how the cells will

change from one generation to the next:1. A dead cell will come to life if it has three living

neighbors.2. A living cell with two or three living neighbors, will

stay alive.3. A living cell with fewer than two living neighbors will

die of loneliness.4. A living cell with more than three living neighbors

will die of overcrowding.

O

O

O O O

O O

O O

O

O

O O

O O

O

O O

O O

O

O

O O O


Conway’s Life (2)

• Surprisingly complex behavior can sometimes emerge from these simple rules.

• This diagram shows a successive sequence of generations of Conway’s Life.

• This pattern is known as a glider. • There is also a pattern known as a glider gun which

constantly fires out gliders.• See examples of life


One-Dimensional Cellular Automata (1)• A single line of cells. Each cell thus has two

immediate neighbors. • It is usual to have rules that take into account the

cells on either side of the immediate neighbors as well.

• Usually, the cell itself is also taken into account, meaning that each cell’s future is determined by 5 cells.

• 1-D Cellular Automata often use totalistic rules, meaning that the number of living cells out of the 5 is all that determines the cell’s state in the next generation.


One-Dimensional Cellular Automata (2)

• Hence, there are 32 possible rule sets. One such set of rules might be:

0 1 2 3 40 0 1 1 1

• This says a cell can only survive if it has two, three or four neighbors.

• This rule can be seen applied in four generations in the following diagram:

O O O O O O O O

O O O O O O O O O O

O O O O O O O O O O O

O O O O O O O O O O O O


Self-Reproducing Systems

• Von Neumann proposed a self-reproducing system based on cellular automata.

• Langton invented loops:• Each loop consists of 94 cells.• Contains all the information that is needed to reproduce

itself.• Langton loops Applet

• Ultimately we may have robots that can obtain the raw materials necessary to produce new versions of themselves.

• This would be useful for exploring other planets.


Evolution

• The changes in cellular automata involve single-step selection, while evolutionary systems involve cumulative selection (Dawkins, 1991).

• Survival of the fittest means that creatures that are fit are more likely to reproduce than those that are less fit.

• This idea is modeled exactly in systems such as genetic algorithms.


Evolution Strategies

• Similar to hill-climbing.• A set of numeric parameters is varied from generation to

generation by making normally distributed changes to the values.

• If the offspring is a better solution than the parent, then the process repeats from the offspring.

• Otherwise, the offspring is rejected, and a new attempt is made.

• This is asexual reproduction – a single parent produces a single offspring.


Genetic Programming

• A method used to evolve LISP S-expressions.• The S-expressions are represented as trees.• A random set of expressions is generated, and the

“fittest” individuals reproduce to produce the next generation.

• Mutation and crossover are used (see chapter 14).• Diagram shows an example

of a tree representation of anS-expression.


Evolutionary Programming• Evolves finite state automata to solve the problem of

identifying the next item in a sequence:

a1, a2, a3, a4, a5, …, an. • A new generation of FSAs is made by applying a set of

mutation operators:• Changing an output symbol• Changing a state transition• Adding a state• Deleting a state• Changing the initial state

• The success of an FSA is determined by seeing how well it predicts the existing sequence.


Classifier Systems (1)• An evolutionary expert system.• Has the following components:

• Detectors which receive inputs from the environment.• Effectors which send outputs and carry out actions.• A rule system, which consists of a population of

classifiers. Each rule has a measure of fitness.• Detectors to determine how well the system is performing.• A bucket brigade algorithm for assigning credit and blame

to classifiers.• A procedure for reproducing classifiers by application of a

set of genetic operators.• A set of message lists – for input, output and internal

messages.


Classifier Systems (2)• The system uses classifiers to determine what outputs to produce, or what

actions to carry out depending on the inputs from the environment. • A classifier has the following form:

(c1, c2, c3, c4, c5) -> M, f • c1 … c5 are the input variables; M is the output or action and f is the fitness

of the classifier.• An example classifier might be:

(4, 2, *, 1, *) -> A2, 9.1 • * represents any input• Determine action

• Exact match = 1• Match * = .5• Sum matches / length * fitness

Rules

(1,2,3,4,5) A1, 0.7

(1,*,*,*,*)A3, 2.4

(4,2,*,1,*) A2, 9.1

(*,9,*,6,2) A3,7.3

(3,4,5,*,*) A4, 4.5

(1,2,*,*,*) A5, 6.2


Classifier Systems (3)

• When a system has classified an input, a new generation of classifiers is produced by allowing the classifiers that provided the best classifications to reproduce.

• A bucket-brigade algorithm is used to assign credit (or blame) to the classifiers.

• Classifier systems can be used to solve a number of problems, including playing games and enabling a virtual robot to explore a virtual terrain.


Artificial Immune Systems• Systems modeled on the immune systems in

humans and other biological creatures.• Used in anti-virus systems, for example.• Also applied in computer security, for

solving combinatorial problems, and for machine learning problems.

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