Cognitive Computing 2012 The computer and the mind LANGTON Professor Mark Bishop.

Cognitive Computing 2012

The computer and the mind

LANGTON

Professor Mark Bishop

(c) Bishop: The computer and the mind

Artificial Life (AL) What is Artificial Life?

Life made by man rather than nature.

Traditional biology An attempt to explain the mechanics of life on Earth Defined by an ‘analytical approach’ to experimentation and theorising.

Conversely ‘Artificial Life’ Is fundamentally ‘synthetic’

investigation by putting things together

Goes beyond ‘life as we know it’ to ‘life as it could be’ Hence it is not limited to investigating carbon chain chemistry; Its aim is to study the dynamics of life itself.


Historical roots The earliest devices that generated their own

behaviour were based on water transport e.g. the Egyptian Clepsydra (around 135 BC)

Early Pneumatics from Hero of Alexandria (1st century AD)

Later, the development of ‘Clockwork Technology’ led to the construction of Vaucanson’s duck

Flapped wings, ate, quacked & digested.

Cf. “If something flaps like a duck; quacks like a duck; eats like a duck then it is a duck...”


Control mechanisms

Vaucanson’s duck had actions that were ‘sequenced’:

The development of sequential controllers led to the development of programmable controllers;

Which in turn were an important step in the development of general-purpose computers.


General purpose computers Computers per se have no intrinsic behaviour, they must always

be instructed what to do. Cf. Lady Lovelace’s objection to machine intelligence.

A program ‘instructs’ the computer to behave like as some ‘machine’ A program is a specification for a machine; We design specific Turing machines for specific tasks;

Universal Turing Machine Suitably programmed it can emulate any machine

Modern PCs.


Formal limits of machine behaviours Computability in principle:

Turing’s Halting problem; Extended by Hopcroft & Ullman to demonstrate that we cannot

algorithmically decide any ‘non-trivial’ aspect of future program behaviour.

Computability in practice: There are many areas in which we do not know how to specify algorithms to

generate certain behaviours; E.g. How to translate perfectly between French and English. Vehicle Identification number to registration plate number (cf. Searle’s error).

Practical computing: There are many tasks for which we can specify an exact algorithmic solution

for simple problems but which for large scale problems take too long to execute;

E.g. Exponential time programs (travelling salesman).


Universal constructors Von Neumann imagined a machine

floating on a ‘pond’ surrounded by lots of machine parts.

Given the description of any machine it will locate the parts and construct that machine.

Given a ‘description of itself’ it will make itself. Need not just to make the machine

but a copy of the description of the machine.

C.f. The RepRap project (Dr. A.Bowyer @ Bath University).


Cellular Automata (CA)

With each time step, the whole system is updated

Every cell is updated by the same local rules

Context sensitive global behviours


‘Self’ reproduction Game of life

Von Neumann’s CA model was proof that self-reproduction was possible by machines Demo - example of glider ‘reproduction’

Information in the description of the ‘reproducing machine’ is typically used in two different ways:

Interpreted Encodes the instructions executed to generate offspring.

Uninterpreted Encodes the description given to offspring.


Linear versus non-linear systems

Linear Systems: Behaviour of the whole is equal to sum of the

behaviour of its parts; Relatively easy to analyse.

Non Linear Systems Behaviour of the whole is more than the sum of

its parts; Difficult to analyse.


Linear versus non-linear systems (2)

Linear Systems: Full understanding of the whole system can be achieved

by the composition of the understanding of the separate parts.

Non Linear Systems: Interaction between component parts is key to system

behaviour; System behaviour is not clear if component parts are

studied separately.


Non-linear systems

The basic building blocks in carbon organisms - amino acids etc. - are not alive themselves.

But when combined in the correct way the system’s dynamic behaviour is life.


Recursively generated objects

E.g. Lindenmayer systems

Simple Linear growth Context free Simple mappings : A -> AB, B -> C, C->A etc

Branching growth (e.g. using these rules - GTYPE): A -> C[B]D B -> A C -> C D -> C(E)A E -> D


Branching growth


Flocking Flocking is a complex, emergent, non-centralised behaviour.

Flocking forms the basis mechanism in the Swarm Intelligence PSO meta-heuristic.

Boids (Craig Reynolds) demonstrates a simulated A-Life flocking behaviour

Boids demo

Each boid follows same behavioural tendencies: Separation: steer to avoid crowding local flock-mates, but maintain minimum

distance Alignment: steer towards the average heading of local flock-mates Cohesion: steer to move toward the average position of local flock-mates.


Biological automata

Genotype: The specification of the system.

Phenotype: The observable physical characteristics and behaviour of the

system.

Morphogenesis The development of the phenotype over time as directed by the

genotype. Cf. Turing, On the chemical nature of morphogenesis (1952); Explains ‘dappling patterns’.


Generalised Genotypes and Phenotypes

In Artificial Life we need to generalise the notion of genotype and phenotype to artificial systems:

Foundations of Genetic Algorithms/Evolutionary computing

GTYPES The unordered set of local rules. Global system behaviour is not specified.

PTYPES The behaviour and structures that emerge from the interactions of

the GTYPE and environment define the PTYPE.


Evolutionary and Genetic Algorithms

Genetic Algorithms (GA) attempt to define the “logical form” of natural selection processes.

Typically a GA implements natural selection by copying (and sometimes varying in some way) the character strings (GTYPE) that represent the fittest PTYPE; the fittest individual(s) being defined by some ‘objective function’.

Varied GTYPES are produced by the application of genetic operators: Reproduction Crossover Mutation Inversion Duplication


Thomas Ray’s Tierra

No ‘engineer designed objective function’; instead computer programs compete for resources: CPU time and memory space.

Their only task is to reproduce themselves.

Reproduction is not exact, and the better performers produce more offspring.

Programs together in an area can increase or diminish each other’s reproductive success.

Like nature there are long periods without change, followed by rapid evolutionary change: see evolution of parasites and ‘anti-bodies’.


EvolutionZ

EVOLUTIONz allows a user to construct, compare, observe, and explore dynamic artificial ecosystems through a 3D interface.

The inhabitants of these ecosystems are artificial animals, each controlled by a neural net, which compete for limited resources and evolve over time.

The program is meant as a fun tool for investigating learning and open-ended evolution.

EvolutionZ demo

Cognitive Computing 2012 The computer and the mind LANGTON Professor Mark Bishop.

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