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

2. (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.

3. (c) Bishop: The computer and the mind 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 (1 st 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...”

4. (c) Bishop: The computer and the mind 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.

5. (c) Bishop: The computer and the mind 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.

6. (c) Bishop: The computer and the mind 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).

7. (c) Bishop: The computer and the mind 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).

8. (c) Bishop: The computer and the mind 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

9. (c) Bishop: The computer and the mind ‘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.

10. (c) Bishop: The computer and the mind 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.

11. (c) Bishop: The computer and the mind 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.

12. (c) Bishop: The computer and the mind 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.

13. (c) Bishop: The computer and the mind 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

14. (c) Bishop: The computer and the mind Branching growth

15. (c) Bishop: The computer and the mind 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.

16. (c) Bishop: The computer and the mind 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’.

17. (c) Bishop: The computer and the mind 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.

18. (c) Bishop: The computer and the mind 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

19. (c) Bishop: The computer and the mind 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’.

20. (c) Bishop: The computer and the mind 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