Artificial Intelligence
 Movie Artificial Intelligence by Steven
Spielberg
 Five year studies at universities of Utrecht,
Amsterdam, Groningen and Maastricht

Artificial Intelligence
 The concept that machines can be improved to assume some capabilities normally thought to be like human intelligence such as learning, adapting, self-correction, etc.
 The extension of human intelligence through the use of computers, as in times past physical power was extended through the use of mechanical tools.

Artificial Intelligence
 On May 11, 1997, an IBM computer named
Deep Blue whipped world chess champion
Garry Kasparov in the deciding game of a sixgame match

Artificial Intelligence
 First Robot World Cup Soccer Games held in
Nagoya, Japan in 1997
 Goal: team of robots beats the FIFA World
Cup champion in 2050

Artificial Intelligence
 Alan Turing
 Turing Award
 Turing Machine
 Turing Test

Artificial Intelligence
 Turing Test

Artificial Intelligence
 Natural language processing: it needs to be able to communicate in a natural language like English
 Knowledge representation: it needs to be able to have knowledge and to store it somewhere
 Automated reasoning: it needs to be able to do reasoning based on the stored knowledge
 Machine learning: it needs to be able to learn from its environment

Artificial Intelligence
 Turing Machine

Time Complexity
 Turing machine gives notion of computability
 Time complexity: how many steps does it take to find an answer?
 Combinatorial Explosion
 Problems that are computable in polynomial time (class P)
 Problems that are verifiable in polynomial time (class NP)
 P equals NP ?

Natural Computing
 Computing carried on or inspired by (gleaned from) nature

Natural Computing
 Computers are to Computer Science as
Comic Books to Literature (Joosen)

Natural Computing
 Natural Computing
– Evolutionary Computing
– Molecular Computing
– Gene Assembly in Ciliates

Evolutionary Computing

Evolutionary Computing
Initialize population, evaluate
(terminate) select mating partners select survivors evaluate recombine mutate

Examples
 Evolutionary Art
 Nozzle

 Genotype: 8 bits
 Phenotype:
– integer
1*2 7 + 0*2 6 + 1*2 5 + 0*2 4 + 0*2 3 + 0*2 2 + 1*2 1 + 1*2 0
= 163
– a real number between 2.5 and 20.5
2.5 + 163/256 (20.5 - 2.5) = 13.9609
– schedule

Example: Mutation before 1 1 1 1 1 1 1 after
1 1 1 0 1 1 1
m

Example: Recombination
 Each chromosome is cut into 2 pieces which are recombined cut cut
1 1 1 1 1 1 1 0 0 0 0 0 0 0 parents
1 1 1 0 0 0 0 0 0 0 1 1 1 1 offspring
.

 Expected number of times f i equals f i
/ average fitness is selected
 Better (fitter) individuals have:
– more space
– more chance to be selected

Evolutionary Computing
Initialize population, evaluate
(terminate) select mating partners select survivors evaluate recombine mutate

Molecular Computing

Molecular Computing
 Implementation of algorithms in biological hardware, e.g. using DNA molecules and enzymes
 Power lies in massive parallel search
 Test tube may contain easily 10 15 strands of
DNA
 Compared to computers very efficient in energy consumption, storage density and number of operations per second

Molecular Computing

Molecular Computing
 DNA: sequence of nucleotides linked together by strong backbone
 Nucleotides have attached bases A, T, C, G:
– Adenine
– Thymine
– Guanine
– Cytosine
 Watson-Crick complementarity A-T C-G

Molecular Computing
in out

Molecular Computing
 Algorithm
– generate random paths through graph
– keep only paths from the initial to the final node
– keep only paths that enter exactly n nodes
– keep only paths that enter all nodes
– if any paths remain, the graph contains a
Hamiltonian path

Molecular Computing
 For each node, take unique random sequence over A, C, T, G
 For each node, the sequence is of the same length

Molecular Computing
 For every connection, construct a sequence from the sequences of the two nodes
– Node 1: TATCGGATCG GTATATCCGA
– Node 2: GCTATTCGAG CTTAAAGCTA
 Inverse: GTATATCCGA GCTATTCGAG
 Sequence: CATATAGGCT CGATAAGCTC

Molecular Computing
 Generate random paths through graph
– Mix strings for all nodes with strings for all arrows, together with Ligase enzyme

Molecular Computing
 Apply PCR (Polymerase Chain Reaction) amplification using as primers string for in and complement for string out

Molecular Computing
 Select molecules that encode paths that enter exactly n nodes by running contents of test tube through agarose gel and save DNA strands of the right length

Molecular Computing
 Create single strands by melting
 For each node, select those sequences that anneal to the string of that node

Molecular Computing
 Result: implementation of algorithm in DNA
– First experiment took seven days
– Now possible in seven seconds

Molecular Computing
 Operations: denaturing, annealing, separation, selection, multiplying
 Simulation of Turing Machine is possible
 Problems:
– PCR and separation procedures are error prone
– DNA may form non-existing pseudo-paths
– DNA may form hairpin loops
– Scalability

Molecular Computing
 Combine Evolutionary Computing with
Molecular Computing (EDNA project)
– Use potential errors as feature
– Huge population sizes
– Automation of DNA processing necessary
 Many more techniques from molecular biology can be used
– Plasmids
– Restriction Enzymes
– Fluorescence

Gene Assembly in Ciliates

Ciliates
 Very ancient ( ~ 2 . 10 9 years ago)
 Very rich group ( ~ 10000 genetically different organisms)
 Very important from the evolutionary point of view

Ciliates

Ciliates
 DNA molecules in micronucleus are very long
(hundreds of kilo bps)
 DNA molecules in macronucleus are genesize, short (average ~ 2000 bps)

Gene Assembly in Ciliates

Gene Assembly in Ciliates

Gene Assembly in Ciliates
 Micronucleus: cell mating


Macronucleus: RNA transcripts (expression)
Micro: I
0
M
1
I
1
M
2
I
2
M
3
… I k
M k
I k+1
 M = P
1
N P
2
 Macro: permutation of (possibly rotated)
M
1
,…, M k and I
0
,…, I k+1 are removed

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Molecular Operators

Gene Assembly
 Pointer structures
 Linked Lists

Natural Computing
 Computing carried on or inspired by (gleaned from) nature
– Evolutionary Computing
– Neural Computing
– Molecular Computing
– Quantum Computing
– Ant Computing