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Bioinspired Computing

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1 Bioinspired Computing
Lecture 1: Overview & Biased History

2 Objectives On completion of this module, students should be able to: understand both the appeals and limitations of natural systems as the inspiration for artificially intelligent systems; understand of some of the many artificial intelligence techniques that have been inspired by natural systems (eg. artificial neural networks, evolutionary computing, ant-like systems); implement one or more of these systems to solve a particular problem (eg., using an artificial network to control a simple simulated robot).

3 Syllabus Introduction, history and philosophy of bio-inspired computing; artificial neural networks for pattern recognition; artificial neural networks for control; multi-agent systems and swarm intelligence; bio-inspired graphics and art; evolutionary design; coevolutionary design; simulation modelling in biology and beyond; applications of bio-inspired computing.

4 Today …. Overview talk Outcome Introduction to the module
Insight in why the field is attractive to many people First review of some of the characteristics that bio-inspired approaches share

5 Lecturing Staff Marc de Kamps (
Started in high energy physics Postdoc in cognitive modelling Neural networks Spiking neurons simulation Vision, attention (in vision), neural representation of language Slides based on last year’s lectures A long string of original contributions over the years: Seth Bullock, Jason Noble, Netta Cohen Slides will be handed out All slides currently available, but these will be updated from last year’s version as the weeks progress (so don’t assume things will be exactly the same as last year!) Next year: third year module prereq.: maths

6 Timetabling This module has two consecutive lecture slots
Mondays 12:00 – 14:00 Roger Stevens LT12 Demonstration slot allocated Thursday 15:00 – 16:00 Roger Stevens LT04 Demonstration sessions will only be given some weeks - notified in advance in lecture and/or newsgroup. WATCH THE MODULE NEWSGROUP FOR UPDATES

7 Resources Course Website:
Lecture Slides Module Outline Reading Lists Assignments Useful Links Newsgroups: and Reading available in library, or via links on course page. Each other Talk about the material. Talk about the assignments. Help each other; feel free to work together, but Submit only you own personal original work.

8 Bio-inspired computing Biological computation Artificial Intelligence
What is this module all about? Bio-inspired computing Biological computation Artificial Intelligence

9 What is Bio-inspired computing?
What is computing? Answer can be given in some way: Universal Turing Machine Example: compute planet trajectories

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16 The Birth of AI In the 1940s Alan Turing was already speculating on…
…the possibility of general computer intelligence – abstract games: good initial tasks ‘requiring little contact with the outside world’… …the potential for a computer chess player – search algorithms to used to find good moves… …a way of deciding whether a computer was intelligent – the Turing Test is a totally disembodied interrogation (but a somewhat situated one)… Artificial Intelligence is an attempt to simulate reasoning as: abstract, formal, disembodied, symbol manipulation. Note that Turing was also interested in BiC – “The chemical basis of morphogenesis” (52)

17 What is Bio-inspired computing?
What is computing? Answer can be given in some way: Universal Turing Machine Example: compute planet trajectories Fixed recipe Chop up state space Define mapping Write program Run it -> Done Since ENIAC we’ve been upgrading the UTM Strong AI

18 Criticism of AI Sometimes unfair: "As soon as someone gets a computer to do it, people say: 'That's not what we meant by intelligence.' People subconsciously are trying to preserve for themselves some special role in the universe." - Michael Kearns The AI Effect: "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?" See

19 SHRDLU By 1972, Terry Winograd’s invented a simulated robot arm surrounded by blocks of various shapes and colours on a table. SHRDLU could… understand natural language instructions ask questions to disambiguate these instructions act & reason in a simulated “blocksworld” environment answer questions about the world & its own reasoning In Winograd’s words, it was “a computer program which ‘understands’ language in a limited domain.” SHRDLU’s algorithms carried out model building, search, planning and logical deduction. See for example:

20 A Conversation with SHRDLU
> Pick up a big red block. OKAY. > Grasp the pyramid. I DON’T KNOW WHICH PYRAMID YOU MEAN. > What does the box contain? THE BLUE PYRAMID AND THE BLUE BLOCK. > I own blocks which are not red, but I don’t own anything which supports a pyramid. I UNDERSTAND. > Do I own the box? NO. (adapted from Dreyfus’ 1979 What Computers Can’t Do)

21 Is that really how our brain works?
John Von Neumann, father of modern high-speed computers also thought about neuro-computation and tried, for the first time, to construct a meaningful comparison between brain and computer power. Von Neumann argued that the brain must employ digital computation. Figuring in the number of neurons, connections, and estimates of computational speed and statistical noise in the brain, he then concluded that the brain could not be explained by logic alone. In fact, he apparently postulated (and began writing) an alternative theory but died soon after.

22 The manuscript (published post mortem) ends as follows:
“The Language of the Brain is Not the Language of Mathematics … whatever language the central nervous system is using, it is characterized by less logical and arithmetical depth than what we are normally used to … Consequently, there exist here different logical structures from the ones we are ordinarily used to... … whatever the system is, it cannot fail to differ considerably from what we consciously and explicitly consider as mathematics.” (John Von Neumann, The Computer and the Brain, 1958.) In a recent commentary, Harold Morowitz writes: “Von Neumann challenged the validity of the underlying conceptualizations we use to study the brain and compare it with computers. Yet, what is surprising, given the great esteem for John Von Neumann, is that no one has taken up on his argument and fully developed its consequences in the mind versus artificial intelligence arguments that had been waging the last few years.”

23 Criticism of AI Frame problem Symbol grounding problem
(see eg. Pfeifer, “Understanding Intelligence”) Frame problem Symbol grounding problem

24 Frame Problem (McCarthy & Hayes, 1969)
Example Wikipedia Predicates which can depend on time t are called fluents A situation where a door is closed, the light is off and and the door is opened at t = 1 can be represented by: ┐open(0) ┐on(0) true →open(1) Direct expression of what is known, but insufficient to correctly draw consequences

25 Frame Problem The following situation is logically consistent with the known situation: ┐open(0) open(1) ┐on(0) ┐on(1) The following too: ┐on(0) on(1) This makes no sense causally, but perfect sense logically A superficial solution requires frame axioma’s: on(0) ≡ on(1)

26 Frame Problem To formulate frame axioms for every condition, action combination that must not change is not an option Solution possible but subtle, unlikely for living creatures “The frame problem in philosophy is therefore the problem of how a rational agent bounds the set of beliefs to change when an action is performed.” (Wikipedia) Biological creatures (including humans) seem not overly troubled by it …

27 Symbol Grounding Problem (Searle 1980; Harnard 1990)
In AI rules are used to operate on symbols The rules operate independent of the meaning of symbols This is not a problem as long as there is a human around to make sense of the symbols Powerful formal reasoning Difficulties with interpretation Computer vision, speech recognition, interpretation still difficult

28 Chess vs. Football Chess Discrete Full Information Single Opponents Turn Taking Limited Options per Turn Intellectual, disembodied Optimal Strategy Exists? Demands General Intelligence? Formal, Analytical, Symbolic Football Continuous Partial Information Heterogeneous Teams Continuous Confrontation Unlimited Options Physical, embodied No Optimal Strategy? Demands Specialist Skills? Dynamic, Physical, Reactive Can the problems faced by footballers be solved through symbol processing and heuristic search? Commentary v. playing?

29 “Intelligence w/out Reason”
Rodney Brooks (“Intelligence Without Reason”): Critic of the AI approach & strong proponent of embodiment and situatedness in bioinspired computing (BIC). AI, he claims, followed the abstract route due to technological gaps in the 40s & 50s. Today (1991), he says, it’s time to move on. Brooks recognised that life-like systems are often intelligent to some degree, yet reasoning is primarily considered to be a human attribute. Rather than modelling complicated human behaviour, why not start simple?

30 “There are a number of key aspects characterizing this style of work
[Situatedness] The robots are situated in the world - they do not deal with abstract descriptions but with the here and now of the world directly influencing the behavior of the system. [Embodiment] The robots have bodies and experience the world directly their actions are part of a dynamic with the world and have immediate feedback on their own sensations. [Intelligence] They are observed to be intelligent - but the source of intelligence is not limited to just the computational engine. It also comes from the situation in the world, the signal transformations within the sensors, and the physical coupling of the robot with the world. [Emergence] The intelligence of the system emerges from the system's interactions with the world and from sometimes indirect interactions between its components - it is sometimes hard to point to one event or place within the system and say that is why some external action was manifested.” (Brooks 91)

31 Why Not The Whole Iguana? (Dennett 78)
Traditional computing is task-oriented (vertical). To survive, animals have to be good across the board! Ant Cricket Iguana Human Walking Sex Memory Chess Vertical Horizontal Rather than build parts of human intelligence, why not build an entire much simpler intelligence?

32 Performance without CPU
Locomotion Examples of dynamic movers: (museonside.mpg was shown in the lecture) Compare:

33 How do biological systems deal with these issues?
Biological performance is dynamic flexible, adaptive robust, noise/defect tolerant

34 Slime moulds Free-ranging amoebae aggregation migratory slugs fruiting body mound When food is plentiful, amoebae live & multiply independently. When food is scarce, they secrete a chemical signal that attracts amoebae towards each other. As cells aggregate they form a mound, then a slug, and eventually fruiting bodies. The slug can migrate to a more desirable location. When a slug reaches an appropriate place, the cells differentiate to form spores and a stalk. The fruiting body consists of mature spore cells on top of dead stalk cells. These spore cells fall off, starting their own colonies.

35 Collective behaviour in
the slime mould

36 Chemical waves in slime mould

37 DNA preserving fortunate accidents

38 Powerful idea Does not really depend on actual mechanism
Can be used in artificial systems? Yes, evolve hardware, programs Good search technique!

39 The slime mould… Is no more than a collective manifestation of amoeba
It self-organised in the face of duress (food deprivation, pressure for self-preservation/self-replication). It is completely decentralised/distributed: There is no leader. Each amoeba follows simple rules. Individual amoebas are not reliable. The amoeba is adaptive; its function is context-dependent. Interactions among different levels of organisation The solution is defined in terms of the system at large (slime mould and its survival, not individual amoeba).

40 Performance without CPU (2)
EU project hydra ATRON hardware videos Self-configuring BOEING:

41 Some unifying concepts/inspirations
Eliminate pre-imposed hierarchies, eliminate leaders Allow a decentralised (distributed) approach and parallelism Allow simple rules for cooperation among components Allow interactions among different levels of organisation Define solutions in terms of system-wide variables Computers need not be as complex as biological machines. By incorporating key principles we hope to achieve much improved functionality.

42 What might computer scientists learn from biological systems?
Some limitations of today’s computers: Computers crash! (OS usually to blame) The complexity of computer programs is limited. The biggest programs are less complicated than the simplest biological cell. Computers don’t self-replicate. They don’t self-organise. They don’t fix themselves. Computers are always right (i.e. they follow their programs correctly), but don’t always give the answers we want to know. Computers are unforgiving.

43 Potential implications:
Pretty soon computers will miniaturise to molecular scales. This transition will require noise/defect-tolerance self-assembly/self-organisation at the hardware level Software will need to cope with ever-increasing complexity. Can software self-organise? Self-evolving software is already in use both in traditional computing & robotics. Computer systems are networked in massive complex and dynamic architectures. Distributed approaches to managing networks/grids are being implemented. Specifically, ant algorithms are used in re-organising internet connectivity in the face of node failures.

44 Properties of biological systems
Collective behaviour self-organisation, self-assembly and self-repair self-replication/reproduction Emergence: Organisation of structure & function is achieved by the system as a coherent, organic and autonomous entity, even though this whole consists of components which are themselves autonomous. The whole is greater than the sum of the parts.

45 Collective computations finds reasonable solutions
Biological creatures are sometimes faced with very hard problems Solution does exist, but takes impossible time to solve Collective action sometimes produces a solution that is reasonable in short time

46 The problem: Directed Hamiltonian Path
Given a graph with directed edges, find a Hamiltonian Path, i.e. a path which starts at one node, finishes at another, and goes through all other nodes exactly once. Variant of the travelling salesman problem where all roads are the same length: A salesman wants to travel over a fixed set of roads between N different towns without ever coming back to a town he has already visited. Find a sequence of fights which goes from Fresno to Boston landing at all other airports exactly once.

47 How hard is it? A P problem is one which can be solved in polynomial time, basically at a rate which is some fixed power of N, where N is the size of the problem. An NP hard problem is one for which no one knows an algorithm which does not take exponential time (2 or some other power of a number > 1). Methods taking exponential operations can work out whether or not such a route exists and report it if it does, but even for small problems they take too much time to be practical. Any NP problem can be transformed to another in P time – so solve one efficiently and can solve any NP problem efficiently!

48 Emergent solutions We will look at solutions that emerge from systems of interacting basic components in: Neural Networks (2x; different!) Swarm Intelligence DNA computing

49 Open questions Can human cognition be understood this way?
Is human cognition a dynamical process, which self-organizes, is emergent, …? Does the brain have executive modules? There are manifest differences: e.g. symbols can not be copied

50 Examples Collective behaviour
self-organisation, self-assembly and self-repair self-replication/reproduction emergence What are the underlying principles that give rise to this behaviour?

51 Pattern formation in nature

52 From Pattern Formation to Computing?
Conjecture: If principles of pattern formation underlie the development of a foetus, why not the development of program or an electronic circuit? Computers need not be as complex as biological machines. Maybe by incorporating key principles from biology we can already achieve much improved functionality. First baby steps: Pattern formation could be useful if… We can extract minimal principles to mimic it. We can harness the patterns for useful computation.

53 Why do we care? Computer hardware is “hard”, but must it be?
What about ad-hoc configurations of computer and communications networks? Pattern formation is an example of parallel processing that is highly versatile, adaptive, and robust. Can we simplify existing algorithms using similar concepts? Simulations of natural pattern formation processes are a growing industry for art, movies, and even music.

54 Lindenmayer Systems L-systems define sets of local rules for making patterns. Requirements: an initial term and a set of rules. A simple rewriting system: Rule: A  AB would develop as follows: A … AB … ABB … ABBB… Adding rule B  BA, gives: A … AB … ABBA … ABBA BAAB … ABBA BAAB BAAB ABBA … ABBA BAAB BAAB ABBA BAAB ABBA ABBA BAAB … ABBA BAAB BAAB ABBA BAAB ABBA ABBA BAAB BAAB ABBA ABBA BAAB ABBA BAAB BAAB ABBA … Notice: early strings are sub-components of later strings. Also, strings are self similar at all levels.

55 Geometric Rewriting Systems
Replace letters with geometries: Grow pictures, not sentences… Turtle graphics approach: Imagine a turtle that can only understand three commands: F (move forward a distance d, drawing a line); + (turn left through ao), - (turn right through ao). Set d=1, a=90. Give turtle initial string of commands (say F-F-F-F). Then, apply set of rewrite rules again and again and again… (The resulting patterns will be examples of quadratic Koch islands) Example rewrite rule: F  F-F+F+FF-F-F+F

56 From lines to trees To generate more life-like shapes…
First, we need a way of representing branches. To do this, Lindenmayer employs a bracketing notation: [ Before carrying on, push the current state of the turtle onto the top of a pushdown stack (i.e., remember the current position and orientation of the turtle for later). ] Before carrying on, pop the state from the top of the stack and make it the current state of the turtle. This will often move the turtle to a different (earlier) part of the shape, or rotate it, or perhaps alter its state in some other way. How does stack system affect L-system’s behaviour?

57 Branching structures & self-similarity
Imagine a simple L-System featuring brackets… Initiator:F Rewrite Rules:F  F [+F] [-F] F ; a=30o 0: F Recursion + branching = tree-like structures 3: etc. 1: F[+F][-F]F 2: F[+F][-F]F [+F[+F]-F]F] [-F[+F]-F]F] F[+F][-F]F Note rescaling Tree-like structures and other shapes that look the same on every scale, are called self-similar systems. These self-similar structures are simple examples of fractals.

58 Exploring Simple L-Systems
It isn’t hard to discover initial states, rules, and constants which produce recognisably plant-like structures. Image courtesy M. Masse However, when generalised to three dimensions, coupled with appropriate textures and colouration, and altered to include leaves, flowers, etc., L-systems have been used to generate some truly remarkable life-like images. Images authored by V. Nový

59 Overview of next weeks

60 Topics in bio-inspired computing
Multi-agent systems and Swarm Intelligence Artificial neural networks Evolutionary design and genetic algorithms Co-evolutionary design Artificial life Robotics and control Interfacing biology with silicon

61 Food foraging in Ant colonies
A computer simulation of an ant colony performing an efficient search: Blue spot in the middle is an ant nest; other 3 spots represent food sources. Ants (red dots) use chemical signals (green) to find the shortest path to the food and alter their signals as the food is depleted. Ant algorithms are implemented in many real-world systems.

62 System wide variables (Felleman & van Essen, 1991)

63 Interaction between hierachies (Felleman & van Essen, 1991)

64 Self-organisation and interaction with environment
Orientation columns human cortex

65 Emergence

66 Extraction essential principles
Feedforward perceptron networks

67 Extraction essential principles
Working memory

68 Overview end

69 or What is BIC and what does it want to achieve? Bio-inspired
Biological computation Artificial Intelligence Bio-inspired computing or

70 Bioinspired programming
Some of the best bioinspired applications to computing are the ones we would never associate with biology. Perhaps the most successful and pervasive bioinspired application is… Object-Oriented Programming

71 Alan Kay by Scott Gasch While studying at the University of Utah he learned about the innovative Sketchpad program developed by Ivan Sutherland and began programming in Simula. Borrowing ideas from this and other systems, as well as from his background in biology and mathematics, he formulated his "biological and algebraic analogy.“ Kay postulated that the ideal computer would function like a living organism; each "cell" would behave in accord with others to accomplish an end goal but would also be able to function autonomously. Cells could also regroup themselves in order to attack another problem or handle another function.

72 How it works What good is this? Autonomous cells
Messages (data, sender & receiver addresses) Operations (contained in message) Cell differentiation (context-dependent functionality) What good is this? ‘Building’ programs the way civil engineers design buildings. programmers can create objects to mimic generic conceptual building blocks: No need for a new language with each application. Inspiration & technical basis for the MacIntosh OS and subsequent windowing based systems (NextStep, Microsoft Windows, X-Windows, Motif, etc.).

73 Consider No biological creature: Developed the wheel Flew to the moon
Communications via radio Much of engineering is not bio-inspired

74 Human level cognition Seems to combine much of both worlds
Symbol processing, reasoning, exploration Use of tools Locomotion Adaptation, genetic variation, reproduction Social structures (like ants, termites)

75 Required Reading (at home)
Popular Reading “John McCarthy: The uncommon logician of common sense”, in Shasha & Lazere (1995). “Alan Kay: A clear romantic vision”, ibid. “Computers and Brains”, in Morowitz (1997). Required Reading (at home) “Intelligence without reason” – Brooks (1991).

76 Some basics This module is for you. Think, read, ask questions, tinker, and have fun! Warning: Some of the material covered is, or recently was, cutting edge. There will be programming, some biology, some philosophy. There is a lot of material to cover in lectures, but… lecture slides will not tell the whole story. You must attend lectures and demo sessions The module courseware – BEAST – will be introduced over the coming weeks. Help with C++, installation etc. will be given in demo sessions. Originality and innovation will be rewarded. Show enthusiasm, play, contribute original code.

77 Assessment 2-Hour Exam (60%) - answer 3 questions of 4
2 assignments (40%) of which 1st assignment (15%) will be announced shortly 2nd assignment includes BEAST analysis & programming project. (25%) will be announced shortly. Additional exercises may be given in demo sessions. Assignments & exam must be passed to pass module.

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