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2005-02-01 1

Trendsin Artificial Intelligence

and Artificial Life

Bruce MacLennan

Dept. of Computer Science

www.cs.utk.edu/~mclennan

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Traditional Definition ofArtificial Intelligence

• “Artificial Intelligence (AI) is the part ofcomputer science concerned with designingintelligent computer systems,

• that is, systems that exhibit thecharacteristics we associate withintelligence in human behavior —

• understanding language, learning,reasoning, solving problems, and so on.” — Handbook of Artif. Intell., vol. I, p. 3

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Traditional AI

• Long-term goal: equaling or surpassing humanintelligence

• Approach: attempt to simulate “highest” humanfaculties:– language, discursive reason, mathematics, abstract

problem solving

• Cartesian assumption: our essential humannessresides in our reasoning minds, not our bodies– Cogito, ergo sum.

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Example of PropositionalKnowledge Representation

IF1) the infection is primary-bacteremia, and

2) the site of the culture is one of the sterile sites, and

3) the suspected portal of entry of the organism is thegastrointestinal tract,

THENthere is suggestive evidence (.7) that the identity of the

organism is bacteroides.

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Formal Knowledge-Representation Language

• Spot is a dog• Spot is brown• Every dog has four

legs• Every dog has a tail• Every dog is a

mammal• Every mammal is

warm-blooded

• dog(Spot)• brown(Spot)• ( x)(dog(x)

four-legged(x))• ( x)(dog(x) tail(x))• ( x)(dog(x)

mammal(x))• ( x)(mammal(x)

warm-blooded(x))

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Graphical Representation(Semantic Net)

mammal

dog

Spot

warm-blooded

four-legs

tail

brown

ExampleInference

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Five Stages of Skill Acquisition1. Novice

• learns facts & rules to apply to simple “context-free” features

2. Advanced Beginner• through experience, learns to recognize similar situations

3. Competence• uses developing sense of relevance to deal with volume of facts

4. Proficiency• analytical thinking is supplemented by intuitive organization &

understanding

5. Expertise• skillful behavior is automatic, involved, intuitive, and fluent.

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The Cognitive Inversion• Computers can do some things very well that are difficult

for people– e.g., arithmetic calculations

– playing chess & other abstract games

– doing proofs in formal logic & mathematics

– handling large amounts of data precisely

• But computers are very bad at some things that are easy forpeople (and even some animals)– e.g., face recognition & general object recognition

– autonomous locomotion

– sensory-motor coordination

• Conclusion: brains work very differently from digitalcomputers

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The 100-Step Rule

• Typical recognitiontasks take less thanone second

• Neurons take severalmilliseconds to fire

• Therefore then can beat most about 100sequential processingsteps

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“The New AI”• A new paradigm that emerged in mid-80s• Convergence of developments in:

– philosophy– cognitive science– artificial intelligence

• Non-propositional knowledge representation– imagistic representation & processing– propositional knowledge as emergent

• Neural information processing– connectionism (implicit vs. explicit representation)– =critical dependence on physical computation

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Imagistic Representation

• Much information isimplicit in an image

• But can be extractedwhen needed

• Humans haveprototype images foreach basic category

• Brains use a kind ofanalog computing forimage manipulation

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Multiple Intelligences(Howard Gardner)

• linguistic

• logico-mathematical

• spatial

• musical

• bodily-kinesthetic

• naturalistic

• intrapersonal

• interpersonal

• existential

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Artificial Emotions?• Have been neglected (in cognitive science & AI)

due to Cartesian bias• Importance of “emotional intelligence” now

recognized• Emotions “tag” information with indicators of

relevance to us• Emotions serve important purposes in

– motivating & directing behavior– modulating information processing

• Artificial emotions will be essential inautonomous robotics

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Propositional Knowledge asEmergent & Approximate

• System may only appear to be following rules– a spectrum of rule-like behavior

• Recognition of situation can be fuzzy & context-sensitive

• Extraction of relevant elements can be context-sensitive

• May explain subtlety & sensitivity of rule-likebehavior in humans & other animals

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Neural InformationProcessing

• 100-Step Rule & CognitiveInversion show brainsoperate on differentprinciples from digitalcomputers– “wide & shallow” vs. “narrow & deep”

• How do brains do it?• Can we make neurocomputers?

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Neural Density in Cortex

• 148 000 neurons / sq. mm

• Hence, about 15 million / sq. cm

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Relative Cortical Areas

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Macaque Visual System

(fig. from Van Essen & al. 1992)

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Hierarchyof

MacaqueVisualAreas

(fig. from Van Essen & al. 1992)

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Bat AuditoryCortex

(figs. from Suga, 1985)

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Neurocomputing

• Artificial Neural Networks– implemented in software on conventional computers

– are trained, not programmed

– “second-best way of doing anything”

– poor match between HW & SW

• Neurocomputers– goal: design HW better suited to neurocomputing

– massively-parallel, low-precision, analog computation

– electronic? optical? chemical? biological?

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How Dependent is Intelligenceon its Hardware?Traditional View

• Brain is no more powerful than Turing machine• Human intelligence is a result of the program

running on our brains (Cartesian dualism)• The same program could be run on any Universal

TM• In particular, it could run on a digital computer

and make it artificially intelligent• Ignores “performance” (as opposed to

“competence”)

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ConnectionistView

• Information processing on digital computers(hardware) is fundamentally different from that inbrains (wetware)

• The flexible, context-sensitive cognition weassociate with human intelligence depends on thephysical properties of biological neurons

• Therefore, true artificial intelligence requiressufficiently brain-like computers(neurocomputers)

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Natural Computation

• Computation occurring in nature or inspiredby computation in nature

• Characteristics:– Tolerance to noise, error, faults, damage– Generality of response– Flexible response to novelty– Adaptability– Real-time response– Optimality is secondary

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Embodied Intelligence

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Importance ofEmbodied Intelligence

• Traditional (dualist) view: mindis essentially independent of the body– in principle, could have an intelligent “brain in a vat”

• Now we understand that much of our knowledgeis implicit in the fact that we have a body

• Also, our body teaches us about the world• Structure of body is foundation for structure of

knowledge• A “disembodied intelligence” is a contradiction in

terms?

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Embodied Artificial Intelligence

• Therefore a genuine artificial intelligencemust be:– embedded in a body

– capable of interacting significantly with itsenvironment

• We expect the intelligence to develop as aconsequence of interaction of its body withan environment including other agents

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“Social Interaction”Rodney Brooks’ Lab

(Humanoid Robotics Group, MIT)

• Cog attending tovisual motion

• Orients head & eyes tomotion

• (Arm & hand motionare not relevant tointeraction)

(video < Brooks’ lab, MIT)

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Giving the Computer a Face(Brooks’ Lab, MIT)

(image < Brooks’ lab, MIT) 2005-02-01 30

Kismet (Brooks’ Lab, MIT)

• Example of three-wayconversationalinteraction

• Models:– head & eye orientation

– motion tracking

– turn taking

– facial expression

• Does not “understand”speech

(video < Brooks’ lab, MIT)

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Autonomous Robots

• The ultimate test of intelligence is to be able tofunction effectively in a complex naturalenvironment

• Natural environments do not come parsed intocontext-free categories

• Natural environments are characterized bycomplexity, unpredictability, uncertainty,openness, & genuine novelty

• There is also a practical need for autonomousrobots

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Starting Small

• In science, it’s generally considered prudentto start by studying the simplest instances ofa phenomenon

• Perhaps it is premature to attempt human-scale artificial intelligence

• It may be more fruitful to try to understandthe simplest instances of intelligentbehavior

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Collective Intelligence

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Mound Buildingby Macrotermes Termites

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Structure of Mound

figs. from Lüscher (1961) 2005-02-01 36

Fungus Cultivator Ants

• “Cultivate” fungi underground• Construct “gardens”• Plant spores• Weed out competing fungi• Fertilize with compost from chewed leaves

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Intelligent Behavior ofHarvester Ants

• Find shortest path to food• Prioritize food sources based on distance & ease

of access• Adjust number involved in foraging based on:

– colony size– amount of food stored– amount of food in area– presence of other colonies– etc.

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Adaptive Significanceof Ant Trail Formation

• Selects most profitable from array of food sources

• Selects shortest route to it– longer paths abandoned within 1–2 hours

• Adjusts amount of exploration to quality ofidentified sources

• Collective decision making can be as accurate andeffective as some individual vertebrate animals

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Simulation

• Nest scent diffuses from nest

• Ants carrying food:– move toward increasing nest scent

– deposit trail pheromone

• Ants not carrying food:– move toward increasing trail pheromone (if

scent is strong)

– wander, otherwise

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Slime Mold(Dictyostelium discoideum)

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Complete Life Cycle

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Aggregation Stage

• Triggered byexhaustion of food

• Aggregate bychemotaxis

• Form expandingconcentric rings andspirals

• Up to 125 000individuals

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Spiral Waves

• Spirals accelerate cell aggregation (18 vs. 3 µm/min.)• Waves propagate 120 – 60 µm/min.• 1 frame = 36 sec.

(video < Zool. Inst., Univ. München)

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Movement of Young Slug

• Time-lapse (1 frame = 10 sec.)• Note periodic up-and-down movement of tip

(video < Zool. Inst., Univ. München) 2005-02-01 48

Movement of Older Slug

• Note rotating prestalk cells in tip

• Pile of anterior-like cells on prestalk/prespore boundary

• Scale bar = 50 µm, 1 frame = 5 sec.

(video < Zool. Inst., Univ. München)

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Migration of Older Slug

• Scale bar = 100 µm, 1 frame = 20 sec.

(video < Zool. Inst., Univ. München) 2005-02-01 50

Early Culmination

• During early culmination all cell in prestalk rotate

• Scale bar = 50 µm, 1 frame = 25 sec.

(video < Zool. Inst., Univ. München)

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Emergence• The appearance of macroscopic patterns,

properties, or behaviors• that are not simply the “sum” of the

microscopic properties or behaviors of thecomponents– non-linear but not chaotic

• Macroscopic order often described by fewer& different variables than microscopic order– e.g. ant trails vs. individual ants– order parameters

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Self-Organization• Order may be imposed from outside a

system– to understand, look at the external source of

organization

• In self-organization, the order emerges fromthe system itself– must look at interactions within system

• In biological systems, the emergent orderoften has some adaptive purpose– e.g., efficient operation of ant colony

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Some Principles ofEmergence & Self-Organization

• Many non-linearly interacting agents• Microdecisions lead to macrobehavior• Circular causality (macro / micro feedback)• Distributed information storage &

processing• Cooperation + competition• Diversity• Amplification of random fluctuations

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Artificial Life

“Genghis” from Brooks’ lab (MIT)

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Definition

• Artificial Life is “the study of man-madesystems that exhibit behaviors characteristicof natural living systems” (Langton)

• “ALife” includes:– synthetic self-reproducing chemical systems,

etc.– some autonomous robots– electronic life forms “living” in a computer’s

memory

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Microrobots

• We don’t know enough about humanintelligence to reproduce it in a machine,

• but issues of:– embodied intelligence

– autonomous activity

– social context of intelligence

• may be explored by means of microrobots

• Many potential applications

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“Ant” Microrobots (Brooks, MIT)

• About 1 cubic inch

• 17 sensors

• Can communicate witheach other

• Goal: push limits ofmicrorobotics

• Goal: explore socialinteractions inspired byant colony

• Applications: explosivesdisposal, Mars exploration

(image < Brooks’ lab, MIT) 2005-02-01 58

Clustering Around “Food”

• “Food” amongst otherobjects in environment

• First “ant” toencounter food,signals others

• Others cluster at foodsource

(video < Brooks’ lab, MIT)

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Tag Game

• “It” robot wandersuntil bumps something

• Transmits “Tag”

• A “Not It” robotreplies “I got tagged”

• First becomes “Not It”

• Second becomes “It”

(video < Brooks’ lab, MIT) 2005-02-01 60

Coordination & Communicationvia Ad Hoc Networks

• Wireless & mobile communication

• No fixed network structure (pattern ofinterconnectivity)

• Each node discovers & keeps track of whichother nodes it can communicate with

• Messages are routed in accordance withcurrent configuration of nodes

• Self-organize & adapt like social networks

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“Smart Dust”• Currently available “motes”:

– Bottle-cap size– $100–$200 each ($1 in 5 years)– Sense temperature, light, motion, energy use, GPS, gas,

pressure, …– Set up ad hoc network– Battery lasts for years– 8K program memory, 512K RAM– coded in C, runs TinyOS

• See Dust Inc. <http://www.dust-inc.com>• Privacy issues?

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(fig. < IEEE Computer)

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Nanobots

• How small can we go?

• Viruses & bacteriashow how robotscould be implementedat micrometer scale

• Genetically engineer:– existing organism

– new organism

• Apply same principlesto nonorganic robot

(video < Hybrid Medical Animation) 2005-02-01 64

How Could Nanobots beConstructed?

• Example: viral protein robot arm– Bio-Nano Robotics Group, Rutgers et al.

• Uses the protein that a virus usesto pierce cell wall

• 10 nm linear motion

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Computing withMicroorganisms

• Bacteria and other microorganisms have have alarge amount of “junk DNA”

• Can be genetically engineered to create internalartificial biochemical networks– Cf.: Gary Sayler’s use of GE’d bacteria for

bioremediation

• GE’d bacteria can cooperate through chemicalsignals, for:– emergent computation– microrobotics & nanorobotics

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Simulation of Self-OrganizedAggregation & Protective

Differentiationin Artificial Bacteria

• Demonstration that simple artificial bacteriacan aggregate when distressed

• Can differentiate functionally for collectiveprotection

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Normal State

• Normal activity:wandering &reproducing

• At this point, ambienttemperature decreasedbelow criticaltemperature

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Aggregating Distressed Bacteria

• Distressed bacteriaemit aggregation signal(green)

• Move toward higherconcentrations

• In highest densities,bacteria are in “interiorstate” (purple)

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Spread of Hibernation Signal

• When critical densityreached, hibernationsignal (purple) emitted

• Causes “interior”bacteria to becomedormant (blue)

• Others die & secretecyst material (grey)

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Completion of Spore

• Protective cystmaterial surroundsdormant bacteria

• Colony in upper rightdoes not reach criticalmass

• Non-dormant bacteriaare dying

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Reanimation of Bacteria

• Return to favorableconditions causesdormant bacteria toreanimate

• Consume cyst material& break out of spore

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Return to Normal State

• Return to wandering& reproducing

• Recovery ofpopulation

• Will form spores againif unfavorableconditions return

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The General-PurposeBacterial Robot

• An assortment ofgeneral genetic circuits

• Ensemble of usefulsensors & effectors

• GE to customize operation

• Genetic circuits blocked or enabled by chemical &other means

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Adaptation in Artificial Life

• Learning (individual & collective)

• Self-repair (individual & collective)

• Reproduction (individual & collective)

• Artificial evolution

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Conclusions: Prospects

• Human-scale intelligence: We don’t knowenough (yet)

• Collective intelligence: More promising– requires few (or no) neurons / agent

• Autonomous robotics: Steady progresstoward greater autonomy

• Many applications of AI ideas, short of full-scale intelligence

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Social Issues

• Human-scale intelligence– inevitable?

• Software Agents– privacy questions

• Microrobots– privacy questions

• Nanobots– environmental issues

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Thank you!