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Adaptive RoboticsAdaptive RoboticsCOM2110COM2110

Autumn Semester 2008 Autumn Semester 2008 Lecturer: Amanda SharkeyLecturer: Amanda Sharkey

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Robots in the newsRobots in the news

Bath University: a robot that jumps like a Bath University: a robot that jumps like a grasshopper and rolls like a ballgrasshopper and rolls like a ball

Created by PhD student Rhodri ArmourCreated by PhD student Rhodri Armour Can roll in any direction, and can jump Can roll in any direction, and can jump

over obstaclesover obstacles Small motors build up energy in the Small motors build up energy in the

springy spherical exoskeleton by springy spherical exoskeleton by compressing it.compressing it.

Avoids problems of robots with legs and Avoids problems of robots with legs and robots with wheelsrobots with wheels

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Behaviour-based robotics versus Behaviour-based robotics versus GOFAIGOFAI

Control mechanisms: Control mechanisms: Subsumption architectureSubsumption architectureArtificial Neural Nets (ANNs) Artificial Neural Nets (ANNs)

learning rules and learning rules and limitationslimitationsGenetic algorithmsGenetic algorithms

Biological inspirationBiological inspirationForms of learning in biological Forms of learning in biological organismsorganismsOrganisation of biological systemsOrganisation of biological systemsBiological modellingBiological modelling

ExamplesExamplesEarly robots, Humanoid robots, Early robots, Humanoid robots, Examples of research papers, Examples of research papers, ApplicationsApplications

Last week – AI, Magic and DeceptionLast week – AI, Magic and Deception

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a) Provide a brief account of the following terms, a) Provide a brief account of the following terms, and their relevance to behaviour-based robotics:and their relevance to behaviour-based robotics:

(I) embodiment (10%)(I) embodiment (10%) (ii) reactivity (10%)(ii) reactivity (10%) (iii) stigmergy (10%)(iii) stigmergy (10%)

b) Consider, with reference to the notion of b) Consider, with reference to the notion of autopoiesis, whether or not strong embodiment is autopoiesis, whether or not strong embodiment is possible. (30%)possible. (30%)

   c) Identify and discuss what you see as the main c) Identify and discuss what you see as the main

strengths and weaknesses of the new approach strengths and weaknesses of the new approach to Artificial Intelligence. (40%)to Artificial Intelligence. (40%)

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Classical AI (GOFAI)Classical AI (GOFAI) E.g. chess player, expert systems or E.g. chess player, expert systems or

traditional planning systems like traditional planning systems like STRIPS, or GPS (General Problem STRIPS, or GPS (General Problem Solver) Solver)

Emphasis on manipulation of Emphasis on manipulation of symbols, planning and reasoningsymbols, planning and reasoning

Centralised systemsCentralised systems SequentialSequential

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CognitivismCognitivism

Cognition is the manipulation of abstract Cognition is the manipulation of abstract representations by explicit formal rulesrepresentations by explicit formal rules

Knowledge of the world as sentence like Knowledge of the world as sentence like descriptions using symbolsdescriptions using symbols

Symbol – stands for objects and Symbol – stands for objects and conceptsconcepts

E.g. CYC project for creating common-E.g. CYC project for creating common-sense reasonersense reasoner

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Problems with Classical Problems with Classical systemssystems

Lack of robustnessLack of robustness May not perform well in noisy conditions, or May not perform well in noisy conditions, or

when some components break downwhen some components break down Lack of generalisationLack of generalisation

May not perform well in novel situationsMay not perform well in novel situations Real time processingReal time processing

Likely to be slowerLikely to be slower centralisedcentralised

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Further problems with Further problems with Classical AIClassical AI

Little consideration of interaction Little consideration of interaction between agent and the real worldbetween agent and the real world

Frame problemFrame problem How to model changeHow to model change

Symbol groundingSymbol grounding How to link the symbols being How to link the symbols being

manipulated with the real worldmanipulated with the real world

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Frame problemFrame problem

Daniel Dennett (1987)Daniel Dennett (1987) Robot with propositional representationsRobot with propositional representations E.g. INSIDE(R1,ROOM) E.g. INSIDE(R1,ROOM)

ON(BATTERY,WAGON) ON(BATTERY,WAGON) Spare battery in room with time bombSpare battery in room with time bomb R1 plans to pull wagon and battery out of R1 plans to pull wagon and battery out of

room. But bomb also on wagonroom. But bomb also on wagon R1D1 considering implications of actions. R1D1 considering implications of actions.

But still deciding whether removing wagon But still deciding whether removing wagon would change the colour of the walls when would change the colour of the walls when bomb explodesbomb explodes

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Back to the drawing board. “We must teach it the Back to the drawing board. “We must teach it the difference between relevant implications and difference between relevant implications and irrelevant implications. So they developed a irrelevant implications. So they developed a method of tagging implications as either relevant method of tagging implications as either relevant or irrelevant to the project at hand and installed or irrelevant to the project at hand and installed the method in R2D1. They found it sitting outside the method in R2D1. They found it sitting outside the room.the room.

““Do something” they yelled. “I am” it retorted. “ Do something” they yelled. “I am” it retorted. “ I’m busily ignoring some thousands of I’m busily ignoring some thousands of implications I have determined to be irrelevant. implications I have determined to be irrelevant. Just as soon as I find an irrelevant implication, I Just as soon as I find an irrelevant implication, I put it on the list of those I must ignore and”put it on the list of those I must ignore and”

The bomb went offThe bomb went off

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Symbol grounding problem Symbol grounding problem and the Chinese Roomand the Chinese Room

Gedanken (thought) experimentGedanken (thought) experiment Imagine a person in a room, who has a set of rule books. Imagine a person in a room, who has a set of rule books.

Sets of symbols are passed in to them, and they can Sets of symbols are passed in to them, and they can process them, using the rule books, and send symbols outprocess them, using the rule books, and send symbols out

The symbols going in are Chinese questionsThe symbols going in are Chinese questions The symbols going out are Chinese answersThe symbols going out are Chinese answers The room seems to understand ChineseThe room seems to understand Chinese But the person in the room does not understand ChineseBut the person in the room does not understand Chinese Similarly, a question answering computer program does not Similarly, a question answering computer program does not

understand languageunderstand language Computers don’t understand – they just manipulate Computers don’t understand – they just manipulate

symbols that are meaningless to them.symbols that are meaningless to them.

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Related papersRelated papers Harnad, S. (1990) The symbol Harnad, S. (1990) The symbol

grounding problem. Physica D. 42, grounding problem. Physica D. 42, 335-46.335-46.

Searle, J.S. (1980) Minds, Brains and Searle, J.S. (1980) Minds, Brains and Programs. Behavioural and Brain Programs. Behavioural and Brain Sciences, 3, 417-24.Sciences, 3, 417-24.

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Behaviour-based AIBehaviour-based AI AKA – embodied cognitive science, new AI, AKA – embodied cognitive science, new AI,

new wave AInew wave AI Brooks, 1986 subsumption architectureBrooks, 1986 subsumption architecture

Emphasis on intelligence emerging from the Emphasis on intelligence emerging from the interaction of organism with the environment, interaction of organism with the environment, and close coupling between sensors and and close coupling between sensors and motorsmotors

Brooks, 1990 “Intelligence without Brooks, 1990 “Intelligence without Representation” and Behaviour-based Representation” and Behaviour-based RoboticsRobotics

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Key concepts in Embodied Cognitive Key concepts in Embodied Cognitive ScienceScience

EmbodimentEmbodiment SituatednessSituatedness

Emphasis on interaction with the environmentEmphasis on interaction with the environment Biological inspirationBiological inspiration

StigmergyStigmergy EmergenceEmergence Reactive behaviourReactive behaviour DecentralisationDecentralisation

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Reactive roboticsReactive robotics Grey Walter’s electronic tortoisesGrey Walter’s electronic tortoises Taxis and tropismTaxis and tropism

Phototropism, PhototaxisPhototropism, Phototaxis PhonotaxisPhonotaxis Coastal seaslugCoastal seaslug

Geotaxis, negative and postive phototaxisGeotaxis, negative and postive phototaxis

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BiologyBiology Biological modellingBiological modelling

Cricket phonotaxisCricket phonotaxis Catagylphis desert antCatagylphis desert ant Task allocationTask allocation Understanding by building: synthetic modellingUnderstanding by building: synthetic modelling

Biological inspirationBiological inspiration Sorting (Holland and Melhuish)Sorting (Holland and Melhuish)

StigmergyStigmergy EmergenceEmergence Minimal representationMinimal representation

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Biological inspirationBiological inspiration Swarm robotics and swarm intelligenceSwarm robotics and swarm intelligence Keep it simple: Minimal representation and Keep it simple: Minimal representation and

reactive systemsreactive systems Innate knowledgeInnate knowledge

Fixed action patternsFixed action patterns Learning and evolutionLearning and evolution

Classical conditioningClassical conditioning Operant conditioningOperant conditioning Neural netsNeural nets Genetic AlgorithmsGenetic Algorithms

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MechanismsMechanisms Subsumption architecture Subsumption architecture Braitenberg vehiclesBraitenberg vehicles McCulloch and Pitts neuronsMcCulloch and Pitts neurons

Main characteristicsMain characteristics Neural Nets and learning algorithmsNeural Nets and learning algorithms

Strengths and limitationsStrengths and limitations Hebbian learning Hebbian learning Delta ruleDelta rule Generalised delta ruleGeneralised delta rule

Genetic AlgorithmsGenetic Algorithms Evolving neural nets Evolving neural nets

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Strengths and Limitations of Strengths and Limitations of reactive systemsreactive systems

A reactive system is one “A reactive system is one “where sensors and where sensors and motors are directly linked and which always motors are directly linked and which always react to the same sensory state with the same react to the same sensory state with the same motor action”motor action” (Nolfi and Floreano, 2000) (Nolfi and Floreano, 2000)

E.g. Grey Walter’s electronic tortoisesE.g. Grey Walter’s electronic tortoises

E.g: a reactive robot with Braitenberg E.g: a reactive robot with Braitenberg controller - simple neural network e.g. fully controller - simple neural network e.g. fully connected perceptrons without internal layers connected perceptrons without internal layers or any form of internal organisation.or any form of internal organisation.

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Did Brooks et al reject the idea of internal Did Brooks et al reject the idea of internal representation?representation?

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CriticismsCriticisms

Criticisms of approach Criticisms of approach

see Anderson (2003)see Anderson (2003)

No complex intelligent creature can get by without No complex intelligent creature can get by without representations….Kirsh (1991)representations….Kirsh (1991)

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CriticismsCriticisms Ford et al (1994): concerned that “The Ford et al (1994): concerned that “The

situationalists are attacking the very idea of situationalists are attacking the very idea of knowledge representation – the notion that knowledge representation – the notion that cognitive agents think about their environments, cognitive agents think about their environments, in large part, by manipulating internal in large part, by manipulating internal

representations of the worlds they inhabitrepresentations of the worlds they inhabit””

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CriticismsCriticisms Vera and Simon (1993) argue that Vera and Simon (1993) argue that

proponents of situated action are not proponents of situated action are not saying something different to proponents saying something different to proponents of physical symbol systemsof physical symbol systems Situated action proponents claimSituated action proponents claim (according (according

to Vera and Simon)to Vera and Simon) No internal representationsNo internal representations Direct access to affordances of the environmentDirect access to affordances of the environment No use of symbolsNo use of symbols

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CriticismsCriticisms But Vera and Simon point out that minimal But Vera and Simon point out that minimal

representations are usedrepresentations are used E.g. Pengi, and the notion of “the bee that E.g. Pengi, and the notion of “the bee that

is chasing me now” is still a symbol.is chasing me now” is still a symbol. ““If the SA approach is suggesting simply that If the SA approach is suggesting simply that

there is more to understanding behaviour than there is more to understanding behaviour than describing internally generated, symbolic, goal-describing internally generated, symbolic, goal-directed planning, then the symbolic approach directed planning, then the symbolic approach has never disagreed.”(Vera and Simon, 1993has never disagreed.”(Vera and Simon, 1993))

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Pengi explanationPengi explanation Agre and Chapman (1987) Agre and Chapman (1987) Pengi is a simulated agent that plays Pengi is a simulated agent that plays

the video game Pengothe video game Pengo Plays without planning or using Plays without planning or using

representationsrepresentations E.g. escaping from “the bee that is E.g. escaping from “the bee that is

chasing me now”, down “the corridor chasing me now”, down “the corridor I’m running down”I’m running down”

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Also, limitations to reactive Also, limitations to reactive systemssystems

Embodied and situated systems can Embodied and situated systems can sometimes solve quite complicated tasks sometimes solve quite complicated tasks without internal representations.without internal representations.

E.g. E.g. sensory-motor coordinationsensory-motor coordination (exploiting (exploiting agent-environment interaction) can solve agent-environment interaction) can solve tasks, as agent can select favourable tasks, as agent can select favourable sensory patterns through motor actions.sensory patterns through motor actions.

But there are limits.But there are limits.

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Examples of problems that can be solved Examples of problems that can be solved through through sensory-motor coordinationsensory-motor coordination

Perceptual aliasingPerceptual aliasing Sensory ambiguitySensory ambiguity

Clearly simple behaviours such as obstacle Clearly simple behaviours such as obstacle avoidance can be accomplished without avoidance can be accomplished without internal represenationinternal represenation

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Perceptual aliasingPerceptual aliasing: two or more objects : two or more objects generate the same sensory pattern, but generate the same sensory pattern, but require different responses.require different responses.

E.g. Khepera robot in environment with 2 E.g. Khepera robot in environment with 2 objectsobjects one with a black top to be avoided, one with a black top to be avoided, One with white top to be approachedOne with white top to be approached

Khepera: 8 infrared proximity sensors and a Khepera: 8 infrared proximity sensors and a linear camera with a view angle of 30 degrees.linear camera with a view angle of 30 degrees.

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If robot approaches object which is not in view If robot approaches object which is not in view angle of camera, it will receive an ambiguous angle of camera, it will receive an ambiguous sensory pattern.sensory pattern.

Solution – to turn towards object, so it is in view Solution – to turn towards object, so it is in view angle of camera, and sensory pattern angle of camera, and sensory pattern disambiguated.disambiguated.

An example of An example of active perceptionactive perception.. Similar behaviour found in fruit fly Drosophila, Similar behaviour found in fruit fly Drosophila,

which moves to shift perceived image to certain which moves to shift perceived image to certain location of visual field.location of visual field.

But limits to this strategy – will only be effective when robot But limits to this strategy – will only be effective when robot can find at least one sensory state not affected by aliasing can find at least one sensory state not affected by aliasing problemproblem..

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Example of Example of active restructuringactive restructuring: Scheier et al 1998.: Scheier et al 1998. Khepera should approach large and avoid small Khepera should approach large and avoid small

cylindrical objects in a walled area.cylindrical objects in a walled area. Robot receives information from 6 frontal proximity Robot receives information from 6 frontal proximity

sensors.sensors. Neural network trained to discriminate between Neural network trained to discriminate between

patterns corresponding between cylindrical objects patterns corresponding between cylindrical objects and walls, or between different sizes of cylindrical and walls, or between different sizes of cylindrical object.object.

Poor performance on large/small cylindrical objectsPoor performance on large/small cylindrical objects

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Problem is that sensory patterns belonging Problem is that sensory patterns belonging to different categories overlap.to different categories overlap.

““put differently, the distance in sensor space for data put differently, the distance in sensor space for data originating from one and the same object can be large, originating from one and the same object can be large, while the distance between two objects from different while the distance between two objects from different categories can be small” (Scheier et al 1998)categories can be small” (Scheier et al 1998)

But Scheier et al (1998) used artificial But Scheier et al (1998) used artificial evolution to select the weights for robot’s evolution to select the weights for robot’s neural controllers.neural controllers.

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Near optimal performance after 40 generations.Near optimal performance after 40 generations. Fittest individuals moved in the environment until Fittest individuals moved in the environment until

they perceived an object (large or small).they perceived an object (large or small). Then they circled the objectThen they circled the object Circling behaviour resulted in different sensory Circling behaviour resulted in different sensory

patterns for different sizes of object.patterns for different sizes of object. i.e sensory-motor coordination allowed robots to i.e sensory-motor coordination allowed robots to

obtain sensory patterns that could be easily obtain sensory patterns that could be easily discriminateddiscriminated..

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So, some difficult tasks can be solved by So, some difficult tasks can be solved by exploiting environmental constraints exploiting environmental constraints through sensory-motor coordination and through sensory-motor coordination and active perception.active perception.

But, not allBut, not all An alternative to simple reactive An alternative to simple reactive

behaviour – robots that can exploit behaviour – robots that can exploit internal dynamical status.internal dynamical status. By using neural network with recurrent By using neural network with recurrent

connectionsconnections

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Summary to dateSummary to date Classical AI vs Behaviour-based AIClassical AI vs Behaviour-based AI What is reactive behaviour?What is reactive behaviour? What can it accomplish? What can it accomplish?

Simple tasks, e.g. obstacle avoidanceSimple tasks, e.g. obstacle avoidance Some harder tasks by exploiting the Some harder tasks by exploiting the

environment and active perceptionenvironment and active perception Limits – example of tasks that require some Limits – example of tasks that require some

internal states.internal states.

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Tasks that require reasoningTasks that require reasoning::- activities that involve predicting the behaviour of - activities that involve predicting the behaviour of

other agentsother agents - activities which require responses to action in the - activities which require responses to action in the

future e.g. avoiding future dangersfuture e.g. avoiding future dangers- activities that require understanding from an activities that require understanding from an

objective perspective e.g. following advice, or a objective perspective e.g. following advice, or a new receipe.new receipe.

- Problem solving e.g how many sheets of paper Problem solving e.g how many sheets of paper needed to wrap a packageneeded to wrap a package

- Creative activities e.g. language use, musical Creative activities e.g. language use, musical performance.performance.

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Mataric (2001) identifies “behaviour-based Mataric (2001) identifies “behaviour-based systems as an alternative to reactive systems as an alternative to reactive systems.systems.

She identifies strengths and weaknesses She identifies strengths and weaknesses of reactive systems.of reactive systems. Strengths: real-time responsiveness, Strengths: real-time responsiveness,

scalability, robustnessscalability, robustness Weaknesses: lack of state, inability to look into Weaknesses: lack of state, inability to look into

past or future.past or future.

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Mataric (2001) characterisation of Mataric (2001) characterisation of types of controltypes of control Reactive control: don’t think, reactReactive control: don’t think, react Deliberative control: think hard, then Deliberative control: think hard, then

act.act. Hybrid control: think and act Hybrid control: think and act

independently in parallelindependently in parallel Behaviour-based control: think the way Behaviour-based control: think the way

you act.you act.

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Behaviour-based controlBehaviour-based control Behaviours added incrementally – Behaviours added incrementally –

simplest first.simplest first. Behavioural modules can use internal Behavioural modules can use internal

representations when necessaryrepresentations when necessary But no centralised knowledge But no centralised knowledge

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Related idea Related idea Action oriented representationsAction oriented representations (Clark, 1997(Clark, 1997))

Use of minimal representations – e.g. when looking Use of minimal representations – e.g. when looking for coffee cup, you search for yellow object.for coffee cup, you search for yellow object.

..

partial models of the world which include only those partial models of the world which include only those aspects that are necessary to allow agents to aspects that are necessary to allow agents to achieve their goals. (Brooks 1991).achieve their goals. (Brooks 1991).

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But if the same body of information were But if the same body of information were needed in several activities, might be more needed in several activities, might be more economical to deploy a more action-neutral economical to deploy a more action-neutral encoding.encoding.

E.g. if knowledge about an object’s location to E.g. if knowledge about an object’s location to be used for many different purposes, might be be used for many different purposes, might be better to generate a single action-independent better to generate a single action-independent inner map that could be accessed by multiple inner map that could be accessed by multiple routines.routines.

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Reactive issueReactive issue Change from traditional AI: different emphasis Change from traditional AI: different emphasis

on importance of mental representations.on importance of mental representations.

Embodied and situated approach: minimal Embodied and situated approach: minimal internal representations best viewed as partial internal representations best viewed as partial models of the world which include only those models of the world which include only those aspects that are necessary to allow agents to aspects that are necessary to allow agents to achieve their goals (Brooks, 1991)achieve their goals (Brooks, 1991)

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Embodiment – robots deal with real Embodiment – robots deal with real objects in the real world, not symbolsobjects in the real world, not symbols

Does that mean they can really be Does that mean they can really be said to be intelligent and capable of said to be intelligent and capable of thought?thought?

Discuss …..Discuss …..

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EmbodimentEmbodiment Key concept in embodied and Key concept in embodied and

situated AIsituated AI Idea that robots are physically embodied Idea that robots are physically embodied

and can act on the worldand can act on the world

Does the use of embodied robots make Does the use of embodied robots make Strong AI possible?Strong AI possible?

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Weak AI:Weak AI: computer is valuable tool for study of computer is valuable tool for study of mind – ie can formulate and test hypotheses mind – ie can formulate and test hypotheses rigorouslyrigorously

Strong AI:Strong AI: appropriately programmed computer appropriately programmed computer really is a mind, can be said to understand, has really is a mind, can be said to understand, has cognitive states.cognitive states.

Strong AI:Strong AI: “the implemented program, by itself, is “the implemented program, by itself, is constitutive of having a mind. The implemented constitutive of having a mind. The implemented program, by itself, guarantees mental life” Searle program, by itself, guarantees mental life” Searle (1997)(1997)

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Problems: how can symbols have Problems: how can symbols have meaning? (Searle and Chinese room)meaning? (Searle and Chinese room)

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Two possible solutionsTwo possible solutions Symbol grounding (not covered here) Symbol grounding (not covered here) Situated and embodied cognitionSituated and embodied cognition

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Situated and Embodied Situated and Embodied cognitioncognition

Exemplified by Rodney BrooksExemplified by Rodney Brooks Approach emphasises construction of Approach emphasises construction of

physical robots embedded in and physical robots embedded in and interacting with the environmentinteracting with the environment No central controllerNo central controller Subsumption architectureSubsumption architecture No symbols to groundNo symbols to ground Intelligence is found in interaction of robot with Intelligence is found in interaction of robot with

its environment.its environment.

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Is strong embodiment possible?Is strong embodiment possible? Sharkey and Ziemke (1998) – only Sharkey and Ziemke (1998) – only

weak embodimentweak embodiment Robots are allopoietic, not Robots are allopoietic, not

autopoietic machines.autopoietic machines.

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AutopoiesisAutopoiesis Etymologically (means) self-makingEtymologically (means) self-making Abstract description of self organising Abstract description of self organising

systems.systems. Maturana and Varela (1972) Autopoiesis Maturana and Varela (1972) Autopoiesis

and Cognition: the realisation of the living.and Cognition: the realisation of the living. An autopoietic system is defined in terms An autopoietic system is defined in terms

of its organisation:of its organisation:

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““a network of processes of production a network of processes of production (transformation and destruction) of components (transformation and destruction) of components that produces the components which (i) through that produces the components which (i) through their interactions and transformation continuously their interactions and transformation continuously regenerate the network of processes (relations) regenerate the network of processes (relations) that produced them: and (ii) constitute it (the that produced them: and (ii) constitute it (the machine) as a concrete entity in the space in machine) as a concrete entity in the space in which they (the components) exist by specifying which they (the components) exist by specifying the topological domain of its realization as such a the topological domain of its realization as such a network. (Maturana and Varela, 1980).network. (Maturana and Varela, 1980).

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For life, embodiment is required.For life, embodiment is required.““Autopoiesis in the physical space [is] a Autopoiesis in the physical space [is] a

necessary and sufficient condition for a necessary and sufficient condition for a system to be a living one.” (Maturana system to be a living one.” (Maturana and Varela, 1980)and Varela, 1980)

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Living organisms – organised as a unitary Living organisms – organised as a unitary wholewhole

Basic phenomenon: self organisation of a Basic phenomenon: self organisation of a single cell.single cell.

Core of autopoiesis: the self-production of the Core of autopoiesis: the self-production of the organism’s boundary as a unitary systemorganism’s boundary as a unitary system

““a living system is an autopoietic machine whose a living system is an autopoietic machine whose function it is to create and maintain the unity that function it is to create and maintain the unity that distinguishes it from the medium in which it exists” distinguishes it from the medium in which it exists” (Sharkey and Ziemke(Sharkey and Ziemke, 2000), 2000)

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.. Maturana and Varela: Maturana and Varela: Machines made by humans are Machines made by humans are

allopoieticallopoietic. . The components are The components are produced by other processes that are produced by other processes that are independent of the organisation of the independent of the organisation of the machine.machine.

Robots – no multicellular solidarity (or Robots – no multicellular solidarity (or living cells) . Sensors, controllers and living cells) . Sensors, controllers and actuators are not integrated into body.actuators are not integrated into body.

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Cell: first living system which determines Cell: first living system which determines its own boundaries (cell membrane)its own boundaries (cell membrane)

Emphasis on self-constructed, self-Emphasis on self-constructed, self-maintaining bodily boundary.maintaining bodily boundary.

An autopoietic machine such as a living An autopoietic machine such as a living system is a special type of homeostatic system is a special type of homeostatic machine for which the fundamental machine for which the fundamental variable to be maintained is its own variable to be maintained is its own organisation.organisation.

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Imagine colony of self-maintaining robotsImagine colony of self-maintaining robots Assembler robots to build robots out of partsAssembler robots to build robots out of parts Transplant robots able to replace damaged partsTransplant robots able to replace damaged parts Tinker robots able to manage some types of damageTinker robots able to manage some types of damage Some artificial evolution, improving their design.Some artificial evolution, improving their design.

But still engineering – the constituents of the But still engineering – the constituents of the system will not have been autonomously system will not have been autonomously generated, but manufacturedgenerated, but manufactured..

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Is strong embodiment possible?Is strong embodiment possible?

A robot is not a living system, and not A robot is not a living system, and not autopoietic, so it does not autopoietic, so it does not experience experience the the world – there is no “self” there to do the world – there is no “self” there to do the experiencing.experiencing.

its ‘experience’ is no different from that of its ‘experience’ is no different from that of an electronic tape measurean electronic tape measure

(No phenomenal embodiment)(No phenomenal embodiment)

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Also there is an important difference Also there is an important difference between animals and robots. between animals and robots.

Animals have coevolved with their Animals have coevolved with their environmentsenvironments

In a robot, the intimate relationship In a robot, the intimate relationship between the body of a living organism and between the body of a living organism and its environment is missing.its environment is missing.

(no mechanistic embodiment)(no mechanistic embodiment)

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Weak embodiment is possible.Weak embodiment is possible. Possible to model mechanistic theories of Possible to model mechanistic theories of

animal behaviouranimal behaviour Possible to use robots to study how artificial Possible to use robots to study how artificial

agents can enact their own environmental agents can enact their own environmental embedding.embedding.

I.e. can study an allopoietic machine as if it I.e. can study an allopoietic machine as if it were autopoietic, and this can yield scientific were autopoietic, and this can yield scientific insights.insights.

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SummarySummary Classical AI vs Embodied Cognitive ScienceClassical AI vs Embodied Cognitive Science

Newer approach emphasises connection to the Newer approach emphasises connection to the environment and embodimentenvironment and embodiment

What is a reactive robot?What is a reactive robot? Limitations of reactive systemsLimitations of reactive systems Minimal representations and interaction with Minimal representations and interaction with

environmentenvironment Can a robot really be embodied?Can a robot really be embodied?

Does embodiment solve problems of Strong AI?Does embodiment solve problems of Strong AI? Not living, and not autopoieticNot living, and not autopoietic Has not evolved together with environmentHas not evolved together with environment

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Where will adaptive robotics go next?Where will adaptive robotics go next? Beyond stigmergy – local communicationBeyond stigmergy – local communication Epigenetic systems – development of Epigenetic systems – development of

knowledge and representation as a result of knowledge and representation as a result of interaction with the worldinteraction with the world

Language evolutionLanguage evolution Social robotics – capitalising on our Social robotics – capitalising on our

anthropomorphism.anthropomorphism.