Insects, Robots

VT

2

Insects, Robots

Barry Smith

http://ifomis.de

3

4

The Technological Background

How the world became part of the World Wide Web

the cheese

5

Sources

“Motion in Databases: Issues and Possible Solutions”

Ouri Wolfson (University of Illinois)

“Intersection of GI and IT Spatial Databases”

Max J. Egenhofer (University of Maine)

6

Information Technologies

Global Positioning Systems (GPS)

7

Digital cameras


8

Digital video cameras


9

Meteorological sensors

chemical

biological

…wearable computers


Microsensors

10

Location based services

Examples:

Where is the closest gas station? How do I get there?

Track my pet/child/prisoner

11


12


Wall Street Journal May 8, 2000: Location-based services a killer application for the wireless internet

Strategy Analytics: consumer lbs a $7B market in North America by 2005

Why now? – Proliferation of portable/wearable/wireless devices

13

Moving Objects Database Technology

Query example:How often is bus #5 late by more than 10

minutes at station 20?

GPS

GPS

GPS

Wireless link

14


Trigger example:Send message when helicopter is in a given

geographic area (trigger)

GPS

GPS

GPS

Wireless link

15


Query example:List trucks that will reach destination within

20 minutes (future query)

GPS

GPS

GPS

Wireless link

16


Present query:

List taxi cabs within 1 mile of my location

GPS

GPS

GPS

Wireless link

17

PalmPilot context aware

Display the wiring/plumbing behind this wall

Display seismographic features of a terrain a geologist is viewing

Display vital signs of a patient a doctor is examining

18

European Media Lab, Heidelberg

Tourism information services

Intelligent, speaking camera plus map display

Display all non-smoking restaurants within walking distance of the castle

Read out a history of the building my camera is pointing to

19

Mobile e-commerce

Inform a person located at L who needs items of a given sort where he can buy them (a) most quickly (b) most cheaply (c) at 2am.

Inform a person walking past a bar of his buddies in the bar

20

Further Applications

Digital battlefieldEmergency responseAir traffic controlSupply chain managementMobile workforce managementDynamic allocation of bandwidth in cellular

network

21

From Fodor to Gibson

22

Traditional Syntactic/Semantic Approach to Information

Systems011011101010001000100010010010010010010001001111001001011011110110111011

derived intentionality

23

You can’t get orange juice into the computer

have to use ‘strings’ instead

classes, categories, entities, concepts …

24

String-Arrays vs. Objects

ghjui123

xxxxx xxxxx

25

Fodor’s Methodological Solipsism

011011101010001000100010010010010010010001001111001001011011110110111011

26

Humans, Machines, and the Structure of Knowledge

Harry M. CollinsSEHR, 4: 2 (1995)

27

Knowledge-down-a-wireImagine a 5-stone weakling having his brain loaded with the knowledge of a champion tennis player.

He goes to serve in his first match

-- Wham! –

his arm falls off.

He just doesn't have the bone structure or muscular development to serve that hard.

28

Types of knowledge/ability/skill

1. those that can be transferred simply by passing signals from one brain/computer to another.

2. those that can’t:

29

Sometimes it is the body which knows (the hardware)

30

Sometimes it is the world which knows

31

I know where the book is= I know how to find it

I know what the square root of 2489 is= I know how to calculate it

I know how to recognize the presence of a tiger

= by smell, noise … (in real-world context)

32

A. Clark, Being There

humans can accomplish much without building detailed, internal models; we rely on

Epistemic action =

manipulating Scrabble tiles – using the re-arranged pieces as basis for brain's pattern-completing abilities

writing one large number above another to multiply them with pen on paper

33

A. Clark, Being There

we can rely also on

External scaffolding = maps, models, tools, language, culture

we act so as to simplify cognitive tasks by "leaning on" the structures in our environment.

34

Not all calculations are done inside the head

Not all thinking is done inside the head

35



2. those that can’t: -- here the "hardware" is important;abilities/skills contained (a) in the body(b) in the world

36

From

The Methodological Solipsist Approach to Information Processing

ToThe Ecological Approach to Information

Processing

37

Fodorian Psychology

To understand human cognition we should study the mind/brain in abstraction from its real-world environment

(as if it were a hermetically sealed Cartesian ego)

38

Gibsonian Ecological Psychology

To understand human cognition we should study the moving, acting human person as it exists in its real-world environment

and taking account how it has evolved into this real-world environment

We are like tuning forks – tuned to the environment which surrounds us

39

Fodorian View of Information Systems

To understand information systems we should study their manipulation of syntactic strings

40

Gibsonian Ecological View of Information Systems

To understand information systems we should study the hardware as it exists embedded in its real-world environment

and taking account the environment for which it was designed and built

Information systems are like tuning forks – they resonate in tune to their surrounding environments

41

Functioning of Information System intelligible only as part of

environment

0110

1110

1010

0010

0010

0010

0100

1001

0010

0100

0100

1111

0010

0101

1011

1101

1011

1011

42

The problem of ontology

different groups use incompatible terminologies

43

Understanding how we can resolve these incompatibilities

is a difficult problemwhose solution

might throw light also on how the human mind

copes with large amounts of highly variegated types of data

44

Consider for example how the human mind

copes with complex phenomena in the social realm (e.g. speech acts of promising)which involve:

experiences (speaking, perceiving), intentions, language, action, deontic powers, background habits, mental competences, records and representations

45

How resolve incompatibilities between the terminologies used by different groups?

“ONTOLOGY” = the solution of first resort

(compare: kicking a television set)

But what does ‘ontology’ mean?

Current most popular answer: a collection of terms and definitions satisfying constraints of description logic

46

Description logic

a decidable logic

(thus much weaker than first-order predicate logic)

for manipulating hierarchies of terms

47

Example: The Semantic Web

Vast amount of heterogeneous data sourcesNeed dramatically better support at the level of metadataThe ability to query and integrate across different conceptual systems:The currently preferred answer is: The Semantic Web

48

Tim Berners-Lee, inventor of the internet

‘sees a more powerful Web emerging, one where documents and data will be annotated with special codes allowing computers to search and analyze the Web automatically. The codes … are designed to add meaning to the global network in ways that make sense to computers’

and built out of these ingredients:OWL (Ontology Web Language)RDF (Resource Descriptor Framework) DAML (Darpa Agent Mark-up Language)

Washington Post, January 30, 2003

49

hyperlinked vocabularies, called

‘ontologies’ will be used by Web authors

‘to explicitly define their words and concepts as they post their stuff online.

‘The idea is the codes would let software "agents" analyze the Web on our behalf, making smart inferences that go far beyond the simple linguistic analyses performed by today's search engines.’

50

Practical problems

of the semantic web:

who will police the coding?

51

But these ‘ontologies’ are just term hierarchies

manipulated by means of the very simple logical tools of description logic

52

Example: The Gene Ontology (GO)

hormone ; GO:0005179

%digestive hormone ; GO:0046659 %peptide hormone ; GO:0005180 %adrenocorticotropin ; GO:0017043 %glycopeptide hormone ; GO:0005181 %follicle-stimulating hormone ; GO:0016913

53

as tree

hormone

digestive hormone peptide hormone

adrenocorticotropin glycopeptide hormone

follicle-stimulating hormone

54

Problem: There exist multiple databases

genomic cellular

structural phenotypic

… and even for each specific type of information, e.g. DNA sequence data, there exist several databases of different scope and organisation

55

What is a gene?GDB: a gene is a DNA fragment that can be

transcribed and translated into a protein

Genbank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype

(from Schulze-Kremer)

GO does not tell us which of these is correct, or indeed whether either is correct, and it does not tell us how to integrate data from the corresponding sources

56

Example: The Enterprise Ontology

A Sale is an agreement between two Legal-Entities for the exchange of a Product for a Sale-Price.

A Strategy is a Plan to Achieve a high-level Purpose.

A Market is all Sales and Potential Sales within a scope of interest.

57

Harvard Business Review, October 2001

… “Trying to engage with too many partners too fast is one of the main reasons that so many online market makers have foundered. The transactions they had viewed as simple and routine actually involved many subtle distinctions in terminology and meaning”

58

Example: Medical Nomenclature Ontologies

Unified Medical Language System (UMLS):

blood is a tissueSystematized Nomenclature of Medicine (SNOMED):

blood is a fluid

59

Example: Statements of Accounts

Company Financial statements may be prepared under either the (US) GAAP or the (European) IASC standards

These allocate cost items to different categories depending on the laws of the countries involved.

60

Job:

to develop an algorithm for the automatic conversion of income statements and balance sheets between the two systems.Not even this relatively simple problem has been satisfactorily resolved

… why not?

Because the very same terms mean different things and are applied in different ways in different cultures

61

Assumptions

Communication / compatibility problems should be solved automatically

(by machine)

Hence ontologies must be applications running in real time

62

Application ontology:

Ontologies are inside the computer

thus subject to severe constraints on expressive power

(effectively the expressive power of description logic)

63

Experience shows

that there can be no mechanical solution to the problems of data integration

in domains like medicine or genetics,

or in the domain of really existing commercial transactions

64

The problem in every case

is one of finding an overarching framework for good definitions,

definitions which will be adequate to the nuances of the domain under investigation

Recall the case of blood

65

Application ontology cannot solve the data-integration problem

because of its roots

1. in knowledge representation / knowledge mining

2. in Quinean philosophy

66

different conceptual systems

67

need not interconnect at all

68

we cannot make incompatible concept-systems interconnect

just by looking at concepts, or knowledge – we need some tertium quid

69

Application ontology

has its philosophical roots in Quine’s doctrine of ontological commitment and in the ‘internal metaphysics’ of Carnap/Putnam Roughly, for an application ontology the world and the semantic model are one and the sameWhat exists = what the system says exists

70

Quineanism:

ontology is the study of the ontological commitments or presuppositions embodied in scientific theories (or in the beliefs of experts)

71

Quineanism, too, faces the integration problem

If an ontology is the set of ontological commitments of a theory, how can we cope with questions pertaining to the relations between the objects to which different theories are committed?

(Recall the Vienna Circle program of the Unity of Science)

72

What is needed

is some sort of wider common framework

sufficiently rich and nuanced to allow concept systems deriving from different theoretical / data sources to be hand-callibrated

73

What is needed

is not an Application Ontology

but

a Reference Ontology

(something like old-fashioned metaphysics)

74

Reference Ontology

An ontology is a theory of a domain of entities in the world

Ontology is outside the computer

seeks maximal expressiveness and adequacy to reality

and sacrifices computational tractability for the sake of representational adequacy

75

Belnap

“it is a good thing logicians were around before computer scientists;

“if computer scientists had got there first, then we wouldn’t have numbers

because arithmetic is undecidable”

76

It is a good thing

Aristotelian metaphysics was around before description logic, because otherwise we would have only hierarchies of terms/concepts/stringsand no reality,no universals,no individual instances …

77

Reference Ontology

a theory of the tertium quid

– called reality –

needed to hand-callibrate database/terminology systems

78

Methodology

Get ontology right first

(realism; descriptive adequacy; rather powerful logic);

solve tractability problems later

79

Recall:

GDB: a gene is a DNA fragment that can be transcribed and translated into a protein


80

Ontology

Note that terms like ‘fragment’, ‘region’, ‘name’, ‘carry’, ‘trait’, ‘type’

… along with terms like ‘part’, ‘whole’, ‘function’, ‘substance’, ‘inhere’ …

are ontological terms in the sense of traditional (philosophical) ontology

81

Basic Formal Ontology

BFOThe Vampire Slayer

82

BFOnot just a system of categoriesbut a formal theory with definitions, axioms, theoremsdesigned to provide formal resources for

the building of reference ontologies for specific domains

the latter should be of sufficient richness that terminological incompatibilities can be resolved intelligently rather than by brute force

83

MedO = BFO’s medical ontology

will include sub-domains like thesecell ontologydrug ontologyprotein ontology gene ontology

here, too, an ontological fusion problem arisesHow are these to be knitted together within a

single theory? Answer: we need a theory of granularity

84

Part Two

Insects, Robots

85

86

Computerized Agents

computer systems situated in an environment capable of flexible, autonomous action in

that environmentinteracting with other agents, including:

communicating, negotiating, coordinating actions

often within some organizational context

87

Rodney Brooks

88

Orthodox methodology

described by Brooks

as the SMPA view

89

SMPASense Model Plan Act

the agent first senses its environment through sensors

then uses this data to build a model of the world

then produces a plan to achieve goals

then acts on this plan

90

SMPA

belongs to the same methodological universe as Application Ontology

If we want to build an intelligent agent, there need to be representations inside the agent

of the domain within which the agent acts

The agent’s reasoning processes act not on the real-world environment but on these representations (‘models’)

91

Brooks’ Engineering Approach

takes its inspiration from evolutionary biology

lends very little weight to the role of representations or models

AI should use the world in all its complexity in producing systems that react directly to the world

92

The starting-point for our understanding of intentionality

should be the insect’s relations to its surrounding physical environment

93

94

Intentionality tactile and chemical

http://liftoff.msfc.nasa.gov/Shared/News2001/Microbe/e-coli_med.jpg

95

The movement of E. colibiased random walk (from C. Emmrich, plus H. C. Berg,

1988, "A physicist looks at bacterial chemotaxis"):

In the absence of a stimulus, E. coli simply wanders around, smoothly swimming by rotating its flagella counterclockwise. These runs are terminated by chaotic events, called tumbles, when flagella rotate clockwise. Following a tumble, the cell runs again, picking a new direction, more or less at random. When the cells swim in a spatial gradient of a chemical attractant, runs that happen to carry it up the gradient are extended, whereas those that happen to carry it down the gradient are not. Thus, the cell drifts in a favourable direction …

96

The life of E. coli

falling down sugar wells

97

the bacterium is a single cell,

Thus it does not have a multicelled nervous system

But it has receptor molecules acting as sensors, it has a signal transduction system, and a highly complex machinery of movable flagella.

Different receptors react to different stimuli, including single oxygen molecules as well as bigger carbohydrate molecules.

See Bruce Alberts et al.: The molecular biology of the cell

98

E. coli bacteria

react to differences in concentrations of sugar molecules with a behavior shift (as a dog reacts to a smelt trace of another animal)

99

Need for a functional level

Even though we might describe the elementary

particles of every atom in the E. coli by theories in high energy physics, this would not give us a clue to the functional meanings of the specific macromolecules of a cell.

A transmembrane transport ion channel protein is not simply a protein, it is a specific kind of protein with a specific function.

100

Therre is a difference

between a purely chemical system, and a system that is at once chemical AND biological.

Only biologically organized chemical systems exhibit functionality.

We don't see the spontaneous formation of cytochrome c enzymes outside biological systems. It is the result of a long evolutionary process, that has constructed the sequence information in the gene for that specific sequence of amino acids required to perform the function of cytochrome c.

101

Attribution of intentionality

does not depend upon the existence of a nervous system

we can ascribe simple biological intentionality to single, movable cells;

intentionality is dependent only upon the existence of sensors, information mediation (automatic interpretation, if you like) and motor responses resulting in adaptable behavior.

102

Frederik Stjernfelt, Biosemiotics and Formal Ontology", Semiotica 127 - 1/4 1999, 537-66

103

E coli bacteria

are attracted by peaks of sugar density

– but they can be fooled

104

Brooks’ Engineering Approach

An intelligent system embodies a number of distinct layers of activity (compare: sub-personal layers of human cognition)

These layers operate independently and connect directly to the environment outside the system

Each layer operates as a complete system that copes in real time with a changing environment

Layers evolve through interaction with the environment (artificial insects/vehicles …)

105

Brooks: An intelligent system

must be situated

it is situatedness which gives the processes within each layer meaning

because

the world serves to unify the different layers together and to make them compatible/mutually adjusting

106

Organisms, especially humans, fix their beliefs not only in their heads but in their worlds, as they attune themselves differently to different parts of the world as a result of their experience. And they pull the same trick with their memories,not only by rearranging their parsing of the world (their understanding of what they see), but by marking it. They place traces out there [and this] changes what they will be confronted with the next time it comes around. Thus they don't have to carry their memories with them.

“Intelligence without Representation”

107

J. J. Gibson The Ecological Approach to Visual Perception

we are like (multi-layered) tuning forks – tuned to the environment which surrounds us

(we have evolved in such a way as to be attuned to our environment;

in part because we ourselves have created it via what Lewontin calls ‘ecosystem engineering’)

108

Organisms are tuning forks

They have evolved to resonate automatically and directly to those quality regions in their niche which are relevant for survival

– perception is a form of automatic resonation– when the insect stumbles through uneven

terrain the insect’s motor system is resonating to the reality beyond

109

Merlin Donald

Origins of the modern mind: Three stages in the evolution of culture and cognitionCambridge, MA: Harvard University Press, 1991

110

Merlin Donald

radical transition in the emergence of modern human culture when humans began to construct elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematicsfrom this point human biological memory becomes an inadequate vehicle for storing and processing our collective knowledge. from this point the modern mind is a hybrid structure built from vestiges of earlier biological stages together with new

external symbolic memory devices

111

A New Biological Theory of Intentionality

– cognitive beings like ourselves resonate to speech acts and to linguistic records

– cognitive beings like ourselves resonate deontically

– mathematicians resonate to the structures of mathematical reality

112

Gibson’s Ecological Approach

To understand cognition we should study the moving, acting organism as it exists in its real-world environment

and for human organisms this is a social environment which includes records and traces of prior actions in the form of communication systems (languages), storage systems (libraries), transport systems (roads), legal systems

Searle, De Soto

113

Gibson: Environment as Array of Affordances

“The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or evil.”

James J. Gibson, The Ecological Approach to Visual Perception

The environment of a commercial organism includes affordances such as prices.

114

Gibson’s theory of surface layout

Niches = systems of barriers, openings, pathways to which organisms are specifically attuned

Including: temperature gradients, patterns of movement of air or water molecules, electro-chemical signals guiding the movements of micro-organisms

But also: traffic signs, instructions posted on notice boards or displayed on the computer screen – reality is marked by signs

115

How we resonate deontically

Compare the way in which the physical properties of roads help people to obey

the traffic laws when driving

Deal with obligations, norms via the comparison with roads

116

Roger G. Barker’s Eco-Behavioral Science

Gibson: Ecological Psychology of Perception

Barker: Ecological Psychology of Social Action

P. Schoggen, Behavior Settings: A Review and Extension of Roger G. Barker’s Ecological

Psychology. Stanford, CA: Stanford University Press, 1989.

117

Roger Barker: Niche as Behavioral SettingNiches are recurrent settings which serve as the environments for our everyday activities:

a newspaper kiosk in the morning rush-hour,

your table in the cafeteria,

the 5pm train to Long Island.

118

Behavior Settings

Each behavior setting is associated with certain standing patterns of behavior.

We are tuned to an environment of behavior settings

119

The Systematic Mutual Fittingness of Behaviour and Setting

The behaviour and the physical objects … are intertwined in such a way as to form a pattern that is by no means random: there is a relation of harmonious fit between the standard patterns of behaviour occurring within the unit and the pattern of its physical components.

120

Settings, for Barker,

are natural units in no way imposed by an investigator.

To laymen they are as objective as rivers and forests

— they are parts of the objective environment that are experienced as directly as rain and sandy beaches are experienced. (Barker 1968, p. 11)

121

Recall how the human mind

copes with complex phenomena in the social realm (e.g. speech acts of promising)which involve:

experiences (speaking, perceiving), intentions, language, action, deontic powers, background habits, mental competences, records and representations

122

Barker on Unity of Social Reality

“The conceptual incommensurability of phenomena which is such an obstacle to the unification of the sciences does not appear to trouble nature’s units.

Within the larger units, things and events from conceptually more and more alien sciences are incorporated and regulated.”

123

Barker on Unity of Social Reality

“As far as our behaviour is concerned, … even the most radical diversity of kinds and categories need not prevent integration”

Because we have been tuned both phylogenetically and ontogenetically to resonate to environments like this

124

A theory of intentionality

must be a (biologically based) theory of the sorts of environments, on different levels of granularity, into which human beings have evolved

our patterns of behavior and cognition on different levels are unified together not via some central monad but by the world itself

(our environments fit together physically)

125

Humans, Machines, and the Structure of Knowledge

Harry M. CollinsSEHR, 4: 2 (1995)

126

Knowledge-down-a-wireImagine a 5-stone weakling having his brain loaded with the knowledge of a champion tennis player.

He goes to serve in his first match

-- Wham! –

his arm falls off.

He just doesn't have the bone structure or muscular development to serve that hard.

127



2. those that can’t

(Philosophers like Fodor have mainly concentrated on the first type of knowledge)

128

Sometimes it is the body (the hardware) which knows

129

and sometimes it is the world outside which knows

130

From

The Methodological Solipsist Approach to Intentionality

ToThe Ecological Approach to Intentionality

131

Fodorian Psychology

To understand human cognition we should study the mind/brain in abstraction from its real-world environment

(as if it were a hermetically sealed Cartesian ego)

132

I know where the book is= I know how to find it

I know what the square root of 2489 is= I know how to calculate it

I know how to recognize the presence of a tiger

= by smell, noise … (in real-world context)

133

So what is the ontology of blood?Part Three

134

135

We cannot solve this problem just by looking at concepts (by engaging in further acts of

knowledge mining)

136

concept systems may be simply incommensurable

137

the problem can only be solved

by taking the world itself into account

138

A Reference Ontology

is a theory of realityIt should do for mutually incompatible

terminology systems what the world itself does for the different cognitive and sensori-motor systems in our bodies

It should be the real-world surrogate for information systems

139

A Reference Ontology

is a theory of reality

But how is this possible?

How can we get beyond our concepts?

How can we produce a theory of reality itself?

Answer: ontology must be maximally opportunistic

140

What is a gene?GDB: a gene is a DNA fragment that can

be transcribed and translated into a protein


141

GO was built by and for biologists

biologists know what they are talking about, even when they use different underlying definitions of terms like ‘gene’

-- because they have the world there before them

142

Competing definitions of standard biological terms

represent slightly different parsings of one and the same reality

-- ‘the lung’ = the lung walls / the lung walls plus cavities …

cf. archeology – two senses of ‘strata’

143

“Maximally opportunistic”

means:

drawing on 2 millennia of philosophical research

pertaining to realism, scepticism, error, theory change, and the language/concept/world relation

but also pertaining to the structure of reality itself and to the relations between different scientific disciplines

144

Maximally opportunistic

means:

don’t just look at beliefs

look at the objects themselves

from every possible direction,

formal and informal

scientific and non-scientific …

145

It means further:

looking at concepts and beliefs in the context of a wider view which includes independent ways to access the objects at issue

above all including physical ways (involving the use of physical measuring instruments)

146

And also:

taking account of tacit knowledge of those features of reality of which the domain experts are not consciously aware

look not at the concepts or representations of a passive observer

but rather at agents, at organisms acting in the world

147

Medical students

often describe medical education as akin to learning a telephone directory by heart

they are able to unify and make sense of their knowledge only when they go out into the world and have physical contact with actual patients

148


means:

look not at what the expert says

but at what the expert does

Experts have expertise = knowing how

Ontologists skilled in extracting knowledge that from knowing how

The experts don’t know what the ontologist knows

149


means:look at the same objects at different levels of granularity:

150

We then recognize

that the same object can be apprehended at different levels of granularity:

at the perceptual level blood is a liquid

at the cellular level blood is a tissue

151

Once again

it is the world itself which unifies these two perspectives together

152

Conclusion

153

Shimon Edelman’s Riddle of Representation

two humans, a monkey, and a robot

are looking at a piece of cheese;

what is common

to the representational processes

in their visual systems?

154

Answer:

The cheese, of course

155

156

select out the good conceptualizations

those which have a reasonable chance of being integrated together into a single ontological system because they are

• based on tested principles• robust• conform to natural science

157

Partitions should be cuts through reality

a good medical ontology should NOT be compatible with a conceptualization of disease as caused by evil spirits

158

A piece of old philosophy

Molyneux’s problem:

Suppose you have been blind all your life, then had an operation and could suddenly see.

Could you identify a cube and a ball just by looking at them, or would you have to feel them as you'd always done?

159

This too is a problem of integration

How do we integrate our visual and tactile cognitive systems

Orthodox answer, Jackendoff: There are interfaces between the different modules within the cognitive apparatus

(operating in real time …)

160

The empirical data does not help to resolve this problem

A congenitally blind person who has regained sight can distinguish objects previously learned by touch alone: he can perceive the difference between a sphere and a cube. But it is not clear that he can recognize or name objects: some can and some cannot. Variations in patient's history and intelligence, and in experimental conditions, confound the results

Documents

Insects, Robots