Van Deemter, WORD, May 2010 Not Exactly Vagueness as Original Sin? Kees van Deemter University of Aberdeen.

Slides:



Advertisements
Similar presentations
A Semantic Model for Vague Quantifiers Combining Fuzzy Theory and Supervaluation Theory Ka Fat CHOW The Hong Kong Polytechnic University The title of.
Advertisements

(Fuzzy Set Operations)
Probability.
Basic Terms in Logic Michael Jhon M. Tamayao.
© Negnevitsky, Pearson Education, Lecture 4 Fuzzy expert systems: Fuzzy logic Introduction, or what is fuzzy thinking? Introduction, or what is.
Generation of Referring Expressions: Managing Structural Ambiguities I.H. KhanG. Ritchie K. van Deemter University of Aberdeen, UK.
Kees van Deemter (AC, Dec 09) Vagueness Facilitates Search Kees van Deemter Computing Science University of Aberdeen.
Van Deemter, Riga, Jan Not Exactly Vagueness as Original Sin? Kees van Deemter University of Aberdeen Scotland.
Kees van Deemter. For Institut Nicod, Jan 2009 Vagueness as Original Sin from measurement to semantic theory Kees van Deemter University of Aberdeen Scotland,
EMBL Forum, Dec 2010 Not Exactly Vagueness as Original Sin? Kees van Deemter University of Aberdeen.
CS CS1512 Foundations of Computing Science 2 Lecture 23 Probability and statistics (4) © J.
Kees van Deemter (SSE, Jan '10) Vagueness Facilitates Search Kees van Deemter Computing Science University of Aberdeen.
CS1512 Foundations of Computing Science 2 Week 3 (CSD week 32) Probability © J R W Hunter, 2006, K van Deemter 2007.
Vagueness: a problem for AI Kees van Deemter University of Aberdeen Scotland, UK.
Kees van Deemter, Dublin, Trinity College, May 2009 What utility can do for NLG: the case of vague language Kees van Deemter University of Aberdeen Scotland,
HIT Summer School 2008, K.v.Deemter Vagueness: a problem for AI Kees van Deemter University of Aberdeen Scotland, UK.
Fuzzy Logic 11/6/2001. Agenda General Definition Applications Formal Definitions Operations Rules Fuzzy Air Conditioner Controller Structure.
Modelling uncertainty in 3APL Johan Kwisthout Master Thesis
A set is a collection of objects A special kind of set Fuzzy Sets
Chapter 11: Conclusion What does it all mean? © 2014 Cynthia Weber.
What is Logic? Forget everything you think you know about logic. Forget everything everything you have ever read about logic. Logic is not the same as.
Symbolic Logic Lesson CS1313 Spring Symbolic Logic Outline 1.Symbolic Logic Outline 2.What is Logic? 3.How Do We Use Logic? 4.Logical Inferences.
Fuzzy Sets and Fuzzy Logic
Fuzzy Expert System  An expert might say, “ Though the power transformer is slightly overloaded, I can keep this load for a while”.  Another expert.
Lecture 4 Fuzzy expert systems: Fuzzy logic
Soft Computing. Per Printz Madsen Section of Automation and Control
CLASSICAL LOGIC and FUZZY LOGIC. CLASSICAL LOGIC In classical logic, a simple proposition P is a linguistic, or declarative, statement contained within.
THERE IS NO GENERAL METHOD OR FORMULA WHICH IS ‘CORRECT’. YOU CAN PROBABLY IGNORE SOME OF THIS ADVICE AND STILL WRITE A GOOD ESSAY… BUT FOLLOWING IT MAY.
Fuzzy Expert System Fuzzy Logic
AI TECHNIQUES Fuzzy Logic (Fuzzy System). Fuzzy Logic : An Idea.
Fuzzy Expert Systems. Lecture Outline What is fuzzy thinking? What is fuzzy thinking? Fuzzy sets Fuzzy sets Linguistic variables and hedges Linguistic.
Me Talk Good One Day When Language and Logic Fail to Coincide.
Fuzzy Logic and Sun Tracking Systems Ryan Johnson December 9, 2002 Calvin College ENGR315A.
1 Chapter 18 Fuzzy Reasoning. 2 Chapter 18 Contents (1) l Bivalent and Multivalent Logics l Linguistic Variables l Fuzzy Sets l Membership Functions l.
WELCOME TO THE WORLD OF FUZZY SYSTEMS. DEFINITION Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept.
Algebra Problems… Solutions
Fuzzy Logic BY: ASHLEY REYNOLDS. Where Fuzzy Logic Falls in the Field of Mathematics  Mathematics  Mathematical Logic and Foundations  Fuzzy Logic.
Fuzzy Logic Jan Jantzen Logic is based on set theory, and when we switch to fuzzy sets it will have an effect on.
FUZZY LOGIC Babu Appat. OVERVIEW What is Fuzzy Logic? Where did it begin? Fuzzy Logic vs. Neural Networks Fuzzy Logic in Control Systems Fuzzy Logic in.
COMP14112: Artificial Intelligence Fundamentals L ecture 3 - Foundations of Probabilistic Reasoning Lecturer: Xiao-Jun Zeng
9/3/2015Intelligent Systems and Soft Computing1 Lecture 4 Fuzzy expert systems: Fuzzy logic Introduction, or what is fuzzy thinking? Introduction, or what.
Lecture for Week Spring.   Introduction to Propositional Logic  Types of Proposition  Operator and Truth table Agenda.
Fuzzy Logic. Lecture Outline Fuzzy Systems Fuzzy Sets Membership Functions Fuzzy Operators Fuzzy Set Characteristics Fuzziness and Probability.
Fuzzy Logic. WHAT IS FUZZY LOGIC? Definition of fuzzy Fuzzy – “not clear, distinct, or precise; blurred” Definition of fuzzy logic A form of knowledge.
Mark shelton | merrick cloete saman majrouh | sahithi jadav.
MIDTERM EXAMINATION THE MIDTERM EXAMINATION WILL BE ON FRIDAY, MAY 2, IN THIS CLASSROOM, STARTING AT 1:00 P.M. BRING A BLUE BOOK. THE EXAM WILL COVER:
 Definition Definition  Bit of History Bit of History  Why Fuzzy Logic? Why Fuzzy Logic?  Applications Applications  Fuzzy Logic Operators Fuzzy.
1 Asst. Prof. Dr. Sukanya Pongsuparb Dr. Srisupa Palakvangsa Na Ayudhya Dr. Benjarath Pupacdi SCCS451 Artificial Intelligence Week 9.
The Reality of Logic David Davenport Computer Eng. Dept., Bilkent University, Ankara Turkey.
Logical Systems and Knowledge Representation Fuzzy Logical Systems 1.
HERE WE ARE IN CONTEMPORARY MATH. WHERE ARE WE GOING TO USE THIS ANYWAY?
Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.
Fuzzy Logic 1. Introduction Form of multivalued logic Deals reasoning that is approximate rather than precise The fuzzy logic variables may have a membership.
Lecture 4 Fuzzy expert systems: Fuzzy logic n Introduction, or what is fuzzy thinking? n Fuzzy sets n Linguistic variables and hedges n Operations of fuzzy.
Introduction to Fuzzy Logic and Fuzzy Systems
Artificial Intelligence CIS 342
Computational Intelligence
Paradoxes 2nd Term 2017 Dr. Michael Johnson
Fuzzy Logic and Fuzzy Sets
Fuzzy Control Tutorial
CLASSICAL LOGIC and FUZZY LOGIC
FUZZIFICATION AND DEFUZZIFICATION
Computational Intelligence
A-level Computer Science
1. A VAGUE CONCEPT: BALDNESS
Not Exactly Vagueness as Original Sin?
© Negnevitsky, Pearson Education, Lecture 4 Fuzzy expert systems: Fuzzy logic Introduction, or what is fuzzy thinking? Introduction, or what is.
Starting out with formal logic
Habib Ullah qamar Mscs(se)
Presentation transcript:

van Deemter, WORD, May 2010 Not Exactly Vagueness as Original Sin? Kees van Deemter University of Aberdeen

van Deemter, WORD, May 2010 Plan of the talk 1. Vagueness is hard to avoid 2. We are often vague for good reasons 3. Vagueness is a problem 4. How to model vagueness formally?

van Deemter, WORD, May Vagueness is hard to avoid Vague words have borderline cases An Aberdeen afternoon in May at 3PM 22 C warm 8 C not warm 15 C ¿warm?

van Deemter, WORD, May 2010 Vague adjectives: warm, cold, large,... Vague nouns: girl, giant, island,... and so on … Most words in ordinary English are vague Vagueness is prevalent in science too Example: species terms

van Deemter, WORD, May 2010 What makes a species? Long thought unproblematic (e.g. Linnaeus 1750) The interbreeding criterion (Mayr, Dobzhansky, 1940) x is same species as y x i nterbreeds with y

van Deemter, WORD, May 2010 Ensatina (Stebbins 1949, Dawkins 2004)

van Deemter, WORD, May 2010 Ensatinas habitat and interbreeding Called a ring species. Logically: eschscholtzii i x i p i o i c i klauberi c o p x eschscholtzii klauberi CENTRAL VALLEY

van Deemter, WORD, May 2010 escholtzii i x i p i o i c i klauberi For example, not i(eschscholtzii,klauberi) Interbreeding predicts overlapping species: {esch,x} {x,p} {p,o} {o,c} {c,klau}

van Deemter, WORD, May 2010 Our own ancestry you stand in relation i with your parents, grandparents,... Let a = the first ancestor such that not i(a,you) Do you and a belong to same species?

van Deemter, WORD, May 2010 Are you and a the same species? Formal Response: No; the interbreeding criterion should be used Many overlapping species s.. s6 s5 s4 s3 s2 s1 time

van Deemter, WORD, May 2010 Are you and a the same species? Formal Response: No; the interbreeding criterion should be used Many overlapping species Standard Response: Yes; species should be defined via the transitive closure of i

van Deemter, WORD, May 2010 Are you and a the same species? Formal Response: No; the interbreeding criterion should be used Many overlapping species Standard Response: Yes; species should be defined via the transitive closure of i All living beings are one species

van Deemter, WORD, May 2010 Interim conclusion Key concepts of science resist precise definition

van Deemter, WORD, May 2010 Vagueness as original sin? (with thanks to Tintoretto)

van Deemter, WORD, May We are often vague for good reasons Strategic vagueness Why are we often more vague than we need to be? (Game theorists, e.g., B. Lipman 2000, 2006) Some tentative answers:

van Deemter, WORD, May 2010 First answer Suppose I say about a day in May: The temperature is 15 C. The rain probability is 20%. Wind speed is 10mph, humidity 55% Easier to digest: A nice-enough Spring day, with light winds and a chance of rain

van Deemter, WORD, May 2010

The numbers use an old-fashioned scale (inches of Mercury) Words like Very Dry and Much Rain help us to understand the scale These words are vague: Does 22.8 count as Rain or Much Rain ?

van Deemter, WORD, May 2010 Aberdeen Computing dept. build programs Input: numbers or formulas (15 C, …) Output: words (Mild, … A nice Spring day) Medical applications too (e.g. BABYTALK) Open questions: Whats best understood? Remembered? Acted on? (Peters et al. 2009, Zikmund-Fisher et al 2007)

van Deemter, WORD, May 2010 Second answer 11m 12m

van Deemter, WORD, May 2010 Height of house 1 =11m Height of house 2 =12m - the 12m house needs to be demolished - the tall house needs to be demolished Comparison is easier than measurement Therefore, we might prefer the tall house

van Deemter, WORD, May 2010 Third answer A politician promising drastic budget cuts, or stable government Game-theory models predict benefits from vague promises (Aragones & Neeman 2000) Unforeseen contingencies could make concrete promises difficult to honour Disappointed voters could hold politician to account

van Deemter, WORD, May Vagueness is a problem Sorites puzzle (Eubulides, 450 BC) One of the top ten unsolved problems of science (The list universe, 2007 AD) 0 hairs is bold (x hairs is bold) (x+1 hairs is bold) therefore, 10 6 hairs is bold Yet 10 6 hairs is not bold

van Deemter, WORD, May 2010

Sorites enhanced by science Eubulides in the audio lab Decibel (dB): measures the loudness of sounds -30dB is inaudible 100dB is very loud differences of 0.5dB cannot be discerned

van Deemter, WORD, May 2010 Eubulides in the audio lab -30dB is inaudible -30dB is indistinguishable from -29.5dB, so -29.5dB is inaudible

van Deemter, WORD, May 2010 Eubulides in the audio lab -29.5dB is inaudible -29.5dB is indistinguishable from -29dB, so -29dB is inaudible

van Deemter, WORD, May 2010 Eubulides in the audio lab dB is inaudible 99.5dB is indistinguishable from 100dB, so 100dB is inaudible !!

van Deemter, WORD, May 2010 The new sorites argument as a whole -30dB is inaudible -30dB is indistinguishable from -29.5dB, so -29.5dB is inaudible -29.5dB is indistinguishable from -29dB, so -29dB is inaudible dB is inaudible 99.5dB is indistinguishable from 100dB, so 100dB is inaudible !!

van Deemter, WORD, May 2010 Vagueness as ignorance The concept bald does have sharp boundaries, but speakers do not know them A surprisingly popular view (Williamson 1994, Sorensen 2001, Tuck 2009) Contradicted by empirical evidence

van Deemter, WORD, May 2010 Were all different Colour terms like red (Hilbert 1987, R.Parikh 2000) People cannot distinguish the same colours pigment on lens and retina; sensitivity of photo receptors Time words like evening (Reiter et al. 2005) Is dinner time relevant? The time of year?

van Deemter, WORD, May 2010 So, … Vagueness is not just a matter of ignorance Models of logic and language ought to embrace vagueness

van Deemter, WORD, May 2010 For analysing the meaning of language, mathematical logic is the tool of choice Classical logic is built on crisp dichotomies George Boole ( ) gave the first mathematical account A statement is either true or false (1 or 0) Nice and simple: Booles paradise

van Deemter, WORD, May 2010 Window in Lincoln Cathedral

van Deemter, WORD, May 2010 audible in classical logic audible inaudible x dB

van Deemter, WORD, May 2010 audible in classical logic x dB audible inaudible Indistinguishable x+ x-

van Deemter, WORD, May 2010 Semi-classical logics use dichotomies too Context-aware logics (Kamp 1981) use Just-Noticeable Difference E.g., loudness: JND 1dB JNDs mistakenly modelled as crisp Crispness contradicted by empirical evidence Subtler models are needed

van Deemter, WORD, May 2010 We have seen: 1. Vagueness is everywhere 2. We are vague for a reason 3. Vagueness is a problem

van Deemter, WORD, May How to model vagueness?

van Deemter, WORD, May 2010 Some like it crisp Blastland & Dilnot (2008): false clarity Substances that are poisonous Genes that cause a condition Another example: Vagueness as ignorance

van Deemter, WORD, May 2010 Two cultures (compare C.P. Snow) Engineers & psychophysicists: approximations, real numbers, Gaussian distributions, Philosophers, linguists, and most logicians: crisp dichotomies (true/false, 1/0). They inhabit Booles Paradise!

van Deemter, WORD, May 2010 Continuous logics Date back to J.Łukasiewicz 1920 and M.Black 1937 Map statements to numbers between 0 and 1

van Deemter, WORD, May 2010 Fuzzy logic (L.Zadeh 1975) [φ] = degree of truth of φ [1000 hairs is bald] < [100 hairs bald] Negation: [not φ] = 1- [φ] Disjunction: [φ or ] = max([φ],[ ]) Conjunction: [φ & ] = min([φ],[ ])

van Deemter, WORD, May 2010 sorites paradox in Fuzzy Logic As x increases, Bald(x) becomes less true: [Bald(0)] = 1 [Bald(10 3 )] 0.5 [Bald(10 6 )] 0 Each premiss Bald(x) Bald(x+1) is almost true

van Deemter, WORD, May 2010 Problems for Fuzzy Logic Is 1000 hairs bald or somewhat bald? [Bald(1000)] = 0.5 [SwBald(1000)] = 0.5 Consider Bald(1000) or SwBald(1000) Fuzzy Logic assigns a strangely low value:

van Deemter, WORD, May 2010 Problems for Fuzzy Logic Is 1000 hairs bald or somewhat bald? [Bald(1000)] = 0.5 [SwBald(1000)] = 0.5 Consider Bald(1000) or SwBald(1000) Fuzzy Logic assigns a strangely low value: [Bald(1000) or SwBald(1000)] = max(0.5, 0.5) = 0.5

van Deemter, WORD, May 2010 A better way (e.g., Edgington 1992,1996) [ ] = probability of someone agreeing with [ or ] = [ ] + [ ] - [ & ] [Bald(1000) or SwBald(1000)] = = 1

van Deemter, WORD, May 2010 Booles 2-valued paradise was such an attractive place

van Deemter, WORD, May 2010 When vagueness is taken seriously... Truthfulness becomes problematic We didnt know that smoking causes cancer Not exactly true Falsification & Belief Revision Are all ravens black? What about this grey-black one? Not exactly black

van Deemter, WORD, May 2010 Questions for linguists, logicians, philosophers, computer scientists Wed better rise to the challenge!

van Deemter, WORD, May 2010 Not Exactly: in Praise of Vagueness Oxford University Press, Jan Part 1: Vagueness in science and daily life Part 2: Theories of vagueness Part 3: Vagueness in Artificial Intelligence

van Deemter, WORD, May 2010 The End With thanks to Judith Masthoff (for Homer Simpsons coiffure)

van Deemter, WORD, May 2010

Dawkins on species terms Let us use names as if they really reflected a discontinuous reality, but let's privately remember that (...) it is no more than a convenient fiction, a pandering to our own limitations. Tyranny of the discontinuous mind. (Dawkins 2004, The Ancestors Tale)

van Deemter, WORD, May 2010 Why is the fiction of species convenient? Links between species have gone extinct When xan and oreg are extinct: esch i xan i pi i oreg i cro i klau Three separate species!