Dictionary of Arguments


Philosophical and Scientific Issues in Dispute
 
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The author or concept searched is found in the following 5 entries.
Disputed term/author/ism Author
Entry
Reference
Exterior/interior Stroud I 206
General/Special/skepticism/verificationism/generalization/interior/exterior/Stroud: Descartes with him the special is representative and can therefore be generalized. - VerificationismVsGeneralization: it considers it suspicious: not apply statements of the system to the system itself. - StroudVsCarnap: the problem interior/exterior is not the same as that of the general and special. - StroudVsCarnap: the sentence that Descartes does not know whether he is sitting by the fire is not meaningless, only in connection to the skeptical presumption that it is not verifiable. - Problem: the verificationism could come easily in the situation to have to assume that then all of our everyday language would be useless. ---
I 211
Naturalized epistemology/QuineVsCarnap/Stroud: denies the need for an external position - so that the interior/exterior-problem is avoided. ---
I 214
QuineVsKant: no a priori "knowledge".

Stroud I
B. Stroud
The Significance of philosophical scepticism Oxford 1984

Generality Stroud I 206
General/Special/skepticism/verificationism/generalization/interior/exterior/Stroud: Descartes with him the special is representative and can therefore be generalized. - VerificationismVsGeneralization: it considers it suspicious: not apply statements of the system to the system itself. - StroudVsCarnap: the problem interior/exterior is not the same as that of the general and special. - StroudVsCarnap: the sentence that Descartes does not know whether he is sitting by the fire is not meaningless, only in connection to the skeptical presumption that it is not verifiable. - Problem: the verificationism could came easily in the situation to have to assume that all of our everyday language would be useless. ---
I 264
Public/knowledge/Stroud: there are indeed general statements about knowledge: e.g. that someone knows something about Sicily of the 4th century.. - E.g. that no one knows the causes of cancer. - VsMoore: that he does not achieve a general statement about knowledge, but is not due to a lack of generality.

Stroud I
B. Stroud
The Significance of philosophical scepticism Oxford 1984

Genetic Algorithms Norvig Norvig I 126
Genetic Algorithms/Norvig/Russell: A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states rather than by modifying a single state. The analogy to natural selection is the same as in stochastic beam search, except that now we are dealing with sexual rather than asexual reproduction.
Norvig I 127
Like beam searches, GAs begin with a set of k randomly generated states, called the population. Each state, or individual, is represented as a string over a finite alphabet – most commonly, a string of 0s and 1s.
Norvig I 128
Like stochastic beam search, genetic algorithms combine an uphill tendency with random exploration and exchange of information among parallel search threads. The primary advantage, if any, of genetic algorithms comes from the crossover operation. Yet it can be shown mathematically that, if the positions of the genetic code are permuted initially in a random order, crossover conveys no advantage.
Norvig I 155
In the 1950s, several statisticians, including Box (1957)(1) and Friedman (1959)(2), used evolutionary techniques for optimization problems, but it wasn’t until Rechenberg (1965)(3) introduced evolution strategies to solve optimization problems for airfoils that the approach gained popularity. In the 1960s and 1970s, John Holland (1975)(4) championed genetic algorithms, both as a useful tool and as a method to expand our understanding of adaptation, biological or otherwise (Holland, 1995)(5). The artificial life movement (Langton, 1995)(6) takes this idea one step further, viewing the products of genetic algorithms as organisms rather than solutions to problems. VsGenetic algorithms: Most comparisons of genetic algorithms to other approaches (especially stochastic hill climbing) have found that the genetic algorithms are slower to converge (O’Reilly and Oppacher, 1994(7); Mitchell et al., 1996(8); Juels and Wattenberg, 1996(9); Baluja, 1997)(10).
VsVs: Such findings are not universally popular within the GA community, but recent attempts within that community to understand population-based search as an approximate form of Bayesian learning might help close the gap between the field and its critics (Pelikan et al., 1999)(11). >Genetic Programming/Norvig.


1. Box, G. E. P. (1957). Evolutionary operation: A method of increasing industrial productivity. Applied
Statistics, 6, 81–101.
2. Friedman, G. J. (1959). Digital simulation of an evolutionary process. General Systems Yearbook, 4, 171–184.
3. Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Library translation 1122, Royal Aircraft Establishment
4. Holland, J. H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press.
5. Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. Addison-Wesley.
6. Langton, C. (Ed.). (1995). Artificial Life. MIT Press.
7. O’Reilly, U.-M. and Oppacher, F. (1994). Program search with a hierarchical variable length representation: Genetic programming, simulated annealing and hill climbing. In Proc. Third Conference on Parallel Problem Solving from Nature, pp. 397–406
8. Mitchell, M., Holland, J. H., and Forrest, S. (1996). When will a genetic algorithm outperform hill climbing? In Cowan, J., Tesauro, G., and Alspector, J. (Eds.), NIPS 6. MIT Press.
9. Juels, A. and Wattenberg, M. (1996). Stochastic hill climbing as a baseline method for evaluating genetic algorithms. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E. (Eds.), NIPS 8, pp. 430–6.
MIT Press.
10. Baluja, S. (1997). Genetic algorithms and explicit search statistics. In Mozer, M. C., Jordan, M. I., and Petsche, T. (Eds.), NIPS 9, pp. 319–325. MIT Press 11. Pelikan, M., Goldberg, D. E., and Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm.
In GECCO-99: Proc. Genetic and Evolutionary Computation Conference, pp. 525–532.

Norvig I
Peter Norvig
Stuart J. Russell
Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010

Genetic Programming Norvig Norvig I 155
Genetic Programming/Russell/Norvig: The field of genetic programming is closely related to genetic algorithms. The principal difference is that the representations that are mutated and combined are programs rather
Norvig I 156
than bit strings. The programs are represented in the form of expression trees; the expressions can be in a standard language such as Lisp or can be specially designed to represent circuits, robot controllers, and so on. Crossover involves splicing together subtrees rather than substrings. This form of mutation guarantees that the offspring are well-formed expressions, which would not be the case if programs were manipulated as strings. Interest in genetic programming was spurred by John Koza’s work (Koza, 1992(1), 1994(2)), but it goes back at least to early experiments with machine code by Friedberg (1958)(3) and with finite-state automata by Fogel et al. (1966)(4).
VsGenetic Programming: As with genetic algorithms, there is debate about the effectiveness of the technique. Koza et al. (1999)(5) describe experiments in the use of genetic programming to design circuit devices. Good overview texts on genetic algorithms are given by Mitchell (1996)(6), Fogel (2000)(7), and Langdon and Poli (2002)(8), and by the free online book by Poli et al. (2008)(9).



1. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press
2. Koza, J. R. (1994). Genetic Programming II: Automatic discovery of reusable programs. MIT Press.
3. Friedberg, R. M. (1958). A learning machine: Part I. IBM Journal of Research and Development, 2, 2–13.
4. Fogel, L. J., Owens, A. J., and Walsh, M. J. (1966). Artificial Intelligence through Simulated Evolution.
Wiley.
5. Koza, J. R., Bennett, F. H., Andre, D., and Keane, M. A. (1999). Genetic Programming III: Darwinian invention and problem solving. Morgan Kaufmann
6. Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press.
7. Fogel, D. B. (2000). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence.
IEEE Press.
8. Langdon, W. and Poli, R. (2002). Foundations of Genetic Programming. Springer 9. Poli, R., Langdon, W., and McPhee, N. (2008). A Field Guide to Genetic Programming. Lulu.com.

Norvig I
Peter Norvig
Stuart J. Russell
Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010

Semantics Black II 166
"General Semantics" / nominalism / Alfred Korzybski / Black: educational movement U.S. after World War 1 - VsLogic - distortions call on diseases - "cow" should be replaced by "Bessie" - Vsgeneral terms - VsAbstraction

Black I
Max Black
"Meaning and Intention: An Examination of Grice’s Views", New Literary History 4, (1972-1973), pp. 257-279
In
Handlung, Kommunikation, Bedeutung, G. Meggle (Hg) Frankfurt/M 1979

Black II
M. Black
The Labyrinth of Language, New York/London 1978
German Edition:
Sprache. Eine Einführung in die Linguistik München 1973

Black III
M. Black
The Prevalence of Humbug Ithaca/London 1983

Black IV
Max Black
"The Semantic Definition of Truth", Analysis 8 (1948) pp. 49-63
In
Truth and Meaning, Paul Horwich Aldershot 1994


The author or concept searched is found in the following controversies.
Disputed term/author/ism Author Vs Author
Entry
Reference
Descartes, R. Carnap Vs Descartes, R. VI 226
Ego/Carnap: is a class of elementary experiences. No bundle, because classes do not consist of their elements! CarnapVsDescartes: the existence of the ego is not a primordial fact of the given. From "cogito" does not follow "sum". Carnap: the ego does not belong to expression of the fundamental experience. But the "this experience". Thinking/RussellVsDescartes: "it thinks". (> Lichtenberg). ("Mind", p.18).
Stroud I 196
KantVsDescartes/CarnapVsDescartes. Frame/Reference system/Carnap/Stroud: for Carnap there is no point of view from which one can judge a frame as adequate or inadequate. That would be an "external" question.
Kant/Stroud: Kant's parallel to this is transcendental idealism: if things were independent of us, skepticism would be inevitable.
Problem: the transcendental idealism should not be crossed with the verification principle. Is Carnap's own positive theory better off here? That is a question of its status. It pursues the same goal as Kant: to explain the conditions of the possibility of knowledge, but without going beyond the limits of comprehensibility.
General/special/internal/external/generalization/Stroud: it would be necessary to explain how the general sceptical conclusion can be meaningless, even if the particular everyday empirical assertions are meaningful. This cannot simply be because one is general and the other particular.
Descartes/Stroud: the particular is representative in its argument and can therefore be generalized. The uncertainty in the individual case is representative of all our knowledge. This is the strength of the argument.
VerificationismVsGeneralization: he considers this generalization suspicious.
CarnapVsSkepticism/CarnapVsDescartes: statements that make sense within a reference system cannot be applied to the reference system itself.
Stroud: but this is the problem inside/outside and not a question of generality or special.
StroudVsCarnap: so he has to show that movement from the inside out is impossible and not the generalization. But he needed an explanation why the traditional view of the relation between "internal" and "external" questions is wrong if he wants to avoid skepticism. ((s) Why Question).
Special/VerificationismVsDescartes: Thesis: the single sentence of Descartes is meaningless from the beginning. (Because unverifiable). (StroudVsVs).
I 207
StroudVsVerificationism: he must now show why this verdict does not apply to all individual (special) sentences of everyday life. Verificationism would otherwise have to assume that our whole language (everyday language) is meaningless! (Because it is not verifiable according to skeptical criteria). For example "I don't know if explanation is caused by sitting in a draught" or "The aircraft spotter doesn't know if the aircraft is an F" would be damned as senseless! If verificationism condemns certain sentences as meaningless only if they are uttered, for example, by Descartes or another skeptic, he would have to show that there is a deviant use on such occasions. Otherwise he could not even indicate what VsDescartes is supposed to have gone wrong with his utterance. ((s) utterance here = action, not sentence, which should be meaningless, neither true nor false).

Ca I
R. Carnap
Die alte und die neue Logik
In
Wahrheitstheorien, G. Skirbekk (Hg) Frankfurt 1996

Ca II
R. Carnap
Philosophie als logische Syntax
In
Philosophie im 20.Jahrhundert, Bd II, A. Hügli/P.Lübcke (Hg) Reinbek 1993

Ca IV
R. Carnap
Mein Weg in die Philosophie Stuttgart 1992

Ca IX
Rudolf Carnap
Wahrheit und Bewährung. Actes du Congrès International de Philosophie Scientifique fasc. 4, Induction et Probabilité, Paris, 1936
In
Wahrheitstheorien, Gunnar Skirbekk Frankfurt/M. 1977

Ca VI
R. Carnap
Der Logische Aufbau der Welt Hamburg 1998

CA VII = PiS
R. Carnap
Sinn und Synonymität in natürlichen Sprachen
In
Zur Philosophie der idealen Sprache, J. Sinnreich (Hg) München 1982

Ca VIII (= PiS)
R. Carnap
Über einige Begriffe der Pragmatik
In
Zur Philosophie der idealen Sprache, J. Sinnreich (Hg) München 1982

Stroud I
B. Stroud
The Significance of philosophical scepticism Oxford 1984