37,055 research outputs found
A Tale of Two Nortons
This paper considers Norton’s Material Theory of Induction. The material
theory aims inter alia to neutralize Hume’s Problem of Induction. The purpose of the
paper is to evaluate the material theorys capacity to achieve this end. After pulling apart
two versions of the theory, I argue that neither version satisfactorily neutralizes the problem
Observationally Indistinguishable Spacetimes: A Challenge for Any Inductivist
Results on the observational indistinguishability of spacetimes demonstrate the impossibility of determining by deductive inference which is our spacetime, no matter how extensive a portion of the spacetime is observed. These results do not illustrate an underdetermination of theory by evidence, since they make no decision between competing theories and they make little contact with the inductive considerations that must ground such a decision. Rather, these results express a variety of indeterminism in which a specification of the observable past always fails to fix the remainder of a spacetime. This form of indeterminism is more troubling than the familiar indeterminism of quantum theory. The inductive inferences that can discriminate among the different spacetime extensions of the observed past are here called "opaque," which means that we cannot readily see the warrant that lies behind them
There are no universal rules for induction
In a material theory of induction, inductive inferences are warranted by facts that prevail locally. This approach, it is urged, is preferable to formal theories of induction in which the good inductive inferences are delineated as those conforming to universal schemas. An inductive inference problem concerning indeterministic, nonprobabilistic systems in physics is posed, and it is argued that Bayesians cannot responsibly analyze it, thereby demonstrating that the probability calculus is not the universal logic of induction. Copyright 2010 by the Philosophy of Science Association.All right reserved
SkILL - a Stochastic Inductive Logic Learner
Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored
area of Statistical Relational Learning which extends classic Inductive Logic
Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic
Learner, which takes probabilistic annotated data and produces First Order
Logic theories. Data in several domains such as medicine and bioinformatics
have an inherent degree of uncer- tainty, that can be used to produce models
closer to reality. SkILL can not only use this type of probabilistic data to
extract non-trivial knowl- edge from databases, but it also addresses
efficiency issues by introducing a novel, efficient and effective search
strategy to guide the search in PILP environments. The capabilities of SkILL
are demonstrated in three dif- ferent datasets: (i) a synthetic toy example
used to validate the system, (ii) a probabilistic adaptation of a well-known
biological metabolism ap- plication, and (iii) a real world medical dataset in
the breast cancer domain. Results show that SkILL can perform as well as a
deterministic ILP learner, while also being able to incorporate probabilistic
knowledge that would otherwise not be considered
Induction without Probabilities
A simple indeterministic system is displayed and it is urged that we cannot responsibly infer inductively over it if we presume that the probability calculus is the appropriate logic of induction. The example illustrates the general thesis of a material theory of induction, that the logic appropriate to a particular domain is determined by the facts that prevail there
Representation results for defeasible logic
The importance of transformations and normal forms in logic programming, and
generally in computer science, is well documented. This paper investigates
transformations and normal forms in the context of Defeasible Logic, a simple
but efficient formalism for nonmonotonic reasoning based on rules and
priorities. The transformations described in this paper have two main benefits:
on one hand they can be used as a theoretical tool that leads to a deeper
understanding of the formalism, and on the other hand they have been used in
the development of an efficient implementation of defeasible logic.Comment: 30 pages, 1 figur
Metaphysical Explanation and the Inference to the Best Explanation (BA thesis)
Inference to the Best Explanation, roughly put, appeals to the explanatory power of a theory or hypothesis (relative to some data set) as constituting epistemic justification for it. Inference to the Best Explanation (henceforth IBE) is a tool widely employed among all reasoners alike, from the empirical sciences to ordinary life. Philosophical discussions do not differ in the usualness of explanatory appeals of this kind during serious argument. Often enough, the appeal is dialectically blocked, as many of our epistemic peers in philosophy offer reasons to be skeptical of IBE. Our aim with this monograph is to assess one worry that have been raised about this mode of inference: That explanatory power is not truth-conducive. We begin by discussing general features of inferences and then formulating IBE in detail. Afterward, we explicate and apply a canonical understanding of what an explanation is. This will lead to a certain understanding of explanatory power. We undergo a case study to defend the thesis that this kind of explanatory power is indeed epistemically irrelevant – unless, perhaps, when combined with other theoretical virtues. Our conclusion is that the measure what explanations are best requires taking other theoretical virtues into account, such as simplicity and unification. In this case, a complete assessment of IBE requires examining if, when, and how these alleged theoretical virtues are indeed truth-conducive
Inductive Knowledge
This paper formulates some paradoxes of inductive knowledge. Two responses in particular are explored: According to the first sort of theory, one is able to know in advance that certain observations will not be made unless a law exists. According to the other, this sort of knowledge is not available until after the observations have been made. Certain natural assumptions, such as the idea that the observations are just as informative as each other, the idea that they are independent, and that they increase your knowledge monotonically (among others) are given precise formulations. Some surprising consequences of these assumptions are drawn, and their ramifications for the two theories examined. Finally, a simple model of inductive knowledge is offered, and independently derived from other principles concerning the interaction of knowledge and counterfactuals
Recommended from our members
A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
- …