Entitlement, Epistemic Risk, and Scepticism

Episteme ◽

10.1017/epi.2019.27 ◽

2019 ◽

pp. 1-11

Author(s):

Luca Moretti

Keyword(s):

Probabilistic Reasoning ◽

Perceptual Justification ◽

Crispin Wright ◽

Epistemic Risk

AbstractCrispin Wright maintains that the architecture of perceptual justification is such that we can acquire justification for our perceptual beliefs only if we have antecedent justification for ruling out any sceptical alternative. Wright contends that this principle doesn't elicit scepticism, for we are non-evidentially entitled to accept the negation of any sceptical alternative. Sebastiano Moruzzi has challenged Wright's contention by arguing that since our non-evidential entitlements don't remove the epistemic risk of our perceptual beliefs, they don't actually enable us to acquire justification for these beliefs. In this paper I show that Wright's responses to Moruzzi are ineffective and that Moruzzi's argument is validated by probabilistic reasoning. I also suggest that Wright couldn't answer Moruzzi's challenge without weakening the support available for his conception of the architecture of perceptual justification.

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The Evidence of the Senses is no Evidence from the Senses

History of Philosophy and Logical Analysis ◽

10.30965/26664275-01601008 ◽

2013 ◽

Vol 16 (1) ◽

pp. 174-191

Author(s):

Tommaso Piazza

Keyword(s):

Logical Investigation ◽

Perceptual Justification ◽

The Senses ◽

Recent Interpretation

In the first part of this paper I suggest that Dogmatism about perceptual justification – the view that in the most basic cases, perceptual justification is immediate – commits to rejecting Evidentialism, as it commits, specifically, to accounting for the mechanics of perceptual justification otherwise than by maintaining that perceptual experiences justify by providing evidence. In the second part of the paper, by following W. Hopp’s recent interpretation of Husserl’s Sixth Logical Investigation, I suggest that Husserl’s theory of fulfilment provides the basis of the non-evidential account of the mechanics of perceptual justification needed to vindicate Dogmatism.

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LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

International Journal of Computational Intelligence and Applications ◽

10.1142/s146902680100024x ◽

2001 ◽

Vol 01 (03) ◽

pp. 273-285 ◽

Cited By ~ 1

Author(s):

PAUL A. BOXER

Keyword(s):

Bayesian Network ◽

Probabilistic Reasoning ◽

Autonomous Robots ◽

Visual Observation ◽

Spatial Representations ◽

Physical Behavior ◽

Naive Physics ◽

Action At A Distance ◽

General Physical ◽

Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

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Making design decisions under uncertainties: probabilistic reasoning and robust product design

Journal of Intelligent Information Systems ◽

10.1007/s10844-021-00665-6 ◽

2021 ◽

Author(s):

Paul Christoph Gembarski ◽

Stefan Plappert ◽

Roland Lachmayer

Keyword(s):

Product Development ◽

Product Life Cycle ◽

Probabilistic Reasoning ◽

Late Phase ◽

Development Project ◽

Product Development Process ◽

Bayesian Decision ◽

Rotary Valve ◽

Design Decisions ◽

Decision Network

AbstractMaking design decisions is characterized by a high degree of uncertainty, especially in the early phase of the product development process, when little information is known, while the decisions made have an impact on the entire product life cycle. Therefore, the goal of complexity management is to reduce uncertainty in order to minimize or avoid the need for design changes in a late phase of product development or in the use phase. With our approach we model the uncertainties with probabilistic reasoning in a Bayesian decision network explicitly, as the uncertainties are directly attached to parts of the design artifact′s model. By modeling the incomplete information expressed by unobserved variables in the Bayesian network in terms of probabilities, as well as the variation of product properties or parameters, a conclusion about the robustness of the product can be made. The application example of a rotary valve from engineering design shows that the decision network can support the engineer in decision-making under uncertainty. Furthermore, a contribution to knowledge formalization in the development project is made.

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