Naïve Physics

1991 ◽  
pp. 507-553 ◽  
Author(s):  
François E. Cellier
Keyword(s):  
Naive Physics

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

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
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.

scholarly journals False beliefs and naive beliefs: They can be good for you

2009 ◽  
Vol 32 (6) ◽  
pp. 512-513
Author(s):  
Marco Bertamini ◽  
Roberto Casati
Keyword(s):  
False Beliefs ◽  
Test Bed ◽  
Naive Physics ◽  
Good For

AbstractNaive physics beliefs can be systematically mistaken. They provide a useful test-bed because they are common, and also because their existence must rely on some adaptive advantage, within a given context. In the second part of the commentary we also ask questions about when a whole family of misbeliefs should be considered together as a single phenomenon.

The Social World

2020 ◽  
pp. 62-69
Author(s):  
Iris Berent
Keyword(s):  
Newborn Infants ◽  
Social World ◽  
Intuitive Knowledge ◽  
Young Infants ◽  
The Social ◽  
Naive Physics ◽  
Human Faces

This chapter provides a very brief overview of the social capacities of young infants. Specifically, we the author ask whether infants instinctively know that (human) agents are distinct from objects. new see that newborn infants spontaneously imitate agents; they follow their gaze, and they selectively respond to human faces, but not objects. A few months later, infants also seem to know that (unlike objects), the behavior of agents is driven by their beliefs and goals, and they spontaneously prefer “good” agents (those that help others) to “bad” ones (those that hinder others from attaining their goals). These results suggest that young infants possess intuitive knowledge of psychology, distinct from their naïve physics.

2. Learning about the outside world

2014 ◽  
Author(s):  
Usha Goswami
Keyword(s):  
External World ◽  
Natural World ◽  
Early Age ◽  
Memory And Attention ◽  
Types Of Knowledge ◽  
Nature Versus Nurture ◽  
Naive Biology ◽  
The World ◽  
Naive Physics ◽  
Attention Function

‘Learning about the outside world’ looks at the nature versus nurture debate and asks: how do infants and toddlers learn about their world? Babies learn a vast amount from observation. Research shows that the observations that babies make are organized mentally into certain types of knowledge. ‘Naive psychology’ is about how they learn how to behave. Observing objects teachers babies how the external world works. This is ‘naive physics’. ‘Naive biology’ refers to how they learn about the natural world. Studies have shown that although babies appear to be fairly unaware of the world around them, both memory and attention function from an early age.

Naive Physics Reasoning: A Commitment to Substance-Based Conceptions

2000 ◽  
Vol 18 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Miriam Reiner ◽  
James D. Slotta ◽  
Michelene T. H. Chi ◽  
Lauren B. Resnick
Keyword(s):  
Naive Physics

Event Structure without naïve Physics

2019 ◽  
pp. 170-204
Author(s):  
Henk J. Verkuyl
Keyword(s):  
Building Block ◽  
Event Structure ◽  
Cognitive Capacity ◽  
The Real ◽  
Real Nature ◽  
Number Systems ◽  
Naive Physics

What is the real nature of the aspectual division between perfective and imperfective as revealed by the well-known in/for-test? The answer is founded on the idea that this division between completion and incompletion mirrors our cognitive capacity to shift between discreteness and continuity as expressed in the number systems N and R. To get at the real contribution of a verb to aspectual information, the first step is to determine the basic atemporal building block making a tenseless verb stative or non-stative. For this, verbhood is to be understood aspectually in a very strict way abstracting from the contribution of arguments. It follows that one has to get ‘below’ event structure in order to see why the in/for-test works as it turns out to do (or in some cases not).

Commonsense Knowledge Representation II

2011 ◽  
pp. 334-336
Author(s):  
Phillip Ein-Dor
Keyword(s):  
Knowledge Representation ◽  
Physical World ◽  
Knowledge Bases ◽  
Real Problem ◽  
Commonsense Knowledge ◽  
The Everyday ◽  
Naive Physics ◽  
Artificial World ◽  
Representation Formalism ◽  
Order Predicate Calculus

Early attempts to implement systems that understand commonsense knowledge did so for very restricted domains. For example, the Planes system [Waltz, 1978] knew real world facts about a fleet of airplanes and could answer questions about them put to it in English. It had, however, no behaviors, could not interpret the facts, draw inferences from them or solve problems, other than those that have to do with understanding the questions. At the other extreme, SHRDLU (Winograd, 1973) understood situations in its domain of discourse (which it perceived visually), accepted commands in natural language to perform behaviors in that domain and solved problems arising in execution of the commands; all these capabilities were restricted, however, to SHRDLU’s artificial world of colored toy blocks. Thus, in implemented systems it appears that there may be a trade off between the degree of realism of the domain and the number of capabilities that can be implemented. In the frames versus logic debate (see Commonsense Knowledge Representation I - Formalisms in this Encyclopedia), the real problem, in Israel’s (1983) opinion, is not the representation formalism itself, but rather that the facts of the commonsense world have not been formulated, and this is more critical than choice of a particular formalism. A notable attempt to formulate the “facts of the commonsense world” is that of Hayes [1978a, 1978b, 1979] under the heading of naïve physics. This work employs first-order predicate calculus to represent commonsense knowledge of the everyday physical world. The author of this survey has undertaken a similar effort with respect to commonsense business knowledge (Ein-Dor and Ginzberg 1989). Some broader attempts to formulate commonsense knowledge bases are cited in the section Commonsense Knowledge Bases.

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