Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 34 for “"Causal Reasoning"”.
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Fault diagnosis based on causal reasoning
A "causal" expert system based on hypothetical reasoning and its application to a Mark 45 turret gun's lower hoist are described. HOIST is a system that performs fault diagnosis without the use of a domain expert or "shallow rules". Rather its "knowledge" is coded directly from a structural …
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The Conundrum of Causal Reasoning in Elephants
<p>Causal reasoning is marked by the ability to mentally reconstruct the missing part of a sequence in order to reproduce an outcome. While research on causal reasoning has been done with children, the results of the studies have been inconsistent. A standardized paradigm for comparative causal …
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Understanding children’s causal reasoning during collaborative discussions
… aims to understand the construction of multilink causal reasoning chains during collaborative discussions in elementary school classrooms. The construction of reasoning chains was investigated in 24 collaborative discussions involving 160 underserved fifth-grade children. The effects of group …
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Bridging crowd and machine intelligence to inspire causal reasoning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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A computational characterization of domain-based causal reasoning development in children
… the mechanisms that underlie the development of causal reasoning in children. I create a behavior-level model, an explanatory theory, and an explanation-level model that account for the developmental stages. I implement these models on top of the Genesis Story Understanding System. The result is …
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Seeing versus Doing: Causal Bayes Nets as Psychological Models of Causal Reasoning
Diese Dissertation geht der Frage nach, wie Menschen Vorhersagen über die Folgen von aktiven Interventionen in kausalen Systemen zu treffen, wenn sie diese Systeme zuvor nur passiv beobachtet haben. Die Theorie der kausalen Bayes-Netze (Spirtes, Glymour & Scheines, 1993; Pearl, 2000) stellt einen …
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Developing a learning progression for energy and causal reasoning in socio-ecological systems
This study looks at the way K-12 students display reasoning in their explanations of energy and socio-ecological events using both interview and written assessments.
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COGNITIVE CHARACTERISTICS AS PREDICTORS OF CHILDREN'S UNDERSTANDING OF SAFETY AND ACCIDENT PREVENTION (REFLECTION-IMPULSIVITY, CAUSAL REASONING, PARENT STYLE)
<p>In the present study, children's level of causal reasoning and cognitive style were considered as possible predictors of their understanding of safety and accident prevention. Understanding of safety and accident prevention were operationalized as differentiation of safe and unsafe situations …
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Using biological and chemical information to improve understanding of drug mechanism of action on the systems-level
… be used with methods such as machine learning, causal reasoning and pathway enrichment to gain insights on compound MoA across different levels of biology. This presents an opportunity to investigate approaches for integrating information sources and computational methods for the elucidation of …
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Towards Rigorously Tested & Reliable Machine Learning for Health
… reliable models is complicated by the need for causal reasoning and robust performance. To support decision-making, we want to draw causal conclusions about the impact of model recommendations (e.g., will recommending a particular drug lead to better patient outcomes?). Moreover, we want our …
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Formalizing Causal Models Through the Semantics of Conditional Independence
Many foundational tools in causal inference are based on graphical structure and can involve complex conditions that obscure the underlying causal logic. Given the inherent complexity and subtlety of cause-and-effect phenomena, establishing formal guarantees about these tools is both challenging …
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Discovering Heterogeneous Causal Effects in Relational Data
… data. There is growing interest in deriving causal insights from such data which is inherently relational in nature. Causal inference in relational settings has to account for interference, where a unit's outcome may be influenced by the treatments or outcomes of other units. Despite recent …
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Toward meaningful communication: engineering semantics with the geometry of conceptual spaces
… property information. Next, we introduce a causal reasoning-based mechanism into the proposed system, which allows the semantic communication system to determine which semantic elements are most important for performing a given task. This reasoning mechanism enables the system to achieve …
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Influence of collaborative group work on English language learner's oral narratives
… work generated significantly longer chains of reasoning (many 5-7 link chains) than students who had received direct instruction (many 1-2 link chains). The results suggest collaborative group work is an effective instructional approach to foster ELL’s oral narrative skill and causal reasoning. …
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The role of temporal factors and prior knowledge in causal learning and judgment
Causal relationships are all around us: wine causes stains; matches cause flames; foods cause allergic reactions. Next to language, it is hard to imagine a cognitive process more indicative of human intelligence than causal reasoning. To understand how people accomplish these feats, two major …
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Natively probabilistic computation
… a special case. I use it to solve probabilistic reasoning problems from statistical physics, causal reasoning and stereo vision. Finally, I introduce stochastic digital circuits that model the probability algebra just as traditional Boolean circuits model the Boolean algebra.
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Improving Observational Causality Using Machine Learning
Causality is at the heart of many machine learning questions whether we know it or not, and we need to explicitly incorporate causal reasoning in order to answer them effectively. By a similar token, traditional causal inference methods can benefit from machine learning to adapt to more complex …
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Stochastic architectures for probabilistic computation
… and motion perception, perceptual learning and causal reasoning via inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity microprocessors - by exploiting stochasticity. I will show how this natively stochastic approach follows naturally from the …
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INVESTIGATING CAUSAL SELECTION: CAUSAL JUDGMENTS BETWEEN NORMS AND EXPECTATIONS
… both in philosophy and in cognitive science: causal selection. Experimental studies have shown that various factors—such as norms, agent knowledge, and the causal structure of the situation —affect people's attribution of different degrees of causality to events with the same dependency …
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The Social Cognition of Domestic Dogs (Canis familiaris) During Cross-Species Interactions with Humans
… of human verbal phrases; 4) the understanding causal reasoning; and 5) whether hemispheric emotional processing in the brain is associated with ear temperature. I investigated the communicative repertoire of dogs using a citizen science approach, thus maximising the data collection potential. …
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