The Experts below are selected from a list of 17553 Experts worldwide ranked by ideXlab platform
Bart Verheij - One of the best experts on this subject based on the ideXlab platform.
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a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the Correct treatment of statistical evidence is important.
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demonstration of a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.
Cory T Forbes - One of the best experts on this subject based on the ideXlab platform.
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scientific practices in elementary classrooms third grade students scientific Explanations for seed structure and function
Science Education, 2014Co-Authors: Laura Zangori, Cory T ForbesAbstract:Elementary science standards emphasize that students should develop conceptual understanding of the characteristics and life cycles of plants (National Research Council, 2012), yet few studies have focused on early learners’ reasoning about seed structure and function. The purpose of this study is twofold: to (a) examine third-grade students’ formulation of Explanations about seed structure and function within the context of a commercially published science unit and (b) examine their teachers’ ideas about and instructional practices to support students’ formulation of scientific Explanations. Data, collected around a long-term plant investigation, included classroom observations, teacher interviews, and students’ written artifacts. Study findings suggest a link between the teachers’ ideas about scientific Explanations, their instructional scaffolding, and students’ written Explanations. Teachers who emphasized a single “Correct Explanation” rarely supported their students’ Explanation-construction, either through discourse or writing. However, one teacher emphasized the importance of each student generating his/her own Explanation and more frequently supported students to do so in the classroom. The evidentiary basis of her students’ written Explanations was found to be much stronger than those from students in the other two classrooms. Overall, these findings indicate that teachers’ conceptions about scientific Explanations are crucial to their instructional practices, which may in turn impact students’ Explanation-construction.
Sjoerd T Timmer - One of the best experts on this subject based on the ideXlab platform.
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a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the Correct treatment of statistical evidence is important.
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demonstration of a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.
Laura Zangori - One of the best experts on this subject based on the ideXlab platform.
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scientific practices in elementary classrooms third grade students scientific Explanations for seed structure and function
Science Education, 2014Co-Authors: Laura Zangori, Cory T ForbesAbstract:Elementary science standards emphasize that students should develop conceptual understanding of the characteristics and life cycles of plants (National Research Council, 2012), yet few studies have focused on early learners’ reasoning about seed structure and function. The purpose of this study is twofold: to (a) examine third-grade students’ formulation of Explanations about seed structure and function within the context of a commercially published science unit and (b) examine their teachers’ ideas about and instructional practices to support students’ formulation of scientific Explanations. Data, collected around a long-term plant investigation, included classroom observations, teacher interviews, and students’ written artifacts. Study findings suggest a link between the teachers’ ideas about scientific Explanations, their instructional scaffolding, and students’ written Explanations. Teachers who emphasized a single “Correct Explanation” rarely supported their students’ Explanation-construction, either through discourse or writing. However, one teacher emphasized the importance of each student generating his/her own Explanation and more frequently supported students to do so in the classroom. The evidentiary basis of her students’ written Explanations was found to be much stronger than those from students in the other two classrooms. Overall, these findings indicate that teachers’ conceptions about scientific Explanations are crucial to their instructional practices, which may in turn impact students’ Explanation-construction.
Silja Renooij - One of the best experts on this subject based on the ideXlab platform.
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a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the Correct treatment of statistical evidence is important.
-
demonstration of a structure guided approach to capturing bayesian reasoning about legal evidence in argumentation
International Conference on Artificial Intelligence and Law, 2015Co-Authors: Sjoerd T Timmer, Johnjules Ch Meyer, Henry Prakken, Silja Renooij, Bart VerheijAbstract:Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the Correct Explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.
