445,431 research outputs found
Confidence-Based Reasoning with Local Temporal Formal Contexts
Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.Ministerio de Ciencia e InnovaciĂłn TIN2009-09492Junta de AndalucĂa TIC-606
Adequacy of compositional translations for observational semantics
We investigate methods and tools for analysing translations between programming languages with respect to observational semantics. The behaviour of programs is observed in terms of may- and must-convergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extension
Reasoning over Description Logic-based Contexts with Transformers
One way that the current state of the art measures the reasoning ability of
transformer-based models is by evaluating accuracy in downstream tasks like
logical question answering or proof generation over synthetic contexts
expressed in natural language. However, most of the contexts used are in
practice very simple; in most cases, they are generated from short first-order
logic sentences with only a few logical operators and quantifiers. In this
work, we seek to answer the question how well a transformer-based model will
perform reasoning over expressive contexts. For this purpose, we construct a
synthetic natural language question-answering dataset, generated by description
logic knowledge bases. For the generation of the knowledge bases, we use the
expressive language . The resulting dataset contains 384K
examples, and increases in two dimensions: i) reasoning depth, and ii) length
of sentences. We show that the performance of our DeBERTa-based model,
DELTA, is marginally affected when the reasoning depth is increased and it
is not affected at all when the length of the sentences is increasing. We also
evaluate the generalization ability of the model on reasoning depths unseen at
training, both increasing and decreasing, revealing interesting insights into
the model's adaptive generalization abilities
Moral reasoning of adolescent male offenders: Comparison of sexual and nonsexual offenders
This study compared the moral reasoning abilities of juvenile sex and non-sex offenders using a novel methodology that explored their responses to moral questions in a variety of offending contexts. Seven sexual and nine nonsexual adolescent male offenders from a maximum security detention facility in New South Wales, Australia, were presented with a variety of hypothetical offending situations involving sexual and non sexual offences and asked to discuss these. It was hypothesised that the quality of moral reasoning employed by offenders would be impaired in those offending contexts in which they had prior experience. Responses were assessed using a modified version of the Moral Judgment Interview Standard Issue Scoring Manual (MJI; Colby & Kohlberg, 1987). Assigned levels of moral reasoning ability were verified independently by two expert raters. Responses by sexual offenders in sexual offending contexts and by nonsexual offenders in nonsexual offending contexts were dominated by preconventional reasoning. Both groups employed a greater use of conventional reasoning in non-congruent offending contexts
Elementary students’ conditional reasoning skills: The case of mathematics
Reasoning about conditional “if..then” statements is a central component of logical reasoning. However, a research link between conditional reasoning and mathematics has been reported only for late adolescence and adults (Attridge & Inglis, 2013; Stylianides, Stylianides, & Philippou, 2004; Durand-Guerrier, 2003), despite claims about the pivotal importance of conditional reasoning, i.e. reasoning with if-then statements, in mathematics. To address this issue and shed some light on the the area of conditional reasoning within mathematics in elementary school, three studies were conducted to measure students conditional reasoning and alternative generation skills in two contexts (everyday and mathematical) and investigate various factors (i.e, age, logical form, working memory capacity, alternative generation skills) that might affect conditional reasoning skills at these ages, as well as the potential scaffolding function of different trainings on these skills. Firstly, after having approached the background that frames conditional reasoning in mathematics and everyday context, we reported on a study that explored if it is feasible to survey conditional reasoning skills in everyday contexts and mathematics with primary school students. The findings shown that the applied instrument was accessible to students, and reflected central predictions of Mental Model Theories of conditional reasoning for differences between the two contexts. Moreover, the question, if the ability to generate examples of mathematical concepts, and to generate multiple alternative models for a given premise, has an influence on students ’ conditional reasoning with these concepts, was raised at that point. In this direction this pilot study also aimed at investigating students’ alternative generation outcomes in both contexts. Based on the aforementioned pilot study, the first study addressed the open question, to which extent conditional reasoning with mathematical concepts differs from conditional reasoning in familiar everyday contexts. This study also examined the role of alternatives generation skills on conditional reasoning within an everyday and a mathematical context. The results of study 1 suggest that, consistently with previous findings, even 2nd graders were able to make correct inferences on some logical forms. Controlling for WM, there were significant effects of grade and logical form, with stronger growth on MP and AC than on MT and DA. The main effect of context was not significant, but context interacted significantly with logical form and grade level. The pattern of results was not consistent with the predictions of MMT. The study also indicates that deductive reasoning skills arise from a combination of knowledge of domain-general principles and domain-specific knowledge. In addition, it extends results concerning the gradual development of primary students' conditional reasoning with everyday concepts (Markovits & Barrouillet, 2002) to reasoning with mathematical concepts adding to our understanding about the link between mathematics and conditional reasoning in primary school. Moreover, alternatives generation skills predict correct conditional reasoning in both contexts, but interesting differences occurred. The findings from the everyday context mirror previous results, predicting correct AC and DA reasoning and inhibiting correct MT reasoning. In the mathematical context, alternatives generation predicted correct reasoning in all forms. The main contribution of study 1 is the emphasis on the specific role of mathematical knowledge in conditional reasoning with mathematical concepts. The results of the latter studies inspired the development of a short-term educational intervention. This goal was addressed in study 2 by investigating the effects of two short-term trainings based on alternative generation priming within two contexts (contrary-to-fact and mathematical contexts). The results of this study were mixed, revealing a decrease of definite reasoning scores after the short interventions and an increase in DA reasoning; however, further analysis is required. Ultimately, the studies in this dissertation aimed to gain some evidence in the area of conditional reasoning within mathematics in primary school and contribute to future research on this research field
Moral reasoning of adolescent male offenders: Comparison of sexual and nonsexual offenders
This study compared the moral reasoning abilities of juvenile sex and non-sex offenders using a novel methodology that explored their responses to moral questions in a variety of offending contexts. Seven sexual and nine nonsexual adolescent male offenders from a maximum security detention facility in New South Wales, Australia, were presented with a variety of hypothetical offending situations involving sexual and non sexual offences and asked to discuss these. It was hypothesised that the quality of moral reasoning employed by offenders would be impaired in those offending contexts in which they had prior experience. Responses were assessed using a modified version of the Moral Judgment Interview Standard Issue Scoring Manual (MJI; Colby & Kohlberg, 1987). Assigned levels of moral reasoning ability were verified independently by two expert raters. Responses by sexual offenders in sexual offending contexts and by nonsexual offenders in nonsexual offending contexts were dominated by preconventional reasoning. Both groups employed a greater use of conventional reasoning in non-congruent offending contexts
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
There have been several attempts at modeling context in robots. However,
either these attempts assume a fixed number of contexts or use a rule-based
approach to determine when to increment the number of contexts. In this paper,
we pose the task of when to increment as a learning problem, which we solve
using a Recurrent Neural Network. We show that the network successfully (with
98\% testing accuracy) learns to predict when to increment, and demonstrate, in
a scene modeling problem (where the correct number of contexts is not known),
that the robot increments the number of contexts in an expected manner (i.e.,
the entropy of the system is reduced). We also present how the incremental
model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures,
International Conference on Intelligent Robots (IROS 2018
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