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This study compared the accuracy for the two general approaches to criminal profiling, inductive and deductive. Participants were 213 college students who participated in a 1-hour training session. Participants in the experimental groups were trained in either the inductive or deductive approach to profiling. Participants in the control group were trained in a crime topic unrelated to profiling (rape and sexual assault prevention). Following the training session, participants were asked to review a double sexual homicide case and then profile the offender by completing the Profiling Offender characteristics Questionnaire. Participants trained in the inductive profiling approach were more accurate when profiling the offender’s physical characteristics. Furthermore, the inductive profiling approach led to higher overall accuracy scores compared to the control condition. These results suggest that training naïve profilers to use the inductive approach may improve the accuracy of offender profiles in sexual homicide cases.

Inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.

Inductive reasoning (IR) requires efficient working memory (WM). Research shows that the prefrontal cortex (PFC) is involved during WM tasks and that PFC functioning declines with age. The ability to comprehend and update text-based information requires an intact PFC and efficient WM and IR. The current study presented a series of messages about the investigation of a warehouse fire to 48 young and 48 older adults. One message contained a piece of misinformation which another message corrected later. It was hypothesized that a memory cue to the misinformation with the correction statement should benefit older adults the most during the updating process. A text-based level and situation model level measured updating. The text-based level is only information from the text but is not necessarily verbatim. The situation model level is the overall meaning of the text, including inferences and assumptions. Results show that unlike young adults, older adults are not capable of recalling the text at the text-based level. However, older adults are capable of performing like young adults at the situation model level. This suggests that older adults are capable of updating causal information in text material as long as a memory cue to the misinformation is provided within the correction statement.

Hybrid is a two-level logical framework that supports higher-order abstract syntax (HOAS), where a specification logic (SL) extends the class of object logics (OLs) we can reason about. We develop a new Hybrid SL and formalize its metatheory, proving weakening, contraction, exchange, and cut admissibility; results that greatly simplify reasoning about OLs in systems providing HOAS. The SL is a sequent calculus defined as an inductive type in Coq and we prove properties by structural induction over SL sequents. We also present a generalized SL and metatheory statement, allowing us to prove many cases of such theorems in a general way and understand how to identify and prove the difficult cases. We make a concrete and measurable improvement to Hybrid with the new SL formalization and provide a technique for abstracting such proofs, leading to a condensed presentation, greater understanding, and a generalization that may be instantiated to other logics.

Economists have convulsively maintained the assumption that humans are able to arrive at decisions by perfect deductive rationality, despite the fact empirical evidences are showing otherwise. The contradicting evidences have resulted in a personal view that instead of finding a unified theory about decision-making, a sound approach would be to study how humans in fact are reasoning in specific contexts. The context of interest for this paper is where it could be assumed humans’ persistence of acting rational is determined by the perceived burden of the problem. In this work, the inductive way of arriving at decisions plays an important role, and the paper will present a way of describing this process in a consistent way. The process will be denoted as the actual level of behavioral change, and represent the core property of this paper. Applying the presented theory is most appropriate for situations where it could be assumed the burden of a problem, expressed as a prevalence rate, will drive the behavioral change. The line of reasoning in this paper will therefore be applied to the important arena of fighting the spread of HIV.

Relations among various nodes in the circuit, as captured by static and inductive invariants, have shown to have a positive impact on a wide range of EDA applications. Techniques such as boolean constraint propagation for static learning and assume-then-verify approach to reason about inductive invariants have been possible due to efficient SAT solvers. Although a significant amount of research effort has been dedicated to the development of effective invariant learning techniques over the years, the computation time for deriving powerful multi-node invariants is still a bottleneck for large circuits. Fast computation of static and inductive invariants is the primary focus of this thesis. We present a novel technique to reduce the cost of static learning by intelligently identifying redundant computations that may not yield new invariants, thereby achieving significant speedup. The process of inductive invariant reasoning relies on the assume-then-verify framework, which requires multiple iterations to complete, making it infeasible for cases with a large set of multi-node invariants. We present filtering techniques that can be applied to a diverse set of multi-node invariants to achieve a significant boost in performance of the invariant checker. Mining and reasoning about all possible potential multi-node invariants is simply infeasible. To alleviate this problem, strategies that narrow down the focus on specific types of powerful multi-node invariants are also presented. Experimental results reflect the promise of these techniques. As a measure of quality, the invariants are utilized for untestable fault identification and to constrain ATPG for path delay fault testing, with positive results. / Master of Science

This is a qualitative research that draws Gee‟s Discourse analysis to understand how learners communicate their mathematical reasoning in a multilingual classroom in South Africa. The study involved a Grade 11 class of 25 learners in a township school East of Johannesburg. The research method used was a case study. Data was collected using classroom observations, and document analysis. The study has shown that learners communicate their mathematics reasoning up to a certain level. The way learners communicated their mathematical reasoning depended on the activities that were given by the textbook being used in the classroom, and the questions which the teacher asked during the lessons. From the findings of the study, recommendations were made: the assessment of how learners communicate their mathematical reasoning should have a basis, say the curriculum. If the curriculum states the level of mathematical reasoning which the learners at Grade 11 must reach, then the teacher will have to probe the learners for higher reasoning; mathematics classroom textbooks should be designed to enable learners communicate their mathematical reasoning. The teacher should ask learners questions that require learners to communicate their mathematical reasoning.