This thesis is concerned with pragmatic factors of deontic reasoning, namely scale of violation, aggravating and mitigating circumstances and power of source. Nine experiments are reported investigating deontic reasoning and judgement revision. Experiment 1 established scale of violation as a modifying factor of a working rule with an inferential reasoning task, however, the effects were not transferred to a deductive reasoning task in Experiment 2. Scale of violation and circumstances were found to influence the reasoning of motoring violations with a major offence and aggravating circumstances being rated as more serious and receiving greater fines than a minor offence or mitigating circumstances (Experiments 3 & 4). These effects were also observed with a judgement revision task (Experiment 5). Power of source was included as an additional pragmatic factor and was found to influence the reasoning of conditional statements (Experiment 6), inducements (Experiment 7) and ratings of credibility and probability of outcomes (Experiment 8). The final study (Experiment 9) found significant effects for scale of violation / compliance and power of source within a judgement revision task. However, no difference was observed in the reasoning of superordinate and non-superordinate statements. The findings are explained in terms of the conditional probability hypothesis.

published_or_final_version / Education / Master / Master of Education

Aspect-oriented programming (AOP) aims to enhance modularity and reusability in software systems by offering an abstraction mechanism to deal with crosscutting concerns. But, in most general-purpose aspect languages aspects have almost unrestricted power, eventually conflicting with these goals. This work presents Effective Aspects: a novel approach to embed the pointcut/advice model of AOP in a statically-typed functional programming language like Haskell; along two main contributions. First, we define a monadic embedding of the full pointcut/advicemodel of AOP. Type soundness is guaranteed by exploiting the underlying type system, in particular phantom types and a new anti-unification type class. In this model aspects are first-class, can be deployed dynamically, and the pointcut language is extensible, therefore combining the flexibility of dynamically-typed aspect languages with the guarantees of a static type system. Monads enable us to directly reason about computational effects both in aspects and base programs using traditional monadic techniques. Using this we extend the notion of Open Modules with effects, and also with protected pointcut interfaces to external advising. These restrictions are enforced statically using the type system. Also, we adapt the techniques of EffectiveAdvice to reason about and enforce control flow properties as well as to control effect interference. We show that the parametricity-based approach to effect interference falls short in the presence of multiple aspects and propose a different approach using monad views, a novel technique for handling the monad stack, developed by Schrijvers and Oliveira. Then, we exploit the properties of our model to enable the modular construction of new semantics for aspect scoping and weaving. Our second contribution builds upon a powerful model to reason about mixin-based composition of effectful components and their interference, based on equational reasoning, parametricity, and algebraic laws about monadic effects. Our contribution is to show how to reason about interference in the presence of unrestricted quantification through pointcuts. We show that global reasoning can be compositional, which is key for the scalability of the approach in the face of large and evolving systems. We prove a general equivalence theorem that is based on a few conditions that can be established, reused, and adapted separately as the system evolves. The theorem is defined for an abstract monadic AOP model; we illustrate its use with a simple version of the model just described. This work brings type-based reasoning about effects for the first time in the pointcut/advice model, in a framework that is expressive, extensible and well-suited for development of robust aspect-oriented systems as well as a research tool for new aspect semantics.

La programmation orientée aspect (AOP) vise à améliorer la modularité et la réutilisation des couches logiciels en proposant un mécanisme d’abstraction pour faire face aux préoccupations transversales. Cependant, dans la plupart des langages d’aspects généralistes, les aspects ont un pouvoir presque illimité, rentrant éventuellement en conflit avec ces objectifs. Dans ce travail, nous présentons Effective Aspects : une nouvelle approche pour incorporer le modèle pointcut/advice de l’AOP dans un langage de programmation fonctionnel statiquement typé comme Haskell. Notre travail comprend deux contributions principales. Premièrement, nous définissons un plongement monadique du modèle pointcut/advice complet de l’AOP. La correction du typage est garantie par l’exploitation du système de type sous-jacent, en particulier les types fantômes et une nouvelle classe de type pour faire de l’anti-unification de types. Dans ce modèle, les aspects sont de première classe, peuvent être déployés de façon dynamique, et le langage de pointcuts est extensible, combinant donc la flexibilité des langages d’aspect typés dynamiquement avec les garanties d’un système de type statique. Les monades nous permettent de raisonner directement sur les effets du calcul à la fois dans les aspects et les programmes de base en utilisant des techniques monadiques traditionnelle. Avec ce système, nous étendons la notion de “open modules” avec des effets, et aussi avec les interfaces de pointcut protégés à l’extérieur d’un advice. Ces restrictions sont appliquées statiquement par le système de type. Aussi, nous adaptons les techniques de EffectiveAdvice afin de raisonner sur des propriétés du flot de contrôle. En outre, nous montrons comment contrôler l’interférence des effets en utilisant l’approche fondée sur la paramétricité de EffectiveAdvice. Nous montrons que cette approche n’est pas satisfaisante en présence de multiples aspects et proposons une approche différente en utilisant des vues monadiques, une nouvelle technique pour le traitement de la pile monadique, développée par Schrijvers et Oliveira. Ensuite, nous exploitons les propriétés de notre modèle pour permettre la construction modulaire de nouvelles sémantiques pour la portée d’aspects et le tissage. Notre deuxième contribution s’appuie sur un modèle puissant pour raisonner sur la composition de mixins avec effets et leur interférence, fondée sur un raisonnement équationnelle, paramétrique, et les lois algébriques sur les effets monadiques. Notre contribution est de montrer comment raisonner sur l’interférence en présence de quantification sans restriction pour les pointcuts. Nous montrons que le raisonnement global peut être compositionnelle, ce qui est essentiel pour le passage à l’échelle de l’approche face aux évolutions de grands systèmes. / Aspect-oriented programming (AOP) aims to enhance modularity and reusability in software systems by offering an abstraction mechanism to deal with crosscutting concerns. But, in most general-purpose aspect languages aspects have almost unrestricted power, eventually conflicting with these goals. This work presents Effective Aspects: a novel approach to embed the pointcut/advice model of AOP in a statically-typed functional programming language like Haskell; along two main contributions. First, we define a monadic embedding of the full pointcut/advicemodel of AOP. Type soundness is guaranteed by exploiting the underlying type system, in particular phantom types and a new anti-unification type class. In this model aspects are first-class, can be deployed dynamically, and the pointcut language is extensible, therefore combining the flexibility of dynamically-typed aspect languages with the guarantees of a static type system. Monads enable us to directly reason about computational effects both in aspects and base programs using traditional monadic techniques. Using this we extend the notion of Open Modules with effects, and also with protected pointcut interfaces to external advising. These restrictions are enforced statically using the type system. Also, we adapt the techniques of EffectiveAdvice to reason about and enforce control flow properties as well as to control effect interference. We show that the parametricity-based approach to effect interference falls short in the presence of multiple aspects and propose a different approach using monad views, a novel technique for handling the monad stack, developed by Schrijvers and Oliveira. Then, we exploit the properties of our model to enable the modular construction of new semantics for aspect scoping and weaving. Our second contribution builds upon a powerful model to reason about mixin-based composition of effectful components and their interference, based on equational reasoning, parametricity, and algebraic laws about monadic effects. Our contribution is to show how to reason about interference in the presence of unrestricted quantification through pointcuts. We show that global reasoning can be compositional, which is key for the scalability of the approach in the face of large and evolving systems. We prove a general equivalence theorem that is based on a few conditions that can be established, reused, and adapted separately as the system evolves. The theorem is defined for an abstract monadic AOP model; we illustrate its use with a simple version of the model just described. This work brings type-based reasoning about effects for the first time in the pointcut/advice model, in a framework that is expressive, extensible and well-suited for development of robust aspect-oriented systems as well as a research tool for new aspect semantics.

Following international trends, statistics is a relatively new addition to the South African mathematics curriculum at school level and its implementation was fraught with problems. Since 2001 teaching statistics in the Further Education and Training Phase (Grades 10 to 12) has been optional due to lack of professional development of teachers. From 2014 teaching statistics will be compulsory. This study is therefore timely as it provides information about different discourses in discussions of an ill-structured problem in a data-rich context, as well as in discussions of the meaning of the statistical mean. A qualitative case study of informal statistical reasoning was conducted with a group of students that attended an introductory course in descriptive statistics as part of an honours degree in mathematics education at the University of the Witwatersrand. The researcher was the course lecturer. Transcripts of the discussions in four video recorded sessions at the start of the semester long course form the bulk of the data. The discussions in the first three sessions of the course were aimed at structuring the data-context, or grasping the system dynamics of the data-context, as is required at the start of a cycle of statistical investigation. The discussion in the fourth session was about the syntactical meaning of the mean algorithm. It provides guidelines for meaningful disobjectification of the well-known mean algorithm. This study provides insight into informal statistical reasoning that is currently described as idiosyncratic or verbal according to statistical reasoning models. Discourse analysis based on Sfard’s (2008) theory of Commognition was used to investigate and describe discursive patterns that constrain shifting from colloquial to informal statistical discourse. The main finding is that colloquial discourse that is aimed at decision making in a data-context is incommensurable with statistical discourse, since comparison of data in the two discourses are drawn on incommensurable scales – a qualitative evaluation scale and a quantitative descriptive scale. The problem of comparison on a qualitative scale also emerged in the discourse on the syntactical meaning of the mean algorithm, where average as a qualitative judgement conflicted with the mean as a quantitative measurement. Implications for teaching and teacher education are that the development of statistical discourse may be dependent on alienation from data-contexts and the abstraction of measurements as abstract numerical units. Word uses that confound measurements as properties of objects and measurements as abstract units are discussed. Attention to word use is vital in order to discern evaluation narratives as deed routines from exploration narratives and routines.

<div> <div> <div> <p>Currently there is a need for research that explores students’ understanding of advanced topics in order to improve teaching and learning beyond the context of introductory-level courses. This work investigates students’ reasoning about graphs used in enzyme kinetics. Using semi-structured interviews and a think aloud-protocol, 14 second-year students enrolled in a biochemistry course were provided two graphs to prompt their reasoning, a typical Michaelis-Menten graph and a Michaelis-Menten graph involving enzyme inhibition. Student responses were coded using a combination of inductive and deductive analysis, influenced by the resource-based model of cognition. Results involve a discussion regarding how students utilized mathematical resources to reason about chemical kinetics and enzyme kinetics, such as engaging in the use of symbolic/graphical forms and focusing on surface-level features of the equations/graphs. This work also addresses student conceptions of the particulate-level mechanism associated with competitive, noncompetitive, and uncompetitive enzyme inhibition. Based on the findings of this study, suggestions are made regarding the teaching and learning of enzyme kinetics. </p> </div> </div> </div>

Chiu, Yiu Man Edmund. / Thesis submitted in: December 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 269-248). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Beginning Story --- p.2 / Chapter 1.2 --- Background --- p.3 / Chapter 1.2.1 --- History of Nonmonotonic Reasoning --- p.3 / Chapter 1.2.2 --- Formalizations of Nonmonotonic Reasoning --- p.6 / Chapter 1.2.3 --- Belief Revision --- p.13 / Chapter 1.2.4 --- Network Representation of Knowledge --- p.17 / Chapter 1.2.5 --- Reference from Logic Programming --- p.21 / Chapter 1.2.6 --- Recent Work on Network-type Automatic Reasoning Sys- tems --- p.22 / Chapter 1.3 --- A Novel Inference Network Approach --- p.23 / Chapter 1.4 --- Objectives --- p.23 / Chapter 1.5 --- Organization of the Thesis --- p.24 / Chapter 2 --- The Predicate Inference Network PIN --- p.25 / Chapter 2.1 --- Preliminary Terms --- p.26 / Chapter 2.2 --- Overall Structure --- p.27 / Chapter 2.3 --- Object Layer --- p.29 / Chapter 2.3.1 --- Virtual Object --- p.31 / Chapter 2.4 --- Predicate Layer --- p.33 / Chapter 2.4.1 --- Node Values --- p.34 / Chapter 2.4.2 --- Information Source --- p.35 / Chapter 2.4.3 --- Belief State --- p.36 / Chapter 2.4.4 --- Predicates --- p.37 / Chapter 2.4.5 --- Prototypical Predicates --- p.37 / Chapter 2.4.6 --- Multiple Inputs for a Single Belief --- p.39 / Chapter 2.4.7 --- External Program Call --- p.39 / Chapter 2.5 --- Variable Layer --- p.40 / Chapter 2.6 --- Inter-Layer Links --- p.42 / Chapter 2.7 --- Chapter Summary --- p.43 / Chapter 3 --- Computation for PIN --- p.44 / Chapter 3.1 --- Computation Functions for Propagation --- p.45 / Chapter 3.1.1 --- Computational Functions for Combinative Links --- p.45 / Chapter 3.1.2 --- Computational Functions for Alternative Links --- p.49 / Chapter 3.2 --- Applying the Computation Functions --- p.52 / Chapter 3.3 --- Relations Represented in PIN --- p.55 / Chapter 3.3.1 --- Relations Represented by Combinative Links --- p.56 / Chapter 3.3.2 --- Relations Represented by Alternative Links --- p.59 / Chapter 3.4 --- Chapter Summary --- p.61 / Chapter 4 --- Dynamic Knowledge Update --- p.62 / Chapter 4.1 --- Operations for Knowledge Update --- p.63 / Chapter 4.2 --- Logical Expression --- p.63 / Chapter 4.3 --- Applicability of Operators --- p.64 / Chapter 4.4 --- Add Operation --- p.65 / Chapter 4.4.1 --- Add a fully instantiated single predicate proposition with no virtual object --- p.66 / Chapter 4.4.2 --- Add a fully instantiated pure disjunction --- p.68 / Chapter 4.4.3 --- Add a fully instantiated expression which is a conjunction --- p.71 / Chapter 4.4.4 --- Add a human biased relation --- p.74 / Chapter 4.4.5 --- Add a single predicate expression with virtual objects --- p.76 / Chapter 4.4.6 --- Add a IF-THEN rule --- p.80 / Chapter 4.5 --- Remove Operation --- p.88 / Chapter 4.5.1 --- Remove a Belief --- p.88 / Chapter 4.5.2 --- Remove a Rule --- p.91 / Chapter 4.6 --- Revise Operation --- p.94 / Chapter 4.6.1 --- Revise a Belief --- p.94 / Chapter 4.6.2 --- Revise a Rule --- p.96 / Chapter 4.7 --- Consistency Maintenance --- p.97 / Chapter 4.7.1 --- Logical Suppression --- p.98 / Chapter 4.7.2 --- Example on Handling Inconsistent Information --- p.99 / Chapter 4.8 --- Chapter Summary --- p.102 / Chapter 5 --- Knowledge Query --- p.103 / Chapter 5.1 --- Domains of Quantification --- p.104 / Chapter 5.2 --- Reasoning through Recursive Rules --- p.109 / Chapter 5.2.1 --- Infinite Looping Control --- p.110 / Chapter 5.2.2 --- Proof of the finite termination of recursive rules --- p.111 / Chapter 5.3 --- Query Functions --- p.117 / Chapter 5.4 --- Type I Queries --- p.119 / Chapter 5.4.1 --- Querying a Simple Single Predicate Proposition (Type I) --- p.122 / Chapter 5.4.2 --- Querying a Belief with Logical Connective(s) (Type I) --- p.128 / Chapter 5.5 --- Type II Queries --- p.132 / Chapter 5.5.1 --- Querying Single Predicate Expressions (Type II) --- p.134 / Chapter 5.5.2 --- Querying an Expression with Logical Connectives (Type II) --- p.143 / Chapter 5.6 --- Querying an Expression with Virtual Objects --- p.152 / Chapter 5.6.1 --- Type I Queries Involving Virtual Object --- p.152 / Chapter 5.6.2 --- Type II Queries involving Virtual Objects --- p.156 / Chapter 5.7 --- Chapter Summary --- p.157 / Chapter 6 --- Uniqueness and Finite Termination --- p.159 / Chapter 6.1 --- Proof Structure --- p.160 / Chapter 6.2 --- Proof for Completeness and Finite Termination of Domain Search- ing Procedure --- p.161 / Chapter 6.3 --- Proofs for Type I Queries --- p.167 / Chapter 6.3.1 --- Proof for Single Predicate Expressions --- p.167 / Chapter 6.3.2 --- Proof of Type I Queries on Expressions with Logical Con- nectives --- p.172 / Chapter 6.3.3 --- General Proof for Type I Queries --- p.174 / Chapter 6.4 --- Proofs for Type II Queries --- p.175 / Chapter 6.4.1 --- Proof for Type II Queries on Single Predicate Expressions --- p.176 / Chapter 6.4.2 --- Proof for Type II Queries on Disjunctions --- p.178 / Chapter 6.4.3 --- Proof for Type II Queries on Conjunctions --- p.179 / Chapter 6.4.4 --- General Proof for Type II Queries --- p.181 / Chapter 6.5 --- Proof for Queries Involving Virtual Objects --- p.182 / Chapter 6.6 --- Uniqueness and Finite Termination of PIN Queries --- p.183 / Chapter 6.7 --- Chapter Summary --- p.184 / Chapter 7 --- Lifschitz's Benchmark Problems --- p.185 / Chapter 7.1 --- Structure --- p.186 / Chapter 7.2 --- Default Reasoning --- p.186 / Chapter 7.2.1 --- Basic Default Reasoning --- p.186 / Chapter 7.2.2 --- Default Reasoning with Irrelevant Information --- p.187 / Chapter 7.2.3 --- Default Reasoning with Several Defaults --- p.188 / Chapter 7.2.4 --- Default Reasoning with a Disabled Default --- p.190 / Chapter 7.2.5 --- Default Reasoning in Open Domain --- p.191 / Chapter 7.2.6 --- Reasoning about Unknown Exceptions I --- p.193 / Chapter 7.2.7 --- Reasoning about Unknown Exceptions II --- p.194 / Chapter 7.2.8 --- Reasoning about Unknown Exceptions III --- p.196 / Chapter 7.2.9 --- Priorities between Defaults --- p.198 / Chapter 7.2.10 --- Priorities between Instances of a Default --- p.199 / Chapter 7.2.11 --- Reasoning about Priorities --- p.199 / Chapter 7.3 --- Inheritance --- p.200 / Chapter 7.3.1 --- Linear Inheritance --- p.200 / Chapter 7.3.2 --- Tree-Structured Inheritance --- p.202 / Chapter 7.3.3 --- One-Step Multiple Inheritance --- p.203 / Chapter 7.3.4 --- Multiple Inheritance --- p.204 / Chapter 7.4 --- Uniqueness of Names --- p.205 / Chapter 7.4.1 --- Unique Names Hypothesis for Objects --- p.205 / Chapter 7.4.2 --- Unique Names Hypothesis for Functions --- p.206 / Chapter 7.5 --- Reasoning about Action --- p.206 / Chapter 7.6 --- Autoepistemic Reasoning --- p.206 / Chapter 7.6.1 --- Basic Autoepistemic Reasoning --- p.206 / Chapter 7.6.2 --- Autoepistemic Reasoning with Incomplete Information --- p.207 / Chapter 7.6.3 --- Autoepistemic Reasoning with Open Domain --- p.207 / Chapter 7.6.4 --- Autoepistemic Default Reasoning --- p.208 / Chapter 8 --- Comparison with PROLOG --- p.214 / Chapter 8.1 --- Introduction of PROLOG --- p.215 / Chapter 8.1.1 --- Brief History --- p.215 / Chapter 8.1.2 --- Structure and Inference --- p.215 / Chapter 8.1.3 --- Why Compare PIN with Prolog --- p.216 / Chapter 8.2 --- Representation Power --- p.216 / Chapter 8.2.1 --- Close World Assumption and Negation as Failure --- p.216 / Chapter 8.2.2 --- Horn Clauses --- p.217 / Chapter 8.2.3 --- Quantification --- p.218 / Chapter 8.2.4 --- Build-in Functions --- p.219 / Chapter 8.2.5 --- Other Representation Issues --- p.220 / Chapter 8.3 --- Inference and Query Processing --- p.220 / Chapter 8.3.1 --- Unification --- p.221 / Chapter 8.3.2 --- Resolution --- p.222 / Chapter 8.3.3 --- Computation Efficiency --- p.225 / Chapter 8.4 --- Knowledge Updating and Consistency Issues --- p.227 / Chapter 8.4.1 --- PIN and AGM Logic --- p.228 / Chapter 8.4.2 --- Knowledge Merging --- p.229 / Chapter 8.5 --- Chapter Summary --- p.229 / Chapter 9 --- Conclusion and Discussion --- p.230 / Chapter 9.1 --- Conclusion --- p.231 / Chapter 9.1.1 --- General Structure --- p.231 / Chapter 9.1.2 --- Representation Power --- p.231 / Chapter 9.1.3 --- Inference --- p.232 / Chapter 9.1.4 --- Dynamic Update and Consistency --- p.233 / Chapter 9.1.5 --- Soundness and Completeness Versus Efficiency --- p.233 / Chapter 9.2 --- Discussion --- p.234 / Chapter 9.2.1 --- Different Selection Criteria --- p.234 / Chapter 9.2.2 --- Link Order --- p.235 / Chapter 9.2.3 --- Inheritance Reasoning --- p.236 / Chapter 9.3 --- Future Work --- p.237 / Chapter 9.3.1 --- Implementation --- p.237 / Chapter 9.3.2 --- Application --- p.237 / Chapter 9.3.3 --- Probabilistic and Fuzzy PIN --- p.238 / Chapter 9.3.4 --- Temporal Reasoning --- p.238 / Bibliography --- p.239

The purpose of the study was to develop a revision of the logical reasoning section of the California Test of Mental Maturity which increases its discriminative ability while maintaining an acceptable measure of reliability. Subjects were 102 students of general psychology classes at North Texas State University. All were administered the Logical Reasoning section of the California Test of Mental Maturity in its original form and an experimental revision of it (LRTR). The Wesman Personnel Classification Test was administered at the same time to demonstrate the tests' construct validity. Pearson product-moment correlations, item and homogeneity analyses were run. Results indicated that the revised test correlated significantly with the original test and the WPCT. Internal validity of the revised test was satisfactory, showing an improvement over the original test in terms of clarity, reliability and homogeneity.

abstract: Knowledge Representation (KR) is one of the prominent approaches to Artificial Intelligence (AI) that is concerned with representing knowledge in a form that computer systems can utilize to solve complex problems. Answer Set Programming (ASP), based on the stable model semantics, is a widely-used KR framework that facilitates elegant and efficient representations for many problem domains that require complex reasoning. However, while ASP is effective on deterministic problem domains, it is not suitable for applications involving quantitative uncertainty, for example, those that require probabilistic reasoning. Furthermore, it is hard to utilize information that can be statistically induced from data with ASP problem modeling. This dissertation presents the language LP^MLN, which is a probabilistic extension of the stable model semantics with the concept of weighted rules, inspired by Markov Logic. An LP^MLN program defines a probability distribution over "soft" stable models, which may not satisfy all rules, but the more rules with the bigger weights they satisfy, the bigger their probabilities. LP^MLN takes advantage of both ASP and Markov Logic in a single framework, allowing representation of problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way. This dissertation establishes formal relations between LP^MLN and several other formalisms, discusses inference and weight learning algorithms under LP^MLN, and presents systems implementing the algorithms. LP^MLN systems can be used to compute other languages translatable into LP^MLN. The advantage of LP^MLN for probabilistic reasoning is illustrated by a probabilistic extension of the action language BC+, called pBC+, defined as a high-level notation of LP^MLN for describing transition systems. Various probabilistic reasoning about transition systems, especially probabilistic diagnosis, can be modeled in pBC+ and computed using LP^MLN systems. pBC+ is further extended with the notion of utility, through a decision-theoretic extension of LP^MLN, and related with Markov Decision Process (MDP) in terms of policy optimization problems. pBC+ can be used to represent (PO)MDP in a succinct and elaboration tolerant way, which enables planning with (PO)MDP algorithms in action domains whose description requires rich KR constructs, such as recursive definitions and indirect effects of actions. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

No description available.