This study concerns students' ideas about the existence or otherwise of forces in several dynamical situations involving moving objects and objects at rest. It aims to contribute to a better understanding of students' ideas about dynamics. It differs from previous research (a) in covering a wider-range among students and larger variation in taught Physics background. (b) in attempting to tap less verbal forms of evidence and (c) in attempting to avoid 'scientifism' in terms of the way to approach students and in terms of interpreting results. The empirical part of the study involved 338 students from seven different groups. Data was collected from the above sample. using a questionnaire to which responses were simply graphic indications of the directions of expected forces. and. if possible. the giving of names to these forces. in eight situations presented diagrammatically. In addition. data was collected from a sub-sample. by means of computer games using a screen 'object' obeying Newtonian Mechanics. in a frictional and a non-frictional 'environment'. under the control of the subject. Difficulties in interpreting the last kind of data led to the main study being focussed on the results of the questionnaire. Some results from the computer games are however presented. They are mainly concerned with students' performance when playing in a frictional versus non-frictional 'environment'. Results suggest a better students' performance when playing in a frictional 'environment'. Results obtained with the questionnaire concern: (a) differences between situations in patterns of expected directions. among students of the same group and between groups. Generally the results suggest the existence of common patterns among the students of the same group and systematic differences between patterns of groups with an increase in exposure to physics teaching. namely the attribution of new force directions [e.g. vertical and downwards. opposite to motion). despite the persistence of primitive ideas (e.g. a force along the motion); (b) names given to the different kinds of forces in various directions. Results include a difficulty found in naming forces which existed before teaching. They also give information about how scientific terms are assimilated. Interpretations of the results. mainly taken from a theory of Common Sense Reasoning about motions proposed by Ogborn (1985). seem to give them a reasonable explanation. Although requiring further investigation. this gives some support to claim that students' intuitive ideas about dynamics should be regarded [i] as deriving from a rather general and coherent set of ideas, [ii] as less formalized in terms of the scientific world view and [iii] as having their origin mainly in actions on the world.

This investigation proposes a powerful formalization of common sense knowledge based on function-free normal deduction graphs (NDGs) which form a powerful tool for deriving Horn and non-Horn clauses without functions. Such formalization allows common sense reasoning since it has the ability to handle not only negative but also incomplete information.

The aim of this thesis is to develop theories and formal methods to endow a computing machinery with capabilities to identify, represent, reason and evaluate complex activities that are directed by an individual’s needs, goals, motives, preferences and environment, information which can be inconsistent and incomplete. Current methods for formalising and reasoning about human activity are typically limited to basic actions, e.g., walking, sitting, sleeping, etc., excluding elements of an activity. This research proposes a new formal activity-centric model that captures complex human activity based on a systemic activity structure that is understood as a purposeful, social, mediated, hierarchically organized and continuously developing interaction between people and word. This research has also resulted in a common-sense reasoning method based on argumentation, in order to provide defeasible explanations of the activity that an individual performs based on the activity-centric model of human activity. Reasoning about an activity is based on the novel notion of an argument under semantics-based inferences that is developed in this research, which allows the building of structured arguments and inferring consistent conclusions. Structured arguments are used for explaining complex activities in a bottom-up manner, by introducing the notion of fragments of activity. Based on these fragments, consistent argumentation based interpretations of activity can be generated, which adhere to the activity-centric model of complex human activity. For resembling the kind of deductive analysis that a clinician performs in the assessment of activities, two quantitative measurements for evaluating performance and capacity are introduced and formalized. By analysing these qualifiers using different argumentation semantics, information useful for different purposes can be generated. e.g., such as detecting risk in older adults for falling down, or more specific information about activity performance and activity completion. Both types of information can form the base for an intelligent machinery to provide tailored recommendation to an individual. The contributions were implemented in different proof-of-concept systems, designed for evaluating complex activities and improving individual’s health in daily life. These systems were empirically evaluated with the purpose of evaluating theories and methodologies with potential users. The results have the potential to be utilized in domains such as ambient assisted living, assistive technology, activity assessment and self-management systems for improving health.

Objects move, collide, flow, bend, heat up, cool down, stretch, compress and boil. These and other things that cause changes in objects over time are intuitively characterized as processes. To understand common sense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative Process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called quantity space. This report describes the basic concepts of Qualitative Process theory, several different kinds of reasoning that can be performed with them, and discusses its impact on other issues in common sense reasoning about the physical world, such as causal reasoning and measurement interpretation. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string but not push with it. This report also describes GIZMO, an implemented computer program which uses Qualitative Process theory to make predictions and interpret simple measurements. The represnetations and algorithms used in GIZMO are described in detail, and illustrated using several examples.

This thesis investigates the impact of finetuning the LLaMA 33B language model on partisan news datasets, revealing negligible changes and underscoring the enduring influence of pretraining datasets on model opinions. Training nine models across nine distinct news datasets spanning three topics and two ideologies, the study found consistent demographic representation, predominantly favoring liberal, college-educated, high-income, and non-religious demographics. Interestingly, a depolarizing effect emerged from partisan news finetuning, suggesting that intense exposure to topic-specific information might lead to depolarization, irrespective of ideological alignment. Despite the exposure to contrasting viewpoints, LLaMA 33B maintained its common sense reasoning ability, showing minimal variance on evaluation metrics like Hellaswag accuracy, ARC accuracy, and TruthfulQA MC1 and MC2. These results might indicate robustness in common sense reasoning or a deficiency in synthesizing diverse contextual information. Ultimately, this thesis demonstrates the resilience of high-performing language models like LLaMA 33B against targeted ideological bias, demonstrating their continued functionality and reasoning ability, even when subjected to highly partisan information environments.

We show how the non-monotonic nature of common-sense reasoning can be formalised by circumscription. Various forms of circumscription are discussed. A new form of circumscription, namely naive circumscription, is introduced in order to facilitate the comparison of the various forms. Finally, some issues connected with the automation of circumscriptive reasoning are examined. / Computing / M. Sc. (Computer Science)

We show how the non-monotonic nature of common-sense reasoning can be formalised by circumscription. Various forms of circumscription are discussed. A new form of circumscription, namely naive circumscription, is introduced in order to facilitate the comparison of the various forms. Finally, some issues connected with the automation of circumscriptive reasoning are examined. / Computing / M. Sc. (Computer Science)

Many applications of neural linguistic models rely on their use as pre-trained features for downstream tasks such as dialog modeling, machine translation, and question answering. This work presents an alternate paradigm: Rather than treating linguistic embeddings as input features, we treat them as common sense knowledge repositories that can be queried using simple mathematical operations within the embedding space, without the need for additional training. Because current state-of-the-art embedding models were not optimized for this purpose, this work presents a novel embedding model designed and trained specifically for the purpose of "reasoning in the linguistic domain".Our model jointly represents single words, multi-word phrases, and complex sentences in a unified embedding space. To facilitate common-sense reasoning beyond straightforward semantic associations, the embeddings produced by our model exhibit carefully curated properties including analogical coherence and polarity displacement. In other words, rather than training the model on a smorgaspord of tasks and hoping that the resulting embeddings will serve our purposes, we have instead crafted training tasks and placed constraints on the system that are explicitly designed to induce the properties we seek. The resulting embeddings perform competitively on the SemEval 2013 benchmark and outperform state-of- the-art models on two key semantic discernment tasks introduced in Chapter 8.The ultimate goal of this research is to empower agents to reason about low level behaviors in order to fulfill abstract natural language instructions in an autonomous fashion. An agent equipped with an embedding space of sucient caliber could potentially reason about new situations based on their similarity to past experience, facilitating knowledge transfer and one-shot learning. As our embedding model continues to improve, we hope to see these and other abilities become a reality.