<p>In order that epistemic formulas might be coupled with approximate databases, it is necessary to have a well-defined semantics for the knowledge operator and a method of reducing epistemic formulas to approximate formulas. In this thesis, two possible definitions of a semantics for the knowledge operator are proposed for use together with an approximate relational database: </p><p>* One based upon logical entailment (being the dominating notion of knowledge in literature); sound and complete rules for reduction to approximate formulas are explored and found not to be applicable to all formulas. </p><p>* One based upon algorithmic computability (in order to be practically feasible); the correspondence to the above operator on the one hand, and to the deductive capability of the agent on the other hand, is explored.</p><p>Also, an inductively defined semantics for a"know whether"-operator, is proposed and tested. Finally, an algorithm implementing the above is proposed, carried out using Java, and tested.</p>

In order that epistemic formulas might be coupled with approximate databases, it is necessary to have a well-defined semantics for the knowledge operator and a method of reducing epistemic formulas to approximate formulas. In this thesis, two possible definitions of a semantics for the knowledge operator are proposed for use together with an approximate relational database: * One based upon logical entailment (being the dominating notion of knowledge in literature); sound and complete rules for reduction to approximate formulas are explored and found not to be applicable to all formulas. * One based upon algorithmic computability (in order to be practically feasible); the correspondence to the above operator on the one hand, and to the deductive capability of the agent on the other hand, is explored. Also, an inductively defined semantics for a"know whether"-operator, is proposed and tested. Finally, an algorithm implementing the above is proposed, carried out using Java, and tested.

Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general characteristics and distinguishing traits of algorithms, key domains in everyday life using algorithms (including the potential benefits and risks), and ethical considerations for the use and application of algorithms. Findings also suggested five behaviors that students could use to help them better cope with algorithmic systems and nine teaching strategies to help improve students’ algorithmic literacy. Suggestions also surfaced for alternative forms of assessment, potential placement in the curriculum, and how to distinguish between basic algorithmic awareness compared to algorithmic literacy. Recommendations for expanding on the current Association of College and Research Libraries’ Framework for Information Literacy for Higher Education (2016) to more explicitly include algorithmic literacy were presented.