Belief change research investigates how agents adapt their knowledge with potentially conflicting information. A common formalization is by epistemic states, abstract entities often represented by faithful preorders. Operators describe how epistemic states change with new evidence and are classified by which postulates they satisfy. Different approaches have been suggested for the problem of iterated belief change. Recent work introduces uniform revision that revises an agent’s beliefs based on one static total preorder, therefore lowering representational costs.
In this thesis, an extended epistemic state approach is introduced, based on an agent deterministically switching between total preorders. Challenges for implementations in the area of iterated belief change, like textual representation of total preorders, are pointed out and solutions developed. A tool for the automated certification of postulates for iterated belief change, called Coeus, is implemented for the new operator. Finally, the developed software is evaluated empirically. Coeus is publicly available, and most of its code is open-source.