Abstract: In this paper we address dynamic assumption-based reasoning in open agent systems, where, unavoidably, agents have incomplete knowledge about their environment and about other agents. The interactions among agents in such systems are typically subject to norms, which stipulate what each agent is obliged, permitted, prohibited, empowered etc. to do, while it participates in the system. In such environments agents need to resort to assumptions, in order to establish what actions are appropriate to perform, and they need to do so dynamically, since the environment, the agents that exist in it, the information that is exchanged between them, and the normative relations between them change over time. In earlier work, we had proposed Default Theory construction to support dynamic assumption-based reasoning. We argued that in this way, agents could perform both assumption identification and employment dynamically, contrary to other approaches to assumption-based reasoning, which catered for either one or the other. A shortcoming of this early proposal of ours, though, is that Default Theory construction seems to require proof, which is notably computationally expensive. In this paper we present a computational technique that can be used for this construction in an incremental manner that does not depend on proof, and a prototype tool that we developed for experimentation. In a nutshell, depending on their current knowledge at any given time, agents can identify appropriate candidate assumptions in an ad hoc manner. When such choices need to be revised, agents can reconstruct their view of the possible world in which they find themselves, and establish their revised assumption requirements at run-time.