"Belief change in rational agents (with application to action formalisms, ontologies, and inferentially weak systems)"

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In Knowledge Representation (KR) in Artificial Intelligence, an agent's knowledge will generally be incomplete, possibly inaccurate, and evolving. This will be the case whether the agent is a robot in a physical domain, or a software agent on the web, or a reasoning agent dealing with a corpus of medical information. A crucial problem in KR is to manage such changing information, whether change comes from being told a new fact, or as a result of some executed action, or from sensing the environment. The focus of the proposed research will be on managing belief change. While much work has been carried out in the areas of reasoning about action and belief revision, there are crucial issues that have not been fully addressed or that are not fully understood. For example, it is not clear that the models that we have for belief revision fully characterize all aspects of belief change; as well, research has only recently addressed change in special-purpose KR languages, such as those involving Horn theories or ontologies. Work in the previous grant period focused on the foundations of belief change, with emphasis on iterated change operators and change in inferentially weak systems, along with work in the related areas of preferences and nonmonotonic reasoning. For the coming grant period I will continue working on the foundations of belief change, but also will address three specific interrelated projects concerned with the application of belief change in realistic settings. The first project (with colleagues from SFU, Greece, and Australia) will examine belief change in description logics, specifically with logics used for representing medical ontologies on the one hand, and the so-called semantic web of the other. The second project (with a colleague from Toronto) will be to provide a full high-level specification of an agent able to incorporate information about known action effects, being informed of something, and sensor information, with the goal of being able to revise its knowledge, whether the incorrect knowledge came from incorrect information, fallible sensors, or failed actions. The third project (with a colleague from SFU) will address situational awareness in large-scale (maritime) and local (household assisted living) settings.