A Commonsense-Driven Model of Belief Revision for Dynamic Domains using the Event Calculus

The fields of Artificial Intelligence (AI) and Robotics were strongly connected in the early days of AI, but have since diverged as practitioners of AI focused on problems and algorithms abstracted from the real world, while roboticists, building on their background in mechanical and electrical engineering, concentrated on sensory-motor functions. With advancements in both fields, there is now a renewed interest in bringing the two disciplines closer together. Robots, or any other autonomous entity inhabiting real-world domains, need to deal with incomplete information and uncertainty at various levels of abstraction, from low-level sensory data to high-level knowledge, such as action preconditions and effects. This thesis concentrates on the latter, aiming to keep the Knowledge Base (KB) of an agent both up-to-date and consistent, while performing world-changing or observation actions. Action theories are well-established logical theories, based on classical logic, for reasoning about domains involving dynamically changing environments. Thus, they can inherently deal with change caused by actions. One of the most prominent action languages is the Event Calculus (EC), which incorporates certain useful features for representing causal and narrative information that differentiate it from other similar formalisms. The EC explicitly represents temporal knowledge, enabling reasoning about the effects of a narrative of events along a time line. Given that the logical theory stored in a KB is not always correct, there is also a need to revise KBs as new information is received. The area of belief revision addresses such a change to a KB. In the well-known AGM postulates, belief revision emerges when one has a knowledge base K and a formula α, and the issue is how to consistently incorporate α in K to obtain a new KB K’. This means that some of the beliefs in the original KB must be retracted, but not all of them, since this would be an unnecessary loss of valuable information. What makes things more complicated is that beliefs in a knowledge base have logical consequences, so when giving up a belief one has to decide as well which of the consequences to retain and which to retract. Thus, belief revision is non-trivial as several different ways for performing this operation may be possible. From the EC perspective, there has been extensive work on epistemic extensions of action languages, in general, as well as, on the main EC formalism. However, little attention has been paid to the problem of automatically revising (correcting) a KB in the EC when an observation contradicts the already inferred knowledge, despite mature work on the belief revision field. As the current trend in related research is to identify efficient ways to couple high-level task planning with low-level task execution or feasibility checking, the current work aims to empower such combinations, through the delivery of a more generic high-level formalism that lifts some of the unrealistic assumptions of existing solutions. We propose a generic framework in the context of the EC, along with Answer Set Programming (ASP) encodings of the revision algorithm, accommodating belief revision on top of EC axiomatizations. We consider both the epistemic and non-epistemic case, relying on the possible-worlds representation to give formal semantics to an agent’s belief state. We formalize notions of commonsense revisions that take into consideration different knowledge states, such as factual (or observed) knowledge, default, inferred and also unknown knowledge. We present a methodology and an ASP encoding that can implement the formalism, in which we adapt the existing powerful action theories in a more realistic setting. We also present an optimization algorithm aiming to improve the efficiency of the implementation. Finally, we discuss possible future expansions and improvements of our framework and how this work can form the substrate for further extensions concerning a richer set of commonsense features, along with formal results showing that it is generic enough to be applied to different EC dialects. ́Ενα μοντέλο κοινής λογικής για την αναθεώρηση πεποιθήσεων σε δυναμικά περιβάλλοντα με χρήση Λογισμού Συμβάντων