Situated conditional reasoning

Casini G., Meyer T., Varzinczak I.

Defeasible reasoning  Artificial Intelligence  Linguistics and Language  Logic in Computer Science (cs.LO)  Non-monotonic reasoning  Belief revision  Counterfactual reasoning  FOS: Computer and information sciences  Artificial Intelligence (cs.AI)  Conditional reasoning  Computer Science - Logic in Computer Science  Language and Linguistics  Computer Science - Artificial Intelligence 

Conditionals are useful for modelling many forms of everyday human reasoning but are not always sufficiently expressive to represent the information we want to reason about. In this paper, we make a case for a form of situated conditional. By 'situated', we mean that there is a context, based on an agent's beliefs and expectations, that works as background information in evaluating a conditional, and we allow such a context to vary. These conditionals are able to distinguish, for example, between expectations and counterfactuals. Formally, they are shown to generalise the conditional setting in the style of Kraus, Lehmann, and Magidor. We show that situated conditionals can be described in terms of a set of rationality postulates. We then propose an intuitive semantics for these conditionals and present a representation result which shows that our semantic construction corresponds exactly to the description in terms of postulates. With the semantics in place, we define a form of entailment for situated conditional knowledge bases, which we refer to as minimal closure. Finally, we proceed to show that it is possible to reduce the computation of minimal closure to a series of propositional entailment and satisfiability checks. While this is also the case for rational closure, it is somewhat surprising that the result carries over to minimal closure.

Source: Artificial intelligence (Gen. ed.) 319 (2023). doi:10.1016/j.artint.2023.103917

Publisher: North-Holland, New York , Paesi Bassi

Metrics