Abstract
We propose analyzing conditional reasoning by appeal to a notion of intervention on a simulation program, formalizing and subsuming a number of approaches to conditional thinking in the recent AI literature. Our main results include a series of axiomatizations, allowing comparison between this framework and existing frameworks (normality-ordering models, causal structural equation models), and a complexity result establishing NP-completeness of the satisfiability problem. Perhaps surprisingly, some of the basic logical principles common to all existing approaches are invalidated in our causal simulation approach. We suggest that this additional flexibility is important in modeling some intuitive examples.
Keywords
- Knowledge Representation and Reasoning: Action, Change and Causality
- Knowledge Representation and Reasoning: Common-Sense Reasoning
- Knowledge Representation and Reasoning: Logics for Knowledge Representation
- Knowledge Representation and Reasoning: Non-classical Logics for Knowledge Representation
