On the Conditional Logic of Simulation Models
This work addresses foundational issues in AI logic, offering a novel framework for conditional reasoning that could impact modeling in AI, though it appears incremental in building on prior approaches.
The paper tackles the problem of conditional reasoning in AI by formalizing it through interventions on simulation programs, subsuming existing approaches and invalidating some common logical principles. The results include axiomatizations for comparison and a proof of NP-completeness for the satisfiability problem.
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.