Published on *Computer Science Department at Princeton University* (http://www.cs.princeton.edu)

We evaluate the success of the qualitative physics enterprise in

automating expert reasoning about physical systems. The field has

agreed, in essentials, upon a modeling language for dynamical systems,

a representation for behavior, and an analysis method. The modeling

language consists of generalized ordinary differential equations

containing unspecified constants and monotonic functions; the

behavioral representation decomposes the state space described by the

equations into discrete cells; and the analysis method traces the

transitory response using sign arithmetic and calculus. The field has

developed several reasoners based on these choices over some fifteen

years. We demonstrate that these reasoners exhibit severe limitations

in comparison with experts and can analyze only a handful of simple

systems. We trace the limitations to inappropriate assumptions about

expert needs and methods. Experts ordinarily seek to determine

asymptotic behavior rather than transient response, and use extensive

mathematical knowledge and numerical analysis to derive this

information. Standard mathematics provides complete qualitative

understanding of many systems, including those addressed so far in

qualitative physics. Preliminary evidence suggests that expert

knowledge and reasoning methods can be automated directly, without

restriction to the accepted language, representation and algorithm. We

conclude that expert knowledge and methods provide the most promising

basis for automating qualitative reasoning about physical systems.