One goal of qualitative physics is to capture the mental models of engineers and scientists. This paper shows how Qualitative Process theory can be used to express concepts of engineering thermodynamics. This encoding provides the means to integrate qualitative and quantitative knowledge for solving textbook thermodynamics problems. These ideas have been implemented in a program called SCHISM, which analyzes thermodynamic cycles, such as gas turbine plants and steam power plants. W e describe its analysis of a sample textbook problem and discuss our plans for future work.

Pretense has been implicated as playing a role in the development of many cognitive and social skills. Despite theimportance and ubiquity of this phenomena, few computational models of pretense exist. We propose a model of pretense viaanalogical abduction. We suggest that pretense occurs via structural alignment. Where a mismatch occurs (i.e. something inthe pretend scenario is not the same as its aligned match in an equivalent real-world scenario), a re-representation must takeplace in order for pretense to continue. For example, a seashell may be re-represented as a cup (as in Fein, 1975) or an emptycup may be re-represented as full (as in Onishi et al., 2007). We show that this model can explain results from two empiricalstudies, including failures in pretense.

Young children's performance on certain mapping tasks can be improved by introducing relational language (Gentner, 1998). We show that children's performance on a spatial mapping task can be modeled using the Structure-Mapping Engine (SME) to simulate the comparisons involved. To model the effects of relational language in our simulations, we vary the quantity and nature of the spatial relations and object descriptions represented. The results reproduce the trends observed in the developmental studies of Loewenstein & Gentner (1998; in preparation). The results of these simulations are consistent with the claim that gains in relational representation are a major contributor to the development of spatial mapping ability. We further suggest that relational language can promote relational representation.

What makes humans smart? We think it has to do with our analogical ability—our ability to perceive common relational patterns between different objects, events, or ideas. It is a cornerstone of our higher-order reasoning ability and may have its origins in a relational processing mechanism that allows us to abstract relations using comparison. In this symposium, we present research on the nature of our relational learning ability. We show that findings from infants, preschoolers, elementary school students, and adults reveal common signatures across a wide variety of contexts. These studies provide evidence about how to improve learning of abstract relational concepts and provide insight about how to build better AI models.