Reasoning about changes of existence in objects, such as steam appearing and water disappearing when boiling occurs, is something people do every day. Discovering methods to reason about such changes in existence is a central problem in Native Physiscs. This paper analyzes the problem by isolating an important case, called quantity-conditioned existence. and presnets a general method for solving it. An example generated by an implemented program is exhibited, and remaining open problems are discussed

We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical remindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data.

The Structure-Mapping Engine (SME) has successfully modeled several aspects of human analogical processing. However, it has two significant drawbacks: (l) SME constructs all structurally consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. W e describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. W e describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. W e illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis.

Causality plays an important role in human thinking. Yet we are far from having a complete account of causal reasoning. This paper presents an analysis of causal reasoning about changes in quantities. W e abstract from AI theories of qualitative physics three dimensions along which causal reasoning about quantities may be decomposed. W e then use this framework to make some psychological predictions.

Judgments of similarity and soundness are important aspects of human analogical processing. This paper explores how these judgments can be modeled using SME, a simulation of Centner's structure-mapping theory. W e focus on structural evaluation, explicating several principles which psychologically plausible algorithms should follow. W e introduce the Specificity Conjecture, which claims that naturalistic representations include a preponderance of appearance and low-order information. W e demonstrate via computational experiments that this conjecture affects how structural evaluation should be performed, including the choice of normalization technique and how the systematicity preference is implemented.

Understanding the nature of commonsense reasoning is one ofthe deepest questions of cognitive science. Prior work hasproposed analogy as a mechanism for commonsense reasoning,with prior simulations focusing on reasoning about continuousbehavior of physical systems. This paper examines howanalogy might be used in commonsense more broadly. The twocontributions are (1) the idea of common sense units,intermediate-sized collections of facts extracted fromexperience (including cultural experience) which improvesanalogical retrieval and simplifies inferencing, and (2)analogical chaining, where multiple rounds of analogicalretrieval and mapping are used to rapidly constructexplanations and predictions. We illustrate these ideas via animplemented computational model, tested on examples froman independently-developed test of commonsense reasoning.