Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

BACON.3 is a production system that discovers empirical laws. Although it does not attempt to model the human discovery process in detail, it incorporates some general heuristics that can lead to discovery in a number of domains. The main heuristics detect constancies and trends in data, and lead to the formulation of hypotheses and the definition of theoretical terms. Rather than making a hard distinction between data and hypotheses, the program represents information at varying levels of description. The lowest levels (...) correspond to direct observations, while the highest correspond to hypotheses that explain everything so for observed. To take advantage of this representation, BACON.3 has the ability to carry out and relate multiple experiments, collapse hypotheses with identical conditions, ignore differences to let similar concepts be treated as equal, and to discover and ignore irrelevant variables. BACON.3 has shown its generality by rediscovering versions of the ideal gas law, Kepler's third law of planetary motion, Coulomb's law, Ohm's law, and Galileo's laws for the pendulum and constant acceleration. (shrink)

In this paper, I review two related lines of computational research: discovery of scientific knowledge and causal models of scientific phenomena. I also report research on quantitative process models that falls at the intersection of these two themes. This framework represents models as a set of interacting processes, each with associated differential equations that express influences among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes (...) enables search through the space of model structures and associated parameters to find explanations of time-series data. I discuss the representation of such process models, their use for prediction and explanation, and their discovery through heuristic search, along with their interpretation as causal accounts of dynamic behavior. (shrink)

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge‐lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this (...) approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC‐IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC‐IPM, they rely on the same forms of knowledge—processes and constraints—when constructing explanatory models. (shrink)

This paper examines the task of understanding dialogues in terms of the mental states of the participating agents. We present a motivating example that clarifies the challenges this problem involves and then outline a theory of dialogue interpretation based on abductive inference of these unobserved beliefs and goals, incremental construction of explanations, and reliance on domain-independent knowledge. After this, we describe UMBRA, an implementation of the theory that embodies these assumptions. We report experiments with the system that demonstrate its ability (...) to accurately infer the conversants’ mental states even when some speech acts are unavailable. We conclude by reviewing related research on dialogue and discussing avenues for future study. (shrink)

This paper examines the task of understanding dialogues in terms of the mental states of the participating agents. We present a motivating example that clarifies the challenges this problem involves and then outline a theory of dialogue interpretation based on abductive inference of these unobserved beliefs and goals, incremental construction of explanations, and reliance on domain-independent knowledge. After this, we describe UMBRA, an implementation of the theory that embodies these assumptions. We report experiments with the system that demonstrate its ability (...) to accurately infer the conversants’ mental states even when some speech acts are unavailable. We conclude by reviewing related research on dialogue and discussing avenues for future study. (shrink)

We initially describe a feature-rich discriminative Conditional Random Field (CRF) model for Information Extraction in the workshop announcements domain, which offers good baseline performance in the PASCAL shared task. We then propose a method for leveraging domain knowledge in Information Extraction tasks, scoring candidate document labellings as one-value-per-field templates according to domain feasibility after generating sample labellings from a trained sequence classifier. Our relational models evaluate these templates according to our intuitions about agreement in the domain: workshop acronyms should resemble (...) their names, workshop dates occur after paper submission dates. These methods see a 5% f-score improvement in fields retrieved when sampling labellings from a Maximum-Entropy Markov Model, however we do not observe improvement over a CRF model. We discuss reasons for this, including the problem of recovering all field instances from a best template, and propose future work in adapting such a model to the CRF, a better standalone system. (shrink)

Humans exhibit the remarkable ability to solve complex, multi-step problems despite their limited capacity for search. We review the standard theory of problem solving, which posits that heuristic guidance makes this possible, but we also note that most studies have emphasized the role of domain-specific heuristics, which are not available for unfamiliar tasks, over more general ones. We describe FPS, a flexible architecture for problem solving that supports a variety of different strategies and heuristics, and we report its use in (...) an experiment that studies the effectiveness of two domain-independent criteria. The results suggest that such heuristics can make problem solving far more tractable, and they are generally consistent with our claim that their use offsets the drawbacks of bounded rationality. (shrink)