Chemical discovery as belief revision

In this paper we describe STAHLp, a system that constructs componential models of chemical substances. STAHLp is a descendent of Zytkow and Simon's (1986) STAHL system, and both use chemical reactions and known componential models in order to construct new chemical models. However, STAHLp employs a more unified and effective strategy for recovering from erroneous inferences, based partly on de Kleer's (1984) assumption-based method of belief revision. This involves recording the underlying source beliefs or premises which lead to each inferred reaction or model. Where Zytkow and Simon's system required multiple methods for detecting errors and recovering from them, STAHLp uses a more powerful representation and additional rules which allow a unified method for error detection and recovery. When given the same initial data, the new system constructs the same historically correct models as STAHL, but it has other capabilities as well. In particular, STAHLp can modify data it has been given if this is necessary to achieve consistent models, and then proceed to construct new models based on the revised data

Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing

Is the human language understander a collection of modular processes operating with relative autonomy, or is it a single integrated process? This ongoing debate has polarized the language processing community, with two fundamentally different types of model posited, and with each camp concluding that the other is wrong. One camp puts forth a model with separate processors and distinct knowledge sources to explain one body of data, and the other proposes a model with a single processor and a homogeneous, monolithic knowledge source to explain the other body of data. In this paper we argue that a hybrid approach which combines a unified processor with separate knowledge sources provides an explanation of both bodies of data, and we demonstrate the feasibility of this approach with the computational model called COMPERE. We believe that this approach brings the language processing community significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr

Boy meets goal, boy loses goal, boy gets goal : the nature of feedback between goal-based simulation and understanding systems

We are designing a goal-based planning and simulation system called REACTOR for a multiple-actor world in which partially formulated plans are monitored during execution, providing feedback to the planner. Plan failures that occur are diagnosed by a combination of top-down (plan-synthesis) and bottom-up (plan-understanding) techniques, allowing an informed choice of response to the error. By maintaining separate belief spaces for each actor, we simulate planners who themselves simulate the planning and plan-understanding of other actors

Clustering the annotation space of proteins

BACKGROUND: Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas. RESULTS: Here we report a new approach, named CLAN, which clusters proteins according to both annotation and sequence similarity. This approach is extremely fast, clustering the complete SwissProt database within minutes. It is also accurate, recovering consistent protein families agreeing on average in more than 97% with sequence-based protein families from Pfam. Discrepancies between sequence- and annotation-based clusters were scrutinized and the reasons reported. We demonstrate examples for each of these cases, and thoroughly discuss an example of a propagated error in SwissProt: a vacuolar ATPase subunit M9.2 erroneously annotated as vacuolar ATP synthase subunit H. CLAN algorithm is available from the authors and the CLAN database is accessible at CONCLUSIONS: CLAN creates refined function-and-sequence specific protein families that can be used for identification and annotation of unknown family members. It also allows easy identification of erroneous annotations by spotting inconsistencies between similarities on annotation and sequence levels

Past and present cosmic structure in the SDSS DR7 main sample

We present a chrono-cosmography project, aiming at the inference of the four dimensional formation history of the observed large scale structure from its origin to the present epoch. To do so, we perform a full-scale Bayesian analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS) Data Release 7 main galaxy sample, relying on a fully probabilistic, physical model of the non-linearly evolved density field. Besides inferring initial conditions from observations, our methodology naturally and accurately reconstructs non-linear features at the present epoch, such as walls and filaments, corresponding to high-order correlation functions generated by late-time structure formation. Our inference framework self-consistently accounts for typical observational systematic and statistical uncertainties such as noise, survey geometry and selection effects. We further account for luminosity dependent galaxy biases and automatic noise calibration within a fully Bayesian approach. As a result, this analysis provides highly-detailed and accurate reconstructions of the present density field on scales larger than $\sim~3$ Mpc$/h$, constrained by SDSS observations. This approach also leads to the first quantitative inference of plausible formation histories of the dynamic large scale structure underlying the observed galaxy distribution. The results described in this work constitute the first full Bayesian non-linear analysis of the cosmic large scale structure with the demonstrated capability of uncertainty quantification. Some of these results will be made publicly available along with this work. The level of detail of inferred results and the high degree of control on observational uncertainties pave the path towards high precision chrono-cosmography, the subject of simultaneously studying the dynamics and the morphology of the inhomogeneous Universe.Comment: 27 pages, 9 figure