Polynomial solvability of cost-based abduction

AbstractIn recent empirical studies we have shown that many interesting cost-based abduction problems can be solved efficiently by considering the linear program relaxation of their integer program formulation. We tie this to the concept of total unimodularity from network flow analysis, a fundamental result in polynomial solvability. From this, we can determine the polynomial solvability of abduction problems and, in addition, present a new heuristic for branch and bound in the non-polynomial cases

Dark Matter Subhalos and the X-ray Morphology of the Coma Cluster

Structure formation models predict that clusters of galaxies contain numerous massive subhalos. The gravity of a subhalo in a cluster compresses the surrounding intracluster gas and enhances its X-ray emission. We present a simple model, which treats subhalos as slow moving and gasless, for computing this effect. Recent weak lensing measurements by Okabe et al. have determined masses of ~ 10^13 solar masses for three mass concentrations projected within 300 kpc of the center of the Coma Cluster, two of which are centered on the giant elliptical galaxies NGC 4889 and NGC 4874. Adopting a smooth spheroidal beta-model for the gas distribution in the unperturbed cluster, we model the effect of these subhalos on the X-ray morphology of the Coma Cluster, comparing our results to Chandra and XMM-Newton X-ray data. The agreement between the models and the X-ray morphology of the central Coma Cluster is striking. With subhalo parameters from the lensing measurements, the distances of the three subhalos from the Coma Cluster midplane along our line of sight are all tightly constrained. Using the model to fit the subhalo masses for NGC 4889 and NGC 4874 gives 9.1 x 10^12 and 7.6 x 10^12 solar masses, respectively, in good agreement with the lensing masses. These results lend strong support to the argument that NGC 4889 and NGC 4874 are each associated with a subhalo that resides near the center of the Coma Cluster. In addition to constraining the masses and 3-d location of subhalos, the X-ray data show promise as a means of probing the structure of central subhalos.Comment: ApJ, in press. Matches the published versio

AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge

AI for Science (AI4Science), particularly in the form of self-driving labs, has the potential to sideline human involvement and hinder scientific discovery within the broader community. While prior research has focused on ensuring the responsible deployment of AI applications, enhancing security, and ensuring interpretability, we also propose that promoting openness in AI4Science discoveries should be carefully considered. In this paper, we introduce the concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science with the core principle of maximizing open knowledge translation throughout the scientific enterprise rather than a single organizational unit. We use the established principles of Knowledge Discovery and Data Mining (KDD) to formalize a language around AI4OS. We then discuss three principle stages of knowledge translation embedded in AI4Science systems and detail specific points where openness can be applied to yield an AI4OS alternative. Lastly, we formulate a theoretical metric to assess AI4OS with a supporting ethical argument highlighting its importance. Our goal is that by drawing attention to AI4OS we can ensure the natural consequence of AI4Science (e.g., self-driving labs) is a benefit not only for its developers but for society as a whole.Comment: NeurIPS AI For Science Workshop 2023. 11 pages, 2 figure

A linear constraint satisfaction approach to cost-based abduction

Abstract Santos Jr, E., A linear constraint satisfaction approach to cost-based abduction, Artificial Intelligence 65 (1994) 1-27. Abduction is the problem of finding the best explanation for a given set of observations. Within AI, this has been modeled as proving the observation by assuming some set of hypotheses. Cost-based abduction associates a cost with each hypothesis. The best proof is the one which assumes the least costly set. Previous approaches to finding the least cost set have formalized cost-based abduction as a heuristic graph search problem. However, efficient admissible heuristics have proven difficult to find. In this paper, we present a new technique for finding least cost sets by using linear constraints to represent causal relationships. In particular, we are able to recast the problem as a 0-1 integer linear programming problem. We can then use the highly efficient optimization tools of operations research yielding a computationally efficient method for solving cost-based abduction problems. Experiments comparing our linear constraint satisfaction approach to standard graph searching methodologies suggest that our approach is superior to existing search techniques in that our approach exhibits an expected-case polynomial run-time growth rate